Reviewerly/exHarmony huggingface dataset:
Coded Distributed Computing For Vehicular Edge Computing With Dual-Function Radar Communication

In this paper, we propose a coded distributed computing (CDC)-based vehicular edge computing (VEC) framework. The framework allows a task vehicle (TV) equipped with the dual-function radar communication (DFRC) to offload its computing tasks to the nearby service vehicles (SVs) using the (m, k) maximum distance separable (MDS). The framework is thus able to address the straggler effect that is typically caused by the high mobility of the vehicles. We then formulate an optimization problem for the TV that aims to i) minimize the overall computing latency, ii) minimize the offloading cost, and iii) maximize the radar range subject to the connection duration. For this, we optimize the MDS parameters, i.e., the number of selected SVs (m) and the number of subtasks for coding (k), and the fractions of power allocated to the radar and communication functions. Under the high dynamic vehicular environment, the uncertainty of the SVs’ computing resource and networking resources, we propose a deep reinforcement learning (DRL) algorithm based on Double Deep Q-Network (DDQN) to solve the TV’s problem. To further improve the performance, we propose to incorporate a parameter norm penalty in the loss function. Simulation results show that the proposed DDQN algorithm outperforms both the DQN algorithm and the non-learning algorithm in terms of computation latency, radar range, and offloading cost.
Methods of video quality-improving

Video content has become integral to our daily lives, but poor video quality can significantly reduce viewers' experience and engagement. Various super-resolution methods are used to correct this, thereby reconstructing high-resolution videos from low-resolution ones. Two main categories of super-resolution methods exist traditional image processing and deep learning-based techniques. Deep learning-based techniques, such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Recurrent Neural Networks (RNNs), have shown great promise in enhancing video quality. The article discusses multiple adaptations of contemporary deep learning models to enhance video resolution. It also briefly explains the framework's design and implementation aspects. Lastly, the paper presents an overview and comparative analysis of the VSR techniques' efficiency on various benchmark datasets. At the same time, the paper describes potential challenges when choosing training sets; performance metrics, which can be used to compare different algorithms quantitatively. This work does not describe absolutely all existing VSR methods, but it is expected to contribute to the development of recent research in this field and potentially deepen our understanding of deep learning-based VSR methods, as well as stimulate further research in this area. In this work, new solutions for improving the performance of the methods are proposed, in particular, new quality metrics and datasets for model training. Overall, AI-based methods for VSR are becoming increasingly crucial with the rising demand for high-quality video content
Digital Competencies of Teachers – Strategies of Training and Attitudes During the Pandemic of COVID

The Covid pandemic has created a social situation that teachers had to adapt to, by teaching and learning online. For this, it was necessary to regulate both the legal and methodological framework, as well as the training and development of online learning. At system level, strategies have been developed and teacher training courses have been createdin order to develop the digital skills. In this article we present some examples of such measures taken at national and local level. The correlational study presented aimed to investigate the attitudes of 75 teachers towards the use of digital skills in online learning. The tool used was a questionnaire that allowed the collection of factual data about teachers (seniority, age, job, access to digital resources, their use) and data regarding attitudes towards online learning and the use of digital resources. Based on the statistical analysis, a significant correlation was found between age, teaching experience and receptivity, respectively anxiety about the use of digital technology at teachers of more advanced ages. The data obtained can be part of the analysis of the training needs of teachers for the easier integration of digital media in learning.
Archives Management System in the General Bureau and Papua Province Governor's Office Protocol

Focused research discusses the Archives Management System in the General Bureau and Protocol of the Papua Province Governor's Office. The method used is a qualitative approach.To obtain data, this is done through: Observation, Interviews and Documentation. To analyze various phenomena in the field, the following steps are carried out: Information collection, data reduction, data presentation, and drawing final conclusions. The results of this study show that the planning carried out in archive management is only general in nature, the organizing carried out in archive management has not been carried out effectively, the actuating in archive management has not gone well, so in-depth planning needs to be carried out in accordance with archival procedures starting from the creation of archives, use of archives, maintenance of archives, up to destruction of archives.
COMPARISON OF SPHERICAL TRIGONOMETRY METHOD, JEAN MEEUS ALGORITHM AND GOOGLE QIBLA FINDER IN DETERMINING OF THE QIBLA DIRECTION OF ISLAMIC HOSPITAL

Accuracy in facing the Qibla is an essential part of performing prayers. This vital value is evident when many mosques are built in public places. This article is qualitative with field data sources, namely coordinate points at the Jemursari Islamic Hospital mosque, Surabaya Islamic Hospital, and Al-Irsyad Hospital Surabaya. Once collected, the data was analyzed using three methods for calculating Qibla direction, namely Spherical Trigonometry, Jean Meeus, and Google Qibla Finder. This article found that the three methods obtained the same results at the Jemursari Islamic Hospital at 294°3'5", at the Surabaya Islamic Hospital at 294° 3'6", and at the Al-Irsyad Surabaya Hospital at 294°3'5 ". However, there is a difference between calculations and field measurements of 2°–7°, including within the Qibla deviation tolerance. It can be concluded that these three methods can accurately determine the Qibla direction in various locations. However, re-checking is required if the measurements exceed the tolerance limits.
Determining the Authorship of a Ukrainian-Language Literary Text by Means of Artificial Intelligence from Ultra-Short Excerpts

Purpose. The intelligent search engine Bing can be used as a method and a means of determining the author of a Ukrainian-language test. Bing helps to find information about a text fragment and its author, but the search results may be inaccurate or incomplete. The main purpose of the paper is to study the effectiveness of establishing the authorship of literary texts by state-of-the-art artificial intelligence tools based on ultra-short excerpts. Methodology. Ten Ukrainian authors with a rich body of fiction reflecting various aspects of Ukrainian culture and history were selected, as well as random fragments of 3–7 words each from different works of these authors. An experiment was conducted to determine the authorship of 2,000 fragments. Findings. Using the Python programming language and the skpy package, we developed software that sends questions and receives answers from the Bing bot built into Microsoft Skype. The answers were checked for the name of the author of the phrase and the corresponding title of the work. According to the results, Ivan Franko has the highest percentage of answers where the author's name was mentioned (65%), and Oleksandr Dovzhenko has the lowest result (23%). The answers were analyzed by the length of the fragments. Of course, the longer the length of a text fragment, the greater the likelihood of accurately identifying its authorship. Features of the author's style are manifested in 20–40 % of short fragments. The remaining 60–80% may be commonly used language constructions that the author relayed from the external environment. Originality. In this work, for the first time, the method of checking the authorship of fragments of Ukrainian-language text using the Bing bot with artificial intelligence is presented. A comparative analysis was performed and experiments were given to determine the authorship of short fragments of 3–7 words. It has been established that even quite small fragments of the text have signs characteristic of the original style of the author of artistic works. Practical value. It has been determined to what extent experts in determining the authorship of natural language texts can rely on existing state-of-the-art artificial intelligence tools in combination with an extensive database of texts in the Internet space.
Polarization entanglement distribution over 50 km in ITU 22 channel in all waveguide geometry

We generate indistinguishable photon pairs in an all-waveguide geometry. We use the source to distribute polarization entangled photons spanning 50 km telecom fiber violating Bell-CHSH inequality with S parameter being 2 . 53 ± 0 . 003.
AirPen: Real-Time Gesture-based Interactive Drawing using Computer Vision

Abstract: The project provides someone who uses a system that allows him to draw on video without switching to a separate whiteboard program while he or she is talking. The sphere of education and training has advanced greatly throughout this epidemic era therefore, this project would be quite valuable for speakers who wish to explain their subject by writing or drawing while speaking. The target is either his fingers or a pen, and its movement is traced to create the desired image.
zkFDL: An efficient and privacy-preserving decentralized federated learning with zero knowledge proof

Federated learning (FL) has been widely adopted in various fields of study and business. Traditional centralized FL systems suffer from serious issues. To address these concerns, decentralized federated learning (DFL) systems have been introduced in recent years. With the help of blockchains, they attempt to achieve more integrity and efficiency. However, privacy preservation remains an uncovered aspect of these systems. To tackle this, as well as to scale the blockchain-based computations, we propose a zero-knowledge proof (ZKP)-based aggregator (zkDFL). This allows clients to share their large-scale model parameters with a trusted centralized server without revealing their individual data to other clients. We utilize blockchain technology to manage the aggregation algorithm via smart contracts. The server performs a ZKP algorithm to prove to the clients that the aggregation is done according to the accepted algorithm. Additionally, the server can prove that all inputs from clients have been used. We evaluate our approach using a public dataset related to the wearable Internet of Things. As demonstrated by numerical evaluations, zkDFL introduces verifiability of the correctness of the aggregation process and enhances the privacy protection and scalability of DFL systems, while the gas cost has significantly declined.
Testing LLM performance on the Physics GRE: some observations

With the recent developments in large language models (LLMs) and their widespread availability through open source models and/or low-cost APIs, several exciting products and applications are emerging, many of which are in the field of STEM educational technology for K-12 and university students. There is a need to evaluate these powerful language models on several benchmarks, in order to understand their risks and limitations. In this short paper, we summarize and analyze the performance of Bard, a popular LLM-based conversational service made available by Google, on the standardized Physics GRE examination.
TOD-Flow: Modeling the Structure of Task-Oriented Dialogues

Task-Oriented Dialogue (TOD) systems have become crucial components in interactive artificial intelligence applications. While recent advances have capitalized on pre-trained language models (PLMs), they exhibit limitations regarding transparency and controllability. To address these challenges, we propose a novel approach focusing on inferring the TOD-Flow graph from dialogue data annotated with dialog acts, uncovering the underlying task structure in the form of a graph. The inferred TOD-Flow graph can be easily integrated with any dialogue model to improve its prediction performance, transparency, and controllability. Our TOD-Flow graph learns what a model can, should, and should not predict, effectively reducing the search space and providing a rationale for the model's prediction. We show that the proposed TOD-Flow graph better resembles human-annotated graphs compared to prior approaches. Furthermore, when combined with several dialogue policies and end-to-end dialogue models, we demonstrate that our approach significantly improves dialog act classification and end-to-end response generation performance in the MultiWOZ and SGD benchmarks. Code available at: https://github.com/srsohn/TOD-Flow
PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction

Inductive Conformal Prediction (ICP) provides a practical and effective approach for equipping deep learning models with uncertainty estimates in the form of set-valued predictions which are guaranteed to contain the ground truth with high probability. Despite the appeal of this coverage guarantee, these sets may not be efficient: the size and contents of the prediction sets are not directly controlled, and instead depend on the underlying model and choice of score function. To remedy this, recent work has proposed learning model and score function parameters using data to directly optimize the efficiency of the ICP prediction sets. While appealing, the generalization theory for such an approach is lacking: direct optimization of empirical efficiency may yield prediction sets that are either no longer efficient on test data, or no longer obtain the required coverage on test data. In this work, we use PAC-Bayes theory to obtain generalization bounds on both the coverage and the efficiency of set-valued predictors which can be directly optimized to maximize efficiency while satisfying a desired test coverage. In contrast to prior work, our framework allows us to utilize the entire calibration dataset to learn the parameters of the model and score function, instead of requiring a separate hold-out set for obtaining test-time coverage guarantees. We leverage these theoretical results to provide a practical algorithm for using calibration data to simultaneously fine-tune the parameters of a model and score function while guaranteeing test-time coverage and efficiency of the resulting prediction sets. We evaluate the approach on regression and classification tasks, and outperform baselines calibrated using a Hoeffding bound-based PAC guarantee on ICP, especially in the low-data regime.
How to guess a gradient

How much can you say about the gradient of a neural network without computing a loss or knowing the label? This may sound like a strange question: surely the answer is "very little." However, in this paper, we show that gradients are more structured than previously thought. Gradients lie in a predictable low-dimensional subspace which depends on the network architecture and incoming features. Exploiting this structure can significantly improve gradient-free optimization schemes based on directional derivatives, which have struggled to scale beyond small networks trained on toy datasets. We study how to narrow the gap in optimization performance between methods that calculate exact gradients and those that use directional derivatives. Furthermore, we highlight new challenges in overcoming the large gap between optimizing with exact gradients and guessing the gradients.
Solving the Lipkin model using quantum computers with two qubits only with a hybrid quantum-classical technique based on the Generator Coordinate Method

The possibility of using the generator coordinate method (GCM) using hybrid quantum-classical algorithms with reduced quantum resources is discussed. The task of preparing the basis states and calculating the various kernels involved in the GCM is assigned to the quantum computer, while the remaining tasks, such as finding the eigenvalues of a many-body problem, are delegated to classical computers for post-processing the generated kernels. This strategy reduces the quantum resources required to treat a quantum many-body problem. We apply the method to the Lipkin model. Using the permutation symmetry of the Hamiltonian, we show that, ultimately, only two qubits is enough to solve the problem regardless of the particle number. The classical computing post-processing leading to the full energy spectrum can be made using standard generalized eigenvalues techniques by diagonalizing the so-called Hill-Wheeler equation. As an alternative to this technique, we also explored how the quantum state deflation method can be adapted to the GCM problem. In this method, variational principles are iteratively designed to access the different excited states with increasing energies. The methodology proposed here is successfully applied to the Lipkin model with a minimal size of two qubits for the quantum register. The performances of the two classical post-processing approaches with respect to the statistical noise induced by the finite number of measurements and quantum devices noise are analyzed. Very satisfactory results for the full energy spectra are obtained once noise correction techniques are employed.
Dynamic Online Modulation Recognition using Incremental Learning

Modulation recognition is a fundamental task in communication systems as the accurate identification of modulation schemes is essential for reliable signal processing, interference mitigation for coexistent communication technologies, and network optimization. Incorporating deep learning (DL) models into modulation recognition has demonstrated promising results in various scenarios. However, conventional DL models often fall short in online dynamic contexts, particularly in class incremental scenarios where new modulation schemes are encountered during online deployment. Retraining these models on all previously seen modulation schemes is not only time-consuming but may also not be feasible due to storage limitations. On the other hand, training solely on new modulation schemes often results in catastrophic forgetting of previously learned classes. This issue renders DL-based modulation recognition models inapplicable in real-world scenarios because the dynamic nature of communication systems necessitate the effective adaptability to new modulation schemes. This paper addresses this challenge by evaluating the performance of multiple Incremental Learning (IL) algorithms in dynamic modulation recognition scenarios, comparing them against conventional DL-based modulation recognition. Our results demonstrate that modulation recognition frameworks based on IL effectively prevent catastrophic forgetting, enabling models to perform robustly in dynamic scenarios.
Is Feedback All You Need? Leveraging Natural Language Feedback in Goal-Conditioned Reinforcement Learning

Despite numerous successes, the field of reinforcement learning (RL) remains far from matching the impressive generalisation power of human behaviour learning. One possible way to help bridge this gap be to provide RL agents with richer, more human-like feedback expressed in natural language. To investigate this idea, we first extend BabyAI to automatically generate language feedback from the environment dynamics and goal condition success. Then, we modify the Decision Transformer architecture to take advantage of this additional signal. We find that training with language feedback either in place of or in addition to the return-to-go or goal descriptions improves agents' generalisation performance, and that agents can benefit from feedback even when this is only available during training, but not at inference.
Unnatural Algorithms in Machine Learning

Natural gradient descent has a remarkable property that in the small learning rate limit, it displays an invariance with respect to network reparameterizations, leading to robust training behavior even for highly covariant network parameterizations. We show that optimization algorithms with this property can be viewed as discrete approximations of natural transformations from the functor determining an optimizer's state space from the diffeomorphism group if its configuration manifold, to the functor determining that state space's tangent bundle from this group. Algorithms with this property enjoy greater efficiency when used to train poorly parameterized networks, as the network evolution they generate is approximately invariant to network reparameterizations. More specifically, the flow generated by these algorithms in the limit as the learning rate vanishes is invariant under smooth reparameterizations, the respective flows of the parameters being determined by equivariant maps. By casting this property a natural transformation, we allow for generalizations beyond equivariance with respect to group actions; this framework can account for non-invertible maps such as projections, creating a framework for the direct comparison of training behavior across non-isomorphic network architectures, and the formal examination of limiting behavior as network size increases by considering inverse limits of these projections, should they exist. We introduce a simple method of introducing this naturality more generally and examine a number of popular machine learning training algorithms, finding that most are unnatural.
Diffence: Fencing Membership Privacy With Diffusion Models

Deep learning models, while achieving remarkable performance across various tasks, are vulnerable to member inference attacks, wherein adversaries identify if a specific data point was part of a model's training set. This susceptibility raises substantial privacy concerns, especially when models are trained on sensitive datasets. Current defense methods often struggle to provide robust protection without hurting model utility, and they often require retraining the model or using extra data. In this work, we introduce a novel defense framework against membership attacks by leveraging generative models. The key intuition of our defense is to remove the differences between member and non-member inputs which can be used to perform membership attacks, by re-generating input samples before feeding them to the target model. Therefore, our defense works \emph{pre-inference}, which is unlike prior defenses that are either training-time (modify the model) or post-inference time (modify the model's output). A unique feature of our defense is that it works on input samples only, without modifying the training or inference phase of the target model. Therefore, it can be cascaded with other defense mechanisms as we demonstrate through experiments. Through extensive experimentation, we show that our approach can serve as a robust plug-n-play defense mechanism, enhancing membership privacy without compromising model utility in both baseline and defended settings. For example, our method enhanced the effectiveness of recent state-of-the-art defenses, reducing attack accuracy by an average of 5.7\% to 12.4\% across three datasets, without any impact on the model's accuracy. By integrating our method with prior defenses, we achieve new state-of-the-art performance in the privacy-utility trade-off.