Reviewerly/exHarmony huggingface dataset:
A Survey of Diffusion Models in Natural Language Processing

This survey paper provides a comprehensive review of the use of diffusion models in natural language processing (NLP). Diffusion models are a class of mathematical models that aim to capture the diffusion of information or signals across a network or manifold. In NLP, diffusion models have been used in a variety of applications, such as natural language generation, sentiment analysis, topic modeling, and machine translation. This paper discusses the different formulations of diffusion models used in NLP, their strengths and limitations, and their applications. We also perform a thorough comparison between diffusion models and alternative generative models, specifically highlighting the autoregressive (AR) models, while also examining how diverse architectures incorporate the Transformer in conjunction with diffusion models. Compared to AR models, diffusion models have significant advantages for parallel generation, text interpolation, token-level controls such as syntactic structures and semantic contents, and robustness. Exploring further permutations of integrating Transformers into diffusion models would be a valuable pursuit. Also, the development of multimodal diffusion models and large-scale diffusion language models with notable capabilities for few-shot learning would be important directions for the future advance of diffusion models in NLP.
Quantum complexity phase transitions in monitored random circuits

Recently, the dynamics of quantum systems that involve both unitary evolution and quantum measurements have attracted attention due to the exotic phenomenon of measurement-induced phase transitions. The latter refers to a sudden change in a property of a state of $n$ qubits, such as its entanglement entropy, depending on the rate at which individual qubits are measured. At the same time, quantum complexity emerged as a key quantity for the identification of complex behaviour in quantum many-body dynamics. In this work, we investigate the dynamics of the quantum state complexity in monitored random circuits, where $n$ qubits evolve according to a random unitary circuit and are individually measured with a fixed probability at each time step. We find that the evolution of the exact quantum state complexity undergoes a phase transition when changing the measurement rate. Below a critical measurement rate, the complexity grows at least linearly in time until saturating to a value $e^{\Omega(n)}$. Above, the complexity does not exceed $\operatorname{poly}(n)$. In our proof, we make use of percolation theory to find paths along which an exponentially long quantum computation can be run below the critical rate, and to identify events where the state complexity is reset to zero above the critical rate. We lower bound the exact state complexity in the former regime using recently developed techniques from algebraic geometry. Our results combine quantum complexity growth, phase transitions, and computation with measurements to help understand the behavior of monitored random circuits and to make progress towards determining the computational power of measurements in many-body systems.
Dynamic Data Augmentation via MCTS for Prostate MRI Segmentation

Medical image data are often limited due to the expensive acquisition and annotation process. Hence, training a deep-learning model with only raw data can easily lead to overfitting. One solution to this problem is to augment the raw data with various transformations, improving the model's ability to generalize to new data. However, manually configuring a generic augmentation combination and parameters for different datasets is non-trivial due to inconsistent acquisition approaches and data distributions. Therefore, automatic data augmentation is proposed to learn favorable augmentation strategies for different datasets while incurring large GPU overhead. To this end, we present a novel method, called Dynamic Data Augmentation (DDAug), which is efficient and has negligible computation cost. Our DDAug develops a hierarchical tree structure to represent various augmentations and utilizes an efficient Monte-Carlo tree searching algorithm to update, prune, and sample the tree. As a result, the augmentation pipeline can be optimized for each dataset automatically. Experiments on multiple Prostate MRI datasets show that our method outperforms the current state-of-the-art data augmentation strategies.
Decoupled Kullback-Leibler Divergence Loss

In this paper, we delve deeper into the Kullback-Leibler (KL) Divergence loss and observe that it is equivalent to the Doupled Kullback-Leibler (DKL) Divergence loss that consists of 1) a weighted Mean Square Error (wMSE) loss and 2) a Cross-Entropy loss incorporating soft labels. From our analysis of the DKL loss, we have identified two areas for improvement. Firstly, we address the limitation of DKL in scenarios like knowledge distillation by breaking its asymmetry property in training optimization. This modification ensures that the wMSE component is always effective during training, providing extra constructive cues. Secondly, we introduce global information into DKL for intra-class consistency regularization. With these two enhancements, we derive the Improved Kullback-Leibler (IKL) Divergence loss and evaluate its effectiveness by conducting experiments on CIFAR-10/100 and ImageNet datasets, focusing on adversarial training and knowledge distillation tasks. The proposed approach achieves new state-of-the-art performance on both tasks, demonstrating the substantial practical merits. Code and models will be available soon at https://github.com/jiequancui/DKL.
Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs

Large language models (LLMs) have achieved widespread success on a variety of in-context few-shot tasks, but this success is typically evaluated via correctness rather than consistency. We argue that self-consistency is an important criteria for valid multi-step reasoning in tasks where the solution is composed of the answers to multiple sub-steps. We propose two types of self-consistency that are particularly important for multi-step reasoning -- hypothetical consistency (a model's ability to predict what its output would be in a hypothetical other context) and compositional consistency (consistency of a model's final outputs when intermediate sub-steps are replaced with the model's outputs for those steps). We demonstrate that multiple variants of the GPT-3/-4 models exhibit poor consistency rates across both types of consistency on a variety of tasks.
Distributed CONGEST Algorithms against Mobile Adversaries

In their seminal PODC 1991 paper, Ostrovsky and Yung introduced the study of distributed computation in the presence of mobile adversaries which can dynamically appear throughout the network. Over the years, this setting has been studied mostly under the assumption that the communication graph is fully-connected. Resilient CONGEST algorithms for general graphs, on the other hand, are currently known only for the classical static setting, i.e., where the set of corrupted edges (or nodes) is fixed throughout the entire computation. We fill this gap by providing round-efficient simulations that translate given CONGEST algorithms into equivalent algorithms that are resilient against $f$-mobile edge adversaries. Our main results are: -Perfect-Security with Mobile Eavesdroppers: A translation of any $r$-round $f$-static-secure algorithm into an equivalent $\Theta(f)$-mobile-secure algorithm with $\Theta(r)$ rounds. We also show that the $f$-static-secure algorithms of [Hitron, Parter and Yogev, DISC 2022 & ITCS 2023] can be modified into $f$-mobile-secure algorithms with the same number of rounds. -Resilience with Mobile Byzantine Adversaries: An $f$-mobile-byzantine simulation which is based on a decomposition of the graph into low-diameter edge-disjoint spanning trees. This provides us with near-optimal CONGEST compilers for expander graphs. It also leads to near-optimal compilers in the congested-clique model against $\Theta(n)$-mobile adversaries. For general $(2f+1)$ edge-connected graphs with $f$-mobile adversary, we almost match the bounds known for the $f$-static setting, when provided a trusted pre-processing phase. Our results are based on a collection of tools from interactive coding [Gelles, Found. Trends Theor. Comput. Sci. 2017], linear sketches and low-congestion graph decomposition. The introduced toolkit might have further applications for resilient computation.
GAME LEARNING FISIKA “ASAH OTAK” BERBASIS ANDROID DENGAN APP INVENTOR 2

Serious game is one of the kind that made not only for entertaiment but also has other functions such as learning or education, training, advertising, simulation, health and others. Nowadays many games are already used as a learning or education. Educational games are usually applied based on age stages or its genre. Educational games can help to train The thinking ability and the logic skill for it players. The learning methods that using games are expected to be more interesting and understandable for it players. This paper is discussed about an educational game concept as a learning media for high school students specialized for physics subject. This game is an android-based game that expected to be directly implemented into their smartphones and can be played anywhere. This Physics Learning Game is made used App Inventor 2 (AI2).
An Entropy-Based Method with a New Benchmark Dataset for Chinese Textual Affective Structure Analysis

Affective understanding of language is an important research focus in artificial intelligence. The large-scale annotated datasets of Chinese textual affective structure (CTAS) are the foundation for subsequent higher-level analysis of documents. However, there are very few published datasets for CTAS. This paper introduces a new benchmark dataset for the task of CTAS to promote development in this research direction. Specifically, our benchmark is a CTAS dataset with the following advantages: (a) it is Weibo-based, which is the most popular Chinese social media platform used by the public to express their opinions; (b) it includes the most comprehensive affective structure labels at present; and (c) we propose a maximum entropy Markov model that incorporates neural network features and experimentally demonstrate that it outperforms the two baseline models.
Peningkatan Minat Belajar IPAS Berbantuan Media Gambar Pada Siswa Sekolah Dasar

Interest in learning is an interest possessed by students which can be expressed as a statement indicating that students prefer something over other things, manifested through participation in an activity. Students who have an interest in certain subjects tend to pay greater attention to certain subjects. Social Knowledge learning media in elementary schools, especially in image media, is a tool that can be used to deliver or convey messages in the form of knowledge to students so that learning objectives can be achieved. Indicators of interest in learning in this study include: 1) paying attention to the teaching and learning process, 2) having a liking for lessons, 3) enthusiastic students, 4) enthusiastic in learning, 5) having active learning
Useful Cyber Threat Intelligence Relation Retrieval Using Transfer Learning

The emergence of hacker groups extends the complexity and frequency of cyberattacks. To adapt to the rapidly evolving cyberattacks, acquiring valuable information from security incident reports is critical for businesses to gain visibility into the fast-evolving threat landscape and to timely deploy preventive measures. As such threat intelligence is mostly presented in textual reports, such information needs to be extracted manually by security analysts and is highly dependent on personnel experience. This research proposes a novel cyber threat intelligence extraction system called “CARE” (Cyber Attack Relation Extraction) that extracts critical threat entities and presents their relationship in both graphical and textual forms that help cybersecurity staff quickly grasp the key information from security reports. To capture attack-related information, this study adopts BERT to enhance contextualized word representation and applies transfer learning to extract the relations among threat entities. The evaluation results show that the proposed CARE system achieves a 97% F1-score on relation extraction and that it could retrieve useful threat information effectively.
Noise Impact on a Recurrent Neural Network with a Linear Activation Function

In recent years, more and more researchers in the field of neural networks are interested in creating hardware implementations where neurons and the connection between them are realized physically. The physical implementation of ANN fundamentally changes the features of noise influence. In the case hardware ANNs, there are many internal sources of noise with different properties. The purpose of this paper is to study the peculiarities of internal noise propagation in recurrent ANN on the example of echo state network (ESN), to reveal ways to suppress such noises and to justify the stability of networks to some types of noises. In this paper we analyse ESN in presence of uncorrelated additive and multiplicative white Gaussian noise. Here we consider the case when artificial neurons have linear activation function with different slope coefficients. Starting from studying only one noisy neuron we complicate the problem by considering how the input signal and the memory property affect the accumulation of noise in ESN. In addition, we consider the influence of the main types of coupling matrices on the accumulation of noise. So, as such matrices, we take a uniform matrix and a diagonal-like matrices with different coefficients called "blurring" coefficient. We have found that the general view of variance and signal-to-noise ratio of ESN output signal is similar to only one neuron. The noise is less accumulated in ESN with diagonal reservoir connection matrix with large "blurring" coefficient. Especially it concerns uncorrelated multiplicative noise.
Rainfall Forecasting in Makassar City Using Triple Exponential Smoothing Method

The purpose of this study is to determine the appropriate forecasting method for predicting rainfall in Makassar City in 2022. This research is based on the problems that are often experienced by the people of Makassar City, namely the occurrence of flooding which results in traffic jams due to high x rainfall which continuously occurs. for several days in a row. With this research, people can see the prediction of rainfall and anticipate flooding in Makassar City. The results of the Makassar city rainfall data plot experience increases and decreases (fluctuations), which tend to repeat every year. This shows that Makassar City's rainfall contains seasonal factors. Therefore the method used is one-parameter Brown triple exponential smoothing, three-parameter Holt-Winters additive and Holt-Winters multiplicative. The forecasting results show that the correct method to use is the Holt-Winters multiplicative method with a parameter value of a=0.001 β=0.15 γ=0.002 which produces a minimum value of MAPE = 1.18 MAD = 136.23 compared to the Brown and Holt-Winters methods additive. Forecasting results using the Holt-Winters multiplicative method show that the highest rainfall occurs from December to April 2022.
Standardized Bibliographic Terminology: the Modern State of Art

The history of standardization of bibliographic terminology in the USSR and the Russian Federation is briefly considered . The terms from the field of bibliographic science and practice , reflected in the national standards of SIBID, approved in 2018—2022, are analyzed in detail . Particular attention is paid to the GOST R 7.0.76—2022 standard “ Bibliographing . Bibliographic resources . Terms and definitions ”.
Smart system Based on Augmented Reality for Displaying Cultural Heritage in Oman

Strengthening cultural heritage is very important in countries with rich heritage. Oman is one of those countries with a large and large cultural heritage. There is a variety of archaeological sites in Oman from castles, forts, and others, with rapid developments in modern technologies is important to attract visitors and tourism at archaeological sites. One of the latest technologies that can be used for this purpose is enhanced reality applications. This proposal aims to create a reliable and intelligent system based on enhanced reality technology to display the shapes , paintings, and other things in archaeological sites from castles and forts. The proposed system is allowed to provide a lot of information about the object or location For visitors. In this project we offer an enhanced reality that can be published in archaeological sites in Oman . The project is a motive for a major increase in Omani national income by increasing visitors to these cultural sites.
A Review of CAD systems for Breast Mass Detection in Mammography Based on Deep Learning

Early detection of breast cancer, one of the most common cancers in women worldwide, raises survival rates and lowers the cost of treatment. Although breast cancer detection and classification (CAD) tools have improved, some issues and restrictions still require further research. The creation of breast cancer CAD systems, particularly with deep learning models, was significantly impacted by recent advancements in machine learning and image processing techniques. The current deep learning-based CAD system to identify and categorize masses in mammography is presented in a structured manner in this survey. The survey provides the most popular breast cancer CAD system evaluation metrics, publicly available mammographic datasets, and current image modalities. The survey examines the available literature, emphasizing its strengths and limitations. The survey also emphasizes the difficulties and drawbacks of the current methods for classifying and detecting breast cancer. We point out research gaps and make suggestions for further study. This systematic review may be beneficial for clinicians using CAD systems to diagnose breast cancer early on and for researchers looking for knowledge gaps and making more contributions to the field.
Role-Driven Clustering of Stakeholders: A Study of IoT Security Improvement

The goal of this study is to categorize stakeholders in Internet of Things (IoT) security based on their roles in order to address the challenges associated with managing the large amount of data generated by IoT devices. With the increasing number of connected devices, the security risks also escalate, necessitating skilled stakeholders to mitigate these risks and prevent potential attacks. This study proposes a two-part approach that involves clustering stakeholders according to their responsibilities and determining relevant features to enhance decision-making. By grouping stakeholders into shared categories, the study aims to provide a more efficient and effective method for managing IoT security. The insights gained from this research will not only benefit stakeholders involved in IoT security but also assist policymakers and regulators in developing effective strategies.
An Introduction of Open System Interconnection (OSI) Model and its Architecture

Open System Interconnection (OSI) model is the best means to understand the way the internet works and the communication of computers over a network system. It is considered as the standard model for network communication. This article describes the OSI model with an example and provides a brief description of the seven layers of the OSI model.
Action Recognition via Adaptive Semi-Supervised Feature Analysis

This paper presents a new algorithm for semi-supervised action recognition using adaptive feature analysis. We model the samples of the same action as the same manifold and those of different actions as different manifolds, and integrate feature correlation mining and visual similarity mining into a joint framework. By maximizing the separability between different classes based on the labeled data, the proposed algorithm learns multiple features from labeled data and utilizes the intrinsic geometric structure of the data distribution between labeled and unlabeled data to boost the recognition. We introduce Projected Barzilai-Borwein (PBB) to train a subspace projection matrix as classifier. Extensive experiments on three benchmark datasets demonstrate that our algorithm outperforms the existing semi-supervised action recognition algorithms when there are a few labeled training samples.
Development of Mobile Learning Applications for Android Based on Artificial Intelligence

In order to enable students to study autonomously and without being constrained by time and distance, this research was done to design an M-Learning learning media based on learning media on artificial intelligence. constrained by time and space, as well as to boost students' enthusiasm for studying. The following are the problems that need to be solved by this research: (1) How feasible is an android-based M-learning application for the field of artificial intelligence? (2) What are the students' reactions to the M-learning application built for Android in the Artificial Intelligence course?Research and development, sometimes known as R&amp;D, is the research methodology employed. Students in the informatics engineering department at Raharja University served as the study's research targets. Techniques Instrument reliability and validity assessments as well as questionnaires are employed as data gathering methods respondents. Then, the examination of media viability and student reaction is performed as a data analysis approach. Descriptive analysis was done on the student replies. (1) Media validation by professional validators is calculated at 92.5% by percentage, according to the results. On the basis of this, it can be said that the android-based M-learning application falls within the category of "Very Excellent" and is appropriate for usage. (2) The results of the M-learning application for Android-based students were positive, with a 79.5% response rate.
ISU TERKINI DALAM PEMBELAJARAN SASTRA: KELIMPAHAN INFORMASI, KECERDASAN BUATAN, DAN LITERASI DIGITAL

Chairil Anwar's poems have been discussed by dozens of Indonesian literary critics and academics. Even so, there could still be cases of incorrect identification of Chairil Anwar's poetry by Indonesian literary academics. This article is an investigative research article. Data was obtained from surveys through cyberspace using the Google search engine with certain keywords and applying snowballing techniques. In order to enrich the data in explaining cases of incorrect identification of Chairil Anwar's poetry, class simulations were carried out. The first class simulation resulted in findings of the phenomenon of information overload as the cause of the identification of Chairil Anwar's poetry. In another class simulation, it was found that artificial intelligence in open-access conversation formats such as the GPT Chat which was just present at the time this article was written needs to be closely observed for its development and potential in literary learning. The conclusion of this article is that there is an urgency to increase Indonesian literary literacy in literary learning in tertiary institutions as well as the need for its integration with digital literacy learning. Keywords: Literary Myopia, Digital Literacy, Information Overload, Artificial Intelligence, ChatGPT
Densities of codes of various linearity degrees in translation-invariant metric spaces

Abstract We investigate the asymptotic density of error-correcting codes with good distance properties and prescribed linearity degree, including (sub)linear and nonlinear codes. We focus on the general setting of finite translation-invariant metric spaces, and then specialize our results to the Hamming metric, to the rank metric, and to the sum-rank metric. Our results show that the asymptotic density of codes heavily depends on the imposed linearity degree and the chosen metric.