Reviewerly/exHarmony huggingface dataset:
Design of General Purpose Minimal-Auxiliary Ising Machines

Ising machines are a form of quantum-inspired processing-in-memory computer which has shown great promise for overcoming the limitations of traditional computing paradigms while operating at a fraction of the energy use. The process of designing Ising machines is known as the reverse Ising problem. Unfortunately, this problem is in general computationally intractable: it is a nonconvex mixed-integer linear programming problem which cannot be naively brute-forced except in the simplest cases due to exponential scaling of runtime with number of spins. We prove new theoretical results which allow us to reduce the search space to one with quadratic scaling. We utilize this theory to develop general purpose algorithmic solutions to the reverse Ising problem. In particular, we demonstrate Ising formulations of 3-bit and 4-bit integer multiplication which use fewer total spins than previously known methods by a factor of more than three. Our results increase the practicality of implementing such circuits on modern Ising hardware, where spins are at a premium.
Online-Learnt and Deployed MoDem System

The telecommunications industry has seen a significant increase in the demand for high-quality transmission and processing of digital information. As a result, research efforts have been focused on developing high-performance systems for the modulation and demodulation of digital information signals. These efforts have led to exploring artificial intelligence (AI) techniques for implementing demodulation. However, traditional AI-based demodulation techniques use specific data for supervised training, which results in models that hardly adapt to changing environments. These models do not consider the time variance that can affect a realistic communication chain, where physical devices and their functionalities inevitably influence the signal. Moreover, the non-idealities that characterize the communication channel are subject to unpredictable and random temporal fluctuations. To address these issues, this manuscript proposes a novel system that implements modulation and demodulation of digital signals and can adapt the demodulation to the characteristics of the specific front-end adopted, as well as to those of the communication channel, during execution on the field. The proposed system estimates the main parameters that characterize the received signal and performs neural demodulation at the receiver. This is achieved through an online learning process that takes place at the transmitter and is sent to the receiver in real-time. This approach allows the system to adapt to the statistically varying conditions of ongoing communication, resulting in improved performance and reliability.
FB-SEC-1: A Social Emotion Cause Dataset

“Social emotion” in text mining refers to the emotion experienced by the reader exposed to a text, as opposed to the emotion conveyed from the author’s perspective. Mining the cause of social emotion from text is a new challenging task with a wide range of applications, but its progress is hindered by the lack of annotated datasets. In this paper, we release the first English dataset for social emotion cause. The dataset is based on a well-established corpus of Facebook posts, and it was annotated through a crowdsourcing experiment. Together with the dataset, we provide two baseline models to be used as benchmarks for future studies.
Text Guided Image Inpainting Based on Generative Adversarial Network

In recent years, the generative adversarial network (GAN) has achieved good results in the field of image inpainting. At the same time, the research on the text-generation image method shows that the text-generated image content has achieved good results, and text description can generate semantic content with visual relevance. Therefore, combining GAN and text information can be used to generate image information, which can both make up for the deficiency of GAN and provide guidance for image inpainting. In this paper, a text-guided image inpainting model based on generation adversarial network is proposed. This module contains spatial and channel information to focus on the information related to inpainting task. However, the original generation adversarial network has the problem of gradient disappearance and the quality of the generated image is unstable. In order to stabilize the training process and improve the image repair effect, the model uses the method of least squares generation to improve the network, and further stabilizes the repair effect by introducing the perceptual network.
LDP-Feat: Image Features with Local Differential Privacy

Modern computer vision services often require users to share raw feature descriptors with an untrusted server. This presents an inherent privacy risk, as raw descriptors may be used to recover the source images from which they were extracted. To address this issue, researchers [11] recently proposed privatizing image features by embedding them within an affine subspace containing the original feature as well as adversarial feature samples. In this paper, we propose two novel inversion attacks to show that it is possible to (approximately) recover the original image features from these embeddings, allowing us to recover privacy-critical image content. In light of such successes and the lack of theoretical privacy guarantees afforded by existing visual privacy methods, we further propose the first method to privatize image features via local differential privacy, which, unlike prior approaches, provides a guaranteed bound for privacy leakage regardless of the strength of the attacks. In addition, our method yields strong performance in visual localization as a downstream task while enjoying the privacy guarantee.
Fast Adversarial Training with Smooth Convergence

Fast adversarial training (FAT) is beneficial for improving the adversarial robustness of neural networks. However, previous FAT work has encountered a significant issue known as catastrophic overfitting when dealing with large perturbation budgets, i.e. the adversarial robustness of models declines to near zero during training. To address this, we analyze the training process of prior FAT work and observe that catastrophic overfitting is accompanied by the appearance of loss convergence outliers. Therefore, we argue a moderately smooth loss convergence process will be a stable FAT process that solves catastrophic overfitting. To obtain a smooth loss convergence process, we propose a novel oscillatory constraint (dubbed ConvergeSmooth) to limit the loss difference between adjacent epochs. The convergence stride of ConvergeSmooth is introduced to balance convergence and smoothing. Likewise, we design weight centralization without introducing additional hyperparameters other than the loss balance coefficient. Our proposed methods are attack-agnostic and thus can improve the training stability of various FAT techniques. Extensive experiments on popular datasets show that the proposed methods efficiently avoid catastrophic overfitting and outperform all previous FAT methods. Code is available at https://github.com/FAT-CS/ConvergeSmooth.
Development of a Prototype Program for Separating Overlapping Images Files

The digital world where internet is easily accessible to everyone, cybercrime in cyberspace is on the rise. If the perpetrator has knowledge of cryptography, investigating and solving such crimes becomes extremely challenging. The objective of this research is to study and develop a prototype program for separating overlapping image files which is one of the methods of steganography. Python is the primary programming language used for development, along with file carving technique using file signature to separate overlapping files. In addition, the program creates hash files using MD5 and SHA-1 to ensure the evidence is not tampered with and can be presented in court. The program generates reports summarizing the results of image file separation, both in text and Excel formats. The effectiveness of the program was tested on 678 overlapping image files and the result showed that it took only 13.907 seconds to separate the files, which is significantly faster than manual separation of the same number of files. The researcher hopes that this research will help reduce the time and errors caused by human intervention in file carving for forensic investigations.
Measuring Asymmetric Gradient Discrepancy in Parallel Continual Learning

In Parallel Continual Learning (PCL), the parallel multiple tasks start and end training unpredictably, thus suffering from both training conflict and catastrophic forgetting issues. The two issues are raised because the gradients from parallel tasks differ in directions and magnitudes. Thus, in this paper, we formulate the PCL into a minimum distance optimization problem among gradients and propose an explicit Asymmetric Gradient Distance (AGD) to evaluate the gradient discrepancy in PCL. AGD considers both gradient magnitude ratios and directions, and has a tolerance when updating with a small gradient of inverse direction, which reduces the imbalanced influence of gradients on parallel task training. Moreover, we present a novel Maximum Discrepancy Optimization (MaxDO) strategy to minimize the maximum discrepancy among multiple gradients. Solving by MaxDO with AGD, parallel training reduces the influence of the training conflict and suppresses the catastrophic forgetting of finished tasks. Extensive experiments validate the effectiveness of our approach on three image recognition datasets in task-incremental and class-incremental PCL. Our code is available at https://github.com/fanlyu/maxdo.
SAFE: Machine Unlearning With Shard Graphs

We present Synergy Aware Forgetting Ensemble (SAFE), a method to adapt large models on a diverse collection of data while minimizing the expected cost to remove the influence of training samples from the trained model. This process, also known as selective forgetting or unlearning, is often conducted by partitioning a dataset into shards, training fully independent models on each, then ensembling the resulting models. Increasing the number of shards reduces the expected cost to forget but at the same time it increases inference cost and reduces the final accuracy of the model since synergistic information between samples is lost during the independent model training. Rather than treating each shard as independent, SAFE introduces the notion of a shard graph, which allows incorporating limited information from other shards during training, trading off a modest increase in expected forgetting cost with a significant increase in accuracy, all while still attaining complete removal of residual influence after forgetting. SAFE uses a lightweight system of adapters which can be trained while reusing most of the computations. This allows SAFE to be trained on shards an order-of-magnitude smaller than current state-of-the-art methods (thus reducing the forgetting costs) while also maintaining high accuracy, as we demonstrate empirically on fine-grained computer vision datasets.
Sentiment Analysis for Preventing HIV Policy Comments Using Word2Vec

Public hearing is important to know how most people feel about the problem they encounter. Proposing policy like HIV prevention is one of them that is currently a social issue. This research presents the experiment of sentiment analysis of social media comments from preventing HIV policy problems, with three classes of the sentiment: positive, neutral, and negative. In addition, to show how the models could get the higher accuracy after fine-tuned parameters. We also used Word2Vec to make word embeddings and combined them with a classifier model and deep learning model. To reach the purpose, we do the grid search to get the best parameter set that gives the high accuracy of each model. The result shows Random Forest achieves the highest accuracy of 95.92%, which is a better performance than other works.
A modified UNet network bridged with multiscale context fusion for photovoltaic panel image segmentation

The identification and segmentation of photovoltaic panel areas are crucial for precise defect detection and accurate component localization in photovoltaic power plants. To address the challenges of variable ground resolution, complex backgrounds, and ambiguous boundaries of photovoltaic panel areas in inspection images, we introduce a novel multi-scale adaptive photovoltaic panel image segmentation network named EB7-MCF-UNet. Our network draws inspiration from the proven UNet architecture, employing EfficientNet-B7 as the encoder and introducing a Multi-Scale Context Fusion (MCF) module as an encoder-decoder feature bridge. The efficient MCF module bolsters the network&apos;s ability to handle multiscale variations effectively. To further enhance model adaptability, we introduce data augmentation techniques to diversify the training data, allowing the model to better accommodate various scenes and lighting conditions. Moreover, the feature matching loss with linear weighting is incorporated alongside the cross-entropy loss for EB7-MCF-UNet to minimize the discrepancy between predicted features and labeled maps. Extensive experiments on practical inspection images photographed in various scenes demonstrate the effectiveness of EB7-MCF-UNet for photovoltaic panel image segmentation.
Image blur processing technology based on multi-scale feature network and cross-stage attention mechanism

Aiming at the problem that traditional image deblurring techniques are not effective in dealing with high blurring images, a novel image deblurring method based on multi-scale feature network and cross-stage attention mechanism is proposed. By combining multi-scale feature extraction and cross-stage attention mechanism, the method aims to enhance the acquisition of spatial characteristic information of images while reducing feature loss to improve the effect of image deblurring. In the experiments on the GoPro dataset, compared with the traditional single-scale method, the investigated two-scale processing method improves the peak signal-to-noise ratio by 0.843 dB and grows the structural similarity by 0.0116, while the three-scale processing method further increases the PSNR and SSIM by 0.756 dB and 0.0093, respectively, in comparison with the two-scales.On the Kohler dataset , the present method improves 0.04dB in PSNR and 0.013dB in SSIM compared to other mainstream generative adversarial network algorithms.Compared to the scaled recurrent network, the research method outperforms the Kohler dataset by 0.31dB in PSNR and 0.018dB in SSIM performance.The results show that the combination of multiscale feature network and cross-stage attention mechanism has significant advantages in improving the recovery effect of blurred images and provides an efficient new approach in the field of image processing, especially showing strong potential in dealing with highly blurred images.
Analysis of how teacher evaluation reform in the new era promotes professional development of primary and secondary school teachers

This article explores how teacher evaluation in the new era can promote the professional development of primary and secondary school teachers. Through the analysis of existing literature and practical experiences, a comprehensive evaluation system is proposed to facilitate teachers' professional growth. Key points of the article include the establishment of multidimensional evaluation criteria, the provision of professional development opportunities, and the reinforcement of feedback and support. The research findings indicate that effective teacher evaluation can stimulate teachers' educational enthusiasm, enhance educational quality, and contribute to teachers' personal and career growth.
The path of deep integration of information technology and courses under TPACK model

This article aims to explore the role and value of the TPACK (Technological Pedagogical Content Knowledge) model in promoting the deep integration of information technology and curriculum in the context of educational informatization. By analyzing the Action Plan for Information Technology in Education 2.0, the gap between the popularization of hardware equipment and teachers' insufficient understanding of the integration of information technology and curriculum is revealed. Research has found that the TPACK model provides a new perspective on education and teaching, which is one of the keys to promoting educational modernization, and can also optimize the functions of smart teaching products. The research on deep integration paths based on the TPACK model involves the current status of deep integration of information technology and disciplines, TPACK evaluation model, teacher training and technology platform function development, etc., aiming to provide effective guidance for teacher training and technology development.
Research on the improvement of educational technology and teaching quality in local colleges and universities from the perspective of high-quality development

Educational technology teaching has become particularly important in the context of the current information age. However, there are many problems in educational technology teaching in local universities, such as insufficient technical equipment and foundation, insufficient teacher technical training, uncoordinated technology integration, and insufficient educational technology policy and planning. This paper mainly discusses how to improve the teaching quality of educational technology in local colleges and universities from the perspective of high-quality development, and proposes corresponding solutions to promote the improvement of the teaching quality of educational technology in local colleges and universities.
STATISTICAL AND MACHINE METHODS FOR AUTOMATICALLY EXTRACTING CAUSAL RELATIONSHIPS FROM TEXT (REVIEW)

Until the 2000s, the concept of non-statistical methods was used to solve the problem ofautomatic extraction of causal relationships (CR). These methods used manually constructedlinguistic templates. Obviously, the CR that did not fit into the built templates could not bedefined. Non-statistical methods required constant manual control by experts, up to the evaluation.Almost all methods were aimed at extracting explicit CR. In some methods, attemptswere made to untie the extraction system from a specific subject area. To eliminate the abovedisadvantages, the methods developed in the future began to shift towards statistical dataprocessing and machine learning. In this article, statistical and machine methods of CR e xtractionare considered. A few valuable papers related to the new paradigm of CR extractionwere analyzed. The aim of the research was to evaluate new methods with the ability to identifytheir advantages and disadvantages. The great advantage of machine and statisticalmethods is independence from the subject area while maintaining the accuracy of extraction.Such methods are worse in accuracy, but they are not tied to a specific problem area. Themethods themselves, unlike non-statistical ones, which used linguistic and syntactic comparisonwith templates manually, are focused on finding these templates. Even though machineand statistical methods are mostly independent of the subject area and use large corpora oftext for teaching, they are intended mainly for the English language. There is also no standardizeddata set that would allow methods to be compared with each other. All works devotedto methods ignored the extraction of implicit CR.
Effectiveness of Increasing Vocabulary and Constructing Sentences for High Class 4/5/6 SDLB for the Deaf Using the MMR Method with Video Media at SLB Riverkids and SLB Putra Jaya

Deafness is a condition where a child experiences problems with his ability to hear. Deaf children have a small vocabulary. The small vocabulary is caused by poor listening skills so that children are unable to accept new vocabulary which results in difficulties in communicating. Apart from difficulties in communication, they also have difficulty in constructing sentences. One step that can overcome the above problem is to apply the Maternal Reflective Method (MMR). The Reflective Maternal Method (MMR) emphasizes the mother's way of teaching language to children, where the teaching is carried out scientifically. The aim of this research is to analyze MMR on the ability of deaf children in adding vocabulary and constructing sentences. This type of research is Quasi Experimental research with a one group pretest posttest design, which applies five pretests and five posttests. Analysis technique for testing hypotheses using the SPSS 25 for Windows program.The research results showed a significant difference between the pretest results and the posttest results. This is proven by the output results obtained p is 0.000, which means p&lt;0.05 then H0 is rejected and Ha is accepted. This means that there is a significant increase in the vocabulary and sentence learning outcomes of deaf students.The conclusion of this research is that the Maternal Reflective Method (MMR) can influence the addition of vocabulary and sentence construction in deaf children
ANALISIS MATERIALITAS PADA PROSEDUR AUDIT VOUCHING ATAS AKUN BEBAN OPERASIONAL

In the era of society 5.0, the auditor profession will continue to exist even though the threat of AI (Artificial Intelligence) has come, bearing in mind that there are many human intelligences that cannot be replaced by any robots. An auditor needs to have an understanding of materiality, where this concept relates to how large a misstatement contained in an assertion can be accepted by the auditor so that users of financial statements are not affected by the magnitude of the misstatement. One of the applications of materiality is the vouching audit procedure. Vouching is a test carried out by the auditor to check the authenticity or correctness of the occurrence of a transaction presented in the financial statements through physical source documents. Vouching for operating expense accounts is important to do because there are many findings indicating irregularities in the presentation of operating expenses in the financial statements. The method used is a qualitative research-descriptive analysis based on the results of interviews with 4 people at Public Accounting Firms and Public ABC as samples and observations. This study aims to get answers regarding the importance of materiality in vouching audit procedures for operating expense accounts.
Implementation of Particle Swarm Optimization Feature Selection on Naïve Bayes for Thoracic Surgery Classification

Thoracic surgery is among the operations that are most often performed on patients with lung cancer. Naive Bayes is one of the data mining classification techniques that may be used to handle thoracic surgery data. Therefore, the goal of this study is to assess the precision of all research models using Naive Bayes with and without Particle Swarm Optimization. This study's methodology includes the dataset used, the Naive Bayes algorithm theory, the particle swarm optimization algorithm, test validation using split validation, and performance assessment using the confusion matrix and AUC evaluation approaches. In this inquiry, secondary data are retrieved via the UCI Repository website. Thoracic surgery weight optimization accuracy is increased using particle swarm optimization. The test results of the Naive Bayes technique utilizing the thoracic surgery dataset showed the highest accuracy of 81.91% at a ratio of 80:20 and an AUC value of 0.620. The highest accuracy score is 93.62% with an AUC value of 0.773 at a ratio of 90:10, with three characteristics, namely PRE6, PRE14, and PRE17, having zero weight. This accuracy score was achieved when Particle Swarm Optimization was used to refine feature selection for attribute weighting. As a consequence, Naïve Bayes accuracy in thoracic surgery has increased as a result of attribute weighting on feature selection utilizing Particle Swarm Optimization. In turn, this research contributes to increasing the precision and efficiency with which thoracic surgical data are processed, which benefits lung cancer diagnosis in both speed and accuracy.