Reviewerly/exHarmony huggingface dataset:
PENGGUNAAN MEDIA PAPAN LUKIS KARAKTER KARTUN UNTUK MENINGKATKAN HASIL BELAJAR IPS SISWA KELAS VIII-6 SMP NEGERI 42 BEKASI (Penelitian Tindakan di Kelas VIII-6 SMP Negeri 42 Bekasi Tahun Ajaran 2018/2019)

The main purpose of this research is to know does the use of cartoon character art board as a media could could be increasing the Score and activeness the Social Studies learning result of 41 students in the class of VIII-6, SMP Negeri 42 Bekasi, 2nd semester, education year of 2018/2019. This research was done from March to May 2019. The applied method in this research is Classroom Action Research (PTK) Method. which was done by a collaboration with social science teachers at SMP Negeri 42 Bekasi. 3 stages was applied during the research, they were: Planning, Acting, Observing and Reflecting. This research found the increase of learning result in every stage that was applied. In the 1st stage, there were 51% (21 students) who had score for learning result above the minimum standardize score which is 75. 2nd stage found there were 73% (30 students) who had score for learning result above the minimum standardize score which is 75. And in the 3rd stage, there were 85% (35 students) who had score for learning result above the minimum standardize score which is 75. Moreover, cartoon character art board media able to increase students’ activity to learn as well. Active level percentage in the first cycle was 12%. In the second cycle was increasing to 38%. And in the third cycle significantly increased to 54%. The conclusion of the research is the using of cartoon character art board as a media could increase the Social Studies learning result and the active level of the students, therefor the students could get a better score for social studies subject which is above the minimum standardize score 75.&#x0D; Keywords: Class acted research, Cartoon character art board, Social Studies Learning Outcomes, Students’ activeness.
PENGGUNAAN MEDIA KAHOOT! UNTUK MENINGKATKAN HASIL BELAJAR IPS (Penelitian Tindakan Kelas di Kelas VII-F SMP Negeri 92 Jakarta)

This research aims to find out the use of Kahoot media to increase result of social sciences learning. This is researched in VII-F class with 36 students in Junior High School of 92 Jakarta since March to April 2019. The method used in this research is classroom action research that uses three cycles, each of cycles consists of four steps; Planning, Acting, Observing, and Reflecting. According to research, the learning result occurs enhancement each of cycle. In the first cycle as big as 42% (15 students) reaches score above minimum completeness criteria 72 with average of learning result 68, second cycle becomes 67% (24 students) reaching score above minimum completeness criteria 72 with average of learning result 74, and in the third cycle becomes 83% (30 students) reaching score above minimum completeness criteria 72 with average of learning result 83. Besides the increasing of learning result, student activiness of each cycle also improves, that's in first cycle of activity of active students in the ability to ask 9%, answer 11%, think 14%, and solve problems 14%. Then in second cycle there was an increase in activeness of students who were active in their ability to ask 22%, answer 17%, argue 20%, and solve problems 20%. Then in third cycle in cycle 3 students who are active in the aspect of ability to ask 64%, answer 61%, argue 58%, and solve problems 56%. It can be concluded that the use of Kahoot media can increase the result of social studies learning and increasing the activeness of students in VII-F class at Junior High School of 92 Jakarta.&#x0D; Keyword: Classroom Action Research, Kahoot media, Result of Social Sciences Learning
The Impact of Big Data on Competitive Advantage in Jordanian Tourism and Travel Offices and Companies

This study aimed to clarify the impact of big data on competitive advantage in Jordanian travel and tourism offices and companies.The study relied on the big data analysis dimension, while four dimensions were adopted to measure competitive advantage: growth, flexibility, cost, and innovation.This study was conducted on the Jordanian tourism and travel companies and offices, which numbered (314) offices and companies, the researchers used a proportional stratified random sample, and the sample size was (167).The study found high levels of application of big data and competitive advantage by Jordanian travel and tourism offices and companies.The results of the study also indicated that there is a statistically significant effect of big data on competitive advantage.The researchers presented a set of recommendations that can help Jordanian travel and tourism offices and companies in increasing interest in big data as well as achieving competitive advantage.
Sarcasm Detection in News Headlines: A Comparative Study of Multinomial Naive Bayes, Logistic Regression, and Support Vector Machine

Abstract: Sarcasm is frequently used in news, and it is difficult to detect sarcasm in news headlines for humans and let alone computers. Media outlets constantly make use of sarcasm in their news headlines to target certain set of people and to subtly spread misinformation by confusing viewers, who, in turn, tend to spread their misinterpreted news to their contacts. Hence, it is very important to develop a system that can accurately detect sarcasm. This study proposes a unique method for sarcasm detection in news headlines that makes use of the Multinomial Naive Bayes, Logistic Regression, and Support Vector Machine classifiers with Bag-of-Words representation. We collected and combined two datasets from Kaggle of labeled headlines for training and evaluation. Using the CountVectorizer, we transformed headlines into numerical vectors capturing word occurrences. Our model yielded great test set accuracies of roughly 87.15%, 89.93%, and 90.6% for Multinomial Naive Bayes, Logistic Regression, and Support Vector Machine respectively. The precision, recall, and F1-score demonstrated balanced detection capability, and were also high enough to accurately detect sarcasm. Finally, our study was able to compare the three classifiers and point out each classifier’s strengths and weaknesses, while also suggesting the best classifier for sarcasm detection of news headlines.
Unmasking the Shadows: A Multi-Dimensional Approach for Cyberbullying Detection in Social Media Networks

Abstract: Cyber bullying refers to the use of electronic techniques of intimidation. Historically challenging, there has been a growing awareness of the repercussions for young people. Teenagers and young adults who spend time on social media sites are an easy target for bullies because of the platform's conducive nature to harassment and threats. We can create algorithms that can automatically identify cyberbullying material and distinguish between the language styles of cyberbullies and their victims with the use of machine learning techniques. Over the last decade, social media has seen explosive growth, which has benefits and drawbacks. As the number of social media sites and apps continues to grow, more and more people are able to connect with one another online. directly, ignoring cultural and economic contexts While there are numerous positive outcomes associated with social media use, none exist at this time. The increase of hate speech over the last several years is a special issue that has surfaced. The use of foul language is the major component of hateful statements. Online networking use It might be used to refer to anybody or anything at all. collective of like-minded people. The methods we use to deal with hostility and the steps we take to mitigate it were detailed in this study. People's instantaneous expressions of wrath and animosity on social media are harmful to the sentiments of others. In-depth investigation on the handling of The most accurate machine learning and natural language processing techniques were used to choose the model used to exclude hate speech.
Drivable Road Region Segmentation in Real Time with High Precision using Deep Learning

Abstract: This paper presents a novel approach that addresses the challenging task of real-time drivable road region extraction in computer vision. Semantic segmentation, which involves accurately identifying and segmenting objects in real-time data, is a complex problem. However, deep learning has proven to be a powerful technique for achieving semantic segmentation by automatically identifying patterns without the need for explicit programming. To tackle this task, the paper proposes a fusion of the YOLO algorithm and UNET architecture, leveraging their respective strengths. The YOLO algorithm enables high-speed object detection, while the UNET architecture provides advantages in global location utilization, contextual understanding, and performance, even with limited training samples. Importantly, the proposed method is lightweight, making it suitable for deployment on embedded systems with limited computational power. To optimize memory usage and capture context at different scales, the system employs dilated convolutions for efficient feature extraction. The algorithm exhibits exceptional performance in accurately segmenting irregular objects and handles diverse input data types, including images and videos, in real-time. Overall, this paper contributes significantly to the advancement of computer vision technologies and offers a valuable solution for real-time drivable road region extraction. Its potential applications include addressing driving challenges and enhancing safety in autonomous vehicles and intelligent transportation systems.
Measurement sharpness and disturbance tradeoff

Obtaining information from a quantum system through a measurement typically disturbs its state. The postmeasurement states for a given measurement, however, are not unique and highly rely on the chosen measurement model, complicating the puzzle of information-disturbance. Two distinct questions are then in order. Firstly, what is the minimum disturbance a measurement may induce? Secondly, when a fixed disturbance occurs, how informative is the possible measurement in the best-case scenario? Here, we propose various approaches to tackle these questions and provide explicit solutions for the set of unbiased binary qubit measurements and postmeasurement state spaces that are equivalent to the image of a unital qubit channel. In particular, we show there are different tradeoff relations between the sharpness of this measurement and the average fidelity of the premeasurement and postmeasurement state spaces as well as the sharpness and quantum resources preserved in the postmeasurement states in terms of coherence and discord-like correlation once the measurement is applied locally.
Large Language Model Prompt Chaining for Long Legal Document Classification

Prompting is used to guide or steer a language model in generating an appropriate response that is consistent with the desired outcome. Chaining is a strategy used to decompose complex tasks into smaller, manageable components. In this study, we utilize prompt chaining for extensive legal document classification tasks, which present difficulties due to their intricate domain-specific language and considerable length. Our approach begins with the creation of a concise summary of the original document, followed by a semantic search for related exemplar texts and their corresponding annotations from a training corpus. Finally, we prompt for a label - based on the task - to assign, by leveraging the in-context learning from the few-shot prompt. We demonstrate that through prompt chaining, we can not only enhance the performance over zero-shot, but also surpass the micro-F1 score achieved by larger models, such as ChatGPT zero-shot, using smaller models.
Under-Display Camera Image Restoration with Scattering Effect

The under-display camera (UDC) provides consumers with a full-screen visual experience without any obstruction due to notches or punched holes. However, the semi-transparent nature of the display inevitably introduces the severe degradation into UDC images. In this work, we address the UDC image restoration problem with the specific consideration of the scattering effect caused by the display. We explicitly model the scattering effect by treating the display as a piece of homogeneous scattering medium. With the physical model of the scattering effect, we improve the image formation pipeline for the image synthesis to construct a realistic UDC dataset with ground truths. To suppress the scattering effect for the eventual UDC image recovery, a two-branch restoration network is designed. More specifically, the scattering branch leverages global modeling capabilities of the channel-wise self-attention to estimate parameters of the scattering effect from degraded images. While the image branch exploits the local representation advantage of CNN to recover clear scenes, implicitly guided by the scattering branch. Extensive experiments are conducted on both real-world and synthesized data, demonstrating the superiority of the proposed method over the state-of-the-art UDC restoration techniques. The source code and dataset are available at \url{https://github.com/NamecantbeNULL/SRUDC}.
On the Node-Averaged Complexity of Locally Checkable Problems on Trees

Over the past decade, a long line of research has investigated the distributed complexity landscape of locally checkable labeling (LCL) problems on bounded-degree graphs, culminating in an almost-complete classification on general graphs and a complete classification on trees. The latter states that, on bounded-degree trees, any LCL problem has deterministic worst-case time complexity $O(1)$, $\Theta(\log^* n)$, $\Theta(\log n)$, or $\Theta(n^{1/k})$ for some positive integer $k$, and all of those complexity classes are nonempty. Moreover, randomness helps only for (some) problems with deterministic worst-case complexity $\Theta(\log n)$, and if randomness helps (asymptotically), then it helps exponentially. In this work, we study how many distributed rounds are needed on average per node in order to solve an LCL problem on trees. We obtain a partial classification of the deterministic node-averaged complexity landscape for LCL problems. As our main result, we show that every problem with worst-case round complexity $O(\log n)$ has deterministic node-averaged complexity $O(\log^* n)$. Then we show how using randomization we can speed this up and show that every problem with worst case round complexity $O(\log n)$ has randomized node-averaged complexity $O(1)$. We further establish bounds on the node-averaged complexity of problems with worst-case complexity $\Theta(n^{1/k})$: we show that all these problems have node-averaged complexity $\widetilde{\Omega}(n^{1 / (2^k - 1)})$, and that this lower bound is tight for some problems. The lower bound holds even for the randomized case and the upper bound is a deterministic algorithm.
Hybrid Retrieval-Augmented Generation for Real-time Composition Assistance

Retrieval augmentation enhances performance of traditional language models by incorporating additional context. However, the computational demands for retrieval augmented large language models (LLMs) pose a challenge when applying them to real-time tasks, such as composition assistance. To address this limitation, we propose the Hybrid Retrieval-Augmented Generation (HybridRAG) framework, a novel approach that efficiently combines a cloud-based LLM with a smaller, client-side, language model through retrieval augmented memory. This integration enables the client model to generate effective responses, benefiting from the LLM's capabilities and contextual information. Additionally, through an asynchronous memory update mechanism, the client model can deliver real-time completions swiftly to user inputs without the need to wait for responses from the cloud. Our experiments on five benchmark datasets demonstrate that HybridRAG significantly improves utility over client-only models while maintaining low latency.
Degeneration-Tuning: Using Scrambled Grid shield Unwanted Concepts from Stable Diffusion

Owing to the unrestricted nature of the content in the training data, large text-to-image diffusion models, such as Stable Diffusion (SD), are capable of generating images with potentially copyrighted or dangerous content based on corresponding textual concepts information. This includes specific intellectual property (IP), human faces, and various artistic styles. However, Negative Prompt, a widely used method for content removal, frequently fails to conceal this content due to inherent limitations in its inference logic. In this work, we propose a novel strategy named \textbf{Degeneration-Tuning (DT)} to shield contents of unwanted concepts from SD weights. By utilizing Scrambled Grid to reconstruct the correlation between undesired concepts and their corresponding image domain, we guide SD to generate meaningless content when such textual concepts are provided as input. As this adaptation occurs at the level of the model's weights, the SD, after DT, can be grafted onto other conditional diffusion frameworks like ControlNet to shield unwanted concepts. In addition to qualitatively showcasing the effectiveness of our DT method in protecting various types of concepts, a quantitative comparison of the SD before and after DT indicates that the DT method does not significantly impact the generative quality of other contents. The FID and IS scores of the model on COCO-30K exhibit only minor changes after DT, shifting from 12.61 and 39.20 to 13.04 and 38.25, respectively, which clearly outperforms the previous methods.
A Deep-Learning Method Using Auto-encoder and Generative Adversarial Network for Anomaly Detection on Ancient Stone Stele Surfaces

Accurate detection of natural deterioration and man-made damage on the surfaces of ancient stele in the first instance is essential for their preventive conservation. Existing methods for cultural heritage preservation are not able to achieve this goal perfectly due to the difficulty of balancing accuracy, efficiency, timeliness, and cost. This paper presents a deep-learning method to automatically detect above mentioned emergencies on ancient stone stele in real time, employing autoencoder (AE) and generative adversarial network (GAN). The proposed method overcomes the limitations of existing methods by requiring no extensive anomaly samples while enabling comprehensive detection of unpredictable anomalies. the method includes stages of monitoring, data acquisition, pre-processing, model structuring, and post-processing. Taking the Longmen Grottoes' stone steles as a case study, an unsupervised learning model based on AE and GAN architectures is proposed and validated with a reconstruction accuracy of 99.74\%. The method's evaluation revealed the proficient detection of seven artificially designed anomalies and demonstrated precision and reliability without false alarms. This research provides novel ideas and possibilities for the application of deep learning in the field of cultural heritage.
Towards Autocomplete Strategies for Visualization Construction

Constructive visualization uses physical data units - tokens - to enable non-experts to create personalized visualizations engagingly. However, its physical nature limits efficiency and scalability. One potential solution to address this issue is autocomplete. By providing automated suggestions while still allowing for manual intervention, autocomplete can expedite visualization construction while maintaining expressivity. We conduct a speculative design study to examine how people would like to interact with a visualization authoring system that supports autocomplete. Our study identifies three types of autocomplete strategies and gains insights for designing future visualization authoring tools with autocomplete functionality. A free copy of this paper and all supplemental materials are available on our online repository https://osf.io/nu4z3/?view_only=594baee54d114a99ab381886fb32a126
Enhancing image captioning with depth information using a Transformer-based framework

Captioning images is a challenging scene-understanding task that connects computer vision and natural language processing. While image captioning models have been successful in producing excellent descriptions, the field has primarily focused on generating a single sentence for 2D images. This paper investigates whether integrating depth information with RGB images can enhance the captioning task and generate better descriptions. For this purpose, we propose a Transformer-based encoder-decoder framework for generating a multi-sentence description of a 3D scene. The RGB image and its corresponding depth map are provided as inputs to our framework, which combines them to produce a better understanding of the input scene. Depth maps could be ground truth or estimated, which makes our framework widely applicable to any RGB captioning dataset. We explored different fusion approaches to fuse RGB and depth images. The experiments are performed on the NYU-v2 dataset and the Stanford image paragraph captioning dataset. During our work with the NYU-v2 dataset, we found inconsistent labeling that prevents the benefit of using depth information to enhance the captioning task. The results were even worse than using RGB images only. As a result, we propose a cleaned version of the NYU-v2 dataset that is more consistent and informative. Our results on both datasets demonstrate that the proposed framework effectively benefits from depth information, whether it is ground truth or estimated, and generates better captions. Code, pre-trained models, and the cleaned version of the NYU-v2 dataset will be made publically available.
SoK: Acoustic Side Channels

We provide a state-of-the-art analysis of acoustic side channels, cover all the significant academic research in the area, discuss their security implications and countermeasures, and identify areas for future research. We also make an attempt to bridge side channels and inverse problems, two fields that appear to be completely isolated from each other but have deep connections.
Algorithmic error mitigation for quantum eigenvalues estimation

When estimating the eigenvalues of a given observable, even fault-tolerant quantum computers will be subject to errors, namely algorithmic errors. These stem from approximations in the algorithms implementing the unitary passed to phase estimation to extract the eigenvalues, e.g. Trotterisation or qubitisation. These errors can be tamed by increasing the circuit complexity, which may be unfeasible in early-stage fault-tolerant devices. Rather, we propose in this work an error mitigation strategy that enables a reduction of the algorithmic errors up to any order, at the cost of evaluating the eigenvalues of a set of observables implementable with limited resources. The number of required observables is estimated and is shown to only grow polynomially with the number of terms in the Hamiltonian, and in some cases, linearly with the desired order of error mitigation. Our results show error reduction of several orders of magnitude in physically relevant cases, thus promise accurate eigenvalue estimation even in early fault-tolerant devices with limited number of qubits.