Reviewerly/exHarmony huggingface dataset:
Communication Modulation Recognition Technology Based on Wavelet Entropy and Decision Tree

The signal modulation recognizer is a very critical system component in both military and civilian communication systems. In this research, the characteristics of time domain, frequency domain, and wavelet entropy are extracted from different modulation signals. The wavelet entropy includes wavelet packet energy entropy, scale entropy, and singular entropy. The feature extraction parameters are used as the input of the feature layer of the decision tree model for training and verification. Experiments show that the time-frequency domain, and wavelet entropy feature extraction of the signal can more completely represent the characteristics of the signal, while reducing the number of training calculations, improving training efficiency and recognition accuracy.
Fault-Tolerant Preparation of Quantum Polar Codes Encoding One Logical Qubit

This paper explores a new approach to fault-tolerant quantum computing (FTQC), relying on quantum polar codes. We consider quantum polar codes of Calderbank-Shor-Steane type, encoding one logical qubit, which we refer to as $\mathcal{Q}_1$ codes. First, we show that a subfamily of $\mathcal{Q}_1$ codes is equivalent to the well-known family of Shor codes. Moreover, we show that $\mathcal{Q}_1$ codes significantly outperform Shor codes, of the same length and minimum distance. Second, we consider the fault-tolerant preparation of $\mathcal{Q}_1$ code states. We give a recursive procedure to prepare a $\mathcal{Q}_1$ code state, based on two-qubit Pauli measurements only. The procedure is not by itself fault-tolerant, however, the measurement operations therein provide redundant classical bits, which can be advantageously used for error detection. Fault-tolerance is then achieved by combining the proposed recursive procedure with an error detection method. Finally, we consider the fault-tolerant error correction of $\mathcal{Q}_1$ codes. We use Steane error correction, which incorporates the proposed fault-tolerant code state preparation procedure. We provide numerical estimates of the logical error rates for $\mathcal{Q}_1$ and Shor codes of length $16$ and $64$ qubits, assuming a circuit-level depolarizing noise model. Remarkably, the $\mathcal{Q}_1$ code of length $64$ qubits achieves a logical error rate very close to $10^{-6}$ for the physical error rate $p = 10^{-3}$, therefore, demonstrating the potential of the proposed polar codes based approach to FTQC.
QParallel: Explicit Parallelism for Programming Quantum Computers

We present a language extension for parallel quantum programming to (1) remove ambiguities concerning parallelism in current quantum programming languages and (2) facilitate space-time tradeoff investigations in quantum computing. While the focus of similar libraries in the domain of classical computing (OpenMP, OpenACC, etc.) is to divide a computation into multiple threads, the main goal of QParallel is to keep the compiler and the runtime system from introducing parallelism-inhibiting dependencies, e.g., through reuse of qubits in automatic qubit management. We describe the syntax and semantics of the proposed language extension, implement a prototype based on Q#, and present several examples and use cases to illustrate its performance benefits. Moreover, we introduce a tool that guides programmers in the placement of parallel regions by identifying the subroutines that profit most from parallelization, which is especially useful if the programmer's knowledge of the source code is limited. Support for QParallel can be added to any multithreading library and language extension, including OpenMP and OpenACC.
A Fair Classifier Embracing Triplet Collapse

In this paper, we study the behaviour of the triplet loss and show that it can be exploited to limit the biases created and perpetuated by machine learning models. Our fair classifier uses the collapse of the triplet loss when its margin is greater than the maximum distance between two points in the latent space, in the case of stochastic triplet selection.
Enriching Shot Boundary Detection through DT-DWT and Deep Learning Models

Abstract Numerous techniques exist for detecting shot or scene boundaries, typically relying on visual characteristics of frames and contrasting said features among adjacent frames to identify shot boundaries. Shot Boundary Detection can utilize a range of visual characteristics such as edge feature, gray intensity, motion vector, and color histogram. The accuracy of Shot Boundary identification methods that currently exist has demonstrated a relatively high level of effectiveness. It is imperative to adapt current techniques to accommodate diverse video content types, as contemporary video productions employ a wider range of video creation methods than their predecessors. The presented approach in this methodology examines the different techniques fr the purpose of achieving the shot boundary detection. The Dual Tree – Discrete Wavelet Transformation approach has been contrasted with the deep learning approaches such as Artificial Neural Network and Convolutional Neural Network. The video is given as input on which frame extraction, feature extraction through Entropy Calculation and Mean Log Estimation is realized with the Deep learning methodologies. The outcomes of the shot boundary identification have been effectively compared with one another to determine performance which is displayed in the later sections of this research article.
Genially- based interactive learning media in the sub-theme of the uniqueness In my home town

This development research uses the ADDIE model (Analysis, Design, Development, Implementation , Evaluation). This research was conducted at SD Negeri Cigombong 02, Bogor Regency, and consisted of 31 students. This research was conducted in the even semester of the 2022/2023 school year. The purpose of this research is to solve the problems that fourth grade students face in the learning process. The results of the research show that the developed interactive learning media is feasible for students to use. This can be seen from the validation results carried out by media experts, linguists, and material experts showing a high percentage. Assessments from media experts showed a percentage of 94% "very feasible", linguists showed a percentage of 100% “very feasible", and material experts showed a percentage of 96% "very feasible". The results of the expert validation as a whole show a percentage of 96.6 % with the criteria of "very feasible". The limited test conducted on 31 students obtained a response of 98.8 % in the "very good" category. Based on the results of the study, it was concluded that genially- based interactive learning media is very appropriate for students to use.
THE ADOPTION OF GOOD CORPORATE GOVERNANCE PRINCIPLES HEIGHTEN MOMENTUM ON EMPLOYEE COMMITMENT

Literature has unfolded that employee commitment has proven to be of great influence on workplace performance and productivity. This study therefore aims to investigate if corporate governance principles affect the employee commitment with corporate reputation and organisational trust mediating the dual relationship. A positivist approach using survey method was employed to collect data from employees working in Malaysian companies. A total of 230 sample were analysed using SPSS and Smart PLS. The results show a positive relationship between corporate governance principles and employee commitment as well as corporate reputation. Corporate governance also had a significant effect on organisation trust which in turn leads to employee commitment. The mediating effect of both corporate reputation and organisational trust had strengthened the relationship between the two constructs. upending the wave of employee commitment in human resource research, these findings implied that governance concepts such as fairness, accountability, responsibility, and transparency had positive impact on employee commitment as well. Future research to close the gap also has been highlighted.
Navigating Interfaces with AI for Enhanced User Interaction

This study introduces an innovative framework designed to automate tasks by interacting with UIs through a sequential, human-like problem-solving approach. Our approach initially transforms UI screenshots into natural language explanations through a vision-based UI analysis, circumventing traditional view hierarchy limitations. It then methodically engages with each interface, guiding the LLM to pinpoint and act on relevant UI elements, thus bolstering both precision and functionality. Employing the ERNIE Bot LLM, our approach has been demonstrated to surpass existing methodologies. It delivers superior UI interpretation across various datasets and exhibits remarkable efficiency in automating varied tasks on an Android smartphone, outperforming human capabilities in intricate tasks and significantly enhancing the PBD process.
Sample Efficient Reinforcement Learning with Partial Dynamics Knowledge

The problem of sample complexity of online reinforcement learning is often studied in the literature without taking into account any partial knowledge about the system dynamics that could potentially accelerate the learning process. In this paper, we study the sample complexity of online Q-learning methods when some prior knowledge about the dynamics is available or can be learned efficiently. We focus on systems that evolve according to an additive disturbance model of the form $S_{h+1} = f(S_h, A_h) + W_h$, where $f$ represents the underlying system dynamics, and $W_h$ are unknown disturbances independent of states and actions. In the setting of finite episodic Markov decision processes with $S$ states, $A$ actions, and episode length $H$, we present an optimistic Q-learning algorithm that achieves $\tilde{\mathcal{O}}(\text{Poly}(H)\sqrt{T})$ regret under perfect knowledge of $f$, where $T$ is the total number of interactions with the system. This is in contrast to the typical $\tilde{\mathcal{O}}(\text{Poly}(H)\sqrt{SAT})$ regret for existing Q-learning methods. Further, if only a noisy estimate $\hat{f}$ of $f$ is available, our method can learn an approximately optimal policy in a number of samples that is independent of the cardinalities of state and action spaces. The sub-optimality gap depends on the approximation error $\hat{f}-f$, as well as the Lipschitz constant of the corresponding optimal value function. Our approach does not require modeling of the transition probabilities and enjoys the same memory complexity as model-free methods.
Global Occlusion-Aware Transformer for Robust Stereo Matching

Despite the remarkable progress facilitated by learning-based stereo-matching algorithms, the performance in the ill-conditioned regions, such as the occluded regions, remains a bottleneck. Due to the limited receptive field, existing CNN-based methods struggle to handle these ill-conditioned regions effectively. To address this issue, this paper introduces a novel attention-based stereo-matching network called Global Occlusion-Aware Transformer (GOAT) to exploit long-range dependency and occlusion-awareness global context for disparity estimation. In the GOAT architecture, a parallel disparity and occlusion estimation module PDO is proposed to estimate the initial disparity map and the occlusion mask using a parallel attention mechanism. To further enhance the disparity estimates in the occluded regions, an occlusion-aware global aggregation module (OGA) is proposed. This module aims to refine the disparity in the occluded regions by leveraging restricted global correlation within the focus scope of the occluded areas. Extensive experiments were conducted on several public benchmark datasets including SceneFlow, KITTI 2015, and Middlebury. The results show that the proposed GOAT demonstrates outstanding performance among all benchmarks, particularly in the occluded regions.
Amodal Completion via Progressive Mixed Context Diffusion

Our brain can effortlessly recognize objects even when partially hidden from view. Seeing the visible of the hidden is called amodal completion; however, this task remains a challenge for generative AI despite rapid progress. We propose to sidestep many of the difficulties of existing approaches, which typically involve a two-step process of predicting amodal masks and then generating pixels. Our method involves thinking outside the box, literally! We go outside the object bounding box to use its context to guide a pre-trained diffusion inpainting model, and then progressively grow the occluded object and trim the extra background. We overcome two technical challenges: 1) how to be free of unwanted co-occurrence bias, which tends to regenerate similar occluders, and 2) how to judge if an amodal completion has succeeded. Our amodal completion method exhibits improved photorealistic completion results compared to existing approaches in numerous successful completion cases. And the best part? It doesn't require any special training or fine-tuning of models.
Doing PeaceTech

PeaceTech reports seldom describe the practical trials of mounting a project. Yet those who reflect on the field suggest that the transformative or disruptive potential of PeaceTech lies less in the attempt to use technology and more in the curious and unpredictable ways that PeaceTech ‘doing’ modifies peacebuilding as a political practice. The chapter therefore offers lessons from doing.
On Learning for Ambiguous Chance Constrained Problems

We study chance constrained optimization problems $\min_x f(x)$ s.t. $P(\left\{ \theta: g(x,\theta)\le 0 \right\})\ge 1-\epsilon$ where $\epsilon\in (0,1)$ is the violation probability, when the distribution $P$ is not known to the decision maker (DM). When the DM has access to a set of distributions $\mathcal{U}$ such that $P$ is contained in $\mathcal{U}$, then the problem is known as the ambiguous chance-constrained problem \cite{erdougan2006ambiguous}. We study ambiguous chance-constrained problem for the case when $\mathcal{U}$ is of the form $\left\{\mu:\frac{\mu (y)}{\nu(y)}\leq C, \forall y\in\Theta, \mu(y)\ge 0\right\}$, where $\nu$ is a ``reference distribution.'' We show that in this case the original problem can be ``well-approximated'' by a sampled problem in which $N$ i.i.d. samples of $\theta$ are drawn from $\nu$, and the original constraint is replaced with $g(x,\theta_i)\le 0,~i=1,2,\ldots,N$. We also derive the sample complexity associated with this approximation, i.e., for $\epsilon,\delta>0$ the number of samples which must be drawn from $\nu$ so that with a probability greater than $1-\delta$ (over the randomness of $\nu$), the solution obtained by solving the sampled program yields an $\epsilon$-feasible solution for the original chance constrained problem.
Cover JTT Vol 8 no 2 tahun 2022

Cover JTT Vol 8 no 2 tahun 2022
Use of a generative chatbot as a middleman to improve User Experience in Interactive Fiction games

In the 1970s and 1980s, interactive fiction games emerged and changed the gaming world by enabling players to talk to the machine, make their own decisions, choose their paths, and decide what to collect and do. This freedom captivated players, but at the time, games were limited by programmed scripts that only accepted words present in the game’s code dictionary in a limited format, usually two or three keywords by phrase. However, technological advances have paved the way for improvements in this regard, replacing scripts with artificial intelligence using APIs, such as ChatGPT, for example. Thus, it may be possible to offer players an even more natural and customized experience, where each decision is more flexible with the player’s writing style. In this work, we carried out a communication experiment with the ChatGPT as an intermediary for interactive fiction games and the Frotz game interpreter (a modified version of the Z-Machine interpreter) to assess the feasibility of the approach.
Deep learning approaches for robust deep fake detection

Detecting deepfake images using a deep learning approach, particularly using model Densenet121, involves training a neural network to differentiate between authentic and manipulated images. Deepfakes have gained prominence due to advances in deep learning, especially generative adversarial networks (GANs). They pose significant challenges to the veracity of digital content, as they can be used to create realistic and deceptive media. Deepfakes are realistic looking fake media generated by many artificial intelligence tools like face2face and deepfake, which pose a severe threat to public. As more deepfakes are spreading, we really need better ways to find and prevent them. Deepfake involves creation of highly realistic images and videos and misuse them for spreading fake news, defaming individuals, and possess a significant threat to the integrity of digital content. Our project “Deep Learning Approaches for Robust Deep Fake Detection” aims to address this critical issue by developing a robust system for identification and localization of deep fake content by using ‘Densenet121’ model. This proposed framework seamlessly integrates forgery detection and localization. The dataset used in this project is “140k Real and Fake Faces”, and it consists of 70k real faces from Flickr dataset collected by Nvidia and 70k fake faces sampled from the 1 million Fake faces generated by StyleGAN. For localization purpose, we use GRAD-CAM method to accurately identify the morphed regions. Overall, our goal is to make deepfake detection more effective and reliable in today’s digital landscape.
A survey on deepfake detection through deep learning

Imagine watching a video where Tom Hanks delivers a rousing speech, but you suspect it might be fabricated. This growing concern stems from the rise of "DeepFakes," hyper realistic manipulated videos created using deep learning algorithms. These tools can seamlessly stitch together faces, voices, and body movements, blurring the lines between reality and fiction. While DeepFakes hold promise for entertainment and creative expression, their potential for misuse is significant. Malicious actors could leverage them to spread misinformation, damage reputations, or even influence elections. Thankfully, researchers are developing sophisticated techniques to detect these synthetic creations. This survey delves into the realm of DeepFake detection, exploring various methods employed by deep neural networks (DNNs). We'll dissect how DeepFakes are made, categorize the most common creation techniques, and analyze the strengths and weaknesses of different detection approaches. Furthermore, we'll examine the evolving landscape of DeepFake datasets, which fuel the training and testing of detection models. We'll also discuss the ongoing quest for a universal DeepFake detector, capable of identifying even unseen manipulations. Finally, we'll touch on the ongoing challenges facing both DeepFake creators and detectors, highlighting the arms race that is unfolding in this technological battleground. By shedding light on these advancements and obstacles, we hope to empower audiences with the knowledge to critically evaluate the information they encounter in the digital age.
THE CONVICT'S SELF-ACTUALIZATION AS ONE OF THE PSYCHOLOGICAL CRITERIA FOR HIS CORRECTION

The article considers the possibility of using self-actualization indicators as markers of the convict's formation on the path of correction. It contains the results of an empirical study of the features of self-actualization of men aged 25-29, divided into three groups: previously convicted persons who were again in a pre-trial detention center in the status of suspects and accused; first brought to criminal responsibility, detained in a pre-trial detention center; men with the problem of finding a new job. It was found that the indicators of self-actualization were significantly higher in the group of men who found themselves in a pre-trial detention center for the first time, and they are specific for each of the three groups. It is concluded that the indicators of self-actualization can act as markers of correction of the convicted person (to indicate either his becoming on the path of correction, or the deepening of criminalization). The directions of further research on the problem of self-actualization of suspects, accused, convicted are shown.
Analisis Bibliometrik Publikasi Penelitian Kearsipan di Indonesia Berbasis Data Scopus

The development of archival research in Indonesia can be determined by carrying out bibliometric analysis. The analysis aims to determine the distribution of main information on archival research in Indonesia, journals relevant to archival publications, most cited sources, topic trends based on keywords and treemaps, keyword network maps, and network maps by country. Bibliometric analysis was carried out by using data from Scopus and the keywords (TITLE-ABS-KEY ( archival ) OR TITLE-ABS-KEY ( archives ) OR TITLE-ABS-KEY (archive) AND TITLE-ABS-KEY (Indonesia). Documents obtained were analyzed using R-biblioshiny and VosViewer software. The results of the analysis show that during the years of 1986 to 2023, 462 documents resulting from research on archives in Indonesia were found and published in 351 different sources with an average of 12.31 citations per document. The journal that publishes the most research on archives in Indonesia is the IOP Conference Series: Earth and Environmental Science. Keywords in the field of archives most often used are archives, cultural heritage, Jakarta, conservation, archive, Asia, Bali, breast cancer, colonialism, and decolonization. Trends in topics that are often studied are decolonization, infrastructure, landsat, South Asia, reconstruction, Aceh, colonial, community participation, curriculum, decolonization, fisheries, heritage, deep learning, materiality, tsunami, digital archives, digitalization, and Dutch colonial. Co-occurrence network analysis using VOSviewer produced 4 clusters with 100 keywords with the most archival themes, namely Aceh, anti colonialism, commercialization, decolonization, East Java, fisheries, imperialism, Indian Ocean, and national archives.
A Study on the Method of Korean Culture Education Centered on ‘activities’ from the Perspective of Mutual Cultural Understanding: Focused on Chinese Folktale ‘Mouinyeo’ and Korean Folktale ‘The fairy and the woodcutter’

In this paper, I examined a method of Korean cultural education aimed at mutual cultural understanding. In this case, I focused on ‘activities’ using Chinese and Korean folktales for advanced Chinese learners. First, I reviewed the relevant prior research and found that when using folktales, the activities were mainly focused on Korean culture only. However, even advanced learners have limited knowledge of Korean culture, so I thought it would be more effective to use cultural knowledge reflected in native tales familiar to learners as schemas. These schemas could be applied not once, but continuously and cumulatively in the process of teaching and learning. From this perspective, I first presented the Chinese tale ‘Moeunyeo’ and then the Korean tale ‘The fairy and the woodcutter’, and proposed an activity-based teaching and learning method that utilizes cultural elements reflected in these tales. Based on this, I proposed activities related to cultural elements that can be extracted from the contents of the two tales, and activities in terms of cross-cultural education. At the same time, I also presented reference materials that should be provided to learners related to Korean culture.