Reviewerly/exHarmony huggingface dataset:
Blockchain Adoption Decision-Making Process in Business: An Empirical Study

Recent research has highlighted gaps between blockchain technology and its adoption decision-making process on the corporate level. This paper aims to resolve these gaps, exploring the issues and processes that need to be considered before utilizing blockchain-based solutions in various domains. We collected data using 10 semi-structured interviews among blockchain professionals that have already adopted blockchain in their companies or were involved in the adoption decision-making process. We analyzed the data using the Gioa approach and identified five dimensions that must be considered before blockchain utilization, namely infrastructure, business models, operational processes, management and environmental impact. Additionally, based on the collected data we provide questions to ask before considering blockchain and a final framework that includes 18 sub-themes of the identified dimensions. Our study extends prior frameworks that might help organizations utilize blockchain according to their business strategy. Based on our findings, we also put forward directions for future studies.
PDHSMICK: A partial key-based secure EHRs with distributed cloud for healthcare systems applying MI-CRYSTALS-Kyber

Healthcare, which raises several challenges regarding security and privacy, is the largest sector of society dealing with patients’ health data. Numerous approaches have been developed regarding healthcare; however, these approaches lead to high response time and leakage of transaction privacy. Thus, this work proposes PDHSMICK, a partial key-based secure EHRs with distributed cloud for Healthcare Systems applying Mimetic Interpolation-based CRYSTALS-Kyber (MI-CRYSTAL-Kyber). Primarily, the patient and doctor register with the system. After that, the patient logs in and books an appointment with the doctor. Then, consultation is done, and their details are sent to the Third-Party Auditor (TPA), where they store the information in the log file. Afterwards, the patient verifies the log file and then uploads the information to the concerned Cloud Server (CS). During uploading, the files are split, and the attributes from the files and Distributed Cloud Server (DCS) are extracted. Then, by utilizing Interpolation-based Pearson Correlation Coefficient (I-PCC), a particular DCS is selected. After that, for authorization purposes, a hash code and digital signature are generated by utilizing Exponential based Extensible Output Functions (EX-EOF) and Double Mod-Digital Signature Algorithm (DM-DSA). Concurrently, the files are encrypted by employing MI-CRYSTAL-Kyber techniques. Lastly, the doctor logs on to the system, and hash code and digital signature verification are performed. The system allows the doctor to download the data if the verification matches. Also, the experimental assessment was validated, which shows the proposed technique’s efficacy.
Tourism Itinerary Recommendation using Vehicle Routing Problem Time Windows Based on Shortest Path Method and Analytics Hierarchy Process

Bandung and Lembang are the cities that are chosen by many tourists (local and foreign) as their destinations. Geographically, these cities are placed side-by-side and each city has its own characteristics. As in Bandung, there are many hotels and culinary spots, meanwhile, in Lembang, there are many scenery tourism spots. Tourists usually have limited time to visit all the destinations on holiday, which makes them choose several tourist destinations to visit. In this paper, the tourism itinerary recommendation system is proposed by calculating the most optimal route between destinations and approaching the Vehicle Routing Problem with Time Windows. The optimal route is defined by using Dijkstra the shortest path algorithm by using the collaboration between the road information (road length, traffic condition, travel time, and weather condition) and criteria weight that is determined using the Analytics Hierarchy Process as the graph weights. According to the simulation results, the criteria weights are 6.9%, 62.7%, 18.6%, and 11.9% for Route Length, Traffic Condition, Travel Time, and Weather Condition respectively. Moreover, the optimal number of tourism itinerary plans for the tourist in this research is 4 destinations. As the usage of computational resources, it takes 31.8% of CPU and 61.9% of Memory usage. For the time processing, it increases exponentially as the increment of number of requested stops. The output of this research is expected to be a solution to the tourist itinerary plan, especially in Bandung and Lembang.
Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach

In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble methods, to enhance credit card fraud detection. Through an extensive review of existing literature, we identified limitations in current fraud detection technologies, including issues like data imbalance, concept drift, false positives/negatives, limited generalisability, and challenges in real-time processing. To address some of these shortcomings, we propose a novel ensemble model that integrates a Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. This ensemble model tackles the dataset imbalance problem associated with most credit card datasets by implementing under-sampling and the Synthetic Over-sampling Technique (SMOTE) on some machine learning algorithms. The evaluation of the model utilises a dataset comprising transaction records from European credit card holders, providing a realistic scenario for assessment. The methodology of the proposed model encompasses data pre-processing, feature engineering, model selection, and evaluation, with Google Colab computational capabilities facilitating efficient model training and testing. Comparative analysis between the proposed ensemble model, traditional machine learning methods, and individual classifiers reveals the superior performance of the ensemble in mitigating challenges associated with credit card fraud detection. Across accuracy, precision, recall, and F1-score metrics, the ensemble outperforms existing models. This paper underscores the efficacy of ensemble methods as a valuable tool in the battle against fraudulent transactions. The findings presented lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, crucial as credit card fraud techniques continue to evolve.
Evaluasi Tata Kelola Teknologi Informasi Menggunakan Kerangka Kerja Cobit 5 di SMK XYZ

The application of IT in the field of education has emerged, such as in SMK Muhammadiyah Purworejo. The presence of IT in the organization helps improve school progress both in operational activities and services. However, in the school, there are still some parts that have not been managed properly. Problems that exist in SMK Muhammadiyah Purworejo such as school management activities that are not yet well structured, for example: not having a school payment system, not having a library system service, and the school's vision, mission, and goals that have not been implemented properly.This study attempts to evaluate governance using the COBIT version 5 framework to assist in guiding the management of the school. researchers used the APO domain (sub domain APO-02 and APO-07) and BAI (sub domain BAI06). The selected domain will evaluate for managing strategy, HR, and managing change. Researchers obtained school data from interviews, observations, and distributing questionnaires to respondents (there were 18 people). The results of the questionnaire distribution show that the Purworejo Muhammadiyah Vocational School agrees to hold follow-up changes in the school so that it can realign according to the school's vision, mission, and goals.The average of the APO-02 subdomain is 0.365; APO-07 of 0.157; and BAI-06 of 0.147. The domain gap is still at level 0 (Incomplete Process). The results of the BAI-06 and APO-02 sub-domains are still too far from the expected target, which is at level 3 (Established Process), while APO-07 is the expected target at level 4 (Predictable Process). Recommendations given by researchers from the BAI06 domain require improvement of business processes both from services, and school policies, and always evaluating these changes. APO-02 recommendations on strategic management in terms of improving student services and designing long-term strategies for schools. Meanwhile, APO-07 continues to develop the quality of its human resources by increasing its skills and expertise.
Recycling of a quantum field and optimal states for single-qubit rotations

We introduce a family of quantized field states that can perform exact (entanglement- and error-free) rotations of a two-level atom starting from a specific state on the Bloch sphere. We discuss the similarities and differences between these states and the recently-introduced "transcoherent states." Our field states have the property that they are left unchanged after the rotation, and we find they are the asymptotic states obtained when a field interacts with a succession of identically prepared ancillary atoms. Such a scheme was recently proposed [npj Quantum Information 3:17 (2017)] as a way to "restore" a field state after its interaction with a two-level atom, so as to reuse it afterwards, thus reducing the energy requirements for successive quantum logical operations. We generalize this scheme to find optimal pulses for arbitrary rotations, and also study analytically what happens if the ancillas are in a mixed, rather than a pure state. Consistent with the numerical results in the original proposal, we find that as long as the ancilla preparation error is small (of the order of $1/\bar n$, where $\bar n$ is the average number of atoms in the pulses considered) it will introduce only higher-order errors in the performance of the restored pulse.
PnPNet: Pull-and-Push Networks for Volumetric Segmentation with Boundary Confusion

Precise boundary segmentation of volumetric images is a critical task for image-guided diagnosis and computer-assisted intervention, especially for boundary confusion in clinical practice. However, U-shape networks cannot effectively resolve this challenge due to the lack of boundary shape constraints. Besides, existing methods of refining boundaries overemphasize the slender structure, which results in the overfitting phenomenon due to networks' limited abilities to model tiny objects. In this paper, we reconceptualize the mechanism of boundary generation by encompassing the interaction dynamics with adjacent regions. Moreover, we propose a unified network termed PnPNet to model shape characteristics of the confused boundary region. Core ingredients of PnPNet contain the pushing and pulling branches. Specifically, based on diffusion theory, we devise the semantic difference module (SDM) from the pushing branch to squeeze the boundary region. Explicit and implicit differential information inside SDM significantly boost representation abilities for inter-class boundaries. Additionally, motivated by the K-means algorithm, the class clustering module (CCM) from the pulling branch is introduced to stretch the intersected boundary region. Thus, pushing and pulling branches will shrink and enlarge the boundary uncertainty respectively. They furnish two adversarial forces to promote models to output a more precise delineation of boundaries. We carry out experiments on three challenging public datasets and one in-house dataset, containing three types of boundary confusion in model predictions. Experimental results demonstrate the superiority of PnPNet over other segmentation networks, especially on evaluation metrics of HD and ASSD. Besides, pushing and pulling branches can serve as plug-and-play modules to enhance classic U-shape baseline models. Codes are available.
The Effective Horizon Explains Deep RL Performance in Stochastic Environments

Reinforcement learning (RL) theory has largely focused on proving minimax sample complexity bounds. These require strategic exploration algorithms that use relatively limited function classes for representing the policy or value function. Our goal is to explain why deep RL algorithms often perform well in practice, despite using random exploration and much more expressive function classes like neural networks. Our work arrives at an explanation by showing that many stochastic MDPs can be solved by performing only a few steps of value iteration on the random policy's Q function and then acting greedily. When this is true, we find that it is possible to separate the exploration and learning components of RL, making it much easier to analyze. We introduce a new RL algorithm, SQIRL, that iteratively learns a near-optimal policy by exploring randomly to collect rollouts and then performing a limited number of steps of fitted-Q iteration over those rollouts. Any regression algorithm that satisfies basic in-distribution generalization properties can be used in SQIRL to efficiently solve common MDPs. This can explain why deep RL works neural networks, since it is empirically established that neural networks generalize well in-distribution. Furthermore, SQIRL explains why random exploration works well in practice, since we show many environments can be solved by estimating the random policy's Q-function and then applying zero or a few steps of value iteration. We leverage SQIRL to derive instance-dependent sample complexity bounds for RL that are exponential only in an "effective horizon" of lookahead and on the complexity of the class used for function approximation. Empirically, we also find that SQIRL performance strongly correlates with PPO and DQN performance in a variety of stochastic environments, supporting that our theoretical analysis is predictive of practical performance.
The Logic of Doxastic Strategies

In many real-world situations, there is often not enough information to know that a certain strategy will succeed in achieving the goal, but there is a good reason to believe that it will. The paper introduces the term ``doxastic'' for such strategies. The main technical contribution is a sound and complete logical system that describes the interplay between doxastic strategy and belief modalities.
LLF-Bench: Benchmark for Interactive Learning from Language Feedback

We introduce a new benchmark, LLF-Bench (Learning from Language Feedback Benchmark; pronounced as "elf-bench"), to evaluate the ability of AI agents to interactively learn from natural language feedback and instructions. Learning from language feedback (LLF) is essential for people, largely because the rich information this feedback provides can help a learner avoid much of trial and error and thereby speed up the learning process. Large Language Models (LLMs) have recently enabled AI agents to comprehend natural language -- and hence AI agents can potentially benefit from language feedback during learning like humans do. But existing interactive benchmarks do not assess this crucial capability: they either use numeric reward feedback or require no learning at all (only planning or information retrieval). LLF-Bench is designed to fill this omission. LLF-Bench is a diverse collection of sequential decision-making tasks that includes user recommendation, poem writing, navigation, and robot control. The objective of an agent is to interactively solve these tasks based on their natural-language instructions and the feedback received after taking actions. Crucially, to ensure that the agent actually "learns" from the feedback, LLF-Bench implements several randomization techniques (such as paraphrasing and environment randomization) to ensure that the task isn't familiar to the agent and that the agent is robust to various verbalizations. In addition, LLF-Bench provides a unified OpenAI Gym interface for all its tasks and allows the users to easily configure the information the feedback conveys (among suggestion, explanation, and instantaneous performance) to study how agents respond to different types of feedback. Together, these features make LLF-Bench a unique research platform for developing and testing LLF agents.
Prediction De-Correlated Inference

Leveraging machine-learning methods to predict outcomes on some unlabeled datasets and then using these pseudo-outcomes in subsequent statistical inference is common in modern data analysis. Inference in this setting is often called post-prediction inference. We propose a novel, assumption-lean framework for inference under post-prediction setting, called \emph{Prediction De-Correlated inference} (PDC). Our approach can automatically adapt to any black-box machine-learning model and consistently outperforms supervised methods. The PDC framework also offers easy extensibility for accommodating multiple predictive models. Both numerical results and real-world data analysis support our theoretical results.
Pengaruh Pembelajaran Berbasis Gamifikasi Terhadap Penguasaan Konsep dan Motivasi Siswa pada Materi Siklus Air

The aim of this research is to analyze the influence of gamification-based learning with conventional learning on class V students' mastery of concepts. The quasi-experimental method used in this research. Inferential statistical analysis and descriptive statistics using Microsoft Excel 2016 and SPSS version 20 were used for data processing. Sampling used purposive sampling technique with 31 students from class V-A control class and 33 students from class V-B experimental class. Research findings show that gamification-based learning has a positive effect on students' concept mastery and motivation. Students' mastery of concepts increased from an average score of 56.991 to 80.191. Students' learning motivation after applying gamification-based learning did not increase. The t-test results obtained were sig. = 0.593 ≥ 0.05. Gamification-based learning has a better effect on students' mastery of concepts compared to conventional learning as seen from the t-test results with an average in the experimental class of 80.358 &gt; 72.97 in the control class.
Understanding (Un)Intended Memorization in Text-to-Image Generative Models

Multimodal machine learning, especially text-to-image models like Stable Diffusion and DALL-E 3, has gained significance for transforming text into detailed images. Despite their growing use and remarkable generative capabilities, there is a pressing need for a detailed examination of these models' behavior, particularly with respect to memorization. Historically, memorization in machine learning has been context-dependent, with diverse definitions emerging from classification tasks to complex models like Large Language Models (LLMs) and Diffusion models. Yet, a definitive concept of memorization that aligns with the intricacies of text-to-image synthesis remains elusive. This understanding is vital as memorization poses privacy risks yet is essential for meeting user expectations, especially when generating representations of underrepresented entities. In this paper, we introduce a specialized definition of memorization tailored to text-to-image models, categorizing it into three distinct types according to user expectations. We closely examine the subtle distinctions between intended and unintended memorization, emphasizing the importance of balancing user privacy with the generative quality of the model outputs. Using the Stable Diffusion model, we offer examples to validate our memorization definitions and clarify their application.
Hijacking Context in Large Multi-modal Models

Recently, Large Multi-modal Models (LMMs) have demonstrated their ability to understand the visual contents of images given the instructions regarding the images. Built upon the Large Language Models (LLMs), LMMs also inherit their abilities and characteristics such as in-context learning where a coherent sequence of images and texts are given as the input prompt. However, we identify a new limitation of off-the-shelf LMMs where a small fraction of incoherent images or text descriptions mislead LMMs to only generate biased output about the hijacked context, not the originally intended context. To address this, we propose a pre-filtering method that removes irrelevant contexts via GPT-4V, based on its robustness towards distribution shift within the contexts. We further investigate whether replacing the hijacked visual and textual contexts with the correlated ones via GPT-4V and text-to-image models can help yield coherent responses.
Quantum Private Information Retrieval from Coded Storage Systems

In the era of extensive data growth, robust and efficient mechanisms are needed to store and manage vast amounts of digital information, such as Data Storage Systems (DSSs). Concurrently, privacy concerns have arisen, leading to the development of techniques like Private Information Retrieval (PIR) to enable data access while preserving privacy. A PIR protocol allows users to retrieve information from a database without revealing the specifics of their query or the data they are accessing. With the advent of quantum computing, researchers have explored the potential of using quantum systems to enhance privacy in information retrieval. In a Quantum Private Information Retrieval (QPIR) protocol, a user can retrieve information from a database by downloading quantum systems from multiple servers, while ensuring that the servers remain oblivious to the specific information being accessed. This scenario offers a unique advantage by leveraging the inherent properties of quantum systems to provide enhanced privacy guarantees and improved communication rates compared to classical PIR protocols. In this thesis we consider the QPIR setting where the queries and the coded storage systems are classical, while the responses from the servers are quantum. This problem was treated by Song et al. for replicated storage and different collusion patterns. This thesis aims to develop QPIR protocols for coded storage by combining known classical PIR protocols with quantum communication algorithms, achieving enhanced privacy and communication costs. We consider different storage codes and robustness assumptions, and we prove that the achieved communication cost is always lower than the classical counterparts.
Investigating YOLO Models Towards Outdoor Obstacle Detection For Visually Impaired People

The utilization of deep learning-based object detection is an effective approach to assist visually impaired individuals in avoiding obstacles. In this paper, we implemented seven different YOLO object detection models \textit{viz}., YOLO-NAS (small, medium, large), YOLOv8, YOLOv7, YOLOv6, and YOLOv5 and performed comprehensive evaluation with carefully tuned hyperparameters, to analyze how these models performed on images containing common daily-life objects presented on roads and sidewalks. After a systematic investigation, YOLOv8 was found to be the best model, which reached a precision of $80\%$ and a recall of $68.2\%$ on a well-known Obstacle Dataset which includes images from VOC dataset, COCO dataset, and TT100K dataset along with images collected by the researchers in the field. Despite being the latest model and demonstrating better performance in many other applications, YOLO-NAS was found to be suboptimal for the obstacle detection task.