Reviewerly/exHarmony huggingface dataset:
ANALISIS SENTIMEN DI MEDIA SOSIAL TWITTER DENGAN STUDI KASUS KARTU PRAKERJA

Kartu Prakerja is one of the government's flagship programs in providing training to the workforce. In its implementation there is a lot of information scattered, especially on social media Twitter both in the pros and cons of Kartu Prakerja program. Based on information in the form of tweets that have not been analyzed in depth, it is necessary to analyze sentiment on the Kartu Prakerja in order to obtain appropriate information based on the opinions of netizen s on Twitter. This study discusses sentiment analysis of tweet data with the keyword “Kartu Prakerja” which uses data as many as 6658 tweet data taken in the period May 27 - August 5, 2021. This research uses the Naive Bayes Classification method which has several stages, namely data retrieval, data preprocessing, manual labeling, data training and testing. The solution offered in this study is to create an analysis model that can be used to perform sentiment analysis about Kartu Prakerja on Twitter. Based on the results of this study obtained that the calculation of accuracy obtained a value of 86% for training data and 87% for data testing. This study concluded that the Kartu Prakerja has a positive sentiment by Twitter netizens based on the results of Classification that discusses many positive sentiments such as the benefits, effectiveness and addition of the Kartu Prakerja budget.
Analisis Sentimen dan Klasifikasi Terhadap Tren “UU ITE” di Media Sosial Twitter

Undang-undang Informasi and Transaksi Elektronik abbreviated UU ITE is a law that regulates information and electronic transactions, or information technology in general. This study discusses sentiment analysis from tweet data with keywords “UU ITE” Who uses as much data 7.407 tweet data and re-tweets taken in the period July 21 - August 16, 2021, with details 914 data that has been manually labeled and 6,493 data labeled using Predicting that the data was taken using authentication on the Twitter API and executed using the Python library. This research uses methods Support Vector Machine because it has several advantages including It is capable of handling the classification of two classes, and its implementation is relatively easy. For the support vector machine stage, namely data retrieval, preprocessing data, manual labeling, data training and testing. As for the solution offered in this research is to create an analysis model that can be used to conduct sentiment analysis about the ITE Law on social media Twitter. This research was successful using the Support Vector Machine method to create a sentiment analysis model with an accuracy of 81.20% for data Training and 87% for data testing. This study provides results that UU ITE have negative sentiments by netizens on social media Twitter based on on the results of classification and calculations on the model and tweet data and the number of Negative discussions.
Analysis of Marketing Campaigns Results Through Unsupervised Learning with Apriori Association Modeling

One of the most popular data mining methods for extracting association rules from huge data sets is the apriori algorithm, which can be databases or databanks. One of the entities that have benefited the most from it have been supermarket chains in the area of marketing and advertising campaigns, thanks to the discovery of combinations of products that customers usually buy together, or relationships between items and merchants, and having the knowledge of these combinations allows companies to better plan their marketing campaigns. The article shows the results of an exercise with a databank of marketing campaigns in order to generate association rules, interpret the results, and detect which factors are common to customers who tend to accept marketing offers.
A Discussion on Variants of an Anisotropic Model Applied to Depth Completion

Depth data holds significant importance in numerous applications, ranging from video games to autonomous vehicles. Depth data can be acquired by sensors such as LiDAR sensors. Frequently, acquired depth map has holes or data that presents low confidence level. The task of depth data interpolation or completion stands as a fundamental requirement for such applications. Infinity Laplacian is an interpolator of data that can perform this task. The Infinity Laplacian represents the most straightforward interpolator, adhering to a set of appropriate axioms. In this study, we assessed three different variations of the infinity Laplacian for the purpose of completing sparse depth data. We sub-sampled the publicly available NUY_V2 dataset and evaluated the performance of these models by up-sampling depth data. In this paper, we compared the infinity Laplacian, unbalanced infinity Laplacian, balanced infinity Laplacian, and biased infinity Laplacian. Obtained results show that the balanced infinity Laplacian outperforms the other three models and also many contemporaneous models. The addition of a mechanism that balances between two eikonal operators gives the infinity the capability to reach better performance in the up-sampling task. Moving forward, our focus will be directed towards addressing the challenge of edge interpolation using the color reference image.
Exploring Data Encryption Standard (DES) Through CrypTool Implementation: A Comprehensive Examination and Historical Perspective

DES has been serving as the official federal standard for data encryption since the 70s, until it was replaced in 2001 by the Advanced Encryption Standard (AES). This paper thoroughly examines the encryption and decryption procedure, describing in detail and illustrating each step of the process. Additionally, we showcase the practical implementation of DES using the CrypTool platform, addressing the lack of a comprehensive and user-friendly tutorial on DES within this context. The paper endeavors to address this gap, enhancing the readers’ understanding of the DES operation by delving into its internal mechanism and the transformations the plaintext undergoes to become ciphertext. In addition, we engage in a critical examination of CrypTool's implementation of DES and the platform’s utility as an educational resource, highlighting both its strengths and shortcomings. The review of CrypTool as a learning tool not only provides insights into the practical implementation of DES but also emphasizes the significance of hands-on learning in modern cryptography. Moreover, we present a historical analysis of attempts at breaking the DES cipher, from its approval as a standard until more recent developments, and assess its relevance in today's cryptographic landscape. Ultimately, this paper aspires to serve as a valuable teaching resource for cryptography students and educators, bridging the gap between theoretical knowledge and practical application.
Enhancing trust and security in IoT computing offloading through game theory and blockchain‐based control strategy

Summary Collaboration between IoT end‐devices and edge servers or idle devices enables offloading of computational tasks, providing an effective solution to address inherent limitations in computational resources, storage capacity, and energy efficiency. However, IoT network openness introduces security challenges, such as privacy breaches, data security, and “free‐riding” attack. Securing computational offloading is crucial for service quality and reliability. In this article, we introduce a comprehensive approach to address these challenges. First, trust metrics are performed at the level of resource requester and resource provider respectively, and the trust level of both parties is matched to reduce the adverse effects of malicious devices or servers on computing offloading. Then, a distributed decision‐making method based on game theory is designed to improve the performance of the algorithm and avoid the single‐point‐of‐failure problem through the joint participation of multiple edge servers in decision‐making. Finally, a blockchain‐based task offloading process control strategy is used to deal with the “free‐riding” attack. The results of simulation experiments show the effectiveness of our proposed offloading decision‐making method.
DocBot: A System for Disease Detection and Specialized Doctor Recommendation Using Patient’s Speech of Symptoms

Nowadays, Machine Learning (ML) plays a crucial role in improving healthcare by enabling researchers, doctors, and patients to explore, diagnose, and prevent diseases such as dengue, typhoid, jaundice, pneumonia, and other major ailments. Our research focuses on leveraging ML to detect various diseases from a patient’s speech. The patient will describe their symptoms to the machine, akin to explaining their concerns to a doctor. The machine will then identify the disease and provide primary medication recommendations along with suggesting a specialized doctor for that particular ailment. To optimize our system’s performance, we trained our machine using multiple algorithms and evaluated their results. Our evaluation revealed an accuracy of 86.59% for Naive Bayes, 83.17% for Unhyperd SVM, 98.05% for Hyperd SVM, 97.4% for Decision Tree, and the highest accuracy of 99.35% was achieved by Random Forest.
Cube Attacks on Round-Reduced Grain-128AEAD

Lightweight cryptography aims to design secure and efficient cryptographic algorithms for resource-constrained devices. Traditional cryptographic algorithms may not be readily usable in resource-constrained environments. To standardise cryptographic solutions tailored for such resource-constraint environments, the National Institute of Standards and Technology (NIST) launched the Lightweight Cryptography (LWC) project. Grain-128AEAD is a stream cipher-based finalist in the NIST LWC project. In this work, we examine the security of the initial version of Grain-128AEAD against cube attacks. We present distinguishing attacks on a reduced-round version of the cipher, assuming that the keystream can be observed immediately after the reduced-round initialisation of the pre-output generator. We obtained various cubes of sizes 25 to 45 for reduced-round Grain-128AEAD. The best cube reported in this work can distinguish the output of a 165-round initialisation of Grain-128AEAD with a cube size of 35. The complexity of the distinguishing attack is $$\mathcal {O}(2^{35})$$ . The results are confirmed experimentally. We conclude that even with fewer rounds of initialisation for the first version of Grain-128AEAD, the cipher still has a good security margin against cube attacks.
Improved Test Input Prioritization Using Verification Monitors with False Prediction Cluster Centroids

Deep learning (DL) systems have been remarkably successful in various applications, but they could have critical misbehaviors. To identify the weakness of a trained model and overcome it with new data collection(s), one needs to figure out the corner cases of a trained model. Constructing new datasets to retrain a DL model requires extra budget and time. Test input prioritization (TIP) techniques have been proposed to identify corner cases more effectively. The state-of-the-art TIP approach adopts a monitoring method to TIP and prioritizes based on Gini impurity; one estimates the similarity between a DL prediction probability and uniform distribution. This letter proposes a new TIP method that uses a distance between false prediction cluster (FPC) centroids in a training set and a test instance in the last-layer feature space to prioritize error-inducing instances among an unlabeled test set. We refer to the proposed method as DeepFPC. Our numerical experiments show that the proposed DeepFPC method achieves significantly improved TIP performance in several image classification and active learning tasks.
A predictive model: A study of employment factors for fresh college graduates

The employment situation of fresh college graduates is affected by many factors. In this paper, on the basis of decision tree, the C4.5 method was used to analyze the employment factors of fresh college graduates. An improved C4.5 model was designed by simplifying the calculation formula of the C4.5 method and combining the error tolerance. Experiments were performed on the actual data of fresh college graduates. The results found that the practice level had a great impact on the employment of fresh college graduates, so the training of the practice level should be focused on before graduation. The results of the prediction models showed that the improved C4.5 method had a smaller training error than ID3 and C4.5 methods, a significantly higher prediction accuracy (88.39%), higher precision, recall rate, and F1 value, and a shorter running time (1.642 s); the improved model remained a high accuracy even when the data volume increased. The experimental results verify the reliability of the improved C4.5 model in predicting the employment situation of fresh college graduates. The model can be applied in actual employment guidance.
The methods of task pre‐allocation and reallocation for multi‐UAV cooperative reconnaissance mission

Abstract Nowadays, multi unmanned aerial vehicle (multi‐UAV) systems have been widely used in battlefield. The rationality of mission plan can directly affect the effectiveness of multi‐UAV system. The existing multi‐UAV task allocation model lack a comprehensive modelling of task pre‐allocation and task reallocation issues. However, in actual task execution, task pre‐allocation and task reallocation are a holistic problem. Therefore, based on the background of multi‐UAV cooperative reconnaissance, the authors establish a multi‐UAV cooperative reconnaissance task pre‐allocation and reallocation model (MCRTPR). There are two kinds of task allocation in MCRTPR model. One is task pre‐allocation, which is a static task allocation before the mission begin. Another is task reallocation, that is a dynamic task allocation during the mission. For task pre‐allocation, a particle swarm optimisation algorithm based on experience pool (EPPSO) is proposed. And for task reallocation, the authors design a partial task reallocation algorithm based on contract network protocol (CNP‐PTR). The experimental results show that, compared with some state‐of‐the‐art algorithms, EPPSO can get the lowest fitness value under various experimental conditions, and CNP‐PTR is able to handle task reallocation problem caused by multiple kinds of dynamic events.
Development of the Mobile Vision Enhancement System (MOVES) for Sight-Assist Mechanism

Vision-related diseases can have various effects on the human eye, ranging from minor sight distractions to the most severe cases of total blindness. There are multiple medical treatments available for the patient, as well as alternative methods, including wearable tools, to assist the patient in their daily routine activities. The most commonly used tool is the guide stick, which requires the user to hold it, thus limiting its flexibility. The technology-aided system also depends on the power supply, which also adds to the limitation. Inspired by the solar-based system, this project applies the portable sight-assist system, which is easy to use and comes with wearable design ideas. The proposed Mobile Vision Enhancement System (MOVES) is a solar-powered system that assists the user in detecting obstacles in their path. It is designed to fit in the hat worn by the user. The MOVES is designed using the Arduino Nano Microcontroller Board with a compatible poly-crystalline solar panel for solar-powered systems, an ultrasonic sensor for obstacle detection, and a buzzer for the alert system. The main objectives of the headwear design are that the system operates by using a solar panel, which is practical to place at the top, and the user does not have to hold it by hand, making it a hands-free system. The MOVES system has been tested in controlled environments with various obstacle placements. This is important, as the system must be able to detect any obstacle in the user’s pathway to avoid collisions and endanger the user. Based on the test, the MOVES had proven to be useful in assisting the user in detecting obstacles and helping the user in avoiding any obstacle in their path, with a mean relative error of less than 5%. Thus, MOVES is proposed as an alternative sight-assist system to be used by a visually impaired person.
Electrooculography for control of Emoji board for Patients with Classical Locked-in Syndrome using

Locked-in paralysis is a neurological syndrome caused by damage to the brainstem leading to paralysis called stroke. It results to complete interference to pathway motor controlling eyes, trunk and limbs movement, to breathing, swallowing and vocalization difficulty. In this study, corneo-retinal standing potential of the eye movements is processed to enable the patient to communicate with others. There are equivalent of six emoji signals that the eye of the patient can see and choose, that correspond to the basic need that the desires to make or perform. The study is successful in terms of providing a biomedical device that can aid the patient with classical locked-in syndrome, with the ability to perform basic actions by controlling the emoji board. The signal produced by the eye movement was calibrated with the aid of wavelet transform by denoising the signals to become parallel to the signal that the patient want to select. The device should be calibrated in order to produce an actual control of the emoji board. Thus, different face shapes were considered giving different threshold voltage (μV) for each face shape (oval face, diamond face, heart shape face, round face, long face and square face) then, it was tested for different patients suffering classical locked-in syndrome. The result of the hypothesis testing provided a p value that states that the null hypothesis is rejected telling that there is a significant difference in the threshold voltages produced by the patient and normal people. In addition to our conclusion, the wavelet transform algorithm can be effectively used to solve signal processing in biomedical field. Because they can be applied in a low-cost common microcontrollers for real-time ECG and EOG denoising.
Analysis of Performance in Recognizing the Presence of Sickle Cells between YOLOv3 and YOLOv3-Tiny

Single-board computers (SBCs) have now reached a point at which they can now be used as whole, standalone systems. SBCs can now be used to perform some tasks that have been traditionally performed manually in settings where space is at a premium. One such traditionally manual task is recognizing the presence of sickle cells in blood smear samples using morphology. This study is therefore aimed at testing which object recognition algorithm, YOLOv3 or YOLOv3-Tiny is better suited to automating this task by testing the accuracy and performance of using each algorithm on a Raspberry Pi 4B SBC. The result of the testing shows that in terms of speed, the YOLOv3 algorithm took more time to train and process. Generally, the algorithm took 2 days more to train and 3x more time to process. In terms of accuracy, while the YOLOv3 algorithm is more accurate per prediction than the YOLOv3-Tiny implementation, both models were still able to produce the same accuracy when classifying each slide. It was therefore concluded that for a task that requires very accurate predictions, the YOLOv3 implementation is better but for a task such as in this study, where there is some room for error, 5 false positives or negatives to be exact, the YOLOv3-Tiny implementation is recommended as it was able to process all files within a reasonable time.
Chatbots

This chapter discusses the role that chatbots can play along the customer journey. As text-based conversational agents that can have conversations with consumers through the use of natural language processing softwares, chatbots are now taking over customer service and support. In this chapter, we discuss what these tools can do (e.g., answering queries, handling customer complaints), how they can be best designed for successful customer experiences (e.g., human-like features, conversational style), and what is their impact on consumers’ perceptions and behaviour. We also build on current applications in the market to further discuss the role that these agents could play in the purchase stage and to suggest directions for future research on this topic.
Governing Agents on the Web

This paper explores the interplay of multiagent systems (MAS) with the Web to identify research challenges related to the governance of Web-based MAS. We first consider as a case study the process for allocating donated bodily organs to potential transplant recipients. We then discuss candidate architectures for governance in Web-based MAS that would support this scenario, from which we derive several research questions that emerge in pursuing the aim of agents on the Web.
Voice Assistants

This chapter is focused on voice assistants, conversational agents that communicate with humans through a voice interface and that are commonly used by consumers in their household to complete consumption- (e.g., ordering products) and non-consumption-related tasks (e.g., listening to music). In particular, this chapter is aimed at outlining the current theoretical perspectives exploring the drivers of consumers’ adoption of voice assistants and the impact that the use of these tools has on consumers’ perceptions and behaviours. We also highlight barriers to the adoption and use of these devices. Finally, we discuss how voice assistants impact several touchpoints along the customer journey and discuss how to best understand their role from a theoretical point of view.
Introduction

This chapter provides an overview of what artificial intelligence is and how it is applied in the different technologies used in retail and commercial settings. Moreover, the chapter presents the different technologies covered in the book and provides empirical evidence of how these technologies can be categorised. In particular, using an online consumer sample (N = 600), the study shows how consumers assess the sensory and social functions of different AI solutions. Based on the findings, the chapter proposes a theoretical framework that will be used as a guide throughout the book. Finally, the chapter discusses the concept of customer journey and sets the stage for explaining how different AI solutions can serve several purposes to enhance the customer experience.
Decentralized Finance: Protocols, Risks, and Governance

Financial markets are undergoing an unprecedented transformation. Technological advances have brought major improvements to the operations of financial services. While these advances promote improved accessibility and convenience, traditional finance shortcomings like lack of transparency and moral hazard frictions continue to plague centralized platforms, imposing societal costs. In this paper, we argue how these shortcomings and frictions may be mitigated by the decentralized finance (DeFi) ecosystem. We delve into the workings of smart contracts, the backbone of DeFi transactions, with an emphasis on those underpinning token exchange and lending services. We highlight the pros and cons of the novel form of decentralized governance introduced via the ownership of governance tokens. We argue that the current DeFi infrastructure introduces operational risks to users, which we segment into five primary categories: consensus mechanisms, protocol, oracle, frontrunning, and systemic risks. We conclude by emphasizing the need for future research to focus on the scalability of existing blockchains, the improved design and interoperability of DeFi protocols, and the rigorous auditing of smart contracts.