Reviewerly/exHarmony huggingface dataset:
INFORMATION SYSTEM OF GOODS IN AND OUT USING FIFO METHOD

Information system in and out of inventory using the FIFO method is an information system to determine the inventory inventory in a company using the First In First Out method. FIFO itself is a sales method by selling incoming goods that are issued first to prevent a decline in the quality of goods due to expiration. FIFO inventory information systems have been widely used or developed in companies with various technologies and systems. The problem with EKO PS (Poultry Shop) Shop is that there is no information system available to manage inventory, so it cannot control the available inventory. Therefore, this study will analyze an information system in the form of a web-based FIFO inventory information system, so that inventory information can be known clearly and in detail and sales can be controlled using the FIFO method so that the FIFO inventory information system can be checked or viewed by admin as a user on the system.&#x0D;
vrGazeCore: an open-source package for virtual reality eye-tracking analysis

Virtual reality (VR) is increasingly used in combination with in-headset eye-tracking to study active vision in 360° environments. However, most software currently available to process raw data from in-headset eye-trackers is expensive and black-boxed from researcher control, placing constraints on the flexibility of analysis. Further, such conditions add complexity to traditional eye-tracking processing pipelines, requiring, for example, that researchers rectify head- and eye-centered coordinate systems to define gaze position. Here, we present vrGazeCore, an open-source package for both individual- and group-level analysis of eye-tracking data collected in 360° VR environments. As input, users provide eye-position, in normalized screen coordinates, and head-position (pitch, yaw, roll). vrGazeCore converts eye-position to degrees visual angle (DVA) and rectifies eye- and head-position to derive gaze coordinates in 360° space while simultaneously automating quality assurance of gaze position (e.g., pupil detection confidence). Our package provides multiple derivative measures of gaze position for subsequent analysis: fixation metrics, trial-averaged fixation density maps, and data visualizations. The open-source nature of our package permits the user direct access to all derivatives of gaze, as well as the ability to tailor analysis parameters to their needs. Using vrGazeCore, we demonstrate high precision and accuracy of fixations across two eye-tracking VR systems: Oculus DK2 Pupil Labs (accuracy: 2.00 DVA +/- 0.38 STE; precision: 0.26 DVA +/- 0.05 STE) and HTC Vive Eye (accuracy: 2.63 +/- 0.27 STE; precision: 0.16 +/- 0.08 STE). We additionally provide a template Unity experiment, equipped with functions to collect and save eye-tracking data, to enable users to easily interface with vrGazeCore. Future extensions of vrGazeCore will expand beyond panoramic scenes to environments with translational motion to promote studies of active navigation. In sum, vrGazeCore promotes the study of active vision in immersive scenes, offering a flexible, open-source package for analyzing eye-tracking data from head-mounted VR displays.
The Cyber-Industrialization of Catfishing and Romance Fraud

We examine a new form of online fraud closely related to traditional online romance fraud and catfishing, but which is âindustrializedâ through enterprise business practices, software platforms, and customer service processes. We conducted an inductive analysis of publicly available testimonial and review data provided by current and prior employees of a specific company in the online customer service contract space. Companies hire individuals online to work as âchat moderatorsâ or âcustomer service providersâ who are told that they are to advance engagement on social media platforms. In fact, they are being recruited as âsextingâ workers, paid on a per-text basis to engage in intimate chatting with clients led to believe the workers are female participants on a dating site. The process is mediated via client management processes that monitor employee productivity and monetize all interactions between âclientsâ and âworkers.â The company executes these processes with great efficiency by algorithmically assigning multiple workers to individual clients and assembling background files on clients in real time. We refer to this corporatized form of fraud as Intimacy Manipulated Fraud Industrialization (IMFI) and find that workers serve as both exploiters of their clients as well as victims of the company they work for.
Radio Frequency Signal Dataset Generation Based on LTE System and Variable Channels

Deep learning-based radio frequency fingerprinting (RFF) identification has the potential to enhance the security performance of the physical layer. In recent years, a number of RFF datasets have been proposed to meet the large-scale data requirements for deep learning. However, these datasets are collected from similar channel environments and only contain receiver data. This paper employs different software radio peripherals to generate radio signals. Hence, it is able to adjust the signal's parameters, such as frequency band, modulation style, antenna gain, etc. In this paper, we propose a radio frequency signal dataset based on LTE system and variable channels to more properly characterize the generated signals in the real world. We collect signals at transmitters and receivers to construct the RFF dataset. Moreover, we confirm the dataset's dependability using various machine learning and deep learning methods. The dataset and reproducible code of this paper can be downloaded from GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> GitHub link: https://github.com/njuptzsp/XSRPdataset.
Natural Resource Management using Remote Sensing and Geographic Information Systems

Natural resource management using remote sensing and Geographic Information Systems (GIS) is a powerful approach that combines technology and data to monitor, assess, and make informed decisions about natural resources. Remote sensing involves the use of sensors on satellites, aircraft, drones, or other platforms to gather data about the Earth's surface and atmosphere. GIS, on the other hand, is a system designed to capture, store, analyze, manage, and present spatial or geographic data. Together, these technologies offer numerous benefits for natural resource management. The data is helpful for the regular asset chiefs to comprehend and all the more actually team up with remote sensing researchers to create and apply remote sensing technology to accomplish checking targets.
An Analysis of Innovative Teaching of English Phonetics for Non-English Major Students

Phonetics is the cornerstone of language. The improvement of the proficiency of pronunciation has a great effect on the level of English listening and speaking skills. Learners with high proficiency in pronunciation can communicate effectively in terms of thoughts, culture, and emotions [1]. However, due to the limitations of the language environment, subject characteristics, and traditional teaching methods, non-English majors often face problems such as difficulty in pronunciation and lack of motivation for learning, which raise concerns regarding the English phonetic learning situation among these students. Therefore, innovative phonetic teaching is of great significance to non-English majors. This paper aims to provide some innovative and effective strategies for the innovative teaching of English phonetics for non-English majors to meet the learning needs of students, and to provide useful guidelines for future teaching reform and practice.According to the literature review and personal teaching experience, the article summarizes some innovative strategies that can improve the teaching of non-English majors’ pronunciation, such as using real situations and technical assistance,encouraging cooperative learning, gamifying phonetic learning, and so on. By implementing these strategies, students can improve phonetic sensitivity and pronunciation accuracy, thereby enhancing their listening skills and oral fluency.
The Translation of Pronouns and Repetitions in Indonesian Children’s Story "Petualangan Si Kancil" into French Using Google Translate

Abstract. There are many things to consider in translating children’s stories: behavioral influence on children, level of language difficulty, and suitability to the target culture. A child has limitations in their social interactions and communications. This article analyzes the translation of pronouns and repetitions in a children’s story with Google Translate. As an online application, Google Translate is considered to have the ability to translate words, phrases, clauses, sentences, and even paragraphs. This study aims to reveal the ability of google translate to translate children's stories, especially Indonesian pronouns and repetitions into French. This study used data from the translation of an Indonesian children's story titled “Petualangan Si Kancil” (literal translation: “The Adventures of the Mouse Deer”) by means of Google Translate, adopting descriptive and comparative methods. The results of data analysis show that Google Translate only translates word for word or sentence for sentence, and has not considered the cultural context. In detail, the results are as follow: (i) pronoun translation cannot sufficiently reflect both the writing style and the cultural context of the source text, (ii) translation of repetitions is avoided and replaced with lexical equivalence.Keywords: Children’s Story; Google Translate; Pronouns; Repetitions
Tracked Robot Control with Hand Gesture Based on MediaPipe

Hand gestures are currently considered one of the most accurate ways to communicate in many applications, such as sign language, controlling robots, the virtual world, smart homes, and the field of video games. Several techniques are used to detect and classify hand gestures, for instance using gloves that contain several sensors or depending on computer vision. In this work, computer vision is utilized instead of using gloves to control the robot's movement. That is because gloves need complicated electrical connections that limit user mobility, sensors may be costly to replace, and gloves can spread skin illnesses between users. Based on computer vision, the MediaPipe (MP) method is used. This method is a modern method that is discovered by Google. This method is described by detecting and classifying hand gestures by identifying 21 three-dimensional points on the hand, and by comparing the dimensions of those points. This is how the hand gestures are classified. After detecting and classifying the hand gestures, the system controls the tracked robot through hand gestures in real time, as each hand gesture has a specific movement that the tracked robot performs. In this work, some important paragraphs concluded that the MP method is more accurate and faster in response than the Deep Learning (DL) method, specifically the Convolution Neural Network (CNN). The experimental results shows the accuracy of this method in real time through the effect of environmental elements decreases in some cases when environmental factors change. Environmental elements are such light intensity, distance, and tilt angle (between the hand gesture and camera).The reason for this is that in some cases, the fingers are closed together, and some fingers are not fully closed or opened and the accuracy of the camera used is not good with the changing environmental factors. This leads to the inability of the algorithm used to classify hand gestures correctly (the classification accuracy decrease), and thus response time of the tracked robot's movement increases. That does not present possibility for the system to determine whether the finger is closed or opened.
The Current State of Blockchain Consensus Mechanism: Issues and Future Works

Blockchain is a decentralized ledger that serves as the foundation of Bitcoin and has found applications in various domains due to its immutable properties. It has the potential to change digital transactions drastically. It has been successfully used across multiple fields for record immutability and reliability. The consensus mechanism is the backbone of blockchain operations and validates newly generated blocks before they are added. To verify transactions in the ledger, various peer-to-peer (P2P) network validators use different consensus algorithms to solve the reliability problem in a network with unreliable nodes. The security and reliability of the inherent consensus algorithm used mainly determine blockchain security. However, consensus algorithms consume significant resources for validating new nodes. Therefore the safety and reliability of a blockchain system is based on the consensus mechanism's reliability and performance. Although various consensus mechanisms/algorithms exist, there is no unified evaluation criterion to evaluate them. Evaluating the consensus algorithm will explain system reliability and provide a mechanism for choosing the best consensus mechanism for a defined set of problems. This article comprehensively analyzes existing and recent consensus algorithms' throughput, scalability, latency, energy efficiency, and other factors such as attacks, Byzantine fault tolerance, adversary tolerance, and decentralization levels. The paper defines consensus mechanism criteria, evaluates available consensus algorithms based on them, and presents their advantages and disadvantages.
Device-Independent Quantum Key Distribution with Realistic Single-Photon Source Implementations

We investigate a set-up for Device-Independent Quantum Key Distribution based on single-photon sources. We derive conditions to attain non-zero secret-key rates in terms of their second-order autocorrelation function and the photon efficiency and indistinguishability.
Translation, Association and Augmentation: Learning Cross-Modality Re-Identification From Single-Modality Annotation

Daytime visible modality (RGB) and night-time infrared (IR) modality person re-identification (VI-ReID) is a challenging cross-modality pedestrian retrieval problem. However, training a cross-modality ReID model requires plenty of cross-modality (visible-infrared) identity labels that are more expensive than single-modality person ReID. To alleviate this issue, this paper studies unsupervised domain adaptive visible infrared person re-identification (UDA-VI-ReID) task without the reliance on any cross-modality annotation. To transfer learned knowledge from the labelled visible source domain to the unlabelled visible-infrared target domain, we propose a Translation, Association and Augmentation (TAA) framework. Specifically, the modality translator is firstly utilized to transfer visible image to infrared image, formulating generated visible-infrared image pairs for cross-modality supervised training. A Robust Association and Mutual Learning (RAML) module is then designed to exploit the underlying relations between visible and infrared modalities for label noise modeling. Moreover, a Translation Supervision and Feature Augmentation (TSFA) module is designed to enhance the discriminability by enriching the supervision with feature augmentation and modality translation. The extensive experimental results demonstrate that our method significantly outperforms current state-of-the-art unsupervised methods under various settings, and even surpasses some supervised counterparts, providing a powerful baseline for UDA-VI-ReID.
Development of Vehicle Detection and Counting Systems with UAV Cameras: Deep Learning and Darknet Algorithms

This study focuses on identifying and detecting several types of vehicles, with each vehicle’s position depicted by drone technology or an Unmanned Aerial Vehicle (UAV) camera. The vehicle’s position is captured from a height of 350 to 400 meters above the ground. This study aims to identify the class of vehicles that travel on the highway. The experiment employs several convolutional neural network models, including YOLOv4, YOLOv3, YOLOv7, DenseNet201-YOLOv3, and CSResNext50-Panet-SPP, to identify this type of vehicle. Meanwhile, the Darknet algorithm aids the training process by making it easier to identify the type of vehicle depicted in MP4 movies. Several other Convolution Neural Network (CNN) model experiments were conducted in this study, but due to hardware limitations, only these 5 CNN models could produce an optimal accuracy of up to 70%. Following several experiments, the CSResNext50-Panet-SPP model produced the highest accuracy while detecting 100% of video data using UAV technology, including the volume of vehicles detected while crossing the road. Other CNN models produced high accuracy values, such as DenseNet201- YOLOv3 and YOLOv4 models, which can detect up to 98% to 99% of the time. This research can improve its capabilities by detecting other classes that are affordable by UAV technology but require hardware and peripheral technology to support the training process.
Artificial Intelligence-Generated Content in Intelligent Transportation Systems: Learning to Copy, Change, and Create! [Editor’s Column]

As artificial intelligence (AI), cloud computing, and chip technologies surge forward, AI-generated content (AIGC) is changing incredibly with each passing day. From the perspective of cognition and learning, just as the human learning process goes from imitation to improvement and then to creation, AIGC is going through the process of moving from sensing and twinning the real world (learning to copy) to predicting and improving the real world (learning to change) and, finally, to evaluating and creating the digital world (learning to create).
Analysis the Role of the Internet of Things and Industry 4.0 in Healthcare Supply Chain Using Neutrosophic Sets

In today's global economy, the Healthcare supply chain (HCSC) must be aware of a market that is both complicated and ever-changing. The HCSC in Industry 4.0 aims to give patients the medicine they need as quickly and cheaply as possible. The HCSC can improve operations by automating routine tasks and reducing human error by using new technologies like the "Internet of Things" (IoT); so, there is a need to analyze the role of IoT and Industry 4.0 in the Healthcare Supply Chain. This paper aims to analyze the role of IoT and Industry 4.0 in HCSC. Also, this paper proposes a framework using a single-valued neutrosophic set with multi-criteria decision-making (MCDM) to overcome uncertainty and deal with the various criteria and features. This paper used the 18 criteria and nine suppliers as an application in the healthcare supply chain. The results concluded that the proposed framework could handle uncertain data and give more effective results in analyzing the role of IoT and Industry 4.0 in HCSC.
Named Entity Annotation Projection Applied to Classical Languages

Tariq Yousef, Chiara Palladino, Gerhard Heyer, Stefan Jänicke. Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature. 2023.
Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization

Neural models for abstractive summarization tend to generate output that is fluent and well-formed but lacks semantic faithfulness, or factuality, with respect to the input documents. In this paper, we analyze the tradeoff between abstractiveness and factuality of generated summaries across multiple datasets and models, using extensive human evaluations of factuality. In our analysis, we visualize the rates of change in factuality as we gradually increase abstractiveness using a decoding constraint, and we observe that, while increased abstractiveness generally leads to a drop in factuality, the rate of factuality decay depends on factors such as the data that the system was trained on. We introduce two datasets with human factuality judgements; one containing 10.2k generated summaries with systematically varied degrees of abstractiveness; the other containing 4.2k summaries from five different summarization models. We propose new factuality metrics that adjust for the degree of abstractiveness, and we use them to compare the abstractiveness-adjusted factuality of previous summarization works, providing baselines for future work.
Trained on 100 million words and still in shape: BERT meets British National Corpus

While modern masked language models (LMs) are trained on ever larger corpora, we here explore the effects of down-scaling training to a modestly-sized but representative, well-balanced, and publicly available English text source – the British National Corpus. We show that pre-training on this carefully curated corpus can reach better performance than the original BERT model. We argue that this type of corpora has great potential as a language modeling benchmark. To showcase this potential, we present fair, reproducible and data-efficient comparative studies of LMs, in which we evaluate several training objectives and model architectures and replicate previous empirical results in a systematic way. We propose an optimized LM architecture called LTG-BERT.
Reliable Gradient-free and Likelihood-free Prompt Tuning

Due to privacy or commercial constraints, large pre-trained language models (PLMs) are often offered as black-box APIs. Fine-tuning such models to downstream tasks is challenging because one can neither access the model’s internal representations nor propagate gradients through it. This paper addresses these challenges by developing techniques for adapting PLMs with only API access. Building on recent work on soft prompt tuning, we develop methods to tune the soft prompts without requiring gradient computation. Further, we develop extensions that in addition to not requiring gradients also do not need to access any internal representation of the PLM beyond the input embeddings. Moreover, instead of learning a single prompt, our methods learn a distribution over prompts allowing us to quantify predictive uncertainty. Ours is the first work to consider uncertainty in prompts when only having API access to the PLM. Finally, through extensive experiments, we carefully vet the proposed methods and find them competitive with (and sometimes even improving on) gradient-based approaches with full access to the PLM.
An In-depth Analysis of Implicit and Subtle Hate Speech Messages

The research carried out so far in detecting abusive content in social media has primarily focused on overt forms of hate speech. While explicit hate speech (HS) is more easily identifiable by recognizing hateful words, messages containing linguistically subtle and implicit forms of HS (as circumlocution, metaphors and sarcasm) constitute a real challenge for automatic systems. While the sneaky and tricky nature of subtle messages might be perceived as less hurtful with respect to the same content expressed clearly, such abuse is at least as harmful as overt abuse. In this paper, we first provide an in-depth and systematic analysis of 7 standard benchmarks for HS detection, relying on a fine-grained and linguistically-grounded definition of implicit and subtle messages. Then, we experiment with state-of-the-art neural network architectures on two supervised tasks, namely implicit HS and subtle HS message classification. We show that while such models perform satisfactory on explicit messages, they fail to detect implicit and subtle content, highlighting the fact that HS detection is not a solved problem and deserves further investigation.