Reviewerly/exHarmony huggingface dataset:
The Future of Cyber-Crimes and Cyber War in the Metaverse

The future of cybercrimes and cyber warfare in the metaverse is a topic of concern. Understanding the emerging landscape is crucial. With technological advancements and the pervasive influence of the metaverse, new cyber threats have emerged. This chapter provides an overview of challenges and ramifications associated with cybercrimes and cyber warfare in the metaverse. It explores evolving cyber threats in the metaverse, considering AI, machine learning, and quantum computing. Cybercrimes include virtual asset theft, identity theft, phishing, harassment, and online extortion. Cyber warfare in the metaverse involves state-sponsored attacks, espionage, information warfare, and manipulation of virtual defense systems. Robust cybersecurity measures, collaboration among stakeholders, and cyber literacy are essential to mitigate risks.
A density estimation perspective on learning from pairwise human preferences

Learning from human feedback (LHF) -- and in particular learning from pairwise preferences -- has recently become a crucial ingredient in training large language models (LLMs), and has been the subject of much research. Most recent works frame it as a reinforcement learning problem, where a reward function is learned from pairwise preference data and the LLM is treated as a policy which is adapted to maximize the rewards, often under additional regularization constraints. We propose an alternative interpretation which centers on the generative process for pairwise preferences and treats LHF as a density estimation problem. We provide theoretical and empirical results showing that for a family of generative processes defined via preference behavior distribution equations, training a reward function on pairwise preferences effectively models an annotator's implicit preference distribution. Finally, we discuss and present findings on "annotator misspecification" -- failure cases where wrong modeling assumptions are made about annotator behavior, resulting in poorly-adapted models -- suggesting that approaches that learn from pairwise human preferences could have trouble learning from a population of annotators with diverse viewpoints.
ÚFAL CorPipe at CRAC 2023: Larger Context Improves Multilingual Coreference Resolution

We present CorPipe, the winning entry to the CRAC 2023 Shared Task on Multilingual Coreference Resolution. Our system is an improved version of our earlier multilingual coreference pipeline, and it surpasses other participants by a large margin of 4.5 percent points. CorPipe first performs mention detection, followed by coreference linking via an antecedent-maximization approach on the retrieved spans. Both tasks are trained jointly on all available corpora using a shared pretrained language model. Our main improvements comprise inputs larger than 512 subwords and changing the mention decoding to support ensembling. The source code is available at https://github.com/ufal/crac2023-corpipe.
GPT Struct Me: Probing GPT Models on Narrative Entity Extraction

The importance of systems that can extract structured information from textual data becomes increasingly pro-nounced given the ever-increasing volume of text produced on a daily basis. Having a system that can effectively extract such information in an interoperable manner would be an asset for several domains, be it finance, health, or legal. Recent devel-opments in natural language processing led to the production of powerful language models that can, to some degree, mimic human intelligence. Such effectiveness raises a pertinent question: Can these models be leveraged for the extraction of structured information? In this work, we address this question by evaluating the capabilities of two state-of-the-art language models - GPT-3 and GPT-3.5, commonly known as ChatGPT - in the extraction of narrative entities, namely events, participants, and temporal expressions. This study is conducted on the Text2Story Lusa dataset, a collection of 119 Portuguese news articles whose anno-tation framework includes a set of entity structures along with several tags and attribute values. We first select the best prompt template through an ablation study over prompt components that provide varying degrees of information on a subset of documents of the dataset. Subsequently, we use the best templates to evaluate the effectiveness of the models on the remaining documents. The results obtained indicate that GPT models are competitive with out-of-the-box baseline systems, presenting an all-in-one alternative for practitioners with limited resources. By studying the strengths and limitations of these models in the context of information extraction, we offer insights that can guide future improvements and avenues to explore in this field.
PENGARUH MODEL PEMBELAJARAN KOOPERATIF TIPE NHT DENGAN BERBANTUAN MEDIA PEMBELAJARAN PAPAN PINTAR PANCASILA UNTUK MENINGKATKAN KEAKTIFAN PESERTA DIDIK KELAS I SDN PODOREJO 02

This research aims to find out whether the application of the NHT learning model can increase the active participation of class 1 students at SDN Podorejo 02 in learning activities. The research method used is quantitative descriptive, namely a type of research that describes variables as they are, supported by data in the form of numbers generated from the actual situation. Data collection techniques were carried out using unstructured interviews with class teachers and observation. The results of the research resulted in the application of the NHT-type cooperative learning model which can increase students' active participation in learning. At the first meeting, the active presentation of students was 72% which was in the good classification and at the second meeting it increased to 86% which was in the very good classification.
Driving Training-Based Optimization-Multitask Fuzzy C-Means (DTBO-MFCM) Image Segmentation and Robust Deep Learning Algorithm for Multicenter Breast Histopathological Images

The second most frequent disease in terms of diagnoses is breast cancer, which has had tremendous impact on women’s lives all around the world. The most frequent cause is a tumour formed as a result of abnormal cell divisions of tissues in the breast where rate of growth and proliferation rate assessed using mitotic activity indices. Image segmentation is regarded as a crucial stage in the processing of images. One of the often-used techniques for picture segmentation is FCM (Fuzzy C-Means) clustering. However, there are problems with this approach, including sensitivity to beginning values, becoming stuck in local optimas, and being unable to tell among objects with identical colour intensity. In order to provide improved generalisation over hitherto unexplored domains, the FMD (Fourier Mitosis Detection) technique has also been added to the shift issue in this study. The three components of the FMD method are fuzzy segmentation-based mitotic detection, pixel-level annotation creation, and Fourier-based data augmentation. For segmentation-based mitosis identification, DTBO-MFCM (Driving Training-Based Optimisation- Multitask Fuzzy C-Means) clustering has been introduced. The DTBO algorithm offers a greater capacity for exploration while looking for the optimum answer to a problem, which avoids the algorithm from becoming stuck in local optimas. This technique is carried out at the image level, therefore there is no need for training and it can be put into use very quickly. The thorough mitosis identification procedure is then carried out on these pictures after they have been cropped into tiny patches. The comparison experiments with other mitosis detections also prove proposed system’s efficacy.
Blockchain Voting: A Step Towards Inclusive Democracy

Abstract: The online voting system that incorporates blockchain technology aims to transform the voting process by introducing upgraded trust, transparency and security. Through the utilization of a decentralized blockchain network, the proposed system ensures that each vote is securely recorded and verified, thereby establishing an immutable and tamper-resistant voting mechanism. One of the fundamental feature of the system is eligible voter verification. By leveraging blockchain's distributed nature, the system can verify the identity and eligibility of each voter, ensure that eligible individuals can only participate in the voting system. This authentication process adds an additional layer of security and helps prevent fraudulent activities. Secure vote casting is another crucial component of the proposed voting system. Through the utilization of blockchain, each and every vote is encrypted and securely stored in a decentralized manner. This ensures that votes cannot be altered or manipulated, providing a high level of integrity to the voting process. Additionally, the system emphasizes transparent result tallying. The decentralized nature of blockchain technology allows for the public verification of votes, enabling anyone to audit the voting process. This transparency enhances trust among voters, as it provides them with the ability to independently verify the accuracy of the election results. Overall, the proposed online voting system, empowered by blockchain technology, offers a transparent, secure and tamper-resistant solution for conducting elections. By leveraging the benefits of blockchain, such as decentralization and immutability, the system addresses the challenges faced by traditional voting systems and provides a more reliable and trustworthy platform for the democratic process. It has the potential to enhance the integrity of elections, increase voter turnout, and foster trust in the electoral system.
The two-qubit singlet/triplet measurement is universal for quantum computing given only maximally-mixed initial states

Abstract In order to delineate which minimalistic physical primitives can enable the full power of universal quantum computing, it has been fruitful to consider various measurement based architectures which reduce or eliminate the use of coherent unitary evolution, and also involve operations that are physically natural. In this context previous works had shown that the triplet-singlet measurement of two qubit angular momentum (or equivalently two qubit exchange symmetry) yields the power of quantum computation given access to a few additional different single qubit states or gates. However, Freedman, Hastings and Shokrian-Zini 1 recently proposed a remarkable conjecture, called the ‘STP=BQP’ conjecture, which states that the two-qubit singlet/triplet measurement is quantum computationally universal given only an initial ensemble of maximally mixed single qubits. In this work we prove this conjecture. This provides a method for quantum computing that is fully rotationally symmetric (i.e. reference frame independent), using primitives that are physically very-accessible, naturally resilient to certain forms of error, and provably the simplest possible.
Introduction

Artificial neural network research began in the 1940s with the M-P neuron network model, which firstly proposed by biologist McCulloch and mathematician Pitts, kicking off neural network research [95]. In 1949, Hebb studied adaptive laws in neural systems and proposed Hebb’s rule for improving the strength of connections between neurons, breaking new ground in the study of neural networks. In 1982, Hopfield proposed a recurrent neural network model, which could perform optimal computation and associative memory. In 1986, Rumelhart et al. gave an error back propagation algorithm (i.e. the BP algorithm) for training weights of multilayer perceptrons. Both the forward neural network model and the recurrent neural network model are based on the nonlinear characteristics, adaptability and excitation inhibition of biological neurons, and the main purpose is to design high-performance controllers based on the bionic principle [152]. A controller that can be implemented online must be relatively simple in structure and have online adaptive and learning capabilities. The neural controllers are the preferred method to meet this requirement, especially recurrent neural networks. They not only have the universal approximation property of the forward neural network, but also the dynamical properties and associative memory capabilities that the forward neural network does not have. In early neural network research, neural networks were discussed as single individual to solve relatively homogeneous task, which was related to the task requirement and the scientific problem at the time. The correction of connection weights between neurons is a key concern, which has led to research on the design of neural network learning algorithms. With the advent of the network era, the complex neural networks (i.e. coupled neural networks) show potential advantages in large-scale pattern recognition and image processing [3, 17, 30, 42, 94, 131, 132, 151, 203]. As a special kind of complex networks, the complex neural networks have a number of characteristics that a single neural network does not have. As a result, the dynamic properties of complex neural networks (such as synchronization properties and chaos properties) are widely studied. The research of complex neural networks focuses on the topological structure or connection strength between nodes, rather than focusing on the connection relationships between microscopic neurons. Each node in complex neural networks is composed of neural networks with appropriate dimensions, rather than a single neuron. In this way, the complex neural networks are endowed with some complex networks characteristics, and they have the following features compared with the earlier neural networks.
Machine Learning Data Suitability and Performance Testing Using Fault Injection Testing Framework

Creating resilient machine learning (ML) systems has become necessary to ensure production-ready ML systems that acquire user confidence seamlessly. The quality of the input data and the model highly influence the successful end-to-end testing in data-sensitive systems. However, the testing approaches of input data are not as systematic and are few compared to model testing. To address this gap, this paper presents the Fault Injection for Undesirable Learning in input Data (FIUL-Data) testing framework that tests the resilience of ML models to multiple intentionally-triggered data faults. Data mutators explore vulnerabilities of ML systems against the effects of different fault injections. The proposed framework is designed based on three main ideas: The mutators are not random; one data mutator is applied at an instance of time, and the selected ML models are optimized beforehand. This paper evaluates the FIUL-Data framework using data from analytical chemistry, comprising retention time measurements of anti-sense oligonucleotide. Empirical evaluation is carried out in a two-step process in which the responses of selected ML models to data mutation are analyzed individually and then compared with each other. The results show that the FIUL-Data framework allows the evaluation of the resilience of ML models. In most experiments cases, ML models show higher resilience at larger training datasets, where gradient boost performed better than support vector regression in smaller training sets. Overall, the mean squared error metric is useful in evaluating the resilience of models due to its higher sensitivity to data mutation.
Reality check

Mobile mixed reality (mMR) is increasingly being integrated into health education, however, the affordances and design principles for the facilitation of critical thinking are yet to be explored. The objective of this study is to explore the perceptions of mobile mixed reality and critical thinking in health education. Thematic analysis was undertaken of data collected from focus groups including students, academics, and mobile mixed developers (n=8). The focus groups revealed two main themes: (1) “purposeful critical thinking” including the benefits of freedom of failure in the virtual environment, and (2) “making it meaningful” by incorporating co-design of virtual learning environments while extending or focusing the learning experience to something that could not be achieved any other way. We conclude that the potential for mMR in health education is considerable. Purposeful inclusion of critical thinking could be achieved by students co-designing scenarios that integrate choose-your-own-adventure healthcare pathways in safe, virtual environments.
Web-Based System Information Certificate Services at Klamono District Offices

Klamono District Office still relies on manual procedures for delivering certificates to residents, involving steps such as direct submission of certificate requests at the village office, form filling, and physical submission of supporting documents. To overcome this challenge, the proposal of implementing a dedicated information system tailored to streamline the certificate issuance process has emerged. This study proposes a Certificate Service Information System to elevate operational efficiency at the Klamono District Office. The Extreme Programming system development method is utilized, along with testing through Blackbox Testing and Usability Testing. This research indicates that the developed application can improve the effectiveness and efficiency of services in the certificate issuance process. The Blackbox testing results show a system success rate of 100%, while the usability testing report records a user satisfaction level of 93.1%.
Integrating spatial audio and active acoustics systems

Rooms incorporating Spatial Audio and Active Acoustic systems are being built that facilitate immersive audience experiences of unprecedented musical performances. Composers are writing new pieces, and new versions of existing works are being produced to take advantage of these technologies. Spatial Audio enables pre-recorded, synthesized, and live reinforced sounds to arrive at the listener from any direction and allows the listener to move around in the space and explore the sonic experience. Active Acoustics provides early reflections and reverberance for the performers and audience throughout the room and can be adjusted for each composition. The physical acoustical design of the space needs to provide an appropriate nominal acoustic for both systems and accommodate performance audio that does not come from a fixed position and orientation to listeners who may not be in a fixed location and orientation. Both systems incorporate distributed loudspeakers that are typically integrated into the architecture yet have different electroacoustic objectives. How do these systems coexist? Installation examples that use Spacemap, a multichannel panner that originated in Australia, and Constellation, an Active Acoustics System that uses VRAS technology developed in New Zealand, illustrate design considerations when integrating these systems.
Hubungan Kecemasan Suami dengan Kesiagaan Suami pada Masa Kehamilan Istri Kota Parepare

This study aims to determine the effect of dialog reading methods and improve language skills of early childhood. This study used a single subject research with A-B design. The research subjects were students of Kindergarten A PAUD Anak Ceria Surabaya with information about the development of language skills which were known based on observations, interviews, language proficiency checklists, and formal tests regarding language development of children aged 4 years. Data obtained through analysis of the subject's language skills in baseline (A) with intervention conditions (B) and analysis between baseline (A) and intervention (B). The results showed an increase in children's language skills from 34.3% in baseline to 65.7% in the intervention. The conclusion from this age study shows that the dialogue reading method can improve children's language skills. The implication of this research is that the dialogue reading method can stimulate the improvement of children's language skills as early as possible and for children who have developed language development, especially expressive language. The dilaog reading method can be applied at home and at school.
The Effectiveness of Quantum Teaching Method in Teaching Reading Comprehension

The research is conducted based on the statement of problems. The question of this research is how the effectiveness of the quantum teaching method in teaching reading comprehension at Madrasah Tsanawiyah Sabilu El-Muhtadin? to answer the question, the researcher used quantitative research by implementing quasi experimental design as the research method. The population of this study was the students of ninth grade of MTS Sabilu El-Muhtadin Pandeglang. The sample was divided into two groups, namely control group and experimental group. There were 30 students in control group and 30 students in experimental group. The experimental group was taught by using Quantum teaching method and control group was taught without using Quantum teaching method. After conducting the treatments and evaluating both pre-test and post-test have given, the researcher found that there was significant influence of using quantum teaching method to improve reading comprehension. After collecting and calculating the data, the value of tobservation is bigger than ttable. tobservation = 4.5&gt; ttable = 1.67 (5%), so Ho is rejected, and Ha is accepted. In other words, there is significant difference between group which use Quantum teaching method and group which did not use Quantum teaching method.
Analisis Sentimen Pengguna Indihome dengan Metode Klasifikasi Support Vector Machine (SVM)

Indonesia Digital Home (IndiHome) is a communication service provider from PT Telekomunikasi Indonesia (Telkom) that provides several communication and data service packages such as internet, home telephone and cable television (Usee TV &amp; IP TV) which implements copper and fiber pptic cable services. Currently, IndiHome is implementing a 100% fiber service replacement for all customers in order to produce high data speeds and more reliable services. However, the fact is that fiber optic services often receive complaints from customers due to weather and other factors. It was recorded that in 2020 internet users in Indonesia reached 196.7 million people or 73.7% million of the population and around 51.2% were social media users (Kompas.com, 2020). One of the social media with 6.43 million active users is Twitter. Twitter then became a medium for channeling opinions regarding a service, including the services provided by Indihome. Based on this, a method is needed, namely sentiment analysis to understand whether the opinion is negative or positive. The Support Vector Machine (SVM) is used to create a classification model for sentiment analysis of IndiHome service users' opinions on Twitter with an accuracy of 91.3%.
Model-agnostic Body Part Relevance Assessment for Pedestrian Detection

Model-agnostic explanation methods for deep learning models are flexible regarding usability and availability. However, due to the fact that they can only manipulate input to see changes in output, they suffer from weak performance when used with complex model architectures. For models with large inputs as, for instance, in object detection, sampling-based methods like KernelSHAP are inefficient due to many computation-heavy forward passes through the model. In this work, we present a framework for using sampling-based explanation models in a computer vision context by body part relevance assessment for pedestrian detection. Furthermore, we introduce a novel sampling-based method similar to KernelSHAP that shows more robustness for lower sampling sizes and, thus, is more efficient for explainability analyses on large-scale datasets.