Reviewerly/exHarmony huggingface dataset:
The effect of superposition and entanglement on hybrid quantum machine learning for weather forecasting

Recently, proposed algorithms for quantum computing and generated quantum computer technologies continue to evolve. On the other hand, machine learning has become an essential method for solving many problems such as computer vision, natural language processing, prediction and classification. Quantum machine learning is a new field developed by combining the advantages of these two primary methods. As a hybrid approach to quantum and classical computing, variational quantum circuits are a form of machine learning that allows predicting an output value against input variables. In this study, the effects of superposition and entanglement on weather forecasting, were investigated using a variational quantum circuit model when the dataset size is small. The use of the entanglement layer between the variational layers has made significant improvements on the circuit performance. The use of the superposition layer before the data encoding layer resulted in the use of less variational layers.
Secure multiparty quantum aggregating protocol

Secure multiparty quantum computation is an important and essential paradigm of quantum computing. All the existing aggregating protocols are $(n, n)$ threshold approaches, where $n$ represents the total number of players. If one player is dishonest, the aggregation protocols cannot aggregate efficiently. In this paper, we propose a $(t, n)$ threshold-based aggregating protocol, where $t$ represents the threshold number of players. This protocol uses Shamir's secret sharing, quantum state, SUM gate, quantum Fourier transform, blind matrix, and Pauli operator. This protocol can perform the aggregation securely and efficiently. In this protocol, we simulate this aggregating protocol using the IBM quantum processor to verify the correctness and feasibility.
Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics

One-class classification has been a prevailing method in building deep anomaly detection models under the assumption that a dataset consisting of normal samples is available. In practice, however, abnormal samples are often mixed in a training dataset, and they detrimentally affect the training of deep models, which limits their applicability. For robust normality learning of deep practical models, we propose an unsupervised deep one-class classification that learns normality from pseudo-labeled normal samples, i.e., outlier detection in single cluster scenarios. To this end, we propose a pseudo-labeling method by an adaptive threshold selected by ranking-based training dynamics. The experiments on 10 anomaly detection benchmarks show that our method effectively improves performance on anomaly detection by sizable margins.
Code-Switching by Postgraduate Students in Online Classes

Code-switching is the use of more than one language in one conversation.This phenomenon is not only happening in the bilingual or multilingual family but also in a society where two or more languages are used functionally.This case was found in students' postgraduate online class of the English department at FKIP, University of Mataram.This study aimed to identify types of codeswitching those postgraduate students used and how they used them.It employed a descriptive qualitative method and used observation, recording, and note-taking as instruments to collect the data.The findings show that there are three types of code-switching used by communicators: intra-sentential, inter-sentential, and tag code-switching which belong to Poplack theory.Students switch the languages between the English-Indonesian language and Indonesian-Sasak Language.And the type which is most widely used is intra-sentential code-switching.
Hand Gesture Recognition Method based on Dual-Channel Residual Neural Network

With the changes in the international situation, more stringent requirements have been put forward for counter-terrorism and stability maintenance tasks. Under invisible conditions such as night and shelter, the recognition of gestures becomes very important. The radar has the characteristics of all-weather and has a certain penetration ability. Therefore, radar has an irreplaceable role in solving this problem. Since wideband radar has higher range resolution than narrow-band radar, it can extract finer features of gestures, which has become the main sensing apparatus for gesture recognition. However, gestures are often in a complex background. Furthermore, gestures weak compared to the main body. Therefore, the classification and recognition of human micro-motion based on wideband radar is still a difficult problem. Inspired by the successful application of residual neural network in computer vision, this paper proposes a wideband radar gestures recognition method based on Pyramid Split Attention(PSA) dual-channel residual neural network, which takes the Range-Doppler (RD) map and High Resolution Range Profiles (HRRPs) of human micro-motion as inputs. The effectiveness of this method is verified by experimental data, after the information is convolved, the features are fused, and finally the purpose of classification is achieved. The target recognition rate of this method is 97.83%, which is higher than 95.83% without pyramid split attention, and much higher than 92.33% of the high-resolution range profile based recognition method and 90% of the range Doppler based recognition method, showing the superiority of the proposed method.
LRSNet: Lightweight and Real-time SAR Ship Detection with Semantic Segmentation

Object detection based on deep learning methods plays a significant role in usual tasks. These greatly universal detection algorithms can promote the intelligent interpretation of synthetic aperture radar (SAR) images and help to get more accurate detection results. Different from the current methods directly used in SAR images, we creatively try to apply the semantic segmentation idea to SAR ship detection and complete detection and segmentation at the same time. We think SAR ship detection as a pixel-by-pixel binary classification problem rather than common general detection methods. By this way, the problem of multi-scale ships and small targets can be transformed into the unbalance of foreground and background in SAR images. In this paper, we propose a lightweight but efficient convolutional neural network (CNN) model named LRSNet. LRSNet has fewer parameters, higher efficiency and better detection and segmentation results. On HRSID dataset, the model can achieve 42.6% AP and 59.5% AR, which is better than the yolov4 detection algorithm, and the model size is only 8.4M.
Sequence Distribution Matching for Unsupervised Domain Adaptation in ASR

Unsupervised domain adaptation (UDA) aims to improve the cross-domain model performance without labeled target domain data. Distribution matching is a widely used UDA approach for automatic speech recognition (ASR), which learns domain-invariant while class-discriminative representations. Most previous approaches to distribution matching simply treat all frames in a sequence as independent features and match them between domains. Although intuitive and effective, the neglect of the sequential property could be sub-optimal for ASR. In this work, we propose to explicitly capture and match the sequence-level statistics with sequence pooling, leading to a sequence distribution matching approach. We examined the effectiveness of the sequence pooling on the basis of the maximum mean discrepancy (MMD) based and domain adversarial training (DAT) based distribution matching approaches. Experimental results demonstrated that the sequence pooling methods effectively boost the performance of distribution matching, especially for the MMD-based approach. By combining sequence pooling features and original features, MMD-based and DAT-based approaches relatively reduce WER by 12.08% and 14.72% over the source domain model.
Tools in a Workshop

While the objectives of digital humanities (DH) workshops vary widely and can range from the transfer of knowledge via teaching and learning specific competences to collaborative work on a task or project, computational tools play an important role.They can be a variety of things: the special implementation of a method, taking the form of a raw script in any programming language; specialized research software; a module installable via repositories available through the internet; or an off-the-shelf program for any DH-related data analysis, text annotation, or other computational practice.A workshop, then, designates the event where people meet to learn and to collaborate on one or more specific tasks or it can designate the place of work.A workshop in that sense implies that tools are already there, like the workshop of a smith or a carpenter, where several saws and hammers would be stored somewhere, ready to use.Preparing a workshop as an event requires an anticipation of certain audience qualities such as experience and level of competence regarding skills and knowledge related to the workshop objectives.Offering a workshop as a place takes these preconditions into account and provides tools and a digital workspace to participants.In this chapter, we approach the technical means and computational environments often used in workshops through our experience of implementing a virtual desktop environment (VDE) for introductory DH courses and workshops.Reflecting on our practice of teaching and learning with that virtual desktop solution, we discuss how it relates to the intertwined technical and social conditions of learning and teaching DH tools and methods.We focus on the problem of heterogeneous individual setups and competence levels aligned to access to computational tools, skills, and resources, especially for students and audiences with less prior experience with digital workflows.We describe the possibilities of dealing with a workshop's infrastructure necessities to argue that a shared environment can support workshop objectives by abstracting from participants' systems.This motivates collaborative
Implementing scientific education in the universities and attracting talented youth to science

Society needs young people with an open outlook, ready to act in non-stan­dard circumstances and create new algorithms for solving social problems, based on the latest scientific achievements. Science also constantly needs a timely change of scientific generations, acceptance by the new generation of a new vi­sion of tasks and opportunities of science. All this should be provided by scientific education, which brings young people closer to education already at the level of universities and even senior school classes. Properly organized scientific educa­tion ensures the proper quality of education of citizens, which allows them to adequately orient themselves and realize themselves in new circumstances ac­cording to the axiological matrix. In this context, high-quality scientific educa­tion, characterized by such parameters as a science-centric humanistic outlook, cognitive flexibility, personal creativity, and cognitive-humanistic creative inter­action between subjects, is the most relevant. The article examines demonstra­tive examples and defines the key areas of effective development of the so-called scientific education, STEAM-education. Despite the fact that the principles and methods of scientific education must be introduced at all stages of the acquisition of education by a modern person in the mode of lifelong education, it is precisely in universities that scientific education can be implemented most fully and give the best results: it is the talented youth who already have a sufficiently high level of scientific knowledge and skills of scientific research and at the same time has not yet become institutionally engaged, and therefore socially dependent, but on the contrary, is maximally open to the creation of new knowledge and new social institutions. Thus, scientific education appears not only as a new powerful factor of motivation for academic activity but also as a reliable way for young people to find their place in society and be the vanguard of overcoming the risks of modern social turbulence.
An effective ensemble method for missing data imputation

The presence of missing data in a dataset plays a vital role in the design of classification, clustering, or regression methods. An efficient missing data imputation can enhance the overall performance of a machine learning method. This paper ensembles k-nearest neighbour imputation, local least square imputation, miss forest imputation, and k-means clustering imputation using the bagging approach to handle missing values over a wide range of datasets. The method has been tested with eight different datasets in terms of root mean square error, median absolute percentage error, mean absolute percentage error, and standard deviation. Experimental results show that our method gives a low error rate compared to its closed competitors.
Using Data-Mining to Solve Crimial Cases

Crime is a serious social problem, which affects the improvement of the quality of life and the development of social economy.Under the background of the information age, how to effectively use the collected large amount of crime data to analyze and predict crime is particularly important.Data-mining can extract implicit, unknown and potentially valuable information from a large number of original data.This paper studies how to use data-mining to analyze the intelligence value behind massive data, which has important reference value for the construction of crime detection and control model.
Perancangan Sistem Informasi Pendataan Pelayanan Ibu Hamil Pada Puskesmas Sorong Timur Kota Sorong

Puskesmas Sorong Timur is one of the community health service centers located in Sorong City, West Papua. The recording of pregnant women's data at the Puskesmas is still fairly manual using books and is quite a lot, and it increases every year. Puskesmas requires an information system for collecting information on pregnant women's services. The research methods applied are data collection methods, namely primary data (interviews, and observations) and secondary data (books, theses, journal references, internet media). The system development method is the prototype method. Software used XAMPP, Sublime Text 3, Microsoft Visio 2007, Rational Rose 7, Balsamic Mockup 3, UC Browser. The results of this study display the Main Page of the Website, Login Page, Data Page of Pregnant Women, Admin Page, etc. It can be concluded that the Information System for Data Collection of Pregnant Women Services at the East Sorong Health Center is expected to make it easier to find data on pregnant women and also in presenting the information produced more accurately and on time, making it easier to provide information to the Puskesmas Management and related parties and can facilitate the process of processing baby data and making reports. The advice given in order to improve the quality of the website that has been built in the future will add new features if needed later.
Canva-Based Adaptive Electronic Module on History Subject of Senior High School Using the ASSURE Model: A Developmental Study

This study aims to determine the results of expert validation and the level of effectiveness of learning history using Canva-based adaptive electronic modules with the application of the ASSURE model in collaboration with learning technology. This research is classified as research and development, in which pre-test and post-test were carried out to 36 students of the tenth grade to be able to find out the level of effectiveness of the product being developed. Data analysis techniques used involved paired statistics, paired correlation, paired sample t-test and normality test. Based on the T-test in the small group test, it is known that the t test value was 25.31 (df = 8) and the t test value in the large group was 58.14 (df = 35) with a significance value of 0.00. The significance value was smaller than the 5% confidence level threshold value (0.00 &lt;0.05) so it can be concluded that there was a significant difference between the pre-test and post-test in the small and large group tests. Based on the results of learning using Canva-based adaptive e-modules, it can be used as a learning resource for students. This is evidenced by an increase in learning outcomes in small groups with an effectiveness value of 0.98 with high effectiveness qualifications and the results of large group trials with an effectiveness value of 0.97 with high effectiveness qualifications. Based on these results, it can be concluded that there is an increase in student learning outcomes after using Canva-based adaptive e-modules as a source of learning.
Bounds on positive operator-valued measure based coherence of superposition

Quantum coherence is a fundamental feature of quantum physics and plays a significant role in quantum information processing. By generalizing the resource theory of coherence from von Neumann measurements to positive operator-valued measures (POVMs), POVM-based coherence measures have been proposed with respect to the relative entropy of coherence, the l 1 norm of coherence, the robustness of coherence and the Tsallis relative entropy of coherence. We derive analytically the lower and upper bounds on these POVM-based coherence of an arbitrary given superposed pure state in terms of the POVM-based coherence of the states in superposition. Our results can be used to estimate range of quantum coherence of superposed states. Detailed examples are presented to verify our analytical bounds.
CYBER SECURITY

"Cyber security has become a critical concern for individuals, businesses, and governments around the world. With the increasing reliance on technology and the internet, cyber attacks and data breaches have become more frequent and sophisticated. In this article, we examine the current state of cyber security and the most common types of cyber threats, including malware, phishing, and ransomware. We also explore best practices for individuals and organizations to protect themselves from cyber attacks, such as regularly updating software and using strong passwords. The results of our research highlight the importance of staying vigilant and proactive in the face of growing cyber threats. In conclusion, we discuss the need for continued investment in cyber security research and development to help keep pace with the evolving threat landscape”. Keywords: Cyber security, cyber attacks, technology, internet, data breaches, malware, phishing, ransomware, best practices, protection, investment, research and development.
PENERAPAN SISTEM NOTIFIKASI CHAT DAN PAYMENT GATEWAY PADA SISTEM INFORMASI PEMBAYARAN SPP BERBASIS WEBSITE

Along with today's rapid technological developments, the application of information systems to support business processes will be beneficial in operational activities within an institution. To be able to manage payment transactions for Education Development Contributions (SPP) better, it is necessary to build an Information System that can help manage data and transactions to be optimal. Therefore we need a payment information system that can facilitate SMA Negeri 1 Palangka Raya in dealing with the payment of tuition fees that are not yet optimal. In this study, a tuition payment information system was created that could be used for recording tuition data, cash, and non-cash payments, as well as informing parents/guardians of students about tuition payments and invoices. This study uses the Waterfall software development method which has stages of analysis, design, program code generation, testing, and maintenance. Non-cash payments and Whatsapp message delivery on applications use features on Midtrans and Whatsapp API. Unit testing is carried out using the Blackbox method. Based on the results of the research, it was found that this tuition payment information system could facilitate the school and students. The SPP Payment Information System is used as a means of managing SPP payment data for schools, non-cash SPP payments for parents, and information on SPP payments for both.
EFL Learners’ Difficulties in Speaking English

This study aims to investigate the difficulties faced by EFL leaners’ in speaking English. The identification of the difficulties covers the six aspects of speaking skill, namely, pronunciation, grammar, vocabulary, fluency, comprehension, and task. The The participants of the study were the first semester non-English Department students of Universitas Tidar who took English as General Course Class. The research data were collected from the students’ spoken production and responses to the questionnaire. The results of the study show that there are three major difficulties identified from the students’ responses. They are grammar (48%), pronunciation (27%), vocabulary (12%), and the other aspects less than 10%.
Lightweight Privacy-Preserving Distributed Recommender System Using Tag-Based Multikey Fully Homomorphic Data Encapsulation

Recommender systems facilitate personalized service provision through the statistical analysis and model training of user historical data (e.g., browsing behavior, travel history, etc). To address the underpinning privacy implications associated with such systems, a number of privacy-preserving recommendation approaches have been presented. There are, however, limitations in many of these approaches. For example, approaches that apply public key (fully) homomorphic encryption (FHE) on different users. historical ratings under a unique public key of a target recommendation user incur significant computational overheads on resource-constrained local users and may not be scalable. On the other hand, approaches without utilizing public key FHE can neither resist chosen ciphertext attack (CCA), nor be straightforwardly applied to the setting of distributed servers. In this paper, a lightweight privacy-preserving distributed recommender system is proposed. Specifically, we present a new cryptographic primitive (i.e., tag-based multikey fully homomorphic data encapsulation mechanism; TMFH-DEM) designed to achieve CCA security for both input privacy and result privacy. TMFH-DEM enables a set of distributed servers to collaboratively execute efficient privacy-preserving outsourced computation on multiple inputs encrypted under different secret keys from different data owners, without using public key FHE. Building on TMFH-DEM, we propose a lightweight privacy-preserving distributed recommender system, which flexibly returns all the recommended items with certain predicted ratings for all target users. Formal security proof shows that our proposal achieves both user historical rating data privacy and recommendation result privacy. Findings from our evaluations demonstrate its practicability in terms of scalability, recommendation accuracy, computational and communication efficiency.
Evaluation of Language Models on Romanian XQuAD and RoITD datasets

Natural language processing (NLP) has become a vital requirement in a wide range of applications, including machine translation, information retrieval, and text classification. The development and evaluation of NLP models for various languages have received significant attention in recent years, but there has been relatively little work done on comparing the performance of different language models on Romanian data. In particular, the introduction and evaluation of various Romanian language models with multilingual models have barely been comparatively studied. In this paper, we address this gap by evaluating eight NLP models on two Romanian datasets, XQuAD and RoITD. Our experiments and results show that bert-base-multilingual-cased and bertbase- multilingual-uncased, perform best on both XQuAD and RoITD tasks, while RoBERT-small model and DistilBERT models perform the worst. We also discuss the implications of our findings and outline directions for future work in this area.