Reviewerly/exHarmony huggingface dataset:
Optimal implementation of two-qubit linear-optical quantum filters

We design optimal interferometric schemes for the implementation of two-qubit linear-optical quantum filters diagonal in the computational basis. The filtering is realized by the interference of the two photons encoding the qubits in a multiport linear-optical interferometer, followed by conditioning on presence of a single photon in each output port of the filter. The filter thus operates in the coincidence basis, similarly to many linear-optical unitary quantum gates. The implementation of a filter with linear optics may require an additional overhead in terms of a reduced overall success probability of the filtering and the optimal filters are those that maximize the overall success probability. We discuss in detail the case of symmetric real filters and extend our analysis also to asymmetric and complex filters.
Lossy Compression using Adaptive Polynomial Image Encoding

In this paper, an efficient lossy compression approach using adaptive-block polynomial curve-fitting encoding is proposed. The main idea of polynomial curve fitting is to reduce the nu ...
Pemanfaatan Videoscribe Dalam Peningkatan Kompetensi Guru

The learning media used by teachers are still relatively simple, such as student worksheets and student books published by the Ministry of Education and Culture and PowerPoint and learning videos that are already on YouTube. Not yet found a teacher who uses VideoScribe and PowerPoint-based multimedia. The majority of PowerPoints are only used as a media presentation with simple features. Learning with the media used by teachers is still not too exciting for students' attention. Students assume that the learning media used by teachers have nothing new that makes students interested in understanding the subject matter. The impact that occurs is the lack of interest, motivation, and enthusiasm of students in participating in learning; consequently, student learning achievement is still below the KKM. The solution to the problem offered is related to the existing problem is the implementation of training activities and mentoring the development of learning media based on Sparkol VideoScribe for Muhammadiyah 4 Yogyakarta Middle School teachers. The effectiveness of this training was measured by the effectiveness questionnaire and the number of teachers who can use VideoScribe.
Improvement of Green Procurement Performance in the Global Supply Chain: Evidence from Enterprise Resource Planning and Social Network Analysis

The purpose of this paper is to present a plan on how to improve the operational efficiency of the company during the Covid-19 period. Previous research has been reviewed in order to gain further knowledge of the problem; primarily on the operational performance, as well as on the supply chain management of the company. The results have shown that, on the basis of a social network analysis, the Procurement Engineer is the most influential individual in the management of information within the organization, where the Procurement Engineer is also the employee who has a strong link with the transfer of data to top management, the Ishikawa diagram indicates that the present environment is the primary cause of the company's problems in finding products, while the Tree diagram shows the only approach to the issue of insufficient employees is by supplying training for new or existing workers or by assigning staff from another department to serve permanently in a department where there is a lack of staff. In addition, the Enterprise Resource Planning (ERP) framework will strengthen the green supply chain management of the business as it will help to reduce the loss of time waiting for or processing the company's materials, thereby reducing the cost as well. This research offers proof of the pre-Covid 19 and post-Covid 19 impacts on green supply chain operational performance.
Clustered Approach for Clone Detection in Social Media

With the popularity of Online Social Networks (OSN), the number of different types of digital attacks has been increased causing lots of damages to their users. Identity Clone Attack (ICA) is one of the leading among them which illegally uses the information of a genuine user by duplicating it in another fake profile. These attacks severely affect a true and innocent identity since it can be misused by another malicious profile. Hence these clone profiles need to be identified and removed in order to increase the protection of users. This study introduces a model to detect clone profiles on Facebook by using a dataset that consists of profiles with attributes and network connections. Though the initial dataset was real, it was modified to make some artificial clones. The process of detection included three main stages, namely, filter by name, cluster using weighted categorical attributes and measure the strength of friend relationships among profiles, which follow one after another respectively. Finally, the list of possible clones with their percentages representing the amount of duplicability to a given victim profile was presented as the output of the model. Instead suggesting the exact clones, the representation of this duplicability percentages makes this approach more practical since there are many similar profiles but not clones. With the use of Agglomerative hierarchical clustering algorithm and Jaccard similarity measurement, a low average within cluster distance in cluster density performance and a precision of 88.75% has shown in the results. The present study highly focuses on the distribution of the dataset, where the calculation of weights for the attributes, similarity threshold and even the selection of the clustering algorithm is done based on it and this increases the adjustability of the proposed model to any other dataset. As the future improvements, this newly proposed approach can be extended to find clones of a victim on different platforms and more attributes can be considered for clustering.
Combining Hybrid Approach Redefinition-Multiclass Imbalance (HAR-MI) and Hybrid Sampling in Handling Multi-Class Imbalance and Overlapping

The class imbalance problem in the multi-class dataset is more challenging to manage than the problem in the two classes and this problem is more complicated if accompanied by overlapping. One method that has proven reliable in dealing with this problem is the Hybrid Approach Redefinition-Multiclass Imbalance (HAR-MI) method which is classified as a hybrid approach that combines sampling and classifier ensembles. However, in terms of diversity among classifiers, a hybrid approach that combines sampling and classifier ensembles will give better results. HAR-MI provides excellent results in handling multi-class imbalances. The HAR-MI method uses SMOTE to increase the number of samples in the minority class. However, this SMOTE also has a weakness where an extremely imbalanced dataset and a large number of attributes will be over-fitting. To overcome the problem of over-fitting, the Hybrid Sampling method was proposed. HAR-MI combination with Hybrid Sampling is done to increase the number of samples in the minority class and at the same time reduce the number of noise samples in the majority class. The preprocessing stages at HAR-MI will use the Minimizing Overlapping Selection under Hybrid Sampling (MOSHS) method, and the processing stages will use Different Contribution Sampling. The results obtained will be compared with the results using Neighbourhood-based under-sampling. Overlapping and Classifier Performance will be measured using Augmented R-Value, the Matthews Correlation Coefficient (MCC), Precision, Recall, and F-Value. The results showed that HAR-MI with Hybrid Sampling gave better results in terms of Augmented R-Value, Precision, Recall, and F-Value
MOTIVATION FOR LEARNING SPEAKING SKILLS USING VIDEO BLOG (VLOGGING) BASED ON PROJECT LEARNING STUDENTS OF THE INDONESIAN LANGUAGE AND LITERATURE EDUCATION DEPARTMENT AT NUSA CENDANA UNIVERSITY: A PHENOMENOLOGY APPROACH

This study aims to identified students motivation in learning project-based learning (PBL) Indonesian language skills using vlogging media based on meanings and themes seen from the "because motives" and "in order to motives" related to the phenomenon of learning in the classroom. In identifying objectives, this study uses a qualitative research method with the approach of phenomenology from Albert Schutz's perspective. Researcher becomes the main instrument in an effort to obtain and find data. The research subjects were 38 second semester students of the Department of Language and Literature at Nusa Cendana University. Data collection techniques used include observation, questionnaires, and interviews. Data analysis through three stages of the flow model namely reduction, presentation and verification of data and triangulation. The results of the study prove that the teaching strategy associated with vlogs can attract students' learning interest because of their interest in other fields such as multimedia technology and social media. The application of learning PBL-based speaking skills using vlogs can improve speaking competence and gradually improve the mental aspects of students to become more confident to speak and communicate in public. So it can be said that learning PBL-based speaking skills using vlogs is a good and innovative learning strategy in an effort to increase learning motivation and mastery of students' speaking skills.
Optimizing Quantum Teleportation and Dense Coding via Mixed Noise Under Non-Markovian Approximation

Physicists are attracted to open-system dynamics, how quantum systems evolve, and how they can be protected from unnecessary environmental noise, especially when environmental memory effects are non-negligible, as with non-Markovian approximations. There are several methods to solve the master equation of non-Markovian cases and we obtain the solutions of the quantum-state-diffusion equation for a two-qubit system using the perturbation method under the influence of various types of environmental noise, including relaxation noise, dephasing noise and their mixture. It was found that by inducing dephasing noise into the relaxation process strengthens the correlation between qubits by restoring the entanglement lost in the environment, thereby making it possible to optimize quantum teleportation fidelity and dense coding capacity via mixed noise under non-Markovian approximation.
Molecular Machines for Quantum Error Correction

Inspired by biological molecular machines, we explore the idea of an active quantum robot the purpose of which is to delay decoherence. A conceptual model capable of partially protecting arbitrary logical qubit states against single-physical-qubit errors is presented. Implementation of an instance of that model—the entanglement qubot—is proposed using laser-dressed Rydberg atoms. The dynamics of the system are studied using stochastic wave-function methods.6 MoreReceived 11 March 2021Accepted 6 August 2021DOI:https://doi.org/10.1103/PRXQuantum.2.030336Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasBrownian motionCooling & trappingFluctuations & noiseQuantum effects in biological systemsQuantum error correctionQuantum thermodynamicsQuantum InformationAtomic, Molecular & OpticalStatistical PhysicsBiological Physics
Automatic Pectoral Muscles Detection and Removal in Mammogram Images

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; The main aim of the Computer-Aided Detection/Diagnosis system is to assist the radiologists in examining the digital mammograms. Digital mammogram is the most popular screening technique for early detection of breast cancer. One of the problems in breast mammogram analysis is the presence of pectoral muscles region with high intensity in the upper right or left side of most Media-Lateral Oblique views of mammogram images. Therefore, it is important to remove this pectoral muscle from the image for accurate diagnosis results. The proposed method consists of three main steps. In the first step, noise is reduced using Median filtering. In the second step, artifacts removal and breast region extraction are performed using Otsu method. Finally, the pectoral muscle is extracted and removed using the proposed Split Orientation Local Thresholding (SOLTH) algorithm. For this study, a total of 110 mammogram images from the Mini-Mias database (MIAS) were used to evaluate the proposed methodâ€™s performance. The experimental results of automatic pectoral muscle detection and removal were observed by radiologist and showed 90.9% accuracy of acceptable results.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
South German Credit Data Classification Using Random Forest Algorithm to Predict Bank Credit Receipts

Normally, most of the bank's wealth is obtained from providing credit loans so that a marketing bank must be able to reduce the risk of non-performing credit loans. The risk of providing loans can be minimized by studying patterns from existing lending data. One technique that can be used to solve this problem is to use data mining techniques. Data mining makes it possible to find hidden information from large data sets by way of classification. The Random Forest (RF) algorithm is a classification algorithm that can be used to deal with data imbalancing problems. The purpose of this study is to discuss the use of the RF algorithm for classification of South German Credit data. This research is needed because currently there is no previous research that applies the RF algorithm to classify South German Credit data specifically. Based on the tests that have been done, the optimal performance of the classification algorithm RF on South German Credit data is the comparison of training data of 85% and testing data of 15% with an accuracy of 78.33%.
Spoofing Linear Cross-Entropy Benchmarking in Shallow Quantum Circuits

The linear cross-entropy benchmark (Linear XEB) has been used as a test for procedures simulating quantum circuits. Given a quantum circuit C with n inputs and outputs and purported simulator whose output is distributed according to a distribution p over {0,1}ⁿ, the linear XEB fidelity of the simulator is ℱ_C(p) = 2ⁿ 𝔼_{x ∼ p} q_C(x) -1, where q_C(x) is the probability that x is output from the distribution C |0ⁿ⟩. A trivial simulator (e.g., the uniform distribution) satisfies ℱ_C(p) = 0, while Google’s noisy quantum simulation of a 53-qubit circuit C achieved a fidelity value of (2.24 ±0.21)×10^{-3} (Arute et. al., Nature'19).In this work we give a classical randomized algorithm that for a given circuit C of depth d with Haar random 2-qubit gates achieves in expectation a fidelity value of Ω(n/L⋅15^{-d}) in running time poly(n,2^L). Here L is the size of the light cone of C: the maximum number of input bits that each output bit depends on. In particular, we obtain a polynomial-time algorithm that achieves large fidelity of ω(1) for depth O(√{log n}) two-dimensional circuits. This is the first such result for two dimensional circuits of super-constant depth. Our results can be considered as an evidence that fooling the linear XEB test might be easier than achieving a full simulation of the quantum circuit.
Application of Translation Technologies in the Translation of IMTFE Transcripts

Machine translation has grown very rapidly in recent times due to the developments in big data, artificial intelligence, and cloud computing software and techniques. The first generation of Rule-Based Machine Translation has been replaced by fourth generation of Neural Machine Translation based on complex deep learning and networking models. Translation models have also undergone tremendous changes. The traditional translation model fails to meet the needs of the modern language service industry. There still exists doubt whether Translation technologies could enhance the quality in the translation of non-technical texts. So far no one has discussed the application of the technologies in the translation of IMTFE Transcripts. This paper aims to prove that translation technologies can be applied to the translation of IMTFE Transcripts which the author works on. By analyzing the text features of IMTFE Transcripts, and applying different translation technologies in the translation, the author finds that the quality and efficiency of translation have been greatly enhanced. It is concluded that translation technologies can be used to facilitate translation of texts of different kinds. In spite of their drawbacks, translators can still benefit a lot from the adoption of translation technologies.
Face Recognition based on Convolution Neural Network and Scale Invariant Feature Transform

Recently, Face Recognition (FR) has been received wide attention from both the research community and the cyber security industrial companies.Low accuracy of recognition is considered a main challenge when it comes to talking about employing the Artificial Intelligence (AI) for FR.In this work, the Scale Invariant Feature Transform (SIFT) and the Convolutional Neural Networks (CNN) feature extraction methods are utilized to build an AI based classifier.The CNN extracts features through both the convolutional and polling layers, while the SIFT extracts features depending on the scale space, directions, and histograms of points of interest.The features that are extracted by the CNN and the SIFT methods are used as an inputs for the KNN classifier.The experimental results with 400 test images of 40 persons, with 240 images are randomly chosen as training sets and 160 images from test sets, demonstrate in terms of accuracy, sensitivity, and error rate, that the CNN-based KNN classifier achieved better results when compared to the SIFT-based KNN classifier (accuracy = 97%, sensitivity = 93%, error rate = 3%).
Comparison of affinity degree classification with four different classifiers in several data sets

Today, the affinity notion has been widely used in various fields such as biochemistry, business, and computing.In general, affinity is a natural linking of someone with something or attraction to a person, idea, or thing.Affinity can be different; the meaning depends on the situation.In chemistry, affinity was defined as the tendency of a molecule to bind with another molecule.It can also be an identifier for the immobilization or separation that can be carried [1].In comparison, business affinity can be defined as consumer interest in the products [2].Affinity may also be adequate for creating a composed service by linking two services or pointing out a similar service [3].In this study, affinity was defined as the correlation, relationship, similarity, or tendency between two objects [4].Adapted from peer-to-peer data replication [5], the term affinity degree (AD) refers to measuring the degree of correlation between two objects.
Named entity recognition of local adverse drug reactions in Xinjiang based on transfer learning

For deep learning’s insufficient learning ability of a small amount of data in the Chinese named entity recognition based on deep learning, this paper proposes a named entity recognition of local adverse drug reactions based on Adversarial Transfer Learning, and constructs a neural network model ASAIBC consisting of Adversarial Transfer Learning, Self-Attention, independently recurrent neural network (IndRNN), Bi-directional long short-term memory (BiLSTM) and conditional random field (CRF). However, of the task of Chinese named entity recognition (NER), there are only few open labeled data sets. Therefore, this article introduces Adversarial Transfer Learning network to fully utilize the boundary of Chinese word segmentation tasks (CWS) and NER tasks for information sharing. Plus, the specific information in the CWS is also filtered. Combing with Self-Attention mechanism and IndRNN, this feature’s expression ability is enhanced, thus allowing the model to concern the important information of different entities from different levels. Along with better capture of the dependence relations of long sentences, the recognition ability of the model is further strengthened. As all the results gained from WeiBoNER and MSRA data sets by ASAIBC model are better than traditional algorithms, this paper conducts an experiment on the data set of Xinjiang local named entity recognition of adverse drug reactions (XJADRNER) based on manual labeling, with the accuracy, precision, recall and F-Score value being 98.97%, 91.01%, 90.21% and 90.57% respectively. These experimental results have shown that ASAIBC model can significantly improve the NER performance of local adverse drug reactions in Xinjiang.
Training Model and Quality of College Students in English Major

This paper mainly analyzes the training model and quality of college students in English major, aiming to improve the training of English majors in colleges. Firstly, the current problems in the training of college students in English major were summarized, the influencing factors of the training quality were identified, and the implementation strategies and paths for the training model were introduced in details. On this basis, the author designed an evaluation index system, set up an evaluation model for the training quality of college students in English major, and explained the fuzzy decision process in the evaluation. The proposed evaluation model effectively quantifies the training quality of English majors in colleges. The research results provide an important guidance for pinpointing the weak links in the training model of English majors in colleges, and laying a solid basis for improving the training quality.
Developing a Shared Task for Speech Processing on Endangered Languages

Advances in speech and language processing have enabled the creation of applications that could, in principle, accelerate the process of language documentation, as speech communities and linguists work on urgent language documentation and reclamation projects. However, such systems have yet to make a significant impact on language documentation, as resource requirements limit the broad applicability of these new techniques. We aim to exploit the framework of shared tasks to focus the technology research community on tasks which address key pain points in language documentation. Here we present initial steps in the implementation of these new shared tasks, through the creation of data sets drawn from endangered language repositories and baseline systems to perform segmentation and speaker labeling of these audio recordings—important enabling steps in the documentation process. This paper motivates these tasks with a use case, describes data set curation and baseline systems, and presents results on this data. We then highlight the challenges and ethical considerations in developing these speech processing tools and tasks to support endangered language documentation.