Reviewerly/exHarmony huggingface dataset:
Classical variational simulation of the Quantum Approximate Optimization Algorithm

A key open question in quantum computing is whether quantum algorithms can potentially offer a significant advantage over classical algorithms for tasks of practical interest. Understanding the limits of classical computing in simulating quantum systems is an important component of addressing this question. We introduce a method to simulate layered quantum circuits consisting of parametrized gates, an architecture behind many variational quantum algorithms suitable for near-term quantum computers. A neural-network parametrization of the many-qubit wavefunction is used, focusing on states relevant for the Quantum Approximate Optimization Algorithm (QAOA). For the largest circuits simulated, we reach 54 qubits at 4 QAOA layers, approximately implementing 324 RZZ gates and 216 RX gates without requiring large-scale computational resources. For larger systems, our approach can be used to provide accurate QAOA simulations at previously unexplored parameter values and to benchmark the next generation of experiments in the Noisy Intermediate-Scale Quantum (NISQ) era.
GraphEmbeddingviaTopologyandFunctionalAnalysis

Graphs have been ubiquitous in Machine Learning due to their versatile nature in modelling real world situations .Graph embedding is an important precursor to using graphs in Machine Learning , and much of performance of algorithms developed later depends heavily on this. However very little theoretical work exists in this area , resulting in the proliferation of several benchmarks without any mathematical validation , which is detrimental .In this paper we present an analysis of deterministic graph embedding in general , using tools from Functional Analysis and Topology . We prove several important results pertaining to graph embedding which may have practical importance .One limitation of our work in it's present form is it's applicable to deterministic embedding approaches only, although we strongly hope to extend it to random graph embedding methods as well in future.We sincerely hope that this work will be beneficial to researchers working in field of graph embedding.
Citation Study of Selected Indian LIS Journals using Google Scholar

As reflected in Google Scholar, the citations obtained by the articles published during 2015-2019 in the three leading Indian LIS journals entitled SRELS, DESIDOC Journal of Library &amp; Information Technology (DJLIT) and JILA have been analysed in the present study. The study mainly covers year-wise publication of articles and the citations received by these articles, calculation of immediacy index, h-index and impact factor, authorship pattern, collaboration trend, highly cited papers, most prolific authors and citation report. The finding of the study reveals that average number of articles published in DJLIT and ALIS are same and the articles published in DJLIT have received maximum number of citations followed by SRELS. It is also found that impact factor for DJLIT was almost 2 in 2019 whereas it was less than 1 for SRELS and JILA during the marked period.
Hybrid Method of Recommender System to Decrement Cold Start and Sparse Data Issues

Background and Objectives: The main purpose of the recommender system is to estimate the user's desire and provide a list of items predicted using the appropriate information. Recommender systems offering suggestions items to users face the two challenges of cold start and sparse data. Methods: This paper aims to propose a novel method to overcome the cold start and sparse data challenges in recommender systems. Singular value decomposition is one of the most common methods to reduce sparse data in recommender systems by reducing dimensions. However, the basic singular value decomposition can only extract the feature vectors of users and items, potentially resulting in lower recommendation precision. Using similarity criteria between entities, in addition to being able to reduce cold start that can solve the problem of singular value decomposition through extracting more refined factor vectors. Besides, considering the context dimensions as the third dimension of the matrix requires the use of another flexible algorithm, such as tensor factorization offering a viable solution to minimize the sparse data challenge. We propose a novel method named TCSSVD. First, a two-level matrix is obtained through similarity criteria between the user and the item to reduce the cold start challenge. The second step, to reduce the challenge of sparse data, contextual information is used with the help of tensor in the two level singular value decomposition. Results: Two data sets IMDB and STS due to the exerting of users feature, items feature and contextual information to review the proposed method. In order to the accuracy of the prediction of the criterion RMSE with an accuracy of 95%. However, since the user's rating of the item is of particular importance in the recommender systems. The method TCSSVD compared to the methods tensor factorization, HOSVD, BPR, and CTLSVD are used evaluation measure Precision, Recall, F1-score, and NDCG. Conclusion: The findings indicate that the use of innovative similarity criteria to extract user attributes, items to reduce the cold start and the use of contextual information through the tensor in the TCSSVD in order to sparse data. The results contribute to improving the accuracy of the suggestions given to users in recommender systems.  ======================================================================================================Copyrights©2021 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.======================================================================================================
Using Formalistic Approach to Analyse Malay Artefact, Kukuran into its Classification

Initially the creation of kukuran probably began as a simple seat straddled with a sharp iron grater protruding at the end. From a simple shape of kukuran, it evolved to more complex styles of design. The splendid achievement of Malay civilization is reflected in the form of art physically and spiritually. The formal analysis is the work of describing and assessing the visual and material appearance of artworks. This formal analysis stressed the development of a technical language for description of the art work, so that the analysis can be depicted and addressed. Formal analysis describes the work of art and trying to understand the artists or craftsmen want to convey. These visual images are analysed into formalistic and pre-iconographical in order to reveal to its characteristics. It establishes categories of systematic definitions and thought to express point of view about the kukuran characteristics. The characteristics of kukuran constitute the basis for its classification. The analysis embarks on three major stages which are characteristics extraction, characteristics comparison, and classification techniques. The characteristics extraction stage captures the visual formal composition qualities of kukuran. After extracting the required characteristics, the image than been compare to the characteristics in appropriate manner. Later, these visual images have describes and clarifies the visual composition and vital ornaments representational in order to reveal its significance characteristics. Finally, these characteristics classified accordingly to its analysis of the extraction characteristics. Finally, the finding concludes that kukuran design are divided into four major group which are zoomorphic design, anthropomorphic design, modern design and utility design. It shows the splendid achievement of Malay civilization reflected in the form of art physically and spiritually.
MENGATASI MOTIVASI BELAJAR RENDAH SISWA DENGAN TEKNIK BEHAVIORAL

Motivation is very important in students to increase interest in learning. In fact there are students who do have low motivation in learning which impacts on their learning outcomes. This study aims to improve low learning motivation at MA Nurul Ummah sampit by applying a Behavioral approach. This research is a qualitative study with a case study approach. Data collection in this study uses interview, observation and documentation. The object of this study is low learning motivation. The subject of this study was one of the XII students at MA Nurul Ummah Sampit. Data analysis techniques in this study are data reduction, data presentation, and drawing conclusions. For data validity checking techniques are using source triangulation and method triangulation. The results of this study indicate that the application of Behavioral techniques succeeded in increasing low learning motivation. The increase in motivation of students from low categories becomes a high category after carrying out individual counseling.
Ball tracking algorithm for mobile devices

Object tracking seeks to determine the object size and location in the following video frames, given the appearance and location of the object in the first frame. The object tracking approaches can ...
Transformation of Smart Text Processing: Emoji Classification for Arabic and Turkish Languages

Over the last decade, the emerging development and popularization of communication tools, as well as the evolution of smart media applications in mobile devices, brought about the transformation of language. Emojis or the lingua franca of social media caused new models in the analysis of language pragmatics. Even if short modalities have existed in the transmission of sentiment context, the unicity of emojis in all languages created new challenges especially in the sentiment spectrum of the exchange of utterances. The discrete nature of emojis brings also a new perspective to label the state of a sentence to cluster lexical features assigned to emojis and to compare them in a multilingual corpora. Thus, smart text processing urges to transform emojis as a multimodal bias to reveal ideas and feelings in unimodal or multimodal approaches. In this study, we created a bilingual Twitter corpora for Arabic and Turkish languages. In the literature, there are few studies highlighting the comparative effects of emojis for both languages. Even though they belong to different language families, similar lexical features become critical for the analysis of sentiment in textual context. Therefore, we aimed to analyze the effects of sentiment classification on Arabic and Turkish tweets. Emojis have been classified as priors of text features. Sentiment analysis has been carried out using multinomial Naive Bayes (NB) and Support Vector Machines (SVM). Our corpora has been prepared using generic expressions without limiting them in a specific field. Consequently, we conclude that evaluation metrics indicate better classification performance for Turkish language through SVM whereas NB is found more accurate for Arabic language.
Development of a Visual Assessment System for Meningiomas Using Deep-Learning Technology: A Multicenter Study

Background: Accurate assessment is important for meningioma patients in magnetic resonance imaging (MRI). The purpose of this study was to establish an automated visual evaluation system to facilitate diagnosis and treatment.Methods: The system, designed with a cascade network structure, was developed with deep-learning technology for automatic function of tumor detection, visual assessment, and grading prediction. Patients were retrospectively collected from two institutions. Deep-learning models were trained on the largest meningioma dataset so far involved 97500 MR images of 625 patients from the first institution. Specifically, a convolutional neural network model was established first to segment the tumor images, followed by rendering algorithms for spatial reconstruction. Then, another model was trained with the segmented tumor images for grading prediction. After training and validating performances in the first dataset, we integrated our models as a system, and tested the robustness based on performances on the second dataset from the second institution involving different MRI platforms.Findings: The segment model represented worthy-noted performances with Dice coefficients of 0.920±0.009, and the classification model also achieved high accuracy with AUC of 0.918±0.006 and accuracy of 0.901±0.039 when classifying the tumors into low-grade and high-grade meningiomas. The system showed clinical potentials, and represented good performances in the external validation group.Interpretation: Deep-learning based system could potentially serve as a reliable assessment for meningioma patients.Funding Statement: This work was supported by 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University; Key research and development project of science and technology department of Sichuan Province.Declaration of Interests: None declared.Ethics Approval Statement: Committee of Sichuan University have given approval for statistics export and utilization for this study. The obligatory written informed consent was obtained from participants enrolled in this study (written informed consent for patients <16 years old was signed by parents or guardians).
SkillBERT: “Skilling” the BERT to classify skills!

In the age of digital recruitment, job posts can attract a large number of applications, and screening them manually can become a very tedious task. These recruitment records are stored in the form of tables in our recruitment database (Electronic Recruitment Records, referred to as ERRs). We have released a de-identified ERR dataset to the public domain. We also propose a BERT-based model, SkillBERT, the embeddings of which are used as features for classifying skills present in the ERRs into groups referred to as groups. A competency group is a group of similar skills and it is used as matching criteria (instead of matching on skills) for finding the overlap of skills between the candidates and the jobs. This proxy match takes advantage of the BERT's capability of deriving meaning from the structure of competency groups present in the skill dataset. In our experiments, the SkillBERT, which is trained from scratch on the skills present in job requisitions, is shown to be better performing than the pre-trained BERT and the Word2Vec. We have also explored K-means clustering and spectral clustering on SkillBERT embeddings to generate cluster-based features. Both algorithms provide similar performance benefits. Last, we have experimented with different machine learning algorithms like Random Forest, XGBoost, and a deep learning algorithm Bi-LSTM . We did not observe a significant performance difference among the algorithms, although XGBoost and Bi-LSTM perform slightly better than Random Forest. The features created using SkillBERT are most predictive in the classification task, which demonstrates that the SkillBERT is able to capture information about the skills' ontology from the data. We have made the source code and the trained models of our experiments publicly available.
ERROR RECOGNITION WITH REASON TEST TO ENHANCE GRAMMAR CAPABILITY OF ENGLISH LANGUAGE PROGRAM STUDENTS IN UNIVERSITAS ISLAM MAJAPAHIT MOJOKERTO

This study aims aims to answer the problems on the factors that influence the students in comprehending English grammar and analyzing on the implementation of error recognition with reason test in teaching and learning process can improve English grammar comprehension of students of Educational Study Program of Universitas Islam Majapahit Mojokerto qualitatively and quantitatively. The implementation of ERWRT had improved students’ English grammar comprehending that can be seen through  the comparison of the results of pre-treatment which shows that on pre-treatment out of 35 students, there were 11 got the score below 55 as the Passing Grade. Result of column 3 showed that all students got the score above passing grade. The result of C4 Cycle I showed that out of 29 students, only 1 student failed. The result of C4 in cycle II showed that all students succeed. The average of C3 in cycle II escalated from 97.9 to 100. The average of C4 escalated from 77 to 97. In cycle I, there were six grammatical errors. Those were (1) Subject and verb disagreement, (2) using preposition, (3) error in using articles, (4) error in forming passive voice, (5) error in using to be, and (6) using multiple verbs. In non-grammatical errors, there were wrong spelling, wrong diction, and no answer. On cycle II, grammatical and non-grammatical errors still can be found as in cycle I except error in using articles. New errors in cycle II was numbers and noun disagreement. The improvement of grammar comprehension had shown that ERWRT can be used to improve grammar comprehension and writing skills.
Data Mining in Grouping Indihome Customer Data Using the K–Means Clustering Method at PT.Telkom Akses

PT. Telkom Akses is part of the PT. Telekomunikasi Indonesia, Tbk (Telkom) company whose shares the stock and fully owned by Telkom.Telkom is a state owned company and as an IndiHome service Provider. So far, PT. Telkom Akses still has problems in classifying customer data. Based on these problems, PTTA requires a computer application that can cluster customer data and use data mining that can help solve problems that occur using the clustering method.. One of the methods offered to solve this problem is the k-means algorithm. The results of this study, the authors design and design a desktop-based information system that can implement the clustering method to produce customer data clusters according to the needs of PT. Telkom Access
Non-Linear Rewards For Successor Features

Reinforcement Learning algorithms have reached new heights in performance, often overtaking humans on several challenging tasks such as Atari and Go. However, the resulting models learn fragile policies that are unable to transfer between tasks without full retraining. Successor features aim to improve this situation by decomposing the policy into two components: one capturing environmental dynamics and the other modelling reward. Under this framework, transfer between related tasks requires only training the reward component. However, successor features builds upon the limiting assumption that the current reward can be predicted from a linear combination of state features. This paper proposes a novel improvement to the successor feature framework, where we instead assume that the reward function is a non-linear function of the state features, thereby increasing its representational power. After derivation of the new state-action value function, the decomposition includes a second term that learns the auto-correlation matrix between state features. Experimentally, we show this term explicitly models the environment's stochasticity and can also be used in place of ϵ-greedy exploration methods during transfer. The performance of the proposed improvements to the successor feature framework is validated empirically on navigation tasks and control of a simulated robotic arm.
Learning to Balance with Incremental Learning

Classification tasks require balanced distribution of data in order to ensure the learner to be trained to generalize over all classes. In realistic settings, however, the number of instances vary substantially among classes. This typically leads to a learner that promotes bias towards the majority group due to its dominating property. Therefore, methods to handle imbalanced data is crucial for alleviating distributional skews and fully utilizing the under-represented data. We propose a novel training method, Sequential Targeting, that forces an incremental learning setting by splitting the data into mutually exclusive subset and adaptively balancing the data distribution as tasks develop. To address problems that arise within incremental learning, we apply dropout and elastic weight consolidation with our method. It is demonstrated in a variety of experiments on both text and image dataset (IMDB, CIFAR-10, MNIST) and has proven its superiority over traditional methods such as oversampling and under-sampling.
Segmentation of Aggregate and Asphalt in Photographic Images of Pavements

Particle size distribution of aggregate in asphalt pavements is used for determining important characteristics like stiffness, durability, fatigue resistance, etc. Unfortunately, measuring this distribution requires a sieving process that cannot be done directly on the already mixed pavement. The use of digital image processing could facilitate this measurement, for which it is important to classify aggregate from asphalt in the image. This classification is difficult even for humans and much more for classical image segmentation algorithms. In this paper, an expert committee approach was used, including classical adaptive Otsu, k-means vector quantization over a set of 8 principal components obtained from 26 features, and a Gaussian mixture model whose parameters are estimated through the expectation-maximization algorithm. A novel cellular automata approach is used to coordinate these expert opinions. Finally, a simple heuristic is used to reduce sub- and over-segmentation. The segmentation results are comparable to those obtained by a human expert, while the sieve size of the segmented images corresponds very well with that obtained from the sieving process, validating the proposed method of segmentation. The results show that with the digital imaging procedure it was possible to detect particles with a size of 100 m with 90% of success with respect to time-consuming manual techniques. In addition, with these results it is possible to establish the homogeneity of the sample and the distribution of the particles within the asphalt mixture.
Learning to teach in times of the COVID-19 pandemic: our experience at Universidad de Buenos Aires

The aim of this text, is to describe our teaching role in times of pandemic, recounting how we discovered strategies to teach in times of virtual presence, as well as the search for new experiences in teacher training, highlighting the importance of how we turn them into an opportunity for our self-improvement Procedure: We will discuss the strengths and weaknesses that we found while we were going through this new path of online teaching, set up for the first time in the undergraduate courses of our University Conclusion: The main educational accomplishments we achieved during the pandemic course were, to establish a true flipped classroom, and to use ludic strategies that were not common in our classes On the other hand, we highlight the close contact we fulfilled with our students, which led them to recognize with high opinion our study methodology, the knowledge acquired, the strategies used, and our commitment and teaching predisposition
ChePAN: Constrained Black-Box Uncertainty Modelling with Quantile Regression

Most predictive systems currently in use do not report any useful information for auditing their associated uncertainty and evaluating the corresponding risk. Taking it for granted that their replacement may not be advisable in the short term, in this paper we propose a novel approach to modelling confidence in such systems while preserving their predictions. The method is based on the Chebyshev Polynomial Approximation Network (the ChePAN), a new way of modelling aleatoric uncertainty in a regression scenario. In the case addressed here, uncertainty is modelled by building conditional quantiles on top of the original pointwise forecasting system considered as a black box, i.e. without making assumptions about its internal structure. Furthermore, the ChePAN allows users to consistently choose how to constrain any predicted quantile with respect to the original forecaster. Experiments show that the proposed method scales to large size data sets and transfers the advantages of quantile regression to estimating black-box uncertainty.