Reviewerly/exHarmony huggingface dataset:
Kombinasi Single Linkage Dengan K-Means Clustering Untuk Pengelompokan Wilayah Desa Kabupaten Pemalang

K-Means is very dependent on determining the center cluster initial which has an impact on the quality of clusters resulting, in addition to determining the center of cluster the number of k that will be used it can also affect the quality of the cluster from the method K-Means.Poverty is mostly experienced by rural communities, this can be seen from the lack of existing facilities to serve the interests of the community in various fields.To avoid the imbalance that occurs, a development plan is needed in accordance with the characteristics of the welfare of the people in the region.Therefore, we need an effort to group villages so that policy making is right on target.One of the algorithms clustering that is commonly used is the K-Means algorithm because it is quite simple, easy to implement, and has the ability to group large data groups very quickly.However, the K-Means algorithm has a weakness in determining the center cluster initial given.Initialization of centers cluster randomly may result in formation clusters changing (inconsistent).For this reason, the K-Means method needs to be combined with the hierarchical method in determining the center cluster initial.This combination method is called Hierarchical K-Means which is a combination of methods hierarchical and partitioning, where the process is hierarchical used to find the initial center initialization cluster and the process partitioning to get the cluster optimal.The hierarchical method used in this study is the method single linkage.Based on the method Elbow , the recommended amount of k for this study is k = 4.The combination of the single linkage and k-means algorithms with k = 4 in this study results in avalue silhouette coefficient of 0.685 which is a feasible or appropriate cluster category, while the evaluation measurement by Davies The Boulldin Index yielded a value of 0.577.
ANALISIS KEPUASAN PENGGUNA E-LEARNING SEBAGAI PENDUKUNG AKTIVITAS PEMBELAJARAN MENGGUNAKAN METODE EUCS

In this research to make the improvement of convenience that would later give also the satisfaction of any user that has used and be active in e-learning information systems which have been applied in engineering Electronics ft unp. This research using the method of end user computing satisfaction that in short EUCS are used to measure the level of user satisfaction with knowing the contribution factors of content, accuracy, shape, ease of usage and timeliness to be thorough by the author to apply to the scope of the Department of electronics engineering ft unp. This research using quantitative research, then conducted a descriptive analysis, inductive analysis, multiple regression and hypothesis testing. The results of the analysis of the data indicates that the variable in the method of EUCS jointly contribute the same significance of 45% to the satisfaction of users of the e-learning information systems in the Department of electronics engineering achievements in the unp ft. from research that has been done, is expected to be contributed to the Administration's e-learnngi in order to improve the quality of e-learning on the campus of the State University of padang in General and in the Electrical Engineering Universitas Negeri Padang in particular. Keywords: information systems, End User Computing Satisfaction, E-learning.
Generalization of low rank parity-check (LRPC) codes over the ring of integers modulo a positive integer

Abstract Following the work of Gaborit et al. (in: The international workshop on coding and cryptography (WCC 13), 2013) defining LRPC codes over finite fields, Renner et al. (in: IEEE international symposium on information theory, ISIT 2020, 2020) defined LRPC codes over the ring of integers modulo a prime power, inspired by the paper of Kamche and Mouaha (IEEE Trans Inf Theory 65(12):7718–7735, 2019) which explored rank metric codes over finite principal ideal rings. In this work, we successfully extend the work of Renner et al. by constructing LRPC codes over the ring $$\mathbb {Z}_{m}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>Z</mml:mi> <mml:mi>m</mml:mi> </mml:msub> </mml:math> which is not a chain ring. We give a decoding algorithm and we study the failure probability of the decoder.
Multivariate Adaptive Regression Splines (MARS) for Modeling Student Status at Universitas Terbuka

Multivariate Adaptive Regression Splines (MARS) used to model the active student’s status in the Department of Statistics at Universitas Terbuka and determine the factors that influence the response variable. This study consists of 9 variables, namely gender, age, education, marital status, job, initial registration year, number of registrations, credits, and GPA, but after modeling using the MARS method, the explanatory variable can affect the response variable is the initial registration year. Several registrations, GPA, and credits. Based on the results of the R output and using a 95% confidence interval, each base 1 to 10 function is partially significant with the p-value of the base 1-10 function being smaller than 0.05 and simultaneously with a smaller p-value. of 0.05, so that the above model has a significant effect partially or simultaneously on the response variable. From these results, it is concluded that the MARS model is suitable for determining the factors that affect the active status of students.
Exploration of Unknown Environments Using Deep Reinforcement Learning

My research presents a method for efficient exploration of an outdoor, unknown area, which aims to achieve precise coverage of regions of interest within that area. While this method for autonomous exploration was designed for autonomous controllers in unmanned aerial vehicles (UAVs), the concepts apply to any vehicle which uses autonomous navigation. We consider an environment with areas of interest of various sizes littered throughout, and a reinforcement learning agent which is tasked with discovering and mapping these areas in an efficient manner.
Role of Optimizer on Network Fine-tuning for Adversarial Robustness (Student Abstract)

The solutions proposed in the literature for adversarial robustness are either not effective against the challenging gradient-based attacks or are computationally demanding, such as adversarial training. Adversarial training or network training based data augmentation shows the potential to increase the adversarial robustness. While the training seems compelling, it is not feasible for resource-constrained institutions, especially academia, to train the network from scratch multiple times. The two fold contributions are: (i) providing an effective solution against white-box adversarial attacks via network fine-tuning steps and (ii) observing the role of different optimizers towards robustness. Extensive experiments are performed on a range of databases, including Fashion-MNIST and a subset of ImageNet. It is found that the few steps of network fine-tuning effectively increases the robustness of both shallow and deep architectures. To know other interesting observations, especially regarding the role of the optimizer, refer to the paper.
The Use of Camtasia Video, Youtube, and WhatsApp in Online Teaching During Pandemic Situation

The aims of this article is to discuss about the implementation of Camtasia and YouTube in Teaching Writing for English Department Students. The problem of the writer in teaching writing for first semester of English Department students in this pandemic situation is online learning media. Not only learning media, but also for the students who lives in rural area are faced the difficulties to get the internet connection easily if the online learning done through Google meet or Zoom. In order to solve that problem, the writer proposes Camtasia, YouTube, and also WhatsApp to deliver the learning material. Camtasia, YouTube, and WhatsApp is an application that can be used as a learning media. Camtasia itself is used to record the material in the form of video or PPT presentation. After finish recording the material, the Camtasia Video can be shared to the students through YouTube Channel. Then the last step is shared the link through WhatsApp group and continued with discussion session. The Subject of this research is first semester students of English Department Program. The research is done start from August to December 2020. This research is qualitative research. The data for this research are taken form the observation and also interview. The results of this research are: After the implementation of Camtasia, you tube link, and also create the discussion on WA group, more than 95% students are able to write with correct punctuation and use capital letter in their sentences, especially in the beginning of their sentence. Moreover, the students are also able to make a sentences with good arrangement by choosing effective words and appropriate order. Only 3 students who still difficult to develop their idea on writing in a good organization, grammatical order, and content.
Optimized Symmetric Keys Generated using Genetic Algorithm for Fully Homomorphic Encryption System

With growing demands in the computing services and increase in computation power, the computer consumers are migrating towards cloud computing domain to increase the effectiveness in compute, share, manage and to store the resources.The data storage and computations are delegated to the third party to manage it in the cloud that poses breach of confidentiality during computation.The cloud providers should ensure security for the data in rest and data in motion to prevent data disclosure during computation.Data encryption is a best way to secure the data, but practically not feasible to share the secret keys since third party is involved in handling the data.A solution to this problem is to perform the computations without decrypting the cipher text.Fully Homomorphic Encryption (FHE) is an efficient method to provide random computations on the encrypted data stored in cloud without decrypting it.The computation results are also encrypted which can be decrypted from the user domain.Genetic Algorithm (GA) is a heuristic search method based on natural selection.This paper proposes a symmetric key homomorphic encryption based on Genetic Algorithm key generation.Due to the randomness in key generation a strong cryptosystem is designed that makes the Chosen plain text attack more difficult.The results shows that the keys generated by GA are more random than other symmetric key cryptosystems.
Multi-hop Graph Convolutional Network with High-order Chebyshev Approximation for Text Reasoning

Graph convolutional network (GCN) has become popular in various natural language processing (NLP) tasks with its superiority in longterm and non-consecutive word interactions.However, existing single-hop graph reasoning in GCN may miss some important nonconsecutive dependencies.In this study, we define the spectral graph convolutional network with the high-order dynamic Chebyshev approximation (HDGCN), which augments the multi-hop graph reasoning by fusing messages aggregated from direct and long-term dependencies into one convolutional layer.To alleviate the over-smoothing in high-order Chebyshev approximation, a multi-vote based crossattention (MVCAttn) with linear computation complexity is also proposed.The empirical results on four transductive and inductive NLP tasks and the ablation study verify the efficacy of the proposed model.Our source code is available at https://github.com/
Multi-sensor Fusion Detection Method for Vehicle Target Based on Kalman Filter and Data Association Filter

Multi-sensor data fusion is an emerging technology, which has been widely used in medical diagnosis, remote sensing, inertial navigation and many other fields. What’s more, the implementation and application of automatic driving system rely heavily on target detection technology. Due to the high mobility and unpredictability of vehicle-mounted equipment, for automatic vehicles, it is arduous to achieve real-time and accurate vehicle target detection by a single sensor means, thus it is difficult to reliably guarantee the safety and stability. This paper proposes a novel object detection method based on a multi-sensor fusion mechanism, which considers the real-time sensing data from two types of sensors including radar and camera. It collects multi-vehicle speed and position information efficiently and reliably. Then, it filters and integrates data according to Extended Kalman Filter, Data Association Filter and some other methods. Furthermore, vehicle-borne equipment makes intelligent decision based on the data. In addition to theoretical support, the designed simulation results also show that the multi-sensor fusion mechanism can detect target vehicles efficiently and accurately, and it has superiority in the stability and accuracy of perception than single sensor sensing method.
Implementasi Augmented Reality pada Mata Pelajaran Komputer dan Jaringan Dasar di SMK Nufa Citra Mandiri Berbasis Android

In the process of teaching and learning activities in schools, there are still many that only use learning media in the form of books. This causes boredom in the learning process. Especially in basic computer and network subjects, where in this subject there is a lot of visual recognition and developing forms of technology. Good visual presentation is very important to support the learning process. Augmented Reality (AR) is a learning medium that is currently still being used in the teaching and learning process. AR is an application that combines the real world with the virtual world in two-dimensional or three-dimensional forms which are projected in a real environment at the same time. AR technology is very appropriate to be implemented to support the visualization process in learning activities, because it can present visualization in 3-dimensional form. So that the visualization is more interactive and more interesting. In the learning process at SMK Nufa Citra Mandir, they still rely on books only, especially in computer and basic network subjects which really need visualization of the devices discussed. The application of AR technology to basic computer and network subjects at SMK Nufa Citra Mandiri is expected to help in the learning process. In its development, the design method used is the Extreme Programming (XP). Purpose of this research is to create software with AR technology on Android platform, so that students can better understand and get to know computer tools and computer network devices contained in basic computer and network subjects.
Symmetric Component Caching for Model Counting on Combinatorial Instances

Given a propositional formula ψ, the model counting problem, also referred to as #SAT, seeks to compute the number of satisfying assignments (or models) of ψ. Modern search-based model counting algorithms are built on conflict-driven clause learning, combined with the caching of certain subformulas (called components) encountered during the search process. Despite significant progress in these algorithms over the years, state-of-the-art model counters often struggle to handle large but structured instances that typically arise in combinatorial settings. Motivated by the observation that these counters do not exploit the inherent symmetries exhibited in such instances, we revisit the component caching architecture employed in current counters and introduce a novel caching scheme that focuses on identifying symmetric components. We first prove the soundness of our approach, and then integrate it into the state-of-the-art model counter GANAK. Our extensive experiments on hard combinatorial instances demonstrate that the resulting counter, SymGANAK, leads to improvements over GANAK both in terms of PAR-2 score and the number of instances solved.
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS MIND MAPPING DENGAN POWERPOINT DI SMA ISLAM TERPADU GRANADA SAMARINDA

This research is intended to develop learning media, determine the feasibility, and assess the trial of PowerPoint based on mind mapping as a learning media to the students. This research and development uses Luther's Multimedia Development Life Cycle (MDLC) model. In data collection, this research uses observation, questionnaires, and interviews. The assessment of learning media feasibility involves content experts, media experts, and 23 respondents or students of class XI IPS B at SMA Islam Terpadu Granada Samarinda. Qualitative and quantitative data analyses are conducted. The results of this development research are: (1) the usage of Multimedia Development Life Cycle (MDLC) model through the stages of conceptualizing, designing, content collecting, assembling, testing, and distributing. (2) The validation result on PowerPoint based on mind mapping as a learning media by content expert is in the “very feasible” criteria with 4.73 average score in all aspects. The media expert's assessment is in the criteria of "very feasible" with 4.60 average score. In the trial to the students or respondents, the result is included in "feasible" criteria with 3.77 average score.Keywords: learning media, mind mapping, PowerPoint
SELF-CONCEALMENT IN JEAN WEBSTER’S DADDY LONG LEGS

This research studied the self-concealment of the main character in Daddy Long Legs novel. It is aimed at identifying self-concealment through the main character’s Johari Windows. This research also described fear of self-disclosure. The method used is descriptive-qualitative research This analysis is collaborating the concept of self-concealment with Johari Window. The results showed, Judy Abbott, the main character does self-concealment which is identified by the most dominated hidden area in her Johari Window which is including fear of self-disclosure. By comparing with previous self-concealment analysis in some literary studies, it is found that self-concealment is connected with queerness, deception, and either repression or suppression defense mechanism.
The Utility and Interplay of Gazetteers and Entity Segmentation for Named Entity Recognition in English

Recent papers have introduced methods to incorporate gazetteer features and entity segmentation techniques in neural named entity recognition models.These papers rely on different resources and include features not related to the use of gazetteers, rendering impossible the comparison of the relative effectiveness of the approaches.Here, we provide a comprehensive overview of methods for incorporating gazetteers and for entity segmentation.We evaluate representative methods from each in similar settings for a fair comparison and identify the ones that are consistently better across datasets and input representations.We further show that gazetteers improve entity segmentation and not just entity typing.Hence, we explore their utility in recognizing long entities, a problem for which entity segmentation techniques were developed.Our work explains the mechanisms via which gazetteers improve the performance of neural NER models. C HyperparametersHyperparameters used as same as the ones in https://github.com/guillaumegenthial/tf_ner/models/chars_conv_lstm_crf, except for number of epochs and the minimum number of steps before early stopping which depend on dataset size.Numbers of epochs used were 25, 25, 50 and 50 for CoNLL, Ontonotes, BTC and TTC respectively.Minimum steps were 8000, 15000, 2500 and 2500 for CoNLL, Ontonotes, BTC and TTC respectively.
LNL polycategories and doctrines of linear logic

We define and study LNL polycategories, which abstract the judgmental structure of classical linear logic with exponentials. Many existing structures can be represented as LNL polycategories, including LNL adjunctions, linear exponential comonads, LNL multicategories, IL-indexed categories, linearly distributive categories with storage, commutative and strong monads, CBPV-structures, models of polarized calculi, Freyd-categories, and skew multicategories, as well as ordinary cartesian, symmetric, and planar multicategories and monoidal categories, symmetric polycategories, and linearly distributive and *-autonomous categories. To study such classes of structures uniformly, we define a notion of LNL doctrine, such that each of these classes of structures can be identified with the algebras for some such doctrine. We show that free algebras for LNL doctrines can be presented by a sequent calculus, and that every morphism of doctrines induces an adjunction between their 2-categories of algebras.
Explaining Neural Matrix Factorization with Gradient Rollback

Explaining the predictions of neural black-box models is an important problem, especially when such models are used in applications where user trust is crucial. Estimating the influence of training examples on a learned neural model's behavior allows us to identify training examples most responsible for a given prediction and, therefore, to faithfully explain the output of a black-box model. The most generally applicable existing method is based on influence functions, which scale poorly for larger sample sizes and models. We propose gradient rollback, a general approach for influence estimation, applicable to neural models where each parameter update step during gradient descent touches a smaller number of parameters, even if the overall number of parameters is large. Neural matrix factorization models trained with gradient descent are part of this model class. These models are popular and have found a wide range of applications in industry. Especially knowledge graph embedding methods, which belong to this class, are used extensively. We show that gradient rollback is highly efficient at both training and test time. Moreover, we show theoretically that the difference between gradient rollback's influence approximation and the true influence on a model's behavior is smaller than known bounds on the stability of stochastic gradient descent. This establishes that gradient rollback is robustly estimating example influence. We also conduct experiments which show that gradient rollback provides faithful explanations for knowledge base completion and recommender datasets. An implementation and an appendix are available.
Distance-based K-Means Clustering Algorithm for Anomaly Detection in Categorical Datasets

Real-world data sets also provide knowledge in an unsupervised manner with distinct and complementary aspects.In the field of cluster analysis, a number of algorithms have recently arisen.A priori, it is difficult for a user to determine which algorithm will be most suitable for a given dataset.For this job, algorithms based on graphs give good results.Such algorithms are however, vulnerable to outliers and noises with minimal edge information found in the tree to split a dataset.Thus, in several fields, the need for better clustering algorithms increases and for this reason utilizing robust and dynamic algorithms to improve and simplify the whole process of data clustering has become an important research field.In this paper, a novel distance-based clustering algorithm called the entropic distance based K-means clustering algorithm (EDBK) is proposed to remove the outliers in effective way.This algorithm depends on the entropic distance between attributes of data points and some basic mathematical statistics operations.In this work, experiments are conducted using UCI datasets showed that EDBK method outperforms the existing methods such as Artificial Bee Colony (ABC), kmeans etc.The EDBK achieved 80.71% recall, 79.81% precision and 75.82%F-measure.The results show that the EDBK method not only improve the clustering accuracy (i.e.nearly 92%), but also greatly reduce the interference of outliers to clustering results.