Reviewerly/exHarmony huggingface dataset:
Basic Word Extraction Algorithm Based on Morphological Rules for Balinese Texts

Stemming is the process of extracting the root word of an affixed word. The process is intended to reduce the variations in the word. In this research, we are interested in applying stemming on Balinese language. Previous works on stemming of the Balinese language applied rule-based method but only prefix and suffix were considered. Moreover, the rules were constructed without providing much attention to the morphology of the Balinese language. Rule-based method can be verified and validated with ease on simple problem but fail to do so on problems with high complexity such as Balinese language. To overcome the weaknesses of rule-based stemming on Balinese language, we propose a method that reduce all variations of affix on Balinese language by combining the rule- based approach and the Balinese language morphology. Based on experiments carried out, our proposed method obtained an average stemming accuracy of 99% which is better than 96.67% achieved by the previous method.&#x0D; Keywords: Stemming, Balinese language, Rule-based
AlignMix: Improving representations by interpolating aligned features

Mixup is a powerful data augmentation method that interpolates between two or more examples in the input or feature space and between the corresponding target labels. Many recent mixup methods focus on cutting and pasting two or more objects into one image, which is more about efficient processing than interpolation. However, how to best images is not well defined. In this sense, mixup has been connected to autoencoders, because often autoencoders interpolate well, for instance generating an image that continuously deforms into another. In this work, we revisit mixup from the interpolation perspective and introduce AlignMix, where we geometrically align two images in the feature space. The correspondences allow us to between two sets of features, while keeping the locations of one set. Interestingly, this gives rise to a situation where mixup retains mostly the geometry or pose of one image and the texture of the other, connecting it to style transfer. More than that, we show that an autoencoder can still improve representation learning under mixup, without the classifier ever seeing decoded images. AlignMix outperforms state-of-the-art mixup methods on five different benchmarks.
Private computation of polynomials over networks

This study concentrates on preserving privacy in a network of agents where each agent seeks to evaluate a general polynomial function over the private values of her immediate neighbors. We provide an algorithm for the exact evaluation of such functions while preserving privacy of the involved agents. The solution is based on a reformulation of polynomials and adoption of two cryptographic primitives: Paillier as a Partially Homomorphic Encryption scheme and multiplicative–additive secret sharing. The provided algorithm is fully distributed, lightweight in communication, robust to dropout of agents, and can accommodate a wide class of functions. Moreover, system theoretic and secure multi-party conditions guaranteeing the privacy preservation of an agent’s private values against a set of colluding agents are established. The theoretical developments are complemented by numerical investigations illustrating the accuracy of the algorithm and the resulting computational cost.
Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC

We present an efficient approach for doing approximate Bayesian inference when only a limited number of noisy likelihood evaluations can be obtained due to computational constraints, which is becoming increasingly common for applications of complex models. Our main methodological innovation is to model the log-likelihood function using a Gaussian process (GP) in a local fashion and apply this model to emulate the progression that an exact Metropolis-Hastings (MH) algorithm would take if it was applicable. New log-likelihood evaluation locations are selected using sequential experimental design strategies such that each MH accept/reject decision is done within a pre-specified error tolerance. The resulting approach is conceptually simple and sample-efficient as it takes full advantage of the GP model. It is also more robust to violations of GP modelling assumptions and better suited for the typical situation where the posterior is substantially more concentrated than the prior, compared with various existing inference methods based on global GP surrogate modelling. We discuss the probabilistic interpretations and central theoretical aspects of our approach, and we then demonstrate the benefits of the resulting algorithm in the context of likelihood-free inference for simulator-based statistical models.
The Arguments of plot( )

Many arguments can be used in plot(). In this chapter, we go over the arguments. The arguments are grouped by categories – arguments that affect overall appearance, the appearance of lines and points, and details. We look at the effects on the appearance of graphics by using examples, with data from an R dataset. In this chapter, the function plot.default(), which plots scatterplots, is used for the examples. While most of the arguments in this chapter can be used in all or most versions of plot(), a few are specific to plot.default().
Emoji as an Aid for Information Retrieval: A Viewpoint regarding Metadata, Technology and Improving International Information Seeking

Current metadata schema, internet search engines and databases of scientific literature have an Anglo-American language and cultural bias. While it is possible to catalogue or index non-English, non-Latin script publications and achieve some degree of success in the retrieval of such information, librarians and researchers are often disappointed by inconsistencies in the accuracy of search results for this type of publication and frustrated by the limitations created by the biases embedded in the technology and schema. This paper is the result of a two-year dialog between a Farsi-speaking medical librarian in Iran and an English-speaking metadata librarian in Canada. They present the argument that a metadata schema which incorporates graphical images such as Emoji along with keyword indexing or a controlled vocabulary and new search engines which can make use of the new schema for Boolean searching is a robust potential solution for the difficulties they discussed. While this new information seeking environment that requires both the edition of a graphical facet to metadata schema and new search engines presents the potential to break down significant intercultural barriers, the authors recognize that this undertaking is more than a simple technological or procedural change but would require significant agreement between and work from librarians, standards bodies and researchers.
Blended Learning Design and Praxis for the Coordinated Development of Theory and Practice ‐‐Take Vocational Education as an Example

There are some typical problems in current vocational education: the teachers emphasize too much on technology and ignore the laws of education; the lack of irrigation in scientific and cultural knowledge making students can only repeat the operation of teachers and fail to transfer and apply knowledge well. These lead the vocational education to become a kind of job training. On the basis of the procedural teaching rules, mastery learning theory and situational teaching theory, the blended learning design of theory and coordinated development provides ideas for solving current vocational education problems in using information technology as a medium. It emphasizes the internal logical integration of theoretical and practical knowledge in the process of learning design to focus on the students' learning process, present students' learning results based on data and thus verify the effectiveness of students' learning. Practice shows that the blended learning of theory and coordinated development can increase students' interest in learning, promote the cultivation of students' higher-order thinking ability and improve students' academic performance.
3D-to-2D Distillation for Indoor Scene Parsing

Indoor scene semantic parsing from RGB images is very challenging due to occlusions, object distortion, and viewpoint variations. Going beyond prior works that leverage geometry information, typically paired depth maps, we present a new approach, a 3D-to-2D distillation framework, that enables us to leverage 3D features extracted from large-scale 3D data repository (e.g., ScanNet-v2) to enhance 2D features extracted from RGB images. Our work has three novel contributions. First, we distill 3D knowledge from a pretrained 3D network to supervise a 2D network to learn simulated 3D features from 2D features during the training, so the 2D network can infer without requiring 3D data. Second, we design a two-stage dimension normalization scheme to calibrate the 2D and 3D features for better integration. Third, we design a semantic-aware adversarial training model to extend our framework for training with unpaired 3D data. Extensive experiments on various datasets, ScanNet-V2, S3DIS, and NYU-v2, demonstrate the superiority of our approach. Also, experimental results show that our 3D-to-2D distillation improves the model generalization.
RGB-D Local Implicit Function for Depth Completion of Transparent Objects

Majority of the perception methods in robotics require depth information provided by RGB-D cameras. However, standard 3D sensors fail to capture depth of transparent objects due to refraction and absorption of light. In this paper, we introduce a new approach for depth completion of transparent objects from a single RGB-D image. Key to our approach is a local implicit neural representation built on ray-voxel pairs that allows our method to generalize to unseen objects and achieve fast inference speed. Based on this representation, we present a novel framework that can complete missing depth given noisy RGB-D input. We further improve the depth estimation iteratively using a self-correcting refinement model. To train the whole pipeline, we build a large scale synthetic dataset with transparent objects. Experiments demonstrate that our method performs significantly better than the current state-of-the-art methods on both synthetic and real world data. In addition, our approach improves the inference speed by a factor of 20 compared to the previous best method, ClearGrasp. Code and dataset will be released at https://research.nvidia.com/publication/2021-03_RGB-D-Local-Implicit.
Attention Head Masking for Inference Time Content Selection in Abstractive Summarization

How can we effectively inform content selection in Transformer-based abstractive summarization models? In this work, we present a simple-yet-effective attention head masking technique, which is applied on encoder-decoder attentions to pinpoint salient content at inference time. Using attention head masking, we are able to reveal the relation between encoder-decoder attentions and content selection behaviors of summarization models. We then demonstrate its effectiveness on three document summarization datasets based on both in-domain and cross-domain settings. Importantly, our models outperform prior state-of-the-art models on CNN/Daily Mail and New York Times datasets. Moreover, our inference-time masking technique is also data-efficient, requiring only 20% of the training samples to outperform BART fine-tuned on the full CNN/DailyMail dataset.
Automating Secondary School Libraries: A Web-Based Library Management System

This paper describes the development of a web-based school library management system for secondary schools in Malaysia (WBSSLMS), which aims to provide an effective and efficient way of acquiring, cataloguing, searching, retrieving, downloading and maintaining of library materials. The systems operates in a consortium where secondary school libraries can participate as members to share information resources and still maintain separate library databases. Information gathered from various literature, onsite visits to school libraries, brainstorming sessions with teacher-librarians and observation on the present systems used, have helped produce ideas in designing and implementing the systems. WBSSLMS consists of seven main modules and each is basically targeted to three types of users; i.e. students, teacher-librarians and systems administrators. Any computer, regardless of its operating system, could access any of the modules as long it had Internet access and a browser. The modules are Registration, Acquisition, Cataloguing, Online Public Access Catalogue (OPAC), Circulation, Maintenance and Information Management.. This paper also presents the strengths and limitations of the systems, and also the possible future enhancements. User acceptance tests showed that a high majority of respondents found the systems easy to use. They also found that the modules are complete and have an appealing interface. It is foreseen that WBSSLMS has met the requirements to meet the needs of a library automated systems, as well as a management information systems.
Pemberdayaan Masyarakat melalui Pelatihan Mitigasi dan Kesiapsiagaan Bencana di Nagari Pakan Sinayan

Disasters can come suddenly at anytime and anywhere. Disasters can result in many casualties, injuries, and material loss. Therefore, the community must understand the threat of a disaster, have knowledge about disaster preparedness. The purpose of this activity is to provide education and training on disaster management that may occur in the Pakan Sanayan village, Banuhampu District. The activity was held on August 8, 2020 in Nagari Pakan Sinayan, Banuhampu District, Agam Regency, and was attended by 47 community members. The activity begins with conducting a pretest to the training participants, providing material on potential disaster analysis followed by a discussion on disaster risk analysis that may occur in the Pakan Sinayan area. The material was continued with disaster mitigation and preparedness, especially disasters that could potentially occur in the area, namely landslides, earthquakes, fires and volcanic eruptions. The results of the evaluation showed an increase in public understanding and knowledge of disasters. It is recommended that this activity be carried out continuously to encourage community empowerment in improving disaster preparedness.
Design and Programming of 5 Axis Manipulator Robot with GrblGru Open Source Software on Preparing Vocational Students’ Robotic Skills

The rapid development of technology at the end of this decade, the industrial world is changing the human resources they have using the help of robot technology. The use of a robot was chosen because of the accuracy and precision that it can do in doing the job. In addition, robotic technology requires only one programming to do many things. Thus, there are many advantages for companies by using robotic technology in their production processes. This research aims to designing and building arm robot prototype to teach the robotics programming language. The result of this research is an arm robot prototype with Arduino Mega 2560 based programming. In addition, the robot movement programming uses the open source GrblGru software Grbl based. The GrblGru used for interpreting G-code and to convert pulse and direction information to control the stepper motor. This research concludes that there are 5 importance steps on develop the arm robot prototype, 1. Analyze; 2. Design; 3. Develop; 4. Evaluate; and 5. Implementation. With the GrblGru software the movements of arm robot prototype can work on simulation mode and control mode, there for students easier to study robotics programming language.
Multi-rate attention architecture for fast streamable Text-to-speech spectrum modeling

Typical high quality text-to-speech (TTS) systems today use a two-stage architecture, with a spectrum model stage that generates spectral frames and a vocoder stage that generates the actual audio. High-quality spectrum models usually incorporate the encoder-decoder architecture with self-attention or bi-directional long short-term (BLSTM) units. While these models can produce high quality speech, they often incur O($L$) increase in both latency and real-time factor (RTF) with respect to input length $L$. In other words, longer inputs leads to longer delay and slower synthesis speed, limiting its use in real-time applications. In this paper, we propose a multi-rate attention architecture that breaks the latency and RTF bottlenecks by computing a compact representation during encoding and recurrently generating the attention vector in a streaming manner during decoding. The proposed architecture achieves high audio quality (MOS of 4.31 compared to groundtruth 4.48), low latency, and low RTF at the same time. Meanwhile, both latency and RTF of the proposed system stay constant regardless of input lengths, making it ideal for real-time applications.
Lower Bounds Implementing Mediators in Asynchronous Systems.

Abraham, Dolev, Geffner, and Halpern proved that, in asynchronous systems, a $(k,t)$-robust equilibrium for $n$ players and a trusted mediator can be implemented without the mediator as long as $n > 4(k+t)$, where an equilibrium is $(k,t)$-robust if, roughly speaking, no coalition of $t$ players can decrease the payoff of any of the other players, and no coalition of $k$ players can increase their payoff by deviating. We prove that this bound is tight, in the sense that if $n \le 4(k+t)$ there exist $(k,t)$-robust equilibria with a mediator that cannot be implemented by the players alone. Even though implementing $(k,t)$-robust mediators seems closely related to implementing asynchronous multiparty $(k+t)$-secure computation \cite{BCG93}, to the best of our knowledge there is no known straightforward reduction from one problem to another. Nevertheless, we show that there is a non-trivial reduction from a slightly weaker notion of $(k+t)$-secure computation, which we call $(k+t)$-strict secure computation, to implementing $(k,t)$-robust mediators. We prove the desired lower bound by showing that there are functions on $n$ variables that cannot be $(k+t)$-strictly securely computed if $n \le 4(k+t)$. This also provides a simple alternative proof for the well-known lower bound of $4t+1$ on asynchronous secure computation in the presence of up to $t$ malicious agents.
Interactionism and online identity

This chapter explores how interactionist approaches to research have contributed to understandings of identity in the online context. To do this, two interactionist approaches are discussed: 1) the Goffmanian approach to self and 2) the discursive model of self. Through drawing on live recordings of Facebook Messenger interactions, this chapter reveals how these two interactionist approaches have significantly contributed to existing knowledge on online identity. Concepts such as the presentation of self and facework, along with the systematic focus of methods such as conversation analysis, have revealed the complexity involved in constructing and performing identity online as well as the ways identity is reduced in digital environments. In focusing on two distinct approaches to interactionist work, this chapter unveils the similarities and differences between the Goffmanian and discursive approaches to self, ultimately arguing that, despite their differences in focus, they can work to complement one another in furthering understanding of identity and self online.
Local Metrics for Multi-Object Tracking

This paper introduces temporally local metrics for Multi-Object Tracking. These metrics are obtained by restricting existing metrics based on track matching to a finite temporal horizon, and provide new insight into the ability of trackers to maintain identity over time. Moreover, the horizon parameter offers a novel, meaningful mechanism by which to define the relative importance of detection and association, a common dilemma in applications where imperfect association is tolerable. It is shown that the historical Average Tracking Accuracy (ATA) metric exhibits superior sensitivity to association, enabling its proposed local variant, ALTA, to capture a wide range of characteristics. In particular, ALTA is better equipped to identify advances in association independent of detection. The paper further presents an error decomposition for ATA that reveals the impact of four distinct error types and is equally applicable to ALTA. The diagnostic capabilities of ALTA are demonstrated on the MOT 2017 and Waymo Open Dataset benchmarks.
Proposal of a framework for software product and process quality assurance for very small entities

Nowadays about 76% of software development organizations in Mexico are Very Small Entities (VSEs). Therefore, new models and standards focused on this type of organizations are emerging, such as the ISO/IEC 29110 series of standards. The ISO/IEC 29110 allows improving the software quality focusing on the improvement of their software development process. However, VSEs also require complementary the quality assurance focusing on the software product characteristics. Unfortunately, the achievement of more than one standard in VSEs is complicated, and most of the time impossible. As solution to this situation, this paper presents a proposal, which aims to establish the basis of a quality assurance framework covering the process and product perspective, allowing the inclusion of product quality characteristics from ISO/IEC 25010 to the basic profile of ISO/IEC 29110.
Employing Divergent Machine Learning Classifiers to Upgrade the Preciseness of Image Retrieval Systems

Abstract Content Based Image Retrieval (CBIR) system is an efficient search engine which has the potentiality of retrieving the images from huge repositories by extracting the visual features. It includes color, texture and shape. Texture is the most eminent feature among all. This investigation focuses upon the classification complications that crop up in case of big datasets. In this, texture techniques are explored with machine learning algorithms in order to increase the retrieval efficiency. We have tested our system on three texture techniques using various classifiers which are Support vector machine, K-Nearest Neighbor (KNN), Naïve Bayes and Decision Tree (DT). Variant evaluation metrics precision, recall, false alarm rate, accuracy etc. are figured out to measure the competence of the designed CBIR system on two benchmark datasets, i.e. Wang and Brodatz. Result shows that with both these datasets the KNN and DT classifier hand over superior results as compared to others.
Constraints in Implementing Online Learning during the Covid-19 Pandemic

Abstract One of the effects of the Covid-19 pandemic is the recommendation to carry out online learning. This study aims to explore the constraints faced by teachers, students and parents in implementing online learning during the Covid-19 pandemic. The method of this study is a qualitative research. The respondents were teachers, students, and parents from the first grade, the second grade, the third grade, the fourth grade and the fifth grade at the state elementary school Mangunjiwan 2, Demak, Indonesia. The data were collected using interview and questionnaires and analyzed using a qualitative descriptive analysis. The results showed that the constraints experienced by teachers, students, and parents in implementing online learning during the Covid-19 pandemic include limited internet quota, network disruption internet, students without cellphones, lack of student attention, disturbed parents’ work, and teachers’ difficulty in using technology and designing attractive online learning. This study suggests teachers to increase their skill for online learning and government to support the internet for online learning.