Reviewerly/exHarmony huggingface dataset:
Forecasting of the Cases of Covid-19 Patients in Indonesia using Fuzzy Time Series

The main objective of this study is forecasting of the cases of covid-19 patients in indonesia using fuzzy time series. The data used is from February 1, 2022 to February 28, 2022. The methods used are Fuzzy Time Series (FTS) Chen and FTS Cheng, using first order and second order. FTS is a forecasting method that uses rules and logic on fuzzy sets. The level of prediction accuracy is then calculated based on the Mean Absolute Percentage Error (MAPE) value. The MAPE values of these two methods are then compared to know which method is more suitable in this case study. The results showed that Fisrt Order FTS Chen produced an accuracy of 4,21% and Fisrt Order FTS Cheng produced an accuracy of 4,22%. Second Order FTS Chen and Second Order FTS Chen produced/1the same MAPE, 1,23%./1The results of this study indicate that Second Order FTS Chen and FTS Cheng produce good accuracy and can be used to predict new confirmed cases of Covid 19 sufferers in Indonesia.
Classification of Organizational Data Using the K-Means Algorithm

The University of Muhammadiyah Tasikmalaya (UMTAS) is a private university under the Muhammadiyah association located in Tasikmalaya City, West Java Province. As a form of developing student soft skills, UMTAS carries out many activities, one of which is through student organizations consisting of internal and external organizations. This research was conducted to find out which student organizations are categorized as excellent, good, and not good. The assessment attributes for grouping student organization data are the number of active members, activities in one year, organizational discipline, and achievements. Clustering uses the K-Means algorithm. The results obtained from calculations carried out manually and using the rapidminer application obtained the same results, namely clusters with the "excellent" category totaling 8 data (26.6%), clusters with the "good" category totaling 17 data (56.6%), and clusters with the "not good" category totaling 5 data (16.6%). The results of this study can be used by the head of the bureau of academic administration of student affairs and alumni in providing rewards in the form of priorities in organizational funding, awarding charters and punishments in the form of coaching and revoking student organization decrees.
The Relationship between the Frequency of Technology Use and Electronic Commerce Adoption among Small and Medium-Sized Enterprises in Kuwait

Manuscript type: Research paper Research aims: This study aims to investigate the determinants of SMEs’ e-commerce adoption as well as technological, organisational, and environmental characteristics in Kuwait. Moreover, it aims to study the effect of the frequency of technology use as a moderator in SMEs’ e-commerce adoption. Design/Methodology/Approach: A questionnaire-based survey was conducted among 259 SMEs. Research findings: The results demonstrate a positive relationship between relative advantage, competitive pressure, and e-commerce adoption. Analysis of the level of technology use as a moderator reveals a significant difference in terms of the impact of compatibility, complexity, and supplier or customer pressure on e-commerce adoption. Theoretical contribution/originality: This study may be the first to assess the moderating role of the frequency of technology use in the context of SMEs’ e-commerce adoption. Research limitation/implications: The paper contributes to e-commerce adoption research by highlighting the main influencing factors, identifying several important interactions based on the frequency of technology use, and recognising many practical implications for SME managers. Keywords: E-commerce adoption, Frequency of technology use, Kuwait, SMEs, TOE JEL Classification: M10, M15, M31
Accounting Information and Supply Chain Management Practices in the Era of IR 4.0: The Case of a Japanese Subsidiary in Malaysia

Manuscript type: Research paper Research aims: This study aims to explore the interplay between accounting information and supply chain management (SCM) practices of a Malaysian subsidiary whose shares are largely owned by a Japanese parent company. Design/Methodology/Approach: This study follows a qualitative, case study methodology. The major sources of this study’s data were collected through semi-structured interviews, documentary reviews and informal observations. Research findings: Influenced by its parent company’s management style that concerns aligning business practices with the fourth Industrial Revolution (IR 4.0), the case company has subscribed to a set of accounting systems and SCM practices to manage its complex business activities. As much as accounting systems influence its SCM practices more naturally, SCM practices also influence its accounting practices quite extensively. On the one hand, it has used accounting reports to manage its cashflow, to develop its marketing plan, and to monitor its performance. On the other hand, its SCM practices have shaped the ways pricing and performance measures are set with much negotiation. Theoretical contribution/Originality: Using the Resource Based View (RBV) as a theoretical lens, this study analyses the role of accounting within the context of the supply chain network by exploring how accounting systems influence and/or get influenced by SCM practices. Practitioner/Policy implications: The findings are important in shedding light on the interplay between accounting and SCM in view of their strong networks and mutual roles in relation to the natural supply chain setting of a Japanese subsidiary company by relating to the IR 4.0’s demands. Research limitation/Implications: This study is based on one case study and that the specific accounting information that were reported in the company were not identified. This study has revealed that information sharing has streamlined SCM and influenced performance as members in the supply chain network are able to collaborate in a more integrated and seamless manner to enhance the organisational competitive advantage as highlighted by the RBV theory and as emphasised by IR 4.0. Keywords: Accounting Systems, Case Study, Japanese Subsidiary, SCM Practices, IR 4.0 JEL Classification: M41
AN ANALYSIS OF STUDENTS’ READING COMPREHENSION ACHIEVEMENT AT SECOND SEMESTER STUDENTS OF BHINNEKA PGRI UNIVERSITY IN THE ACADEMIC YEAR 2020/2021

To reading comprehension, especially long texts in foreign languages, some students have problems, such as being lazy and bored. If this problem persists, it will affect student achievement. This study was conducted with the aim of (1) to find how is the students’ reading comprehension achievement at second semester of Bhinneka PGRI University in the academic year 2020/2021. (2) to find what are the factors that affect the students’ reading comprehension achievement at second semester students of Bhinneka PGRI University in the academic year 2020/2021. The study used qualitative research to obtain valid data from 25 participants in class A at second semester in English Depatment. Researchers obtained data on reading comprehension test scores from the results of the TOEFL test questions given by researchers with an average value of 62. To test the truth, the researchers conducted interviews and questionnaire data. The results of the analysis are also through interviews with teachers and students, as well as questionnaires answered by students. It was concluded that learning reading skills in the second semester TOEFL test in English Depatment at the University of Bhinneka PGRI in the 2020/2021 academic year got poor achievements. Because they experience several problems in learning, namely difficulty in accessing the internet, lack of vocabulary that students have, and the material obtained are difficult to capture.
Stochastic Transformer Networks with Linear Competing Units: Application to end-to-end SL Translation

Automating sign language translation (SLT) is a challenging real world application. Despite its societal importance, though, research progress in the field remains rather poor. Crucially, existing methods that yield viable performance necessitate the availability of laborious to obtain gloss sequence groundtruth. In this paper, we attenuate this need, by introducing an end-to-end SLT model that does not entail explicit use of glosses; the model only needs text groundtruth. This is in stark contrast to existing end-to-end models that use gloss sequence groundtruth, either in the form of a modality that is recognized at an intermediate model stage, or in the form of a parallel output process, jointly trained with the SLT model. Our approach constitutes a Transformer network with a novel type of layers that combines: (i) local winner-takes-all (LWTA) layers with stochastic winner sampling, instead of conventional ReLU layers, (ii) stochastic weights with posterior distributions estimated via variational inference, and (iii) a weight compression technique at inference time that exploits estimated posterior variance to perform massive, almost lossless compression. We demonstrate that our approach can reach the currently best reported BLEU-4 score on the PHOENIX 2014T benchmark, but without making use of glosses for model training, and with a memory footprint reduced by more than 70%.
Signal Processing over Multilayer Graphs: Theoretical Foundations and Practical Applications

Signal processing over single-layer graphs has become a mainstream tool owing to its power in revealing obscure underlying structures within data signals. However, many real-life datasets and systems, {including those in Internet of Things (IoT)}, are characterized by more complex interactions among distinct entities, which may represent multi-level interactions that are harder to be captured with a single-layer graph, and can be better characterized by multilayers graph connections. Such multilayer or multi-level data structure can be more naturally modeled by high-dimensional multilayer graphs (MLG)}. To generalize traditional graph signal processing (GSP) over multilayer graphs for analyzing multi-level signal features and their interactions, this work proposes a tensor-based framework of multilayer graph signal processing (M-GSP). Specifically, we introduce core concepts of M-GSP and study properties of MLG spectrum space, followed by fundamentals of MLG-based filter design. To illustrate novel aspects of M-GSP, we further explore its link with traditional signal processing and GSP. We provide example applications to demonstrate the efficacy and benefits of applying multilayer graphs and M-GSP in practical scenarios.
Comparison of cause specific rate functions of panel count data with multiple modes of recurrence

Panel count data refer to the data arising from studies concerning recurrent events where study subjects are observed only at distinct time points. If these study subjects are exposed to recurrent events of several types, we obtain panel count data with multiple modes of recurrence. In the present paper, we propose a nonparametric test for comparing cause specific rate functions of panel count data with more than one mode of recurrence. The test can also be employed to assess whether the competing modes of recurrence are affecting the recurrence times identically. We carry out simulation studies to evaluate the performance of the test statistic in a finite sample setup. The proposed test is illustrated using two real life panel count data sets, one arising from a medical follow up study on skin cancer chemo prevention trial and the other on a warranty database for a fleet of automobiles.
Structured Prediction in NLP -- A survey

Over the last several years, the field of Structured prediction in NLP has had seen huge advancements with sophisticated probabilistic graphical models, energy-based networks, and its combination with deep learning-based approaches. This survey provides a brief of major techniques in structured prediction and its applications in the NLP domains like parsing, sequence labeling, text generation, and sequence to sequence tasks. We also deep-dived into energy-based and attention-based techniques in structured prediction, identified some relevant open issues and gaps in the current state-of-the-art research, and have come up with some detailed ideas for future research in these fields.
Image Processing via Multilayer Graph Spectra

Graph signal processing (GSP) has become an important tool in image processing because of its ability to reveal underlying data structures. Many real-life multimedia datasets, however, exhibit heterogeneous structures across frames. Multilayer graphs (MLG), instead of traditional single-layer graphs, provide better representation of these datasets such as videos and hyperspectral images. To generalize GSP to multilayer graph models and develop multilayer analysis for image processing, this work introduces a tensor-based framework of multilayer graph signal processing (M-GSP) and present useful M-GSP tools for image processing. We then present guidelines for applying M-GSP in image processing and introduce several applications, including RGB image compression, edge detection and hyperspectral image segmentation. Successful experimental results demonstrate the efficacy and promising futures of M-GSP in image processing.
It's not Rocket Science : Interpreting Figurative Language in Narratives

Figurative language is ubiquitous in English. Yet, the vast majority of NLP research focuses on literal language. Existing text representations by design rely on compositionality, while figurative language is often non-compositional. In this paper, we study the interpretation of two non-compositional figurative languages (idioms and similes). We collected datasets of fictional narratives containing a figurative expression along with crowd-sourced plausible and implausible continuations relying on the correct interpretation of the expression. We then trained models to choose or generate the plausible continuation. Our experiments show that models based solely on pre-trained language models perform substantially worse than humans on these tasks. We additionally propose knowledge-enhanced models, adopting human strategies for interpreting figurative language types : inferring meaning from the context and relying on the constituent words' literal meanings. The knowledge-enhanced models improve the performance on both the discriminative and generative tasks, further bridging the gap from human performance.
On the effects of biased quantum random numbers on the initialization of artificial neural networks

Recent advances in practical quantum computing have led to a variety of cloud-based quantum computing platforms that allow researchers to evaluate their algorithms on noisy intermediate-scale quantum (NISQ) devices. A common property of quantum computers is that they can exhibit instances of true randomness as opposed to pseudo-randomness obtained from classical systems. Investigating the effects of such true quantum randomness in the context of machine learning is appealing, and recent results vaguely suggest that benefits can indeed be achieved from the use of quantum random numbers. To shed some more light on this topic, we empirically study the effects of hardware-biased quantum random numbers on the initialization of artificial neural network weights in numerical experiments. We find no statistically significant difference in comparison with unbiased quantum random numbers as well as biased and unbiased random numbers from a classical pseudo-random number generator. The quantum random numbers for our experiments are obtained from real quantum hardware.
Trust Enhancement Issues in Program Repair

Automated program repair is an emerging technology that seeks to automatically rectify bugs and vulnerabilities using learning, search, and semantic analysis. Trust in automatically generated patches is necessary for achieving greater adoption of program repair. Towards this goal, we survey more than 100 software practitioners to understand the artifacts and setups needed to enhance trust in automatically generated patches. Based on the feedback from the survey on developer preferences, we quantitatively evaluate existing test-suite based program repair tools. We find that they cannot produce high-quality patches within a top-10 ranking and an acceptable time period of 1 hour. The developer feedback from our qualitative study and the observations from our quantitative examination of existing repair tools point to actionable insights to drive program repair research. Specifically, we note that producing repairs within an acceptable time-bound is very much dependent on leveraging an abstract search space representation of a rich enough search space. Moreover, while additional developer inputs are valuable for generating or ranking patches, developers do not seem to be interested in a significant human-in-the-loop interaction.
On the Multilingual Capabilities of Very Large-Scale English Language Models

Generative Pre-trained Transformers (GPTs) have recently been scaled to unprecedented sizes in the history of machine learning. These models, solely trained on the language modeling objective, have been shown to exhibit outstanding few-shot learning capabilities in a number of different tasks. Nevertheless, aside from anecdotal experiences, little is known regarding their multilingual capabilities, given the fact that the pre-training corpus is almost entirely composed of English text. In this work, we investigate the multilingual skills of GPT-3, focusing on one language that barely appears in the pre-training corpus, Catalan, which makes the results especially meaningful; we assume that our results may be relevant for other languages as well. We find that the model shows an outstanding performance, particularly in generative tasks, with predictable limitations mostly in language understanding tasks but still with remarkable results given the zero-shot scenario. We investigate its potential and limits in extractive question-answering and natural language generation, as well as the effect of scale in terms of model size.
Formalizing the Gromov-Hausdorff space

The Gromov-Hausdorff space is usually defined in textbooks as "the space of all compact metric spaces up to isometry". We describe a formalization of this notion in the Lean proof assistant, insisting on how we need to depart from the usual informal viewpoint of mathematicians on this object to get a rigorous formalization.
Multistart Algorithm for Identifying All Optima of Nonconvex Stochastic Functions

We propose a multistart algorithm to identify all local minima of a constrained, nonconvex stochastic optimization problem. The algorithm uniformly samples points in the domain and then starts a local stochastic optimization run from any point that is the "probabilistically best" point in its neighborhood. Under certain conditions, our algorithm is shown to asymptotically identify all local optima with high probability; this holds even though our algorithm is shown to almost surely start only finitely many local stochastic optimization runs. We demonstrate the performance of an implementation of our algorithm on nonconvex stochastic optimization problems, including identifying optimal variational parameters for the quantum approximate optimization algorithm.
AraT5: Text-to-Text Transformers for Arabic Language Generation

Transfer learning with a unified Transformer framework (T5) that converts all language problems into a text-to-text format was recently proposed as a simple and effective transfer learning approach. Although a multilingual version of the T5 model (mT5) was also introduced, it is not clear how well it can fare on non-English tasks involving diverse data. To investigate this question, we apply mT5 on a language with a wide variety of dialects--Arabic. For evaluation, we introduce a novel benchmark for ARabic language GENeration (ARGEN), covering seven important tasks. For model comparison, we pre-train three powerful Arabic T5-style models and evaluate them on ARGEN. Although pre-trained with ~49 less data, our new models perform significantly better than mT5 on all ARGEN tasks (in 52 out of 59 test sets) and set several new SOTAs. Our models also establish new SOTA on the recently-proposed, large Arabic language understanding evaluation benchmark ARLUE (Abdul-Mageed et al., 2021). Our new models are publicly available. We also link to ARGEN datasets through our repository: https://github.com/UBC-NLP/araT5.
A Robust Cybersecurity Topic Classification Tool

In this research, we use user defined labels from three internet text sources (Reddit, Stackexchange, Arxiv) to train 21 different machine learning models for the topic classification task of detecting cybersecurity discussions in natural text. We analyze the false positive and false negative rates of each of the 21 model's in a cross validation experiment. Then we present a Cybersecurity Topic Classification (CTC) tool, which takes the majority vote of the 21 trained machine learning models as the decision mechanism for detecting cybersecurity related text. We also show that the majority vote mechanism of the CTC tool provides lower false negative and false positive rates on average than any of the 21 individual models. We show that the CTC tool is scalable to the hundreds of thousands of documents with a wall clock time on the order of hours.
Towards Fine-grained Image Classification with Generative Adversarial Networks and Facial Landmark Detection

Fine-grained classification remains a challenging task because distinguishing categories needs learning complex and local differences. Diversity in the pose, scale, and position of objects in an image makes the problem even more difficult. Although the recent Vision Transformer models achieve high performance, they need an extensive volume of input data. To encounter this problem, we made the best use of GAN-based data augmentation to generate extra dataset instances. Oxford-IIIT Pets was our dataset of choice for this experiment. It consists of 37 breeds of cats and dogs with variations in scale, poses, and lighting, which intensifies the difficulty of the classification task. Furthermore, we enhanced the performance of the recent Generative Adversarial Network (GAN), StyleGAN2-ADA model to generate more realistic images while preventing overfitting to the training set. We did this by training a customized version of MobileNetV2 to predict animal facial landmarks; then, we cropped images accordingly. Lastly, we combined the synthetic images with the original dataset and compared our proposed method with standard GANs augmentation and no augmentation with different subsets of training data. We validated our work by evaluating the accuracy of fine-grained image classification on the recent Vision Transformer (ViT) Model.
Chaos embedded opposition based learning for gravitational search  algorithm

Due to its robust search mechanism, Gravitational search algorithm (GSA) has achieved lots of popularity from different research communities. However, stagnation reduces its searchability towards global optima for rigid and complex multi-modal problems. This paper proposes a GSA variant that incorporates chaos-embedded opposition-based learning into the basic GSA for the stagnation-free search. Additionally, a sine-cosine based chaotic gravitational constant is introduced to balance the trade-off between exploration and exploitation capabilities more effectively. The proposed variant is tested over 23 classical benchmark problems, 15 test problems of CEC 2015 test suite, and 15 test problems of CEC 2014 test suite. Different graphical, as well as empirical analyses, reveal the superiority of the proposed algorithm over conventional meta-heuristics and most recent GSA variants.