Reviewerly/exHarmony huggingface dataset:
Models and Datasets for Cross-Lingual Summarisation

We present a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in a target language. The corpus covers twelve language pairs and directions for four European languages, namely Czech, English, French and German, and the methodology for its creation can be applied to several other languages. We derive cross-lingual document-summary instances from Wikipedia by combining lead paragraphs and articles’ bodies from language aligned Wikipedia titles. We analyse the proposed cross-lingual summarisation task with automatic metrics and validate it with a human study. To illustrate the utility of our dataset we report experiments with multi-lingual pre-trained models in supervised, zero- and few-shot, and out-of-domain scenarios.
Identifying Precautionary Measures for High-Risk Disease from Doctor's Answer Text Using LDA

The Online Health Consultation (OHC), which contains a Q&A collection of various diseases since 2014, has received an increasing number of visits due to the COVID-19. Based on the benefits and increasing health information need for people who seek information in OHC, health information related to precautionary measures to avoid diseases, especially high-risk diseases, become critical because not all seeker and readers of health information are diagnosed with certain diseases. However, It has currently unidentified whether the text of the doctor's answer corpus, especially in high-risk diseases, contains words that imply precautionary. This study aims to find the pattern of doctor's answer for high-risk diseases through the corpus of doctor's answer text on OHC by identifying whether the doctor's answer text contains words that imply precautionary against disease. Thus, it can help health information seekers and readers take precautionary against disease early on. This paper's contribution was to identify precautionary measures from doctor's answer text for high-risk disease in 2014–2021 using the best model of the two models, namely Single LDA (only LDA Method) and Hybrid LDA (a combination of LDA and Collapsed Gibbs Sampling). The results showed that the best model was Hybrid LDA, and medical experts identified groups of words with this model into four domains, namely symptoms/diagnosis, treatments, precautionary measurements, and general text. The pattern that emerges from the identification of precautionary measures shows (1) which precautionary measures are divided based on what disease, (2) Some words that mean precautionary measures also mean treatment or symptom/diagnosis.
DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning

The Mixture-of-experts (MoE) architecture is showing promising results in multi-task learning (MTL) and in scaling high-capacity neural networks. State-of-the-art MoE models use a trainable sparse gate to select a subset of the experts for each input example. While conceptually appealing, existing sparse gates, such as Top-k, are not smooth. The lack of smoothness can lead to convergence and statistical performance issues when training with gradient-based methods. In this paper, we develop DSelect-k: the first, continuously differentiable and sparse gate for MoE, based on a novel binary encoding formulation. Our gate can be trained using first-order methods, such as stochastic gradient descent, and offers explicit control over the number of experts to select. We demonstrate the effectiveness of DSelect-k in the context of MTL, on both synthetic and real datasets with up to 128 tasks. Our experiments indicate that MoE models based on DSelect-k can achieve statistically significant improvements in predictive and expert selection performance. Notably, on a real-world large-scale recommender system, DSelect-k achieves over 22% average improvement in predictive performance compared to the Top-k gate. We provide an open-source TensorFlow implementation of our gate.
Vision-and-Language or Vision-for-Language? On Cross-Modal Influence in Multimodal Transformers

Pretrained vision-and-language BERTs aim to learn representations that combine information from both modalities. We propose a diagnostic method based on cross-modal input ablation to assess the extent to which these models actually integrate cross-modal information. This method involves ablating inputs from one modality, either entirely or selectively based on cross-modal grounding alignments, and evaluating the model prediction performance on the other modality. Model performance is measured by modality-specific tasks that mirror the model pretraining objectives (e.g. masked language modelling for text). Models that have learned to construct cross-modal representations using both modalities are expected to perform worse when inputs are missing from a modality. We find that recently proposed models have much greater relative difficulty predicting text when visual information is ablated, compared to predicting visual object categories when text is ablated, indicating that these models are not symmetrically cross-modal.
Scalable Intervention Target Estimation in Linear Models

This paper considers the problem of estimating the unknown interventiontargets in a causal directed acyclic graph from observational andinterventional data. The focus is on soft interventions in linear structuralequation models (SEMs). Current approaches to causal structure learning eitherwork with known intervention targets or use hypothesis testing to discover theunknown intervention targets even for linear SEMs. This severely limits theirscalability and sample complexity. This paper proposes a scalable and efficientalgorithm that consistently identifies all intervention targets. The pivotalidea is to estimate the intervention sites from the difference between theprecision matrices associated with the observational and interventionaldatasets. It involves repeatedly estimating such sites in different subsets ofvariables. The proposed algorithm can be used to also update a givenobservational Markov equivalence class into the interventional Markovequivalence class. Consistency, Markov equivalency, and sample complexity areestablished analytically. Finally, simulation results on both real andsynthetic data demonstrate the gains of the proposed approach for scalablecausal structure recovery. Implementation of the algorithm and the code toreproduce the simulation results are available at\url{https://github.com/bvarici/intervention-estimation}.
QoS–Aware Web Service Composition using Weight Improved Particle Swarm Optimization

Lots of Web Services are available which differ in their QoS values but can perform a similar task. Discovery mechanism selects the best Web Service according to their QoS values and functional attributes. Cases arise, where the discovery mechanism fails, as a user’s complex query cannot be satisfied by a single Web Service. This can be solved by Web Service composition where multiple Web Services are combined to give a composite Web Service which meet user’s complex query. Our work is mainly focused on composition of Web Services that efficiently meets the user’s query. Different algorithms have been discussed and used by different researchers in this field. One of the most blooming topics is the use of evolutionary algorithms in optimization problems. In our work, we have chosen Particle Swarm Optimization Algorithm approach to discover the best efficient composition. Then, Weight Improved Particle Swarm Optimization Algorithm is used to improve the results which were found to be quite satisfying and efficient.
On Model Calibration for Long-Tailed Object Detection and Instance Segmentation

Vanilla models for object detection and instance segmentation suffer from the heavy bias toward detecting frequent objects in the long-tailed setting. Existing methods address this issue mostly during training, e.g., by re-sampling or re-weighting. In this paper, we investigate a largely overlooked approach -- post-processing calibration of confidence scores. We propose NorCal, Normalized Calibration for long-tailed object detection and instance segmentation, a simple and straightforward recipe that reweighs the predicted scores of each class by its training sample size. We show that separately handling the background class and normalizing the scores over classes for each proposal are keys to achieving superior performance. On the LVIS dataset, NorCal can effectively improve nearly all the baseline models not only on rare classes but also on common and frequent classes. Finally, we conduct extensive analysis and ablation studies to offer insights into various modeling choices and mechanisms of our approach.
Robust Principal Component Analysis and Fractal Methods to Delineate Mineralization-Related Hydrothermally-Altered Zones from ASTER Data: A Case Study of Dehaj Terrain, Central Iran

The Dehaj area, located in the southern part of the Urumieh-Dokhtar magmatic belt, is a well-endowed terrain hosting a number of world-class porphyry copper deposits. These deposits are all hosted in an acidic to intermediate volcano-plutonic sequence greatly affected by various types of the hydrothermal alterations, whether argillic, phyllic or propylitic. Although there are a handful of hitherto-discovered porphyry copper deposits in the area, the geological setting of the area suggests the possibility of finding further deposits. The recognition and delineation of the hydrothermal alterations can pave the way for the discovery of further potential zones that possibly host the porphyry copper deposits. The current work proposes a hybrid methodology applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery by combining the application of dimension reduction and fractal techniques to delineate the hydrothermally-altered zones In order to reduce the dimensionality of multi-band ASTER data, Robust Principal Component Analysis (RPCA) was employed to elicit the traces of hydrothermally-related mineral assemblages including illite, sericite, quartz, kaolinite, epidote, and chlorite. Highlighting the existence of the aforementioned minerals, the extracted components require interpretation, i.e. a boundary is required to constraint the hydrothermally affected zones from the rest of the geological units. In order to tackle such a challenge, the authors introduce the concept of value-pixel fractal technique for the extracted principal components. The Prediction-Area (P-A) plot is used for the validation, which shows that the identified alterations correlate with the mineralization. The results obtained are verified by a geological survey, where a number of samples are collected from the delineated zones. The samples are analyzed by the XRD techniques, finding that this work is successful in classifying the hydrothermally-altered zones.
Special Issue “Data Processing and Modeling on Volcanic and Seismic Areas”

Volcanology, seismology and Earth Sciences in general, like all quantitative sciences, are increasingly dependent on the quantity and quality of data acquired [...]
Integrated Formalization for Temporal Referentialities

It is common knowledge that natural languages are complex. All utterances are uttered directly by the speaker and are described over the passage of time, which is called “temporal referentiality” (called also the “frame of reference”), which is the axis of time. However, not all utterances are described in “enunciative referentiality,” which is the speaker's time axis. For example, utterances that are not described in the present time, such as utterances with fictional meaning, narrative or historical facts, or indirect speech,are not realized in the speaker's time axis. Rather, they require a new axis of time, such as an axis of time that has not been realized or an axis of indirect speech, or an axis of time that has not yet been realized but contains possibilities. Therefore, to analyze various types of utterances, other types of referentialities are required in addition to enunciative referentiality. In addition, enunciative operations that allow the insertion of predicative relations into thetemporal frame of reference should be considered. The purpose of this study is to demonstrate the necessity and applicability of enunciative operations to formally conceptualize various types of situations expressed by predicate relations that form the basis of utterances. For these works, logico-linguistic systems, such as locative detection systems, and the cognitive linguistic theories such as temporal referentiality theory are used. These methodologies can be used to perform enunciative operations, resulting in the most general and canonical abstract schema. This schema is ultimately applicable to any natural language and is the primary abstract form for the computerization of natural languages.
STUDENTS’ PERCEPTION TOWARD THE USE OF BAHASAINDONESIA IN ENGLISH LEARNING CLASSROOM INSMAN 1 REJOTANGAN

ABSTRACTAji, Fungky Kartiko. Registered Number Student. 17203153159.2019.”Students’ Perception Toward The Use of Bahasa Indonesia inEnglish Learning Classroom in SMAN 1 Rejotangan”. Thesis. EnglishEducation Program. State Islamic College Institut (IAIN) of Tulungagung.Advisor. Dr. Nurul Chojimah, M.Pd.Keyword: Perception, Bahasa Indonesia, Learning English.In non-English speaking country such as Indonesia, English has been taughtsince in elementary school until university level because English is considered asglobal language. There are some method for explain English as foreign language inIndonesia, for example is using first language, bilingual or using English itself.However, the issue about the use of first language in English classroom has beendebated for years. There are pros and cons regarding the use of first language inEnglish classroom. In this case, first language refers to Bahasa Indonesia. Manyteachers in Indonesia still use Bahasa Indonesia in their teaching English process.That is why the aimed of this study is to find out students’ perception toward theuse of Bahasa Indonesia.The researcher used descriptive study by using qualitative approach andmethod of collecting data in this research was semi structured interview. In thisresearch the data is selected students’ response of the interview, the researcherchoosed the subjects of study by applying certain criteria. The criteria from thisresearch is ten year students with high score, average score and low score in oneclass. The researcher interviewed students one by one while record the answers ofinterview question. Analyzing of data needs interview transcrips, because it wouldmake researcher easy to analyze data. Therefore, the researcher wrote interviewtranscript based on the results of recording before researcher analyze the data. Inthis study, in analyzing the data, the researcher used qualitative data analysis. Thequalitative data of this research is analyzed by using inductive analysis method. Itbegin with particular piecese of evidence, then pull them together into a meaningfulwholeThe finding of this research study, the ten year student of SMAN 1Rejotangan perceive Bahasa Indonesia as a facilitator in their learning, as a tool tohelp them get a better understanding when learning English. The use of BahasaIndonesia was actually important in their learning English such as when the teacherswere explaining things since the students might not understand the English wordsbeing used in the explanation and using Bahasa Indonesia would make things easierto understand. In other words, students got advantages when teachers used BahasaIndonesia in teaching English classroom, but in certain frequency.
CyBERT: Contextualized Embeddings for the Cybersecurity Domain

We present CyBERT, a domain-specific Bidirectional Encoder Representations from Transformers (BERT) model, fine-tuned with a large corpus of textual cybersecurity data. State-of-the-art natural language models that can process dense, fine-grained textual threat, attack, and vulnerability information can provide numerous benefits to the cybersecurity community. The primary contribution of this paper is providing the security community with an initial fine-tuned BERT model that can perform a variety of cybersecurity-specific downstream tasks with high accuracy and efficient use of resources. We create a cybersecurity corpus from open-source unstructured and semi-unstructured Cyber Threat Intelligence (CTI) data and use it to fine-tune a base BERT model with Masked Language Modeling (MLM) to recognize specialized cybersecurity entities. We evaluate the model using various downstream tasks that can benefit modern Security Operations Centers (SOCs). The fine-tuned CyBERT model outperforms the base BERT model in both the domain-specific MLM evaluation as well as the cybersecurity downstream tasks.
Classifying Divergences in Cross-lingual AMR Pairs

Translation divergences are varied and widespread, challenging approaches that rely on parallel text. To annotate translation divergences, we propose a schema grounded in the Abstract Meaning Representation (AMR), a sentence-level semantic framework instantiated for a number of languages. By comparing parallel AMR graphs, we can identify specific points of divergence. Each divergence is labeled with both a type and a cause. We release a small corpus of annotated English-Spanish data, and analyze the annotations in our corpus.
LibGuides: Management of Engineering and Technology: Journals

This guide is for the NCU Management of Engineering and Technology specialization. It contains resources available in the NCU Library.
The Role of Teachers of Teachers in Online Learning with the Google Classroom Platform (Multicase Study of Middle School in Tulungagung)

The COVID-19 pandemic has had a fundamental impact on the joints of mankind. Starting from health, this pandemic attacks the realm of education. The enactment of regulations by maintaining a distance of approximately one meter has consequences for the education process in Indonesia, requiring elements of education to conduct a distance learning process. This term is holding online learning as a substitute for face-to-face methods to keep it held, namely in the internet network. The implementation of the 2013 curriculum in several schools in Indonesia has an impact on the readiness and role of teachers in the online learning process, one of which is following the new provisions concerning the Regulation of the Minister of Education and Culture Number 68 of 2014 concerning the Role of Information and Communication Technology (ICT) Teachers in the implementation of the 2013 curriculum. as an ICT guidance teacher in schools, including the high school in Tulungagung. The application of distance learning that uses technology in learning or e-learning creates an internet connection as well as information technology learning management system services that support the online learning process, namely Google Classroom. In this paper, the researcher focuses on the role of ICT guidance teachers in online learning using Google Classroom.
Manufacturing and Expertise Organization Using Fuzzy Cognitive Maps

Many early technology and engineering management planning decisions must rely on specialists' assessments of evolving technologies, markets, and business environments due to a lack of available data. Fuzzy Cognitive Maps (FCMs) have been proposed by several authors as a tool for analyzing simulation models that can handle qualitative input and aid in making these kinds of decisions. There is, however, a dearth of documented real-world experience. Research will lead to six phases in the study for practitioners and university students who desire to use FCM technologies for real-world challenges. This is based on a large number of case studies and a thorough review of the relevant literature.
An automatic differentiation system for the age of differential privacy

We introduce Tritium, an automatic differentiation-based sensitivity analysis framework for differentially private (DP) machine learning (ML). Optimal noise calibration in this setting requires efficient Jacobian matrix computations and tight bounds on the L2-sensitivity. Our framework achieves these objectives by relying on a functional analysis-based method for sensitivity tracking, which we briefly outline. This approach interoperates naturally and seamlessly with static graph-based automatic differentiation, which enables order-of-magnitude improvements in compilation times compared to previous work. Moreover, we demonstrate that optimising the sensitivity of the entire computational graph at once yields substantially tighter estimates of the true sensitivity compared to interval bound propagation techniques. Our work naturally befits recent developments in DP such as individual privacy accounting, aiming to offer improved privacy-utility trade-offs, and represents a step towards the integration of accessible machine learning tooling with advanced privacy accounting systems.
PENGGUNAAN MEDIA ZOOM MEETING UNTUK MENINGKATKAN MOTIVASI DAN HASIL BELAJAR SISWA KELAS XI UPW1 PADA MATERI FUNGSI KOMPOSISI DI SMK NEGERI 1 BANDUNG TAHUN PELAJARAN 2021/2022

This study is a classroom action study whose main subject is focused on improving the learning achievement of fourth grade students at MI Karanganyar 01 Pekalongan with 24 students, in thematic subjects students listen to stories from their teachers by applying the lecture method. The problem behind this research is the lack of learning to use the Planted questions method in learning. This study is intended to answer the problem of increasing student achievement through the application of the Planted questions method at MI Karanganyar 01. There are several techniques carried out: 1) Application of formative test instruments on the heroku theme and 2) Observation is used to collect data on student achievement and the implementation of learning using Planted questions method, all of these are documented as archives. this can be seen from the value of the quiz results in each cycle, namely where in the pre-cycle there were 15 students or 62.5% who completed the increase in the first cycle to 18 students or 75.0% and at the end of the second cycle they had reached 22 students or 91.7% completed. Meanwhile, student activity also increased in each cycle where in the first cycle the activity in the good and very good category was 15 students or 62.5% increased to 20 students or 83.3% at the end of the second cycle. Based on the research, it was concluded that the use of the planted question method could improve student achievement at MI Karanganyar 01. ABSTRAKPenelitian ini adalah penelitian tindakan kelas (PTK) bertujuan untuk Peningkatan Motivasi dan Hasil Belajar Materi Fungsi Komposisi pada siswa kelas XI UPW1 SMKN 1 Bandung dengan menggunakan Media Zoom Cloud Meeting. Berdasarkan dari hasil penelitian dan pembahasan dapatlah disimpulkan bahwa: 1) Penggunaan Media Zoom Meeting dapat Meningkatkan Motivasi belajar Siswa Kelas XI UPW1 Pada Materi Fungsi Komposisi di SMK Negeri 1 Bandung Tahun Pelajaran 2021/2022. Dapat terpantau dari Lembar Observasi berupa angket yang diberikan kepada siswa pada siklus I sebesar 59 % dan siklus II sebesar 81 % menunjukan bahwa siswa termotivasi dalam proses pembelajaran dengan media zoom meningkat. 2) Penggunaan Media Zoom Meeting dapat Meningkatkan Hasil Belajar Siswa Kelas XI UPW1 Pada Materi Fungsi Komposisi di SMK Negeri 1 Bandung Tahun Pelajaran 2021/2022. Mengalami peningkatan pada setiap siklus dengan persentase ketuntasan secara klasikal masing- masing siklus yaitu siklus I sebesar 60 % dan siklus II sebesar 86 %.