Reviewerly/exHarmony huggingface dataset:
Neural Circuit Synthesis from Specification Patterns

We train hierarchical Transformers on the task of synthesizing hardware circuits directly out of high-level logical specifications in linear-time temporal logic (LTL). The LTL synthesis problem is a well-known algorithmic challenge with a long history and an annual competition is organized to track the improvement of algorithms and tooling over time. New approaches using machine learning might open a lot of possibilities in this area, but suffer from the lack of sufficient amounts of training data. In this paper, we consider a method to generate large amounts of additional training data, i.e., pairs of specifications and circuits implementing them. We ensure that this synthetic data is sufficiently close to human-written specifications by mining common patterns from the specifications used in the synthesis competitions. We show that hierarchical Transformers trained on this synthetic data solve a significant portion of problems from the synthesis competitions, and even out-of-distribution examples from a recent case study.
Self-Training using Rules of Grammar for Few-Shot NLU.

We tackle the problem of self-training networks for NLU in low-resource environment—few labeled data and lots of unlabeled data. The effectiveness of self-training is a result of increasing the amount of training data while training. Yet it becomes less effective in low-resource settings due to unreliable labels predicted by the teacher model on unlabeled data. Rules of grammar, which describe the grammatical structure of data, have been used in NLU for better explainability. We propose to use rules of grammar in self-training as a more reliable pseudo-labeling mechanism, especially when there are few labeled data. We design an effective algorithm that constructs and expands rules of grammar without human involvement. Then we integrate the constructed rules as a pseudo-labeling mechanism into self-training. There are two possible scenarios regarding data distribution: it is unknown or known in prior to training. We empirically demonstrate that our approach substantially outperforms the state-of-the-art methods in three benchmark datasets for both scenarios.
APLIKASI PENGADUAN MASYARAKAT KECAMATAN SECANGGANG MENGGUNAKAN METODE AGILE DAN FUZZY BERBASIS ANDROID

Public Complaint Service is a one of participation to play a role in building and monitoring the performance of government agencies. In this case the researcher took a case study in Secanggang District where the community in the district found it difficult to convey complaints that exist in their environment. A public complaint service application design is needed to make it easier for the public to submit complaints and their surroundings so they can be conveyed to the relevant parties. The design of a public complaint service application uses agile methodes, especially Extreme Programming because this method is superior in dealing with ever-changing needs to cope with developments that will occur from time to time. Government performance a measurement is also carried out for evaluation and planning in the future. Performance appraisal must be carried out with a good and appropriate method so can to guarantee staticfactory service to the community. In this case, the making of a government service performance appraisal system uses the Fuzzy Tsukamoto Inferenzi System. The parameters used for the fuzzy membership function are based on expert opinion, are admistrative services, public information services, licensing services and health services. The analysis has resulted in a accuracy level of 47.5, which based on the fuzzy output range can be categorized as good.
Multi-modal Retrieval of Tables and Texts Using Tri-encoder Models

Open-domain extractive question answering works well on textual data by first retrieving candidate texts and then extracting the answer from those candidates. However, some questions cannot be answered by text alone but require information stored in tables. In this paper, we present an approach for retrieving both texts and tables relevant to a question by jointly encoding texts, tables and questions into a single vector space. To this end, we create a new multi-modal dataset based on text and table datasets from related work and compare the retrieval performance of different encoding schemata. We find that dense vector embeddings of transformer models outperform sparse embeddings on four out of six evaluation datasets. Comparing different dense embedding models, tri-encoders with one encoder for each question, text and table increase retrieval performance compared to bi-encoders with one encoder for the question and one for both text and tables. We release the newly created multi-modal dataset to the community so that it can be used for training and evaluation.
Detecting Health Advice in Medical Research Literature

Health and medical researchers often give clinical and policy recommendations to inform health practice and public health policy. However, no current health information system supports the direct retrieval of health advice. This study fills the gap by developing and validating an NLP-based prediction model for identifying health advice in research publications. We annotated a corpus of 6,000 sentences extracted from structured abstracts in PubMed publications as ‘“strong advice”, “weak advice”, or “no advice”, and developed a BERT-based model that can predict, with a macro-averaged F1-score of 0.93, whether a sentence gives strong advice, weak advice, or not. The prediction model generalized well to sentences in both unstructured abstracts and discussion sections, where health advice normally appears. We also conducted a case study that applied this prediction model to retrieve specific health advice on COVID-19 treatments from LitCovid, a large COVID research literature portal, demonstrating the usefulness of retrieving health advice sentences as an advanced research literature navigation function for health researchers and the general public.
Sistem Informasi Tanaman Lahan Basah Kebun Raya Sriwijaya

Juairiyah O, Maryani S, Komalasari O. 2019. Wetland information system of Sriwijaya Botanical Garden. In : Herlinda S et al. ( Eds. ), Prosiding Seminar Nasional Lahan Suboptimal 2019, Palembang  4-5 September 2019. pp. 38-44.  Palembang: Unsri Press. The Sriwijaya South Sumatra Botanical Garden area is included in the ecoregion of the Sumatran peat swamp forest with the theme of collecting medicinal plants and Sumatra wetlands with several major zones in its development. It is necessary to develop an information database on the Sriwijaya Botanical Garden that provides information on plants that have been planted and the locations that have been planted. The development goal is to design and build a database of wetland plant information systems in the Sriwijaya Botanical Gardens. The methodology used is the System Development Life Cycle (SDLC) and consists of several stages, namely planning, system requirements analysis, system design, implementation, and maintenance. Information systems run on platforms or operating systems that support website-based applications. Consists of two users namely administrator and guest . There are three main menus, namely the planting zone, planting points that have been planted and the types of plants and their benefits. The application displays data and information on plants in the Sriwijaya Botanical Garden. Keywords: database, medicinal plants, wetland, South Sumatra
When differential privacy meets NLP: The devil is in the detail

Differential privacy provides a formal approach to privacy of individuals. Applications of differential privacy in various scenarios, such as protecting users’ original utterances, must satisfy certain mathematical properties. Our contribution is a formal analysis of ADePT, a differentially private auto-encoder for text rewriting (Krishna et al, 2021). ADePT achieves promising results on downstream tasks while providing tight privacy guarantees. Our proof reveals that ADePT is not differentially private, thus rendering the experimental results unsubstantiated. We also quantify the impact of the error in its private mechanism, showing that the true sensitivity is higher by at least factor 6 in an optimistic case of a very small encoder’s dimension and that the amount of utterances that are not privatized could easily reach 100% of the entire dataset. Our intention is neither to criticize the authors, nor the peer-reviewing process, but rather point out that if differential privacy applications in NLP rely on formal guarantees, these should be outlined in full and put under detailed scrutiny.
STUDENT MOTIVATION IN LEARNING ENGLISHSPEAKING AT TBI SEMESTER I ACADEMIC YEAR2018/2019 AT IAIN TULUNGAGUNG

ABSTRACTSatria, Hanif. Students ID 1723143063. 2019. Student’s motivation in learningEnglish speeaking TBI Semester I 2018 at IAIN Tulungagung. Thesis.English Education Program. Tarbiyah and Education Faculty (FTIK)State Islamic Institute (IAIN) Tulungagung. Advisor: Nany SoengkonoMadayani, S.S, M.Pd.Kata kunci : Motivation, Extrinsic motivation, Intrinsic motivationWhen students come to class, each student has a variety of backgroundsthat are not visible. To help students as individuals who are developing, teachersshould research and classify whatever motivations of students so that the teachercan design a curriculum that is right on target. The purpose of this study is toexamine the students' motivation in learning English speaking in the Englishdepartment majoring in semester 1 of 2018 at IAIN Tulungagung.This research use desciptive qualitative approach. The data source isstudent representatives from the first semester of IAIN Tulungagung. Theinstrument used to collect data is to use interviews and questionnaires. Data willbe analyzed using reduced data, showing data, and drawing conclusions andverification.With this research, researchers expect bias to be a place for parents,students, and lecturers to map student motivation and what students expect afterlearning English speaking in the TBI IAIN Tulungagung department.This study found that students have both intrinsic and extrinsic motivation.Namely true students are really voluntary and without coercion in learningEnglish conversation and the majority of students get environmentalencouragement from parents. The majority of students also admit that they learnspeaking in a karaoke way, because of the difficulty of finding friends to learnEnglish conversation
Continual Learning via Local Module Composition

Modularity is a compelling solution to continual learning (CL), the problemof modeling sequences of related tasks. Learning and then composing modules tosolve different tasks provides an abstraction to address the principalchallenges of CL including catastrophic forgetting, backward and forwardtransfer across tasks, and sub-linear model growth. We introduce local modulecomposition (LMC), an approach to modular CL where each module is provided alocal structural component that estimates a module's relevance to the input.Dynamic module composition is performed layer-wise based on local relevancescores. We demonstrate that agnosticity to task identities (IDs) arises from(local) structural learning that is module-specific as opposed to the task-and/or model-specific as in previous works, making LMC applicable to more CLsettings compared to previous works. In addition, LMC also tracks statisticsabout the input distribution and adds new modules when outlier samples aredetected. In the first set of experiments, LMC performs favorably compared toexisting methods on the recent Continual Transfer-learning Benchmark withoutrequiring task identities. In another study, we show that the locality ofstructural learning allows LMC to interpolate to related but unseen tasks(OOD), as well as to compose modular networks trained independently ondifferent task sequences into a third modular network without any fine-tuning.Finally, in search for limitations of LMC we study it on more challengingsequences of 30 and 100 tasks, demonstrating that local module selectionbecomes much more challenging in presence of a large number of candidatemodules. In this setting best performing LMC spawns much fewer modules comparedto an oracle based baseline, however, it reaches a lower overall accuracy. Thecodebase is available under https://github.com/oleksost/LMC.
Ontologias de alto nível: porque precisamos e como usar

Ontologies are knowledge representation artifacts that, when formals, allow unambiguous representation and interpretable by both humans and computational machines. Depending on the representation scope and the details level against the domain of knowledge, ontologies are classified into domain ontologies, reference ontologies and top-level ontologies. The first two types of ontologies cover representations of some specific domain of knowledge, but with different levels of detail. Top-level ontologies are domain-neutral ontologies that describe general concepts common across all domains. However, why do we need top-level ontologies, what are the role of these ontologies and how to use them in the design of domain or reference ontologies is not always clear. Hence, this work describes the role of top-level ontologies as representational artifacts and their contribution to the reference and domain ontologies building. Additionally, it is presented how to use them in the process of building domain or reference ontologies.
Parameter Prediction for Unseen Deep Architectures

Deep learning has been successful in automating the design of features in machine learning pipelines. However, the algorithms optimizing neural network parameters remain largely hand-designed and computationally inefficient. We study if we can use deep learning to directly predict these parameters by exploiting the past knowledge of training other networks. We introduce a large-scale dataset of diverse computational graphs of neural architectures - DeepNets-1M - and use it to explore parameter prediction on CIFAR-10 and ImageNet. By leveraging advances in graph neural networks, we propose a hypernetwork that can predict performant parameters in a single forward pass taking a fraction of a second, even on a CPU. The proposed model achieves surprisingly good performance on unseen and diverse networks. For example, it is able to predict all 24 million parameters of a ResNet-50 achieving a 60% accuracy on CIFAR-10. On ImageNet, top-5 accuracy of some of our networks approaches 50%. Our task along with the model and results can potentially lead to a new, more computationally efficient paradigm of training networks. Our model also learns a strong representation of neural architectures enabling their analysis.
THE USE OF DEIXIS IN THE MOVIE SCRIPTS OF THELION KING AND TOY STORY

ABSTRACTUlla, Dewi S. Student Registered Number. 17203163105. The Use of Deixis in theMovie Scripsts of the Lion King and Toy Story. Sarjana Thesis. EnglishEducation Department, Teacher Training and Tarbiyah Faculty, StateIslamic Institute (IAIN) of Tulungagung. Advisor: Dr. Nurul ChojimahM.Pd.Key Words: Deixis, Movie ScriptLanguage is one of the most fundamental aspects of human‟s life. Peopleuse language for communication. To understand the meaning in everycommunication which occurs with language, we are necessary to know thecontextual information, like the culture, time and place where the language isuttered. Language and context are two things which cannot be separated eachother. They have relation each others. Contextual meaning is connected with thereference. Every utterance refers to different things based on the speaker‟smeaning. Deixis shows the phenomenon where in understanding the meaning ofcertain words and phrases in an utterance requires contextual information. Moviescript is one of the appropriate objects for analyzing deixis because the sentencesin the movie script belong to spoken language. “Lion King and Toy Story” is theappropriate movie to analyze deixis because the plot of the story occurs in dailylife. So in this research, the writer carries out the research entitled “The Use ofDeixis in the Movie Scripsts of the Lion King and Toy Story.”The objectives in this research are (1) to know how the use of deixis in theLion King differ from the one in Toy Story. In this research, the writer usesdescriptive analysis technique. The data are deixis, the types of deixis and whatthe differ of deixis types are used in “Lion King and Toy Story” and the datasource is movie script of the movie “Lion King and Toy Story.”The result of the research shows that there are five types of deixis in “LionKing and Toy Story” Movie Script, they are person, time, place, discourse, andsocial deixis. There is no significant deixis difference between Lion King andToy Story movie script. Due to the fact that deixis is important to indicateimpermanent reference, English learner should enrich themselves byunderstanding deixis, the types of deixis and how deixis are used in an utterance.
Peculiarities of Application of Cognitive Operations in the Process of Museum Pedagogical Activity

The authors of the article reveal various aspects of cognitive activity in the museum space. They focus on the peculiarities of the formation of cognitive operations in the process of museum-pedagogical activities and proceed from the fact that the museum space is the optimal place, where you can design, identify and formulate various problem tasks for students. The article emphasizes that the initial stage of thinking is the emergence of a problematic situation that can reveal the intellectual capabilities of the child. The article analyzes the levels of thinking and various cognitive operations of thinking, which the authors adapt to the museum space and extracurricular educational institution, it is emphasized that cognitive activity is directly related with creative activity; examples of application of thinking strategies in the museum space are given.
Bearing-Only Ground Slow Target Localization with Unknown Relative Altitude

A state-dependent extended Kalman filter (EKF) algorithm is proposed for bearing-only slow target tracking when the relative height between the observer and the target is unknown. Firstly, we establish the bearing-only target tracking model with unknown relative altitude. Secondly, we study the vinculum between the relative altitude and the target status and present a new Gauss-Helmert state transition model. The Gauss-Newton method is used to predict the state since the Gauss-Helmert model is nonlinear. Finally, we use EKF algorithm to jointly reckon the target location and the relative height. Compared with the augmented EKF algorithm, The algorithm proposed in this paper can predict the unknown state vector accurately.From the simulation result,the proposed state-dependent EKF algorithm has faster convergence speed and higher tracking accuracy.
Language Models are Few-shot Multilingual Learners

General-purpose language models have demonstrated impressive capabilities, performing on par with state-of-the-art approaches on a range of downstream natural language processing (NLP) tasks and benchmarks when inferring instructions from very few examples. Here, we evaluate the multilingual skills of the GPT and T5 models in conducting multi-class classification on non-English languages without any parameter updates. We show that, given a few English examples as context, pre-trained language models can predict not only English test samples but also non-English ones. Finally, we find the in-context few-shot cross-lingual prediction results of language models are significantly better than random prediction, and they are competitive compared to the existing state-of-the-art cross-lingual models and translation models.
The use of digital literacy to optimize remote learning

The implementation of this online learning in remote learning environments carried out at all levels of education units from elementary schools to universities. Thus, during this Covid-19 Pandemic, the learning activities are conducted virtually. The objective of this study is to analyze the use of digital literacy to optimize remote learning. It was descriptive qualitative study which was conducted in a private university in Semarang in two classes consisted of 64 students. The findings showed that the use of digital literacy in remote learning was effective to improve students’ understanding and motivation during this online learning. Further, the digital literacy helped them to engage with new information and technology in remote learning.
Efficient Sampling of Dependency Structure

Probabilistic distributions over spanning trees in directed graphs are a fundamental model of dependency structure in natural language processing, syntactic dependency trees. In NLP, dependency trees often have an additional root constraint: only one edge may emanate from the root. However, no sampling algorithm has been presented in the literature to account for this additional constraint. In this paper, we adapt two spanning tree sampling algorithms to faithfully sample dependency trees from a graph subject to the root constraint. Wilson (1996(’s sampling algorithm has a running time of O(H) where H is the mean hitting time of the graph. Colbourn (1996)’s sampling algorithm has a running time of O(Nˆ3), which is often greater than the mean hitting time of a directed graph. Additionally, we build upon Colbourn’s algorithm and present a novel extension that can sample K trees without replacement in O(K Nˆ3 + Kˆ2 N) time. To the best of our knowledge, no algorithm has been given for sampling spanning trees without replacement from a directed graph.
Iterative GNN-based Decoder for Question Generation.

Natural question generation (QG) aims to generate questions from a passage, and generated questions are answered from the passage. Most models with state-of-the-art performance model the previously generated text at each decoding step. However, (1) they ignore the rich structure information that is hidden in the previously generated text. (2) they ignore the impact of copied words on the passage. We perceive that information in previously generated words serves as auxiliary information in subsequent generation. To address these problems, we design the Iterative Graph Network-based Decoder (IGND) to model the previous generation using a Graph Neural Network at each decoding step. Moreover, our graph model captures dependency relations in the passage that boost the generation. Experimental results demonstrate that our model outperforms the state-of-the-art models with sentence-level QG tasks on SQuAD and MARCO datasets.
Optimal Policies Tend To Seek Power

Some researchers have speculated that capable reinforcement learning agents are often incentivized to seek resources and power in pursuit of their objectives. While seeking power in order to optimize a misspecified objective, agents might be incentivized to behave in undesirable ways, including rationally preventing deactivation and correction. Others have voiced skepticism: human power-seeking instincts seem idiosyncratic, and these urges need not be present in reinforcement learning agents. We formalize a notion of power within the context of Markov decision processes. With respect to a class of neutral reward function distributions, we provide sufficient conditions for when optimal policies tend to seek power over the environment.