Reviewerly/exHarmony huggingface dataset:
Using Planning to Improve Semantic Parsing of Instructional Texts

We develop a symbolic planning-based decoder to improve the few-shot semantic parsing of instructional texts.The system takes long-form instructional texts as input and produces sequences of actions in a formal language that enable execution of the instructions.This task poses unique challenges since input texts may contain long context dependencies and ambiguous and domain-specific language.Valid semantic parses also require sequences of steps that constitute an executable plan.We build on recent progress in semantic parsing by leveraging large language models to learn parsers from small amounts of training data.During decoding, our method employs planning methods and domain information to rank and correct candidate parses.To validate our method, we evaluate on four domains: two household instruction-following domains and two cooking recipe interpretation domains.We present results for few-shot semantic parsing using leaveone-out cross-validation.We show that utilizing planning domain information improves the quality of generated plans.Through ablations we also explore the effects of our decoder design choices.
Contrastive Error Attribution for Finetuned Language Models

Recent work has identified noisy and misannotated data as a core cause of hallucinations and unfaithful outputs in Natural Language Generation (NLG) tasks.Consequently, identifying and removing these examples is a key open challenge in creating reliable NLG systems.In this work, we introduce a framework to identify and remove low-quality training instances that lead to undesirable outputs, such as faithfulness errors in text summarization.We show that existing approaches for error tracing, such as gradient-based influence measures, do not perform reliably for detecting faithfulness errors in NLG datasets.We overcome the drawbacks of existing error tracing methods through a new, contrast-based estimate that compares undesired generations to human-corrected outputs.Our proposed method can achieve a mean average precision of 0.93 at detecting known data errors across synthetic tasks with known ground truth, substantially outperforming existing approaches.Using this approach and re-training models on cleaned data leads to a 70% reduction in entity hallucinations on the NYT dataset and a 55% reduction in semantic errors on the E2E dataset.
Predicting Numerals in Text Using Nearest Neighbor Language Models

Commonsense about quantitative properties is essential for a deep understanding of texts containing numerals.However, naive language models (LMs) treat numerals as string tokens; therefore, they lack an understanding of the magnitudes of numerals, resulting in a difficulty in acquiring the commonsense.In this study, we apply the k-nearest neighbor LM (kNN-LM) to the masked numeral prediction (MNP) task, which measures the quantitative commonsense of LMs.kNN-LM extends pretrained neural LMs with the k-nearest neighbor (kNN) search.Since it can utilize patterns that appear in the datastore for prediction, we expect an improvement in numeral prediction accuracy, which is associated with a high rate of occurrence of out-of-vocabulary (OOV) words.Through experiments, we verified that the retrieval-based method is effective for fine-grained predictions of numerals from context, especially for the OOV numerals.We also compared two different context spans for context representations to improve the accuracy of kNN search by using only the words that are closely related to the masked numeral: the mask and its surrounding words, and the mask and its subsequent words.Our results reveal that using only the embeddings of mask tokens for numerals in kNN search is the most effective approach for realizing MNP tasks.
ZBL2W at SemEval-2023 Task 9: A Multilingual Fine-tuning Model with Data Augmentation for Tweet Intimacy Analysis

This paper describes our system used in the SemEval-2023 Task 9 Multilingual Tweet Intimacy Analysis.There are two key challenges in this task: the complexity of multilingual and zero-shot cross-lingual learning, and the difficulty of semantic mining of tweet intimacy.To solve the above problems, our system extracts contextual representations from the pretrained language models, XLM-T, and employs various optimization methods, including adversarial training, data augmentation, ordinal regression loss and special training strategy.Our system ranked 14th out of 54 participating teams on the leaderboard and ranked 10th on predicting languages not in the training data.Our code is available on Github 1 .
Exploration of multilingual prompts in document-grounded dialogue

Transferring DGD models from high-resource languages to low-resource languages is a meaningful but challenging task.Being able to provide multilingual responses to multilingual documents further complicates the task.This paper describes our method at DialDoc23 Shared Task (Document-Grounded Dialogue and Conversational Question Answering) for generate responses based on the most relevant passage retrieved.We divide it into three steps of retrieval, re-ranking and generation.Our methods include negative sample augmentation, prompt learning, pseudo-labeling and ensemble.On the submission page, we rank 2nd based on the sum of token-level F1, SacreBleu and Rouge-L scores used for the final evaluation, and get the total score of 210.25.
A Modified Strassen Algorithm to Accelerate Numpy Large Matrix Multiplication with Integer Entries

Numpy is a popular Python library widely used in the math and scientific community because of its speed and convenience.We present a Strassen type algorithm for multiplying large matrices with integer entries.The algorithm is the standard Strassen divide and conquer algorithm but it crosses over to Numpy when either the row or column dimension of one of the matrices drops below 128.The algorithm was tested on a MacBook, an I7 based Windows machine as well as a Linux machine running a Xeon processor and we found that for matrices with thousands of rows or columns and integer entries, the Strassen based algorithm with crossover performed 8 to 30 times faster than regular Numpy on such matrices.Although there is no apparent advantage for matrices with real entries, there are a number of applications for matrices with integer coefficients.
SOSIALISASI GERAKAN LITERASI MEMBACA PESERTA DIDIK SDN SEBERANG MESJID 1 BANJARMASIN

The reading interest of the Indonesian people is quite low so it is very important to foster culture and interest in reading. Based on data from the Programme for International Student Assessment (PISA) 2012 shows that Indonesian students are at the 64th level with a score of 396. This proves that the competence of Indonesian students is relatively low. The low interest in reading by students can be seen from the students' reading activities which are quite low. This is also what happened to students at SDN Seberang Mesjid 1 Banjarmasin. This Community Service activity aims to provide socialization to improve the reading literacy of students of SDN Seberang Mesjid 1 Banjarmasin. The method of implementing this community service activity is in the form of socialization activities to students at SDN Seberang Mesjid 1 Banjarmasin. Students at SDN Seberang Mesjid 1 Banjarmasin are able to practice the activities of the School Literacy Movement (GLS) properly and correctly so that students' knowledge and creativity can be useful in the real life of students. Broadly speaking, there are two main points in the implementation of Community Service (PkM) which is carried out at SDN Seberang Mesjid 1Banjarmasin. The first point is to provide motivation to read by giving children's storybooks and the second point is to be skilled in speed reading to obtain information. Positive responses were shown by these community service participants during the activity. Learners gain new knowledge and experiences related to reading literacy and correct reading practices. This is a good provision to carry out activities in a sustainable manner.&#x0D;
Perancangan Sistem Informasi Penjualan E-Commerce Rempeyek Ilhamum-Taza Berbasis Web

Ilhamum-Taza is a micro, small and medium enterprise (UMKM) which is engaged in the production and sale of tempe chips and peanut brittle which have become souvenirs from Jambi. UMKM manage the sale of goods is still manual so that the sale of goods is not optimal. Sales management to be more optimal so that it must use a web based e-commerce information system. The development of this web based e-commerce information system uses the waterfall model and the data flow model uses DFD (Data Flow Diagrams) . This research and design produces an e-commerce information system that will be implemented in UMKM Ilhamum-Taza.
Chapter Ordering in Novels

Understanding narrative flow and text coherence in long-form documents (novels) remains an open problem in NLP.To gain insight, we explore the task of chapter ordering, reconstructing the original order of chapters in novel given a random permutation of the text.This can be seen as extending the well-known sentence ordering task to vastly larger documents: our task deals with over 9,000 novels with an average of twenty chapters each, versus standard sentence ordering datasets averaging only 5-8 sentences.We formulate the task of reconstructing order as a constraint solving problem, using minimum feedback arc set and traveling salesman problem optimization criteria, where the weights of the graph are generated based on models for character occurrences and chapter boundary detection, using relational chapter scores derived from RoBERTa.Our best methods yield a Spearman correlation of 0.59 on this novel and challenging task, substantially above baseline.
PAIGE: Personalized Adaptive Interactions Graph Encoder for Query Rewriting in Dialogue Systems

Unexpected responses or repeated clarification questions from conversational agents detract from the users’ experience with technology meant to streamline their daily tasks. To reduce these frictions, Query Rewriting (QR) techniques replace transcripts of faulty queries with alternatives that lead to responses thatsatisfy the users’ needs. Despite their successes, existing QR approaches are limited in their ability to fix queries that require considering users’ personal preferences.We improve QR by proposing Personalized Adaptive Interactions Graph Encoder (PAIGE).PAIGE is the first QR architecture that jointly models user’s affinities and query semantics end-to-end.The core idea is to represent previous user-agent interactions and world knowledge in a structured form — a heterogeneous graph — and apply message passing to propagate latent representations of users’ affinities to refine utterance embeddings.Using these embeddings, PAIGE can potentially provide different rewrites given the same query for users with different preferences. Our model, trained without any human-annotated data, improves the rewrite retrieval precision of state-of-the-art baselines by 12.5–17.5% while having nearly ten times fewer parameters.
Pengaruh Software SAP Dalam Meningkatkan Kinerja Karyawan Bagian Keuangan Dan Akuntansi Pada PT. Perkebunan Nusantara VI

A Employee performance is of course one of the important components for the company because it affects the company's operations. This study aims to analyze the effect of SAP software in improving employee performance in finance and accounting at PT. Perkebunan Nusantara VI, used a questionnaire as an instrument to take a sample of 30 respondents. The analytical method used in this research is multiple linear regression analysis. The independent variables in this study are software quality, information quality, software user satisfaction and the impact of using accounting software, while the dependent variable is employee performance of 65.3%, the remaining 34.7% is influenced by other variables. The results of this study indicate that the variables of software user satisfaction and the impact of using accounting software have a significant effect on employee performance with a significant value &lt;0.05 with a value of Tcount &lt; Ttable (-2.182 &lt; 2.060) and (6.064 &gt; 2.060). Meanwhile, the variables of software quality and information quality have no effect on employee performance because they have a significant value &gt; 0.05 with a value of Tcount &lt; Ttable (-0.293 &lt; 2.060) and (-1.347 &lt; 2.060). For the overall effect, the value of Fcount &gt; Ftable is (11.870 &gt; 2.76) with a significant value of 0.000 &lt;0.05.
Perancangan Sistem Informasi Persediaan Barang Berbasis Web Pada UD. Kopi Ayam Ras Jambi

Kopi Ayam Ras is a company engaged in the storage and sale of goods (distributor). The process of data collection of incoming and outgoing goods, inventory stock, and reporting of goods is not optimal when searching for data due to the large number of data items and data reservoirs still using a manual system. The purpose of this inventory information system is to solve problems that arise in the system that is currently running at PD. Kopi Ayam Ras is also a good solution for companies to create a faster and more accurate inventory management system. Methods of data collection is done by means of interviews, observation, documentation and analysis of the system used by the company. The result of this research is to produce a web-based inventory information system with the FIFO method that provides convenience in providing information on inventory data through incoming and outgoing goods using data flow diagrams, DFD, ERD and MySQL for data processing.
Sistem Informasi Laporan Laba Rugi Pada UD. Kopi Ayam Ras Jambi

This study discusses the problems that occur in UD. Kopi Ayam Ras Jambi which is related to accounting records through the calculation of the income statement still uses the manual method that has not used a computerized system that is in accordance with accounting standards. From this, this situation makes it difficult for management to calculate the income statement every month. The purpose of this study is to create an Income Statement System using the single-step method, which is expected to assist management in calculating the company's profit and loss for each period. The profit and loss statement using this single step can make it easier for management to calculate the income, costs, and profit and loss generated each period. So that the management of UD. Kopi Ayam Ras Jambi can find out in real terms the company's profit and loss for each accounting period. The results of this system are expected to have an impact on efficiency and effectiveness in UD operations. Kopi Ayam Ras Jambi gets an accurate profit and loss report through maximum results.
Numerical Correlation in Text

Evaluation of quantitative reasoning of large language models is an important step towards understanding their current capabilities and limitations.We propose a new task, Numerical Correlation in Text, which requires models to identify the correlation between two numbers in a sentence.To this end, we introduce a new dataset, which contains over 2,000 Wikipedia sentences with two numbers and their correlation labels.Using this dataset we are able to show that recent numerically aware pretraining methods for language models do not help generalization on this task posing a challenge for future work in this area. 1
Conformal Predictor for Improving Zero-Shot Text Classification Efficiency

Pre-trained language models (PLMs) have been shown effective for zero-shot (0shot) text classification.0shot models based on natural language inference (NLI) and next sentence prediction (NSP) employ cross-encoder architecture and infer by making a forward pass through the model for each label-text pair separately.This increases the computational cost to make inferences linearly in the number of labels.In this work, we improve the efficiency of such cross-encoder-based 0shot models by restricting the number of likely labels using another fast base classifier-based conformal predictor (CP) calibrated on samples labeled by the 0shot model.Since a CP generates prediction sets with coverage guarantees, it reduces the number of target labels without excluding the most probable label based on the 0shot model.We experiment with three intent and two topic classification datasets.With a suitable CP for each dataset, we reduce the average inference time for NLI-and NSP-based models by 25.6% and 22.2% respectively, without dropping performance below the predefined error rate of 1%.
The Importance of Synergetics in the Study of Fiction

The synergetic theoretical and methodological approach is a universal scientific paradigm of the universe, created and developed by crossing three areas: nonlinear modeling, applied philosophy and knowledge in a specific science. The synergetic method plays an important role in determining models of the ontological poetic interpretation of fiction. The nature of instability and contradictions in complex systems is the basis for synergetic research. The interpretation of literary texts corresponding to the characteristics of such a system requires the application of general laws of existence. In a synergetic context, the problem of self-creation, the ordering of meaning is investigated. During the dialogue of the recipient with fictional text, the dynamic state of a complex system is determined. There are synergetic principles of text formation, literacy and existence that obey universal laws which are common to all systems of existence. This article presents forms of synergetic sequencing. In the synergy of the text there are three basic principles of the emerging system: inaccuracy, openness, instability. Hamsa’s verses are a system of self-organizing microscopes as a result of the simultaneous rounding of terms of the semantic medium based on several competing scenarios. A microscript is the smallest unit of a text program that is formed by speech. These microscopes become macromolecules, which, in turn, form the main theme of the work. Synergetic theory has the term criterion of order, which is considered as a central concept.
Identifiability of Homoscedastic Linear Structural Equation Models using Algebraic Matroids

We consider structural equation models (SEMs), in which every variable is a function of a subset of the other variables and a stochastic error. Each such SEM is naturally associated with a directed graph describing the relationships between variables. When the errors are homoscedastic, recent work has proposed methods for inferring the graph from observational data under the assumption that the graph is acyclic (i.e., the SEM is recursive). In this work, we study the setting of homoscedastic errors but allow the graph to be cyclic (i.e., the SEM to be non-recursive). Using an algebraic approach that compares matroids derived from the parameterizations of the models, we derive sufficient conditions for when two simple directed graphs generate different distributions generically. Based on these conditions, we exhibit subclasses of graphs that allow for directed cycles, yet are generically identifiable. We also conjecture a strengthening of our graphical criterion which can be used to distinguish many more non-complete graphs.
APLIKASI PRESENSI PERKULIAHAN MAHASISWA MENGGUNAKAN QR-CODE DAN LOCATION BASED SERVICE BERBASIS ANDROID

Technological advancements have had a significant impact on life, including work and education. In the era of globalization, technological development is very important, including in the learning process. Attendance is an important aspect of student performance assessment. However, the manual timekeeping system has problems such as fraud, low accuracy, and data entry errors. This study aims to design conferencing applications that use QR codes and location-based services (LBS) to simplify and accelerate attendance management. The development method used is prototyping, which allows interaction between developers and users. This application is built using App Inventor on Android operating system, allowing to scan QR codes for class attendance. Attendance data is automatically stored in Google Drive for recording and report generation that teachers can flexibly access. The application of this technology is expected to increase the efficiency and accuracy of attendance as well as the quality of the learning process in higher education institutions.
POLA KOMUNIKASI VIRTUAL DALAM PENGGUNAAN E-SPORT GAMES (STUDI KASUS : MOBILE LEGENDS DI KALANGAN MAHASISWA ILMU KOMUNIKASI UIN SUMATERA UTARA)

This research purpose to know the Virtual Communication Patterns of the game Mobile Legends in the University of Communication Studies of UINSU. The research uses descriptive qualitative research methods with a virtual ethnographic approach. Data collection using interviews to 5 sources and library studies. The results of the research showed that the virtual communication pattern of the Mobile Legends game uses Whatssapp media and game conversation media to invite UINSU Communication Sciences students to play, discuss, and give commands and instructions while the game is taking place using Mobile Legend conversation media using everyday, gaul and informal language as well as using stickers or emoticons available on both media. But few use direct or face-to-face communication. Reactions or feedback obtained such as expressing anger when losing in a game and using words unpolite, happy or happy at the time of winning the game, each player can give instructions and orders at the moment of the game subsug so that they follow the instructions in order to win the game. The communication pattern they use is a network of all channels, because anyone can communicate with others in the game.
Chrisimos: A useful Proof-of-Work for finding Minimal Dominating Set of a graph

Hash-based Proof-of-Work (PoW) used in the Bitcoin Blockchain leads to high energy consumption and resource wastage. In this paper, we aim to re-purpose the energy by replacing the hash function with real-life problems having commercial utility. We propose Chrisimos, a useful Proof-of-Work where miners are required to find a minimal dominating set for real-life graph instances. A miner who is able to output the smallest dominating set for the given graph within the block interval time wins the mining game. We also propose a new chain selection rule that ensures the security of the scheme. Thus our protocol also realizes a decentralized minimal dominating set solver for any graph instance. We provide formal proof of correctness and show via experimental results that the block interval time is within feasible bounds of hash-based PoW.
Let's Get Vysical: Perceptual Accuracy In Visual and Tactile Encodings

In this paper, we explore the effectiveness of tactile data encodings using swell paper in comparison to visual encodings displayed with SVGs for data perception tasks. By replicating and adapting Cleveland and McGill's graphical perception study for the tactile modality, we establish a novel tactile encoding hierarchy. In a study with 12 university students, we found that participants perceived visual encodings more accurately when comparing values, judging their ratios with lower cognitive load, and better self-evaluated performance than tactile encodings. However, tactile encodings differed from their visual counterparts in terms of how accurately values could be decoded from them. This suggests that data physicalizations will require different design guidance than that developed for visual encodings. By providing empirical evidence for the perceptual accuracy of tactile encodings, our work contributes to foundational research on forms of data representation that prioritize tactile perception such as tactile graphics.