Reviewerly/exHarmony huggingface dataset:
Scalable noncontextuality inequalities and certification of multiqubit quantum systems

We propose a family of noncontextuality inequalities and show that they can be used for certification of multiqubit quantum systems. Our scheme, unlike those based on nonlocality, does not require spatial separation between the subsystems, yet it makes use of certain compatibility relations between measurements. Moreover, it is scalable in the sense that the number of expectation values that are to be measured to observe contextuality scales polynomially with the number of qubits that are being certified. In a particular case we also show our scheme to be robust to errors and experimental imperfections. Our results seem promising as far as certification of physical setups is concerned in scenarios where spatial separation between the subsystems cannot be guaranteed.
Anti-Overestimation Dialogue Policy Learning for Task-Completion Dialogue System

A dialogue policy module is an essential part of task-completion dialogue systems.Recently, increasing interest has focused on reinforcement learning (RL)-based dialogue policy.Its favorable performance and wise action decisions rely on an accurate estimation of action values.The overestimation problem is a widely known issue of RL since its estimate of the maximum action value is larger than the ground truth, which results in an unstable learning process and suboptimal policy.This problem is detrimental to RL-based dialogue policy learning.To mitigate this problem, this paper proposes a dynamic partial average estimator (DPAV) of the ground truth maximum action value.DPAV calculates the partial average between the predicted maximum action value and minimum action value, where the weights are dynamically adaptive and problem-dependent.We incorporate DPAV into a deep Q-network as the dialogue policy and.Our method can achieve better or comparable results compared to top baselines on three dialogue datasets of different domains with a lower computational load.In addition, we also theoretically prove the convergence and derive the upper and lower bounds of the bias compared with those of other methods.
Weakly Supervised Text-to-SQL Parsing through Question Decomposition

Text-to-SQL parsers are crucial in enabling non-experts to effortlessly query relational data.Training such parsers, by contrast, generally requires expertise in annotating natural language (NL) utterances with corresponding SQL queries.In this work, we propose a weak supervision approach for training text-to-SQL parsers.We take advantage of the recently proposed question meaning representation called QDMR, an intermediate between NL and formal query languages.Given questions, their QDMR structures (annotated by non-experts or automatically predicted), and the answers, we are able to automatically synthesize SQL queries that are used to train text-to-SQL models.We test our approach by experimenting on five benchmark datasets.Our results show that the weakly supervised models perform competitively with those trained on annotated NL-SQL data.Overall, we effectively train text-to-SQL parsers, while using zero SQL annotations.Question Decomposition: 1. papers 2. #1 in PVLDB 3. authors of #2 4. number of #2 for each #3 5. #3 where #4 is more than 10 1
Probing the Role of Positional Information in Vision-Language Models

In most Vision-Language models (VL), the understanding of the image structure is enabled by injecting the position information (PI) about objects in the image. In our case study of LXMERT, a state-of-the-art VL model, we probe the use of the PI in the representation and study its effect on Visual Question Answering. We show that the model is not capable of leveraging the PI for the image-text matching task on a challenge set where only position differs. Yet, our experiments with probing confirm that the PI is indeed present in the representation. We introduce two strategies to tackle this: (i) Positional Information Pre-training and (ii) Contrastive Learning on PI using Cross-Modality Matching. Doing so, the model can correctly classify if images with detailed PI statements match. Additionally to the 2D information from bounding boxes, we introduce the object's depth as new feature for a better object localization in the space. Even though we were able to improve the model properties as defined by our probes, it only has a negligible effect on the downstream performance. Our results thus highlight an important issue of multimodal modeling: the mere presence of information detectable by a probing classifier is not a guarantee that the information is available in a cross-modal setup.
Causal Distillation for Language Models

Distillation efforts have led to language models that are more compact and efficient without serious drops in performance.The standard approach to distillation trains a student model against two objectives: a task-specific objective (e.g., language modeling) and an imitation objective that encourages the hidden states of the student model to be similar to those of the larger teacher model.In this paper, we show that it is beneficial to augment distillation with a third objective that encourages the student to imitate the causal dynamics of the teacher through a distillation interchange intervention training objective (DIITO).DIITO pushes the student model to become a causal abstraction of the teacher model -a faithful model with simpler causal structure.DIITO is fully differentiable, easily implemented, and combines flexibly with other objectives.Compared against standard distillation with the same setting, DIITO results in lower perplexity on the WikiText-103M corpus (masked language modeling) and marked improvements on the GLUE benchmark (natural language understanding), SQuAD (question answering), and CoNLL-2003 (named entity recognition).
Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks

Code-switching dependency parsing stands as a challenging task due to both the scarcity of necessary resources and the structural difficulties embedded in code-switched languages.In this study, we introduce novel sequence labeling models to be used as auxiliary tasks for dependency parsing of code-switched text in a semi-supervised scheme.We show that using auxiliary tasks enhances the performance of an LSTM-based dependency parsing model and leads to better results compared to an XLM-Rbased model with significantly less computational and space complexity.As the first study that focuses on multiple code-switching language pairs for dependency parsing, we acquire state-of-the-art scores on all of the studied languages.Our best models outperform the previous work by 7.4 LAS points on average.
LastResort at SemEval-2022 Task 5: Towards Misogyny Identification using Visual Linguistic Model Ensembles And Task-Specific Pretraining

In current times, memes have become one of the most popular mediums to share jokes and information with the masses over the internet.Memes can also be used as tools to spread hatred and target women through degrading content disguised as humour.The task, Multimedia Automatic Misogyny Identification (MAMI), is to detect misogyny in these memes.This task is further divided into two sub-tasks: (A) Misogynous meme identification, where a meme should be categorized either as misogynous or not misogynous and (B) Categorizing these misogynous memes into potential overlapping subcategories.In this paper, we propose models leveraging task-specific pretraining with transfer learning on Visual Linguistic models.With our best performing models, we were able to achieve rank 5 th and 10 th on sub-tasks A and B respectively.
GPT-2-based Human-in-the-loop Theatre Play Script Generation

We experiment with adapting generative language models for the generation of long coherent narratives in the form of theatre plays.Since fully automatic generation of whole plays is not currently feasible, we created an interactive tool that allows a human user to steer the generation somewhat while minimizing intervention.We pursue two approaches to long-text generation: a flat generation with summarization of context, and a hierarchical text-to-text two-stage approach, where a synopsis is generated first and then used to condition generation of the final script.Our preliminary results and discussions with theatre professionals show improvements over vanilla language model generation, but also identify important limitations of our approach.
Don’t Forget About Pronouns: Removing Gender Bias in Language Models Without Losing Factual Gender Information

The representations in large language models contain multiple types of gender information.We focus on two types of such signals in English texts: factual gender information, which is a grammatical or semantic property, and gender bias, which is the correlation between a word and specific gender.We can disentangle the model's embeddings and identify components encoding both types of information with probing.We aim to diminish the stereotypical bias in the representations while preserving the factual gender signal.Our filtering method shows that it is possible to decrease the bias of gender-neutral profession names without significant deterioration of language modeling capabilities.The findings can be applied to language generation to mitigate reliance on stereotypes while preserving gender agreement in coreferences. 1
Opening Knowledge Graph Model Building of Artificial Intelligence Curriculum

The knowledge points setting of artificial intelligence curriculum has shortcomings in connection between theory and practices. To overcome the problem, this study designs an open knowledge point design model based on knowledge graph. Fist, to promote the construction of the knowledge graph (KG) of curriculums, associated teaching research was analyzed visually. Then the order and hierarchical structure of the knowledge points were defined, and the ontology structure of curriculum knowledge and the relationship between knowledge points and posts were designed as well. Moreover, an overall logic structure for the construction of the open KG of curriculums was proposed. Results demonstrated that high attention should be paid to the construction and concern of teaching teams for artificial intelligence algorithms and the KG of curriculum construction. Additionally, the opening model can strengthen the openness of the KG of curriculums to reinforce the close connections between classroom knowledge and practices. Research conclusions are conducive to understand the existing problems in the KG of curriculums and provide beneficial references to the integration of information technology and education.
A Research on the Function of Teaching Evaluation for Machine Translation Course

Teaching evaluation refers to the process of measuring and giving value judgment to the process and results of teaching activities by using effective technical means. Through teaching evaluation, teachers can direct the teaching process to develop toward the predetermined goal and effectively complete the teaching task. This paper investigates the function of teaching evaluation in the Machine Translation course in Northeastern University. The function of teaching evaluation for teachers includes adjustment, diagnosis, teaching stimulation, and orientation. The function of teaching evaluation for students includes feedback, guidance, intensification, and purpose orientation. This paper also discusses the influence of teaching evaluation on the psychology of both, teachers and students. Positive evaluation can improve the enthusiasm of teachers and students, but other times, it may reduce their enthusiasm, whereas when negative evaluation and guidance are appropriate, the enthusiasm of teachers and students may improve. This research reveals that a reasonable teaching evaluation plays a huge role in promoting the psychology of both, teachers and students.
Carbonic: A Framework for Creating and Visualizing Complex Compound Graphs

Advances in data generation and acquisition have resulted in a volume of available data of such magnitude that our ability to interpret and extract valuable knowledge from them has been surpassed. Our capacity to analyze data is hampered not only by their amount or their dimensionality, but also by their relationships and by the complexity of the systems they model. Compound graphs allow us to represent the existing relationships between nodes that are themselves hierarchically structured, so they are a natural substrate to support multiscale analysis of complex graphs. This paper presents Carbonic, a framework for interactive multiscale visual exploration and editing of compound graphs that incorporates several strategies for complexity management. It combines the representation of graphs at multiple levels of abstraction, with techniques for reducing the number of visible elements and for reducing visual cluttering. This results in a tool that allows both the exploration of existing graphs and the visual creation of compound graphs following a top-down approach that allows simultaneously observing the entities and their relationships at different scales. The results show the applicability of the developed framework to two use cases, demonstrating the usefulness of Carbonic for moving from information to knowledge.
Design of a Secured Database System Using Blockchain Technology

In this paper, a decentralized electronic voting system that provides inputs and outputs information support to admin/users in order to update their voting information while being capable of exhibiting different properties of blockchain security without the need for a third party.In this research, the Object Oriented Analysis and Design Methodology were adopted.The high level model of the proposed system was also designed and displayed in a format easily understandable to the user.
Developing an Anomaly Detection System for Automatic Defective Products’ Inspection

Since unqualified products cause enterprise revenue losses, product inspection is essential for maintaining manufacturing quality. An automated optical inspection (AOI) system is an efficient tool for product inspection, providing a convenient interface for users to view their products of interest. Specifically, in the screw manufacturing industry, the conventional methods are the human visual inspection of the product and for the inspector to view the product image displayed on the dashboard of the AOI system. However, despite the inspector and the approach used, inspection results strongly depend on the inspector’s experience. Moreover, machine learning algorithms could improve the efficiency of human visual inspection, thus addressing the above problem. Based on these facts, we improved anomaly detection efficiency during product inspection, using product image data from the AOI system to obtain valuable information. This study notably used the visual geometry group network, Inception V3, and Xception algorithms to detect qualified and unqualified products during product image analytics. Therefore, we considered that the analyzed results could be integrated into a proposed cloud system for human–machine interaction. Thus, administrators can receive reminders concerning the anomaly-inspected notification through the proposed cloud system, comprising a message queuing telemetry transport protocol, an application programming interface, and a cloud dashboard. From the experimental results, the above-mentioned algorithms had more than 93% accuracy, especially Xception, which had a better performance during the defective type classification. From our study, the proposed system can successfully apply the obtained data in data communication, anomaly dashboards, and anomaly notifications.
Variance Reduction in Actor Critic Methods (ACM)

After presenting Actor Critic Methods (ACM), we show ACM are control variate estimators. Using the projection theorem, we prove that the Q and Advantage Actor Critic (A2C) methods are optimal in the sense of the $L^2$ norm for the control variate estimators spanned by functions conditioned by the current state and action. This straightforward application of Pythagoras theorem provides a theoretical justification of the strong performance of QAC and AAC most often referred to as A2C methods in deep policy gradient methods. This enables us to derive a new formulation for Advantage Actor Critic methods that has lower variance and improves the traditional A2C method.
Visualization Proposal for Power System Control Rooms Based on Situational Awareness

Due to the increasing size of electric power systems and its monitoring and operation needs, people at control rooms face complex situations, analyzing great amounts of information, protocols, operation states, alarms, and control parameters. Additionally, new renewable energy sources data is taking its place nowadays, hindering the process of quickly reading and interpreting information. This is known as the loss of Situational Awareness, SA, of system operators. This work presents proposals for displaying useful information in control rooms and visualization techniques to support accurate SA. Grid data displays, maps, trending graphics, lists, bars graphics, table plots, hierarchical levels, proximity regions, and connections are also proposed and analyzed, presenting an application case in a microgrid control room.
Human Readability of Data Files

In this era of big data and FAIR data, data formats must be machine interpretable. XML, among other standards, satisfies this requirement. Yet many standardization initiatives cite human readability as a second, key property in data format development. Examples include the development of STAR in the field of structural biology, W3C PROV for provenance, and even the continuing development of XML. This begs the question(s), what is meant by human readability and can this property be measured for a given data format or compared between competing standards? The broad topic of readability is considered with attention to the various aspects of written text which either foster or counter readability. Drawing on efforts in the educational system, a metric is proposed for estimating the relative human readability of structured data within an archival file format. Comparison is made between the same data represented in various formats, including JSON and XML, to help judge whether these standards have accomplished their simultaneous goals of machine interpretability and human readability.
Semantic-Edge-Supervised Single-Stage Detector for Oriented Object Detection in Remote Sensing Imagery

In recent years, significant progress has been made in arbitrary-oriented object detection. Different from natural images, object detection in aerial images remains its problems and challenges. Current feature enhancement strategies in this field mainly focus on enhancing the local critical response of the target while ignoring the target’s contextual information, which is indispensable for detecting remote sensing targets in complex backgrounds. In this paper, we innovatively combine semantic edge detection with arbitrary-oriented object detection and propose a feature enhancement network base on a semantic edge supervision module (SES) that realizes an attention-like mechanism in three dimensions of space, channel, and pyramid level. It helps the network pay attention to the edge features of targets at multiple scales to obtain more regression clues. Furthermore, to solve the problem of dense objects with different directions in remote sensing images, we propose a rotation-invariant spatial pooling pyramid (RISPP) to extract the features of objects from multiple orientations. Based on the two feature enhancement modules, we named the network SE2-Det; extensive experiments on large public datasets of aerial images (DOTA and UCAS-AOD) validate our approach’s effectiveness and demonstrate our detector’s superior performance.
Mathematical Model of the Financial Sustainability of a Public University

Higher education worldwide has witnessed major changes across nations beginning with the last decade of the twentieth century. In the 1990s there appeared a lot of reforms which were deeply rooted in the new public administrative regulations. These had a major effect on the university management at the global level. The general objective of this scientific approach is to study the financial sustainability of a public university in terms of financial indicators. The first part of the research aimed at a scientific-theoretical basis that was based on qualitative research in order to thoroughly investigate the current state of knowledge. The qualitative part is completed by a quantitative study based of a mathematical model on the financial sustainability of Romanian universities. The results indicate all the premises to create a general model of sustainable university that can be applied, depending on the specifics of the university, and that contributes to a good development over an average period of such an institution.
REVISITING RECEPTIVE SKILLS IN ONLINE LEARNING: THE STUDENTS’ ATTITUDE

This research attempts to investigate the English language learners’ attitude on the online learning of receptive skills, namely listening and reading skills, conducted in English Language Education Department of UIN Raden Intan Lampung. One hundred and twenty-four students of the first year of English Education Department participated in this descriptive quantitative research with online survey design. The data were collected by using a close-ended questionnaire of the ABC model of attitude proposed by Jain which revealed three elements; affective, behavior, and cognition. The questionnaire was administered online to the students who had experienced the process of teaching and learning online in listening and reading courses. The result showed that students’ attitude towards online learning of receptive skills indicated as positive attitude on affective aspect (x̄ = 3.04), behavioral aspect (x̄ = 3.01) and cognitive aspect (x̄ = 3.01) which concerned on the online learning platforms, task completion, lecturers’ strategies in delivering online materials, online group discussion, and receptive skills improvement. In spite of the positive results elicited from this research, there were some limitations encountered such as learning platforms provided by the lecturer, their strategies in teaching receptive skills, and task types which had not been revealed in this research. Thus, for the future research who are interested in doing research in education, it is expected to investigate those areas.
Development of the self-awareness scale for secondary school students

This research aimed to explore the quality, develop scoring criteria for the selfawareness scale, and develop a manual for the use of the self-awareness scale for secondary school students.Participants consisted of 966 secondary school students.Quantitative data analysis was conducted using descriptive statistics and confirmatory factor analysis (CFA).To analyze the qualitative data, content analysis was performed.The results were as follows: (1) The self-awareness scale consisted of 3 components: emotional awareness, accurate self-assessment, and self-confidence, consisting of 30 items.The item discrimination power ranged between 0.205 and 0.645.The construct validity using an empirical approach found that the model was consistent with the empirical data.The total score for each component was significantly related to the total score at the .05level, the correlation coefficients were 0.845, 0.876, 0.895, and the reliability was 0.963; (2) For the norms of the self-awareness scale for secondary school students, the T-scores were in the range of 19.34 to 76.69; and (3) The manual for the use of the self-awareness scale for secondary school students contains the objectives, structure, characteristics, and quality of the scale, time required for the test, test procedures, scoring instructions, and scoring criteria.The instructions are clear and understandable.Teachers can use the manual to arrange tests in actual conditions.Self-awareness scale instrument to measure self-awareness has strong internal consistency reliability.