Reviewerly/exHarmony huggingface dataset:
ECNU-SenseMaker at SemEval-2020 Task 4: Leveraging Heterogeneous  Knowledge Resources for Commonsense Validation and Explanation

This paper describes our system for SemEval-2020 Task 4: Commonsense Validation and Explanation (Wang et al., 2020). We propose a novel Knowledge-enhanced Graph Attention Network (KEGAT) architecture for this task, leveraging heterogeneous knowledge from both the structured knowledge base (i.e. ConceptNet) and unstructured text to better improve the ability of a machine in commonsense understanding. This model has a powerful commonsense inference capability via utilizing suitable commonsense incorporation methods and upgraded data augmentation techniques. Besides, an internal sharing mechanism is cooperated to prohibit our model from insufficient and excessive reasoning for commonsense. As a result, this model performs quite well in both validation and explanation. For instance, it achieves state-of-the-art accuracy in the subtask called Commonsense Explanation (Multi-Choice). We officially name the system as ECNU-SenseMaker. Code is publicly available at https://github.com/ECNU-ICA/ECNU-SenseMaker.
Event Graph Construction Based on Disciplinary Procedural Knowledge: Concept Model and Application

Abstract: At present, procedural knowledge is mainly represented by production rules. Although this representation is concise, problems exist on lack of semantic elements and single semantic relationship, which is not conducive to the representation of procedural knowledge and hinders the teaching and application of procedural knowledge. Therefore, this paper firstly analyzes the semantic elements that procedural knowledge should have, and the lack of time and space elements in traditional representation methods. Combining with the cognitive background of procedural knowledge and introducing event cognition, the representation method of procedural knowledge graph and the construction process model are obtained. The verification proves that the event graph is a procedural knowledge representation method with complete semantic roles and rich semantic relations.
Learning and Reasoning for Robot Dialog and Navigation Tasks

Reinforcement learning and probabilistic reasoning algorithms aim at learning from interaction experiences and reasoning with probabilistic contextual knowledge respectively. In this research, we develop algorithms for robot task completions, while looking into the complementary strengths of reinforcement learning and probabilistic reasoning techniques. The robots learn from trial-and-error experiences to augment their declarative knowledge base, and the augmented knowledge can be used for speeding up the learning process in potentially different tasks. We have implemented and evaluated the developed algorithms using mobile robots conducting dialog and navigation tasks. From the results, we see that our robot’s performance can be improved by both reasoning with human knowledge and learning from task-completion experience. More interestingly, the robot was able to learn from navigation tasks to improve its dialog strategies.
Research on improved image recovery algorithm based on Dark-Channel and multi-scale Retinex theory

Due to the unique optical properties of water, the camera took the images underwater often has a series of problems such as low contrast, fog blur in the images, unclear details for noise, blue-green colour casts on the background, and so on. In order to solve these problems, this paper presents an enhanced underwater image enhancement algorithm based on dark channel prior theory and multi-scale Retinex theory(Dark-Retinex algorithms). Dark-Retinex algorithms introduce a guide filter to improve the lack of depth of field in the two-dimensional perspective rate and adapt it to the underwater environment. Aiming at the problem of colour cast, based on the multi-scale Retinex theory, the brightness component of the colour space is enhanced, the ratio of each colour channel to the brightness component in the original figure is obtained, and the grey value of each colour channel is re-determined according to the balance. Using bilateral filtering removes noise’s influence on image quality and reduces the impact of noise on image detail. At the end of the paper, the Dark-Retinex algorithm and four different algorithms are simulation experiments and analyzed. The results show that the Dark-Retinex algorithm has a good effect on the enhancement of the image quality of underwater shooting, eliminates the noise and fog blur in the image, and corrects the colour deviation.
Maneuvering Target Tracking Based on Adaptive Turning Rate Interactive Multiple Model

Real-time tracking maneuvering targets is vital for monitor the target. This paper proposes an adaptive turn rate target tracking algorithm based on the interactive multiple model target tracking algorithm, which calculates the angular velocity according to the target turning radius and velocity in real time. An AIMM algorithm is also proposed, which corrects the probability transition matrix real time using the slope of the model prior probability change, and it reduce the tracking error and improve the robustness of the tracking. Simulation is presented to testify the tracking accuracy and stability of the algorithm proposed in this paper.
Learning maximum entropy policies with QMIX in cooperative MARL

Model-free deep reinforcement learning have been successfully applied to a range of challenging sequential decision making tasks. However, most tasks in the real world consist of multi-agent and require cooperative behaviors among agents. Quite a few methods such as QMIX have been proposed to address the credit assignment problem and learn cooperative policies in MARL so far. However, such methods suffer from lack of deep and adequate explorations, causing miss of the optimal policies. We propose a novel method QMIX-ME for MARL in this paper that learns policies with maximum entropy for exploration inspired by SAC. For the compatibility of applying policies with maximum entropy in MARL, we make modifications as followed. First, we set independent entropy temperatures for each agent and make approximations for total state values. Besides, we adopt self-attention mechanism in individual critic networks to capture relations and avail of information of other agents. We evaluate our method on StarCraft Multi-Agent Challenge (SMAC) and the experiments results show great progress of our method.
An Evaluation of Binary Comparative Lexical Complexity Models

Identifying complex words in texts is an important first step in text simplification (TS) systems.In this paper, we investigate the performance of binary comparative Lexical Complexity Prediction (LCP) models applied to a popular benchmark dataset -the CompLex 2.0 dataset used in SemEval-2021 Task 1.With the data from CompLex 2.0, we create a new dataset contain 1,940 sentences referred to as CompLex-BC.Using CompLex-BC, we train multiple models to differentiate which of two target words is more or less complex in the same sentence.A linear SVM model achieved the best performance in our experiments with an F1-score of 0.86.
A Self-supervised Joint Training Framework for Document Reranking

Pretrained language models such as BERT have been successfully applied to a wide range of natural language processing tasks and also achieved impressive performance in document reranking tasks.Recent works indicate that further pretraining the language models on the task-specific datasets before fine-tuning helps improve reranking performance.However, the pre-training tasks like masked language model and next sentence prediction were based on the context of documents instead of encour aging the model to understand the content of queries in document reranking task.In this paper, we propose a new self-supervised joint training framework (SJTF) with a selfsupervised method called Masked Query Pre diction (MQP) to establish semantic relations between given queries and positive documents.The framework randomly masks a token of query and encode the masked query paired with positive documents, and use a linear layer as a decoder to predict the masked token.In addition, the MQP is used to jointly opti mize the models with supervised ranking ob jective during fine-tuning stage without an ex tra further pre-training stage.Extensive exper iments on the MS MARCO passage ranking and TREC Robust datasets show that models trained with our framework obtain significant improvements compared to original models.
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger

Adversarial training based on the minimax formulation is necessary for obtaining adversarial robustness of trained models. However, it is conservative or even pessimistic so that it sometimes hurts the natural generalization. In this paper, we raise a fundamental question---do we have to trade off natural generalization for adversarial robustness? We argue that adversarial training is to employ confident adversarial data for updating the current model. We propose a novel approach of friendly adversarial training (FAT): rather than employing most adversarial data maximizing the loss, we search for least adversarial (i.e., friendly adversarial) data minimizing the loss, among the adversarial data that are confidently misclassified. Our novel formulation is easy to implement by just stopping the most adversarial data searching algorithms such as PGD (projected gradient descent) early, which we call early-stopped PGD. Theoretically, FAT is justified by an upper bound of the adversarial risk. Empirically, early-stopped PGD allows us to answer the earlier question negatively---adversarial robustness can indeed be achieved without compromising the natural generalization.
A Multi-pronged Redesign to Reduce Gaming the System

Despite almost two decades of interest in reducing gaming the system in interactive learning environments, gaming continues as a key factor reducing student learning outcomes and contributing to poorer learning outcomes. In this study, we redesigned the Kupei learning system by implementing a combined set of three interventions aimed at mitigating the impact of the two gaming behaviors we documented. Our results show evidence of a possible positive effect of the combined gaming prevention intervention at reducing the second type of gaming behavior within our system, however, it was not as successful at mitigating the first type of gaming behavior.
Design Assertions: Executable Assertions for Design Constraints

An assertion is a Boolean expression embedded in a program that must hold during the execution. Executable assertions are a simple but practical way to check assumptions and code logic at runtime. Assertions are written by referring to concrete program states. In this paper, we recognize a variety of assertions that we call design assertions. These are assertions written to ensure design constraints and properties, not detailed implementation decisions, and thus can detect major problems in the implementation such as design drift or corrosion. However, they are written by referring to concrete program states, thus causing readability and maintenance problems. To address these problems, we propose to write design constraints at a higher abstraction level by referring to abstract program states. We explain our approach using the Dart/Flutter platform, but it should work in other languages and platforms with similar assertion facilities.
Developing a Production System for Purpose of Call Detection in Business Phone Conversations

For agents at a contact centre receiving calls, the most important piece of information is the reason for a given call.An agent cannot provide support on a call if they do not know why a customer is calling.In this paper we describe our implementation of a commercial system to detect Purpose of Call statements in English business call transcripts in real time.We present a detailed analysis of types of Purpose of Call statements and language patterns related to them, discuss an approach to collect rich training data by bootstrapping from a set of rules to a neural model, and describe a hybrid model which consists of a transformer-based classifier and a set of rules by leveraging insights from the analysis of call transcripts.The model achieved 88.6 F1 on average in various types of business calls when tested on real life data and has low inference time.We reflect on the challenges and design decisions when developing and deploying the system.
Multilingual Extraction and Categorization of Lexical Collocations with Graph-aware Transformers

Recognizing and categorizing lexical collocations in context is useful for language learning, dictionary compilation and downstream NLP.However, it is a challenging task due to the varying degrees of frozenness lexical collocations exhibit.In this paper, we put forward a sequence tagging BERT-based model enhanced with a graph-aware transformer architecture, which we evaluate on the task of collocation recognition in context.Our results suggest that explicitly encoding syntactic dependencies in the model architecture is helpful, and provide insights on differences in collocation typification in English, Spanish and French. 1
An Empirical Study on Pseudo-log-likelihood Bias Measures for Masked Language Models Using Paraphrased Sentences

In this paper, we conduct an empirical study on a bias measure, log-likelihood Masked Language Model (MLM) scoring, on a benchmark dataset. Previous work evaluates whether MLMs are biased or not for certain protected attributes (e.g., race) by comparing the log-likelihood scores of sentences that contain stereotypical characteristics with one category (e.g., black) versus another (e.g., white). We hypothesized that this approach might be too sensitive to the choice of contextual words than the meaning of the sentence. Therefore, we computed the same measure after paraphrasing the sentences with different words but with same meaning. Our results demonstrate that the log-likelihood scoring can be more sensitive to utterance of specific words than to meaning behind a given sentence. Our paper reveals a shortcoming of the current log-likelihood-based bias measures for MLMs and calls for new ways to improve the robustness of it
Design and Implementation of Virtual Campus System based on VR Technology

This paper analyzes the development status of virtual campus system and the design and implementation of virtual campus system based on VR technology under Web 2.0, This paper puts forward a feasible scheme of using virtual reality technology to realize the campus oriented identification system. It describes the framework, development technology and specific implementation method of the virtual campus system
Image Surface Analysis for Null-screen Testing

Image surface of a plane screen after reflection is analyzed to set the posi- tion of the elements in a null screen test to avoid ambiguities between real and apparent deformation of a freeform surface.
Transition between individually different and common features in skilled drumming movements

Why do professional athletes and musicians exhibit individually different motion patterns? For example, baseball pitchers generate various pitching forms, e.g., variable wind-up, cocking, and follow-through forms. However, they commonly rotate their wrists and fingers at increasingly high speeds via shoulder and trunk motions. Despite the universality of common and individually different motion patterns in skilled movements, the abovementioned question remains unanswered. Here, we focus on a motion required to hit a snare drum, including the indirect phase of task achievement (i.e., the early movement and mid-flight phases) and the direct phase of task achievement (i.e., the hit phase). We apply tensor decomposition to collected kinematic data for the drum-hitting motion, enabling us to decompose high-dimensional and time-varying motion data into individually different and common movement patterns. As a result, individually different motion patterns emerge during the indirect phase of task achievement, and common motion patterns are evident in the direct phase of task achievement. Athletes and musicians are thus possibly allowed to perform individually different motion patterns during the indirect phase of task achievement. Additionally, they are required to exhibit common patterns during the direct phase of task achievement.
Utilization of Big Data in the Japanese Retail Industry and Consumer Purchasing Decision-Making

In recent years, the spread of new information technology has increased the amount of digital data available, and the use of Big data is advancing. On the other hand, the Japanese retail industry, which emphasizes in-store shopping experiences, has been facing serious management deterioration due to the impact of the globalization of EC (Electronic Commerce) companies and foreign-affiliated companies. From the above background, I considered the utilization of Big data and consumer purchasing decision-making. This research used the statistics from “Utilization of digital data in Japanese companies” conducted by the Ministry of Internal Affairs and Communications since 2015, and searched for trends in data utilization from the information released by each company.
Pengaruh Permainan Balok Angka Terhadap Kemampuan Berhitung Anak Usia 5-6 Tahun di PAUD Marqisah

This research is entitled "The Effect of Number Block Game on Counting Ability of Children 5-6 Years Old at Marqisah PAUD, Segamit Village, Semende Darat Ulu, Muara Enim". As for the problem of this research, is there any influence of the number block game on the numeracy ability of children aged 5-6 years at PAUD Marqisah, Segamit Village, Semende Darat Ulu, Muara Enim. 5-6 years in PAUD Marqisah, Segamit Village, Semende Darat Ulu, Muara Enim. This type of research is pre-experimental with one group pre-test-post-test design (one group of objects). The number of samples of children is 10 children consisting of 1 girl and 9 boys. Data collection techniques in the study used initial observation before being treated with learning using number block games, observation was used to collect data during treatment, tests in the form of scores were carried out to determine test results were given treatment and documentation in the form of photos. The data analysis technique used in this study used normality test, homogeneity test and hypothesis testing. The results of the hypothesis in this study obtained tcount = 6, 2616487343 while dk = 10 + 10-2 = 18 with a 5% significance level so that ttable = 1,734 is obtained because tcount = 6, 2616487343 &gt; ttable = it can be concluded that Ho is rejected, meaning that there is an influence the number block game on the numeracy skills of children aged 5-6 years at PAUD Marqisah, Segamit Village, Semende Datar Ulu, Muara Enim.
RecurrentHAR: A Novel Transfer Learning-Based Deep Learning Model for Sequential, Complex, Concurrent, Interleaved, and Heterogeneous Type Human Activity Recognition

Smartphone sensor-based Human Activity Recognition (HAR) is a significant facilitator for many real-world applications such as smart homes, personal healthcare, and illness detection because it has the potential to discover activity patterns in daily life. While solutions for detecting sequential activities like walking or running are already matured, however recognizing additional forms of complex, concurrent, interleaved, and heterogeneous human activities remains a research challenge. Furthermore, several solutions have been developed for specific types of activity complexity. According to the best of our knowledge, there is no common solution for detecting all types of action-based activities. In this paper, we address the problem of recognizing all types of action-based human activities i.e. sequential, complex, concurrent, interleaved, and heterogeneous activities. The intention behind this paper is to present a generic methodology for HAR by leveraging transfer learning and evaluating the performance on a plethora of HAR datasets. In this regard, we have proposed a model called RecurrentHAR, which stands for Recurrent layers for HAR, a novel adversarial knowledge transfer approach that uses the Gated Recurrent Units (GRUs) architecture for smartphone sensor-based HAR. The performance of the proposed model is evaluated through extensive experiments using three public datasets namely WISDM (sequential activities), PAMAP2 (complex, concurrent, and interleaved activities), and KU-HAR (Heterogeneous activities), and the RecurrentHAR model has outperformed compared with other state-of-the-art approaches. The proposed model achieved 96.26%, 94.77%, and 98.98% of F1-score for WISDM, PAMAP2, and KU-HAR datasets respectively. The experimental results provide insight into the applicability of the proposed model and future research possibilities.
On the Problem of UAV Collision Avoidance based on Adaptive Load Factor Control

Autonomous collision avoidance for small unmanned aerial vehicles (UAVs) is an important issue covered in many papers. Nevertheless, many questions remain regarding the implementation of adaptive obstacle bypass control with trajectory optimization. This is especially relevant for fixed-wing UAVs due to their limited maneuverability. This paper proposes a two-step solution. Step 1 is to modify the standard autopilot by adding a load factor control loop; Step 2 is to implement fuzzy model reference adaptive control (fuzzy MRAC) to preserve the quality of obstacle bypass trajectories. Simulations on full 6-DoF, 12-state nonlinear small-UAV models show that this approach is usable and effective. Thus, it enables more flexible control when using UAV collision avoidance algorithms compared to the standard altitude state machine.