Reviewerly/exHarmony huggingface dataset:
Crowd Counting and Abnormal Behavior Detection via Multiscale GAN Network Combined with Deep Optical Flow

Aiming at the problem of low performance of crowd abnormal behavior detection caused by complex backgrounds and occlusions, this paper proposes a single-image crowd counting and abnormal behavior detection via multiscale GAN network. The proposed method firstly designed an embedded GAN module with a multibranch generator and a regional discriminator to initially generate crowd-density maps; and then our proposed multiscale GAN module is added to further strengthen the generalization ability of the model, which can effectively improve the accuracy and robustness of the prediction detection and counting. On the basis of single-image crowd counting, synthetic optical-flow feature descriptor is adopted to obtain the crowd motion trajectory, and the classification of abnormal behavior is finally implemented. The simulation results show that the proposed algorithm can significantly improve the accuracy and robustness of crowd counting and abnormal behavior detection in real complex scenarios compared with the existing mainstream algorithms, which is suitable for engineering applications.
Graph Embedding Framework Based on Adversarial and Random Walk Regularization

Graph embedding aims to represent node structural as well as attribute information into a low-dimensional vector space so that some downstream application tasks such as node classification, link prediction, community detection, and recommendation can be easily performed by using simple machine learning algorithms. The graph convolutional network is a neural network framework for machine learning on graphs. Because of its powerful ability to model graph data, it is currently the best choice for graph embedding. However, most existing graph convolutional network-based embedding algorithms not only ignore the data distribution of the latent codes but also lose the high-order proximity between nodes in a graph, leading to inferior embedding. To mitigate this problem, we investigate how to enforce latent codes to match a prior distribution, and we introduce random walk to preserve high-order proximity in a graph. In this paper, we propose a novel graph embedding framework, Adversarial and Random Walk Regularized Graph Embedding (ARWR-GE), which jointly preserves structural and attribute information. ARWR-GE adopts an adversarial training scheme to enforce the latent codes to match a prior distribution, and by employing the skip-gram model, nodes in a random walk sequence are closer in the latent space. We evaluate our proposed framework by using three real-world datasets on link prediction, graph clustering, and visualization tasks. The results demonstrate that our framework achieves better performance than state-of-the-art graph embedding algorithms.
Intelligent Personal Assistant in Business-Context: Key-feature Evaluation for User Acceptance

Abstract Background The usage of intelligent personal assistants (IPA), such as Amazon Alexa or Google Assistant is increasing significantly, and voice-interaction is relevant for workflows in a business context. Objectives This research aims to determine IPA characteristics to evaluate the usefulness of specific functions in a simulated production system of an Enterprise Resource Planning (ERP) software. A new function called explanation-mode is introduced to the scientific community and business world. Methods/Approach As part of a design science research, an artefact, i.e. an add-on for speech-interaction in business software, was developed and evaluated using a survey among ERP users and researchers. Results In the area of IPA-features, the search-function and speech input for textual fields were recognised as most useful. The newly introduced feature, the explanation mode, was positively received too. There is no significant correlation between the usefulness of features and participant-characteristics, affinity to technology or previous experience with IPAs in a private context, which is in line with previous studies in the private environment leading to the conclusion that the task attraction is the most important element for usefulness. Conclusions Most of the participants agreed that the speech-input is not able to fully substitute standard input devices, such as a keyboard or a mouse, so the IPA is recognised as an addition to traditional input methods. The usefulness is rated high especially for speech-input for long text fields, calling up masks and search-functions.
HinglishNLP at SemEval-2020 Task 9: Fine-tuned Language Models for Hinglish Sentiment Detection

Sentiment analysis for code-mixed social media text continues to be an under-explored area.This work adds two common approaches: fine-tuning large transformer models and sample efficient methods like ULMFiT (Howard and Ruder, 2018).Prior work demonstrates the efficacy of classical ML methods for polarity detection.Fine-tuned general-purpose language representation models, such as those of the BERT family are benchmarked along with classical machine learning and ensemble methods.We show that NB-SVM beats RoBERTa by 6.2% (relative) F1.The best performing model is a majority-vote ensemble which achieves an F1 of 0.707.The leaderboard submission was made under the codalab username nirantk, with F1 of 0.689.
Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas

JSON Schema is maturing into the de-facto schema language for JSON documents. When JSON Schema declarations evolve, the question arises how the new schema will deal with JSON documents that still adhere to the legacy schema. This is particularly crucial in the maintenance of software APIs. In this paper, we present the results of our empirical study of the first generation of tools for checking JSON Schema containment which we apply to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions. We assess two such special-purpose tools w.r.t. their applicability to real-world schemas and identify weak spots. Based on this analysis, we enumerate specific open research challenges that are based on real-world problems.
Optimal Oblivious Priority Queues

In this work, we present the first asymptotically optimal oblivious priority queue, which matches the lower bound of Jacob, Larsen, and Nielsen (SODA'19).Our construction is conceptually simple and statistically secure.We illustrate the power of our optimal oblivious priority queue by presenting a conceptually equally simple construction of statistically secure offline ORAMs with O(log n) bandwidth overhead.
Service quality monitoring in confined spaces through mining Twitter data

Promoting public transport depends on adapting effective tools for concurrent monitoring of perceived service quality.Social media feeds, in general, provide an opportunity to ubiquitously look for service quality events, but when applied to confined geographic area such as a transport node, the sparsity of concurrent social media data leads to two major challenges.Both the limited number of social media messages-leading to biased machine-learning-and the capturing of bursty events in the study period considerably reduce the effectiveness of general event detection methods.In contrast to previous work and to face these challenges, this paper presents a hybrid solution based on a novel finetuned BERT language model and aspect-based sentiment analysis.BERT enables extracting aspects from a limited context, where traditional methods such as topic modeling and word embedding fail.Moreover, leveraging aspect-based sentiment analysis improves the sensitivity of event detection.Finally, the efficacy of event detection is further improved by proposing a statistical approach to combine frequency-based and sentiment-based solutions.Experiments on a real-world case study demonstrate that the proposed solution improves the effectiveness of event detection compared to state-of-the-art approaches.
Implementation of Mobile Entrepreneurial Learning in the Context of Flexible Integration of Traditions and Innovations

The study considers the issue of integrating traditions and principles of mobile learning in entrepreneurship education. The purpose of the research is to study the level of implementation of mobile learning methods in higher education through the example of the Republic of Azerbaijan. The study involves a survey of 99 teachers from 5 universities. The survey results confirmed that both external factors (primarily globalization and widespread introduction of information and communication technologies) and internal factors affect higher education. It has been shown that national traditions do not contradict the best practices of entrepreneurship education and are successfully integrated into mobile learning practices. The level of integration of advanced trends in the transformation of education in the practice of mobile learning has been highly assessed by the teachers (3.44 out of 5 points on the Likert scale). The benefit of mobile learning for implementing the identified advanced trends (3.44 - 4.84 on the Likert scale) has also been highly praised. For example, respect for elders by age, experience, rank; practical focus; comprehensive assessment; rationality; involvement of each family member in hard work are supported by advanced learning methods, including the involvement of skilled lecturers, the increased importance of practical tasks, the use of project approach and startups; the use of mobile applications to assess knowledge; personalization of classes; the introduction of distance learning, etc. Flexible integration of national traditions and innovations can become a competitive advantage of the university and the education system; it is also being successfully implemented in mobile learning. A model for the formation of entrepreneurial competence in students based on the use of mobile applications along with cloud services that takes into account the flexible integration of traditions and innovation has been developed.
A Novel Chaotic System with a Line Equilibrium: Analysis and Its Applications to Secure Communication and Random Bit Generation

In this study, a novel two-parameter, three-dimensional chaotic system is constructed. The system has no linear terms and its equilibrium is a line, so it is a system with hidden attractors. The system is first studied by computation of its bifurcation diagrams and diagram of Lyapunov exponents. Then, the system is applied to two encryption related problems. First, the problem of secure communications is considered, using the symmetric chaos shift keying modulation method. Here, the states of the chaotic system are combined with a binary information signal in order to mask it, safely transmit it through a communication channel, and successfully reconstruct the information at the receiver end. In the second problem, the states of the system are utilized to design a simple rule to generate a bit sequence that possesses random properties, and is thus suitable for encryption related applications. For both applications, simulations are performed through Matlab to verify the soundness of the designs.
Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer’s Disease Detection

Data scarcity is always a constraint on analyzing speech transcriptions for automatic Alzheimer's disease (AD) detection, especially when the subjects are non-English speakers.To deal with this issue, this paper first proposes a contrastive learning method to obtain effective representations for text classification based on monolingual embeddings of BERT.Furthermore, a cross-lingual data augmentation method is designed by building autoencoders to learn the text representations shared by both languages.Experiments on a Mandarin AD corpus show that the contrastive learning method can achieve better detection accuracy than conventional CNN-based and BERTbased methods.Our cross-lingual data augmentation method also outperforms other compared methods when using another English AD corpus for augmentation.Finally, a best detection accuracy of 81.6% is obtained by our proposed methods on the Mandarin AD corpus.
Handling Anomalies of Synthetic Questions in Unsupervised Question Answering

Advances in Question Answering (QA) research require additional datasets for new domains, languages, and types of questions, as well as for performance increases.Human creation of a QA dataset like SQuAD, however, is expensive.As an alternative, an unsupervised QA approach has been proposed so that QA training data can be generated automatically.However, the performance of unsupervised QA is much lower than that of supervised QA models.We identify two anomalies in the automatically generated questions and propose how they can be mitigated.We show our approach helps improve unsupervised QA significantly across a number of QA tasks.
Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems

Recent progress through advanced neural models pushed the performance of task-oriented dialog systems to almost perfect accuracy on existing benchmark datasets for intent classification and slot labeling.However, in evolving real-world dialog systems, where new functionality is regularly added, a major additional challenge is the lack of annotated training data for such new functionality, as the necessary data collection efforts are laborious and time-consuming.A potential solution to reduce the effort is to augment initial seed data by paraphrasing existing utterances automatically.In this paper, we propose a new, data-efficient approach following this idea.Using an interpretation-to-text model for paraphrase generation, we are able to rely on existing dialog system training data, and, in combination with shuffling-based sampling techniques, we can obtain diverse and novel paraphrases from small amounts of seed data.In experiments on a public dataset and with a real-world dialog system, we observe improvements for both intent classification and slot labeling, demonstrating the usefulness of our approach.
Intermediate Self-supervised Learning for Machine Translation Quality Estimation

Pre-training sentence encoders is effective in many natural language processing tasks including machine translation (MT) quality estimation (QE), due partly to the scarcity of annotated QE data required for supervised learning.In this paper, we investigate the use of an intermediate self-supervised learning task for sentence encoder aiming at improving QE performances at the sentence and word levels.Our approach is motivated by a problem inherent to QE: mistakes in translation caused by wrongly inserted and deleted tokens.We modify the translation language model (TLM) training objective of the cross-lingual language model (XLM) to orientate the pretrained model towards the target task.The proposed method does not rely on annotated data and is complementary to QE methods involving pre-trained sentence encoders and domain adaptation.Experiments on English-to-German and English-to-Russian translation directions show that intermediate learning improves over domain adaptated models.Additionally, our method reaches results in par with state-of-the-art QE models without requiring the combination of several approaches and outperforms similar methods based on pre-trained sentence encoders.
Hitachi at SemEval-2020 Task 8: Simple but Effective Modality Ensemble for Meme Emotion Recognition

Users of social networking services often share their emotions via multi-modal content, usually images paired with text embedded in them.SemEval-2020 task 8, Memotion Analysis, aims at automatically recognizing these emotions of so-called internet memes.In this paper, we propose a simple but effective MODALITY ENSEMBLE that incorporates visual and textual deep-learning models, which are independently trained, rather than providing a single multi-modal joint network.To this end, we first fine-tune four pre-trained visual models (i.e., Inception-ResNet, PolyNet, SENet, and PNASNet) and four textual models (i.e., BERT, GPT-2, Transformer-XL, and XLNet).Then, we fuse their predictions with ensemble methods to effectively capture crossmodal correlations.The experiments performed on dev-set show that both visual and textual features aided each other, especially in subtask-C, and consequently, our system ranked 2nd on subtask-C.
Is Killed More Significant than Fled? A Contextual Model for Salient Event Detection

Identifying the key events in a document is critical to holistically understanding its important information.Although measuring the salience of events is highly contextual, most previous work has used a limited representation of events that omits essential information.In this work, we propose a highly contextual model of event salience that uses a rich representation of events, incorporates document-level information and allows for interactions between latent event encodings.Our experimental results on an event salience dataset (Liu et al., 2018) demonstrate that our model improves over previous work by an absolute 2-4% on standard metrics, establishing a new state-of-the-art performance for the task.We also propose a new evaluation metric which addresses flaws in previous evaluation methodologies.Finally, we discuss the importance of salient event detection for the downstream task of summarization.
Context Dependent Semantic Parsing: A Survey

Semantic parsing is the task of translating natural language utterances into machine-readable meaning representations.Currently, most semantic parsing methods are not able to utilize contextual information (e.g.dialogue and comments history), which has a great potential to boost semantic parsing performance.To address this issue, context dependent semantic parsing has recently drawn a lot of attention.In this survey, we investigate progress on the methods for the context dependent semantic parsing, together with the current datasets and tasks.We then point out open problems and challenges for future research in this area.The collected resources for this topic are available at: https://github.com/zhuang-li/
HIT-SCIR at SemEval-2020 Task 5: Training Pre-trained Language Model with Pseudo-labeling Data for Counterfactuals Detection

We describe our system for Task 5 of SemEval 2020: Modelling Causal Reasoning in Language: Detecting Counterfactuals.Despite deep learning has achieved significant success in many fields, it still hardly drives today's AI to strong AI, as it lacks of causation, which is a fundamental concept in human thinking and reasoning.In this task, we dedicate to detecting causation, especially counterfactuals from texts.We explore multiple pre-trained models to learn basic features and then fine-tune models with counterfactual data and pseudo-labeling data.Our team HIT-SCIR wins the first place (1st) in Sub-task 1 -Detecting Counterfactual Statements and is ranked 4th in Sub-task 2 -Detecting Antecedent and Consequence.In this paper we provide a detailed description of the approach, as well as the results obtained in this task 1 .
Managing Feature Compatibility in Kubernetes: Vendor Comparison and Analysis

Kubernetes (k8s) is a kind of cluster operating system for cloud-native workloads that has become a de-facto standard for container orchestration. Provided by more than one hundred vendors, it has the potential to protect the customer from vendor lock-in. However, the open-source k8s distribution consists of many optional and alternative features that must be explicitly activated and may depend on pre-configured system components. As a result, incompatibilities still may ensue among Kubernetes vendors. Mostly managed k8s services typically restrict the customizability of Kubernetes. This paper firstly compares the most relevant k8s vendors and, secondly, analyses the potential of Kubernetes to detect and configure compatible support for required features across vendors in a uniform manner. Our comparison is performed based on documented features, by testing, and by inspection of the configuration state of running clusters. Our analysis focuses on the potential of the end-to-end testing suite of Kubernetes to detect support for a desired feature in any Kubernetes vendor and the possibility of reconfiguring the studied vendors with missing features in a uniform manner. Our findings are threefold: First, incompatibilities arise between default cluster configurations of the studied vendors for approximately 18% of documented features. Second, matching end-to-end tests exist only for around 64% of features and for 17% of features these matching tests are not well developed for all vendors. Third, almost all feature incompatibilities can be resolved using a vendor-agnostic API. These insights are beneficial to avoid feature incompatibilities already in cloud-native application engineering processes. Moreover, the end-to-end testing suite can be extended in currently unlighted areas to provide better feature coverage.
Single Image-based Enhancement Techniques for Underwater Optical Imaging

Underwater color images suffer from low visibility and color cast effects caused by light attenuation by water and floating particles. This study applied single image enhancement techniques to enhance the quality of underwater images and compared their performance with real underwater images taken in Korean waters. Dark channel prior (DCP), gradient transform, image fusion, and generative adversarial networks (GAN), such as cycleGAN and underwater GAN (UGAN), were considered for single image enhancement. Their performance was evaluated in terms of underwater image quality measure, underwater color image quality evaluation, gray-world assumption, and blur metric. The DCP saturated the underwater images to a specific greenish or bluish color tone and reduced the brightness of the background signal. The gradient transform method with two transmission maps were sensitive to the light source and highlighted the region exposed to light. Although image fusion enabled reasonable color correction, the object details were lost due to the last fusion step. CycleGAN corrected overall color tone relatively well but generated artifacts in the background. UGAN showed good visual quality and obtained the highest scores against all figures of merit (FOMs) by compensating for the colors and visibility compared to the other single enhancement methods.
On the fractal nature of complex syntax and the timescale problem

Fundamental to complex dynamic systems theory is the assumption that the recursive behavior of complex systems results in the generation of physical forms and dynamic processes that are self-similar and scale-invariant. Such fractal-like structures and the organismic benefit that they engender has been widely noted in physiology, biology, and medicine, yet discussions of the fractal-like nature of language have remained at the level of metaphor in applied linguistics. Motivated by the lack of empirical evidence supporting this assumption, the present study examines the extent to which the use and development of complex syntax in a learner of English as a second language demonstrate the characteristics of self-similarity and scale invariance at nested timescales. Findings suggest that the use and development of syntactic complexity are governed by fractal scaling as the dynamic relationship among the subconstructs of syntax maintain their complexity and variability across multiple temporal scales. Overall, fractal analysis appears to be a fruitful analytic tool when attempting to discern the dynamic relationships among the multiple component parts of complex systems as they interact over time.