Reviewerly/exHarmony huggingface dataset:
Behavior-Based Machine Learning Approaches to Identify State-Sponsored Trolls on Twitter

In recent years, there has been an increased prevalence of adopting state-sponsored trolls by governments and political organizations to influence public opinion through disinformation campaigns on social media platforms.This phenomenon negatively affects the political process, causes distrust in the political systems, sows discord within societies, and hastens political polarization.Thus, there is a need to develop automated approaches to identify sponsored-troll accounts on social media in order to mitigate their impacts on the political process and to protect people against opinion manipulation.In this paper, we argue that behaviors of sponsored-troll accounts on social media are different from ordinary users' because of their extrinsic motivation, and they cannot completely hide their suspicious behaviors, therefore these accounts can be identified using machine learning approaches based solely on their behaviors on the social media platforms.We have proposed a set of behavioral features of users' activities on Twitter.Based on these features, we developed four classification models to identify political troll accounts, these models are based on decision tree, random forest, Adaboost, and gradient boost algorithms.The models were trained and evaluated on a set of Saudi trolls disclosed by Twitter in 2019, the overall classification accuracy reaches up to 94.4%.The models also are capable to identify the Russian trolls with accuracy up to 72.6% without training on this set of trolls.This indicates that although the strategies of coordinated trolls might vary from an organization to another, they are all just employees and have common behaviors that can be identified.
Collection Development in the Era of Big Deals

Drawing on an original methodology using citations, downloads, and survey data, this paper analyzes journal usage patterns across 28 Canadian universities. Results show that usage levels vary across disciplines and that different academic platforms varied in their importance to different institutions, with for-profit platforms generally exhibiting lower usage. These results suggest economic inefficiencies exist in "big deal" academic journal subscriptions for universities, as most journals in such bundles are seldom or never used. We recommend that universities coordinate resource sharing and negotiate strategies with academic journal expenditures based on shared interests and usage trends.
Experimental Study on Flash Evaporation under Low-pressure Conditions

ABSTRACTFlash evaporation is extensively used in industrial application. In order to investigate flash evaporation, a new experimental system equipped with the high-speed CCD digital camera and data acquisition instruments is established. A series of experiments has been carried out with various initial temperatures from 50.0 C to 90.5 C, initial water heights of 400 mm to 550 mm, and superheats ranging from 1.5 K to 17.8 K. It is revealed that flashing phenomenon process includes three stages: bubbles generation stage, boiling growth stage and boiling reduction stage. Moreover, the heat transfer characteristics and flow mechanism of each stage has explored. In addition, a correlation between the propagation speed of flash evaporation wave and superheat degree was obtained. Results indicated that the intensity of flash evaporation drastically increases with the increasing of superheat.
A distributed framework to orchestrate video analytics across edge and cloud: a use case of smart doorbell

The concept of Internet of Things (IoT) is a reality now. This paradigm shift has caught everyone's attention in a large class of applications, including IoT-based video analytics using smart doorbells. Due to its growing application segments, various efforts exist in scientific literature and many video-based doorbell solutions are commercially available in the market. However, contemporary offerings are bespoke, offering limited composability and reusability of a smart doorbell framework. Second, they are monolithic and proprietary, which means that the implementation details remain hidden from the users. We believe that a transparent design can greatly aid in the development of a smart doorbell, enabling its use in multiple application domains.To address the above-mentioned challenges, we propose a distributed framework to orchestrate video analytics across Edge and Cloud resources. We investigate trade-offs in the distribution of different software components over a bespoke/full system, where components over Edge and Cloud are treated generically. The proposal looks at the smart doorbell case study and uses AWS as a base platform for implementation. To showcase the composability and reusability of the proposed framework, the state of the art ML-and DL-based models are implemented on the affordable Raspberry Pi in the form of an Edge device. This also demonstrates the feasibility of running these models on IoT devices. Moreover, this paper evaluates the proposed framework as well as the state-of-the-art models and presents comparative analysis of them on various metrics (such as overall model accuracy, latency, memory and CPU usage). The evaluation result demonstrates our intuition very well, showcasing that the AWS-based approach exhibits reasonably high object-detection accuracy, low memory and CPU usage when compared to the state-of-the-art approaches, but high latency.
Systematic mapping study: On the coverage of aspect-oriented methodologies for the early phases of the software development life cycle

Although the number of aspect-oriented software development techniques and tools proposed by the scientific literature have been increasing since the late 80′s, the evidence about the benefits that the aspect-oriented paradigm have reached in real-world settings is scarce. Our objective is to identify and classify the aspect-oriented software development methodologies used to reduce the effort and costs of moving from traditional approaches to the aspect-oriented approach in real-world settings. We conducted a systematic mapping study (SMS). Our search strategies retrieved a set of 3212 papers out of which 115 were selected as relevant studies. We defined eight categories to classify these studies: aspect-oriented methodologies proposed for early aspects; development early phases covered; notations; modeling techniques; supporting tools; aspect-oriented methodologies used in real-world settings; benefits reported and unsolved issues. As a result, 39 named methodologies were reported; they cover the business modeling (14 papers), requirements (93 papers), test cases (1 paper) and design (41 papers) phases of the software development life cycle (SDLC); we found 36 different notations, with UML as the most mentioned (66 papers); 22 modeling techniques were found, where the use cases were the ones that appeared the most, on 43 occasions; 43 support tools of which none was repeated in more than 3 articles; 15 applications in real-world settings; 17 benefits, modularization being the most mentioned with 4 occurrences; and 15 pending improvement opportunities. Finally, we have obtained conclusions: the literature analyzed demonstrates that there are no prevailing standards on methodologies and notations, beyond that the most employed are those belonging to the OMG; we have not found any methodology that includes all phases of the SDLC; the diagrams corresponding to OMG’s standards account for 78% of the results in the facet of modeling techniques; there are no tools that have the greatest predilection and the most used is mentioned only three times; in addition, 41% of the articles do not mention any tool; the evidence of aspect-oriented methodologies application in real-world settings reached just the 10%, although the declared benefits are coincident with those promised in the aspect-orientated literature. The mentioned pending issues can guide new studies (RQ8).
Patient Emotion Recognition in Human Computer Interaction System Based on Machine Learning Method and Interactive Design Theory

There are more and more human computer interaction systems (HCIS) in the medical field. Improving the service quality of HCIS and making them more intelligent is an inevitable trend in the future. Emotion recognition is of great significance for patients using HCIS. Some excellent HCIS not only satisfies the needs of patients, but also judges the emotional state of patients based on the results of emotional recognition, thereby providing more intimate medical services. Therefore, emotion recognition is crucial for HCIS. To effectively optimize the correct rate of emotion recognition, a novel emotion recognition framework based on machine learning is proposed. The core of the framework is to select the optimal classifier for different emotional data, and fuse the classification results of each classifier to get the global classification result. Experiments demonstrate that the proposed framework not only improves the accuracy of emotion recognition, but also improves the stability and reliability of the recognition results. The emotion recognition function based on the framework is applied to the HCIS design, so that the HCIS of the medical institution can better serve the patient during use, keep the patient happy, and improve the patient's happiness index and rehabilitation rate.
Help! Need Advice on Identifying Advice

Humans use language to accomplish a wide variety of tasks -asking for and giving advice being one of them.In online advice forums, advice is mixed in with non-advice, like emotional support, and is sometimes stated explicitly, sometimes implicitly.Understanding the language of advice would equip systems with a better grasp of language pragmatics; practically, the ability to identify advice would drastically increase the efficiency of adviceseeking online, as well as advice-giving in natural language generation systems.We present a dataset in English from two Reddit advice forums -r/AskParents and r/needadvice -annotated for whether sentences in posts contain advice or not.Our analysis reveals rich linguistic phenomena in advice discourse.We present preliminary models showing that while pre-trained language models are able to capture advice better than rulebased systems, advice identification is challenging, and we identify directions for future research.
Complex Human Pose Estimation via Keypoints Association Constraint Network

Human pose estimation has attracted enormous interest in the field of human action recognition. When the human pose is complex (such as pose distortion, pose reversal, etc.) or there is background interference (multi-target, shadow, etc.), the keypoints obtained by existing methods of human pose estimation often have incorrect positioning, category, and connection. This paper proposes a novel human pose estimation network KACNet via the keypoint association constraints. The Channel-1 of KACNet is constrained by the distance loss function to obtain the position of keypoints, and the Channel-2 of KACNet is constrained by the association loss function to obtain the relationship of keypoints. Then, the position and relationship of keypoints are fused by the weighted loss function to obtain the keypoints with accurate location, classification, and connection. Experiments on a large number of public datasets and Internet data show that our method can effectively suppress background interference to improve the accuracy of complex human pose estimation. Compared with state-of-the-art human pose estimation methods, the proposed methods can accurately locate, classify, and connect the human body keypoints robustly.
Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm

The bacterial foraging optimization (BFO) algorithm successfully searches for an optimal path from start to finish in the presence of obstacles over a flat surface map. However, the algorithm suffers from getting stuck in the local minima whenever non-circular obstacles are encountered. The retrieval from the local minima is crucial, as otherwise, it can cause the failure of the whole task. This research proposes an improved version of BFO called robust bacterial foraging (RBF), which can effectively avoid obstacles, both of circular and non-circular shape, without falling into the local minima. The virtual obstacles are generated in the local minima, causing the robot to retract and regenerate a safe path. The proposed method is easily extendable to multiple robots that can coordinate with each other. The information related to the virtual obstacles is shared with the whole swarm, so that they can escape the same local minima to save time and energy. To test the effectiveness of the proposed algorithm, a comparison is made against the existing BFO algorithm. Through the results, it was witnessed that the proposed approach successfully recovered from the local minima, whereas the BFO got stuck.
Real-Time Low-Pixel Infrared Human Detection From Unmanned Aerial Vehicles

To improve the speed and accuracy in human detection in Search and Rescue (SAR) operations, this paper presents a novel and highly efficient machine learning empowered system by extending the You Only Look Once (YOLO) algorithm, which is designed and deployed on an embedded system. The proposed approach has been evaluated under real-world conditions on a Jetson AGX Xavier platform and the results have shown a well-balanced system in terms of accuracy, speed and portability. Moreover, the system demonstrates its resilience to perform low-pixel human detection on infrared images received from an Unmanned Aerial Vehicle (UAV) at low-light conditions, different altitudes and postures such as sitting, walking and running. The proposed approach has achieved in a constrained environment a total of 89.26% of accuracy and 24.6 FPS, surpassing the barrier of real-time object recognition.
Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification

Interpretability of predictive models is becoming increasingly important with growing adoption in the real-world.We present RuleNN, a neural network architecture for learning transparent models for sentence classification.The models are in the form of rules expressed in first-order logic, a dialect with well-defined, human-understandable semantics.More precisely, RuleNN learns linguistic expressions (LE) built on top of predicates extracted using shallow natural language understanding.Our experimental results show that RuleNN outperforms statistical relational learning and other neuro-symbolic methods, and performs comparably with black-box recurrent neural networks.Our user studies confirm that the learned LEs are explainable and capture domain semantics.Moreover, allowing domain experts to modify LEs and instill more domain knowledge leads to human-machine cocreation of models with better performance.
VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modeling

In this paper, we tackle the task of definition modeling, where the goal is to learn to generate definitions of words and phrases. Existing approaches for this task are discriminative, combining distributional and lexical semantics in an implicit rather than direct way. To tackle this issue we propose a generative model for the task, introducing a continuous latent variable to explicitly model the underlying relationship between a phrase used within a context and its definition. We rely on variational inference for estimation and leverage contextualized word embeddings for improved performance. Our approach is evaluated on four existing challenging benchmarks with the addition of two new datasets, “Cambridge” and the first non-English corpus “Robert”, which we release to complement our empirical study. Our Variational Contextual Definition Modeler (VCDM) achieves state-of-the-art performance in terms of automatic and human evaluation metrics, demonstrating the effectiveness of our approach.
Decoding Language Spatial Relations to 2D Spatial Arrangements

We address the problem of multimodal spatial understanding by decoding a set of languageexpressed spatial relations to a set of 2D spatial arrangements in a multi-object and multirelationship setting.We frame the task as arranging a scene of clip-arts given a textual description.We propose a simple and effective model architecture SPATIAL-REASONING BERT (SR-BERT), trained to decode text to 2D spatial arrangements in a non-autoregressive manner.SR-BERT can decode both explicit and implicit language to 2D spatial arrangements, generalizes to out-of-sample data to a reasonable extent and can generate complete abstract scenes if paired with a clip-arts predictor.Finally, we qualitatively evaluate our method with a user study, validating that our generated spatial arrangements align with human expectation.
Will it Unblend?

Natural language processing systems often struggle with out-of-vocabulary (OOV) terms, which do not appear in training data.Blends, such as innoventor, are one particularly challenging class of OOV, as they are formed by fusing together two or more bases that relate to the intended meaning in unpredictable manners and degrees.In this work, we run experiments on a novel dataset of English OOV blends to quantify the difficulty of interpreting the meanings of blends by large-scale contextual language models such as BERT.We first show that BERT's processing of these blends does not fully access the component meanings, leaving their contextual representations semantically impoverished.We find this is mostly due to the loss of characters resulting from blend formation.Then, we assess how easily different models can recognize the structure and recover the origin of blends, and find that context-aware embedding systems outperform character-level and contextfree embeddings, although their results are still far from satisfactory.
Generalization to Mitigate Synonym Substitution Attacks

Studies have shown that deep neural networks are vulnerable to adversarial examples -perturbed inputs that cause DNN-based models to produce incorrect results.One robust adversarial attack in the NLP domain is the synonym substitution.In attacks of this variety, the adversary substitutes words with synonyms.Since synonym substitution perturbations aim to satisfy all lexical, grammatical, and semantic constraints, they are difficult to detect with automatic syntax check as well as by humans.In this work, we propose the first defensive method to mitigate synonym substitution perturbations that can improve the robustness of DNNs with both clean and adversarial data.We improve the generalization of DNN-based classifiers by replacing the embeddings of the important words in the input samples with the average of their synonyms' embeddings.By doing so, we reduce model sensitivity to particular words in the input samples.Our algorithm is generic enough to be applied in any NLP domain and to any model trained on any natural language.
Emoji and Self-Identity in Twitter Bios

Emoji are widely used to express emotions and concepts on social media, and prior work has shown that users' choice of emoji reflects the way that they wish to present themselves to the world.Emoji usage is typically studied in the context of posts made by users, and this view has provided important insights into phenomena such as emotional expression and selfrepresentation.In addition to making posts, however, social media platforms like Twitter allow for users to provide a short bio, which is an opportunity to briefly describe their account as a whole.In this work, we focus on the use of emoji in these bio statements.We explore the ways in which users include emoji in these self-descriptions, finding different patterns than those observed around emoji usage in tweets.We examine the relationships between emoji used in bios and the content of users' tweets, showing that the topics and even the average sentiment of tweets varies for users with different emoji in their bios.Lastly, we confirm that homophily effects exist with respect to the types of emoji that are included in bios of users and their followers.
BENNERD: A Neural Named Entity Linking System for COVID-19

We present a biomedical entity linking (EL) system BENNERD that detects named entities in text and links them to the unified medical language system (UMLS) knowledge base (KB) entries to facilitate the corona virus disease 2019 (COVID-19) research.BEN-NERD mainly covers biomedical domain, especially new entity types (e.g., coronavirus, viral proteins, immune responses) by addressing CORD-NER dataset.It includes several NLP tools to process biomedical texts including tokenization, flat and nested entity recognition, and candidate generation and ranking for EL that have been pre-trained using the CORD-NER corpus.To the best of our knowledge, this is the first attempt that addresses NER and EL on COVID-19-related entities, such as COVID-19 virus, potential vaccines, and spreading mechanism, that may benefit research on COVID-19.We release an online system to enable real-time entity annotation with linking for end users.We also release the manually annotated test set and CORD-NERD dataset for leveraging EL task.The BENNERD system is available at
diagNNose: A Library for Neural Activation Analysis

In this paper we introduce diagNNose, an open source library for analysing the activations of deep neural networks.diagNNose contains a wide array of interpretability techniques that provide fundamental insights into the inner workings of neural networks.We demonstrate the functionality of diagNNose with a case study on subjectverb agreement within language models.
TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation

Sign language translation (SLT) aims to interpret sign video sequences into text-based natural language sentences. Sign videos consist of continuous sequences of sign gestures with no clear boundaries in between. Existing SLT models usually represent sign visual features in a frame-wise manner so as to avoid needing to explicitly segmenting the videos into isolated signs. However, these methods neglect the temporal information of signs and lead to substantial ambiguity in translation. In this paper, we explore the temporal semantic structures of signvideos to learn more discriminative features. To this end, we first present a novel sign video segment representation which takes into account multiple temporal granularities, thus alleviating the need for accurate video segmentation. Taking advantage of the proposed segment representation, we develop a novel hierarchical sign video feature learning method via a temporal semantic pyramid network, called TSPNet. Specifically, TSPNet introduces an inter-scale attention to evaluate and enhance local semantic consistency of sign segments and an intra-scale attention to resolve semantic ambiguity by using non-local video context. Experiments show that our TSPNet outperforms the state-of-the-art with significant improvements on the BLEU score (from 9.58 to 13.41) and ROUGE score (from 31.80 to 34.96)on the largest commonly-used SLT dataset. Our implementation is available at this https URL.