Reviewerly/exHarmony huggingface dataset:
Dog Face Detection Using YOLO Network

This work presents the real-world application of the object detection which belongs to one of the current research lines in computer vision. Researchers are commonly focused on human face detection. Compared to that, the current paper presents a challenging task of detecting a dog face instead that is an object with extensive variability in appearance. The system utilises YOLO network, a deep convolution neural network, to~predict bounding boxes and class confidences simultaneously. This paper documents the extensive dataset of dog faces gathered from two different sources and the training procedure of the detector. The proposed system was designed for realization on mobile hardware. This Doggie Smile application helps to snapshot dogs at the moment when they face the camera. The proposed mobile application can simultaneously evaluate the gaze directions of three dogs in scene more than 13 times per second, measured on iPhone XR. The average precision of the dogface detection system is 0.92.
MAT: Multianchor Visual Tracking With Selective Search Region

The core prerequisite of most modern trackers is a motion assumption, defined as predicting the current location in a limited search region centering at the previous prediction. For clarity, the central subregion of a search region is denoted as the tracking anchor (e.g., the location of the previous prediction in the current frame). However, providing accurate predictions in all frames is very challenging in the complex nature scenes. In addition, the target locations in consecutive frames often change violently under the attribute of fast motion. Both facts are likely to lead the previous prediction to an unbelievable tracking anchor, which will make the aforementioned prerequisite invalid and cause tracking drift. To enhance the reliability of tracking anchors, we propose a real-time multianchor visual tracking mechanism, called multianchor tracking (MAT). Instead of directly relying on the tracking anchor inherited from the previous prediction, MAT selects the best anchor from an anchor ensemble, which includes several objectness-based anchor proposals and the anchor inherited from the previous prediction. The objectness-based anchors provide several complementary selective search regions, and an entropy-minimization-based selection method is introduced to find the best anchor. Our approach offers two benefits: 1) selective search regions can increase the chance of tracking success with affordable computational load and 2) anchor selection introduces the best anchor for each frame, which breaks the limitation of solo depending on the previous prediction. The extensive experiments of nine base trackers upgraded by MAT on four challenging datasets demonstrate the effectiveness of MAT.
Cooperative Co-Evolution and MapReduce

Real-word large-scale optimisation problems often result in local optima due to their large search space and complex objective function. Hence, traditional evolutionary algorithms (EAs) are not suitable for these problems. Distributed EA, such as a cooperative co-evolutionary algorithm (CCEA), can solve these problems efficiently. It can decompose a large-scale problem into smaller sub-problems and evolve them independently. Further, the CCEA population diversity avoids local optima. Besides, MapReduce, an open-source platform, provides a ready-to-use distributed, scalable, and fault-tolerant infrastructure to parallelise the developed algorithm using the map and reduce features. The CCEA can be distributed and executed in parallel using the MapReduce model to solve large-scale optimisations in less computing time. The effectiveness of CCEA, together with the MapReduce, has been proven in the literature for large-scale optimisations. This article presents the cooperative co-evolution, MapReduce model, and associated techniques suitable for large-scale optimisation problems.
SelectNet: Learning to Sample from the Wild for Imbalanced Data Training

Supervised learning from training data with imbalanced class sizes, a commonly encountered scenario real applications such as anomaly/fraud detection, has long been considered a significant challenge machine learning. Motivated by recent progress curriculum and self-paced learning, we propose to adopt a semi-supervised learning paradigm by training a deep neural network, referred to as SelectNet, to selectively add unlabelled data together with their predicted labels to the training dataset. Unlike existing techniques designed to tackle the difficulty dealing with class imbalanced training data such as resampling, cost-sensitive learning, and margin-based learning, SelectNet provides an end-to-end approach for learning from important unlabelled data in the wild that most likely belong to the under-sampled classes the training data, thus gradually mitigates the imbalance the data used for training the classifier. We demonstrate the efficacy of SelectNet through extensive numerical experiments on standard datasets computer vision.
Italian Counter Narrative Generation to Fight Online Hate Speech

Counter Narratives are textual responses meant to withstand online hatred and prevent its spreading. The use of neural architectures for the generation of Counter Narratives (CNs) is beginning to be investigated by the NLP community. Still, the efforts were solely targeting English. In this paper, we try to fill the gap for Italian, studying how to implement CN generation approaches effectively. We experiment with an existing dataset of CNs and a novel language model, recently released for Italian, under several configurations, including zero and few shot learning. Results show that even for under-resourced languages, data augmentation strategies paired with large unsupervised LMs can held promising results1
Improving Compression Artifact Reduction via End-to-End Learning of Side Information

We propose to improve neural network-based compression artifact reduction by transmitting side information for the neural network. The side information consists of artifact descriptors that are obtained by analyzing the original and compressed images in the encoder. In the decoder, the received descriptors are used as additional input to a well-designed conditional post-processing neural network. To reduce the transmission overhead, the entire model is optimized under the rate-distortion constraint via end-to-end learning. Experimental results show that introducing the side information greatly improves the ability of the post-processing neural network, and improves the rate-distortion performance.
Face recognition based on adaptive margin and diversity regularization constraints

In recent years, a more robust facial feature can be learned by convolutional neural networks once introducing margins into loss functions. Those methods set a margin for each class manually to squeeze the intra-class variations within each class equally. However, the internal feature distributions of different persons in the real world are highly unbalanced, and the distance between different identities is not uniform either. As a result, applying the same margin on all classes might not lead to higher inter-class differences. To address this problem, this paper proposes an adaptive margin based on feature distribution to squeeze the feature interior spaces of different classes. Simultaneously, because the inter-class margin can adequately represent the distribution of different classes in the feature space, this paper proposes a novel diversity regularization method. The regularization weights of each class are dynamically set depending on their margins. This method proposed in this paper is intuitively interpretable and can be easily applied to other classification scenarios. Experiments on current existing benchmarks have demonstrated the superiority of our method over state-of-the-art competitors.
IMPLEMENTATION OF A GENERIC FRAMEWORK ON CROWD SIMULATION: A NEW ENVIRONMENT TO MODEL CROWD BEHAVIOR AND DESIGN VIDEO GAMES

Crowd behavior is the collective act and gathering of a group of individuals to achieve a shared purpose. Swarm intelligence-based optimization algorithms are usually used to solve complex problems for crowd behavior. Crowd simulations are often used for the analyses that require precision in different domains such as complex structural analysis, image recognition, creating nature-inspired non-player character movements in video games, and more. In this study, a generic crowd simulation framework that can be used to simulate already-available crowd simulation algorithms and design new ones was developed. The test environment layout was generated with the use of a generate-and-test algorithm combined with the crowd simulation algorithms to make sure that the generated content is meeting the requirements of a crowd simulation environment. Within the framework, three different crowd simulation algorithms —firefly algorithm, particle swarm optimization, and artificial bee colony— are generated and also implemented as puzzle-like video games. The results show that all fireflies achieved to gather at the global minimum of the generated layout faster and in a more precise way than the artificial bee colony algorithm and particle swarm optimization algorithm. The developed framework enables a generic and parametric testbed to design and compare different algorithms and to generate video games.
From Constructivist Educational Technology to Mobile Constructivism: How mobile learning serves constructivism?

Constructivist theory of learning suggests that individuals construct meaning through authentic experiences and social interactions. Thanks to the ample learning experiences and opportunities for unlimited interaction anywhere, anytime, mobile technologies have given birth to a concept called mobile learning. However, there seems a need to ground mobile learning on constructivist learning principles, i.e. define a new scope for constructivism: “mobile constructivism.” This paper aimed to explore the relationship between constructivism and mobile learning and how they serve each other. To this end, articles which have based mobile learning activities on constructivist learning principles were reviewed systematically. Results were synthesized and discussed under four major themes: the expansive potential of constructivist educational technology, integrating different kinds of constructivism in mobile learning, social consequences of mobile learning; and elemental principles of “mobile constructivism”
How to Construct Software Knowledge Graph: A Case Study

Knowledge graph has been more and more widely used, but software knowledge graph is still in the initial stage. To explore the way of constructing a software knowledge graph, this paper proposes a case study by using source code, related documents and relations among the information to build software knowledge graph. The source code comes from the open source project Tomcat, the related documents come from various software communities. Different approaches are designed for each type of resources to achieve entity and attribute extraction, and to categorize the relation into internal and external relations to extract. Internal relation is contained in each resource, external relation exists between different resources. And then subtree comparison and mailbox matching are used to align entities, completing the construction of software knowledge graph. Software knowledge graph is applied to knowledge retrieval and association visualization to improve the efficiency of knowledge acquisition.
Data Augmentation for Multiclass Utterance Classification – A Systematic Study

Utterance classification is a key component in many conversational systems. However, classifying real-world user utterances is challenging, as people may express their ideas and thoughts in manifold ways, and the amount of training data for some categories may be fairly limited, resulting in imbalanced data distributions. To alleviate these issues, we conduct a comprehensive survey regarding data augmentation approaches for text classification, including simple random resampling, word-level transformations, and neural text generation to cope with imbalanced data. Our experiments focus on multi-class datasets with a large number of data samples, which has not been systematically studied in previous work. The results show that the effectiveness of different data augmentation schemes depends on the nature of the dataset under consideration.
Pheonix at SemEval-2020 Task 5: Masking the Labels Lubricates Models for Sequence Labeling

This paper presents the deep-learning model that is submitted to the SemEval-2020 Task 5 competition: "Detecting Counterfactuals".We participated in both Subtask1 and Subtask2.The model proposed in this paper ranked 2nd in Subtask2: "Detecting antecedent and consequence".Our model approaches the task as a sequence labeling.The architecture is built on top of BERT; and a multi-head attention layer with label masking is used to benefit from the mutual information between nearby labels.Also, for prediction, a multi-stage algorithm is used in which the model finalize some predictions with higher certainty in each step and use them in the following.Our results show that masking the labels not only is an efficient regularization method but also improves the accuracy of the model compared with other alternatives like CRF.Label masking can be used as a regularization method in sequence labeling.Also, it improves the performance of the model by learning the specific patterns in the target variable.
A Knowledge-Fusion Ranking System with an Attention Network for Making Assignment Recommendations

In recent decades, more teachers are using question generators to provide students with online homework. Learning-to-rank (LTR) methods can partially rank questions to address the needs of individual students and reduce their study burden. Unfortunately, ranking questions for students is not trivial because of three main challenges: (1) discovering students’ latent knowledge and cognitive level is difficult, (2) the content of quizzes can be totally different but the knowledge points of these quizzes may be inherently related, and (3) ranking models based on supervised, semisupervised, or reinforcement learning focus on the current assignment without considering past performance. In this work, we propose KFRank, a knowledge-fusion ranking model based on reinforcement learning, which considers both a student’s assignment history and the relevance of quizzes with their knowledge points. First, we load students’ assignment history, reorganize it using knowledge points, and calculate the effective features for ranking in terms of the relation between a student’s knowledge cognitive and the question. Then, a similarity estimator is built to choose historical questions, and an attention neural network is used to calculate the attention value and update the current study state with knowledge fusion. Finally, a rank algorithm based on a Markov decision process is used to optimize the parameters. Extensive experiments were conducted on a real-life dataset spanning a year and we compared our model with the state-of-the-art ranking models (e.g., ListNET and LambdaMART) and reinforcement-learning methods (such as MDPRank). Based on top- <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mi>k</mi></math> nDCG values, our model outperforms other methods for groups of average and weak students, whose study abilities are relatively poor and thus their behaviors are more difficult to predict.
Artificial Neural Networks Based Solar Radiation Estimation using Backpropagation Algorithm

Being at the cutting edge for a long time, solar energy has found several applications in various areas. Optimal harvesting of solar energy is one of the thrust areas of the researchers and developers in 21 st century. Solar energy optimization solely depends on radiation received by the solar panels. Radiation is measured by various devices and it may be estimated by various estimation models. Solar energy needs to be estimated well in advance if the system to be designed has to be completely dependent on solar energy. This is a very challenging job since solar radiation depends on several parameters such as location changes and seasonal changes. Artificial Neural Network is one of the most preferred technique for the researchers in prediction related cases. This paper proposes a methodology to estimate solar radiation using feed forward back propagation neural network. A three-layer neural network is used here with one hidden layer. The data of 14 stations of Uttar Pradesh, India are obtained data from FAO, UN and it further divided into three sets of â€˜Trainingâ€™, Validationâ€™ and â€˜Testingâ€™.Â  This study is based on nine input parameters and one output parameter. Feed forward back propagation is used here to estimate solar radiation. The proposed model is validated for Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient training algorithms. The obtained results of Mean Square Error (MSE), Regression Values (R), Slope Values (m) and Intercept Values (c) in all three cases are justifying the suitability of the proposed model.
Relief Extraction From a Rough Stele Surface Using SVM-Based Relief Segment Selection

Archaeological steles having a rough surface due to long periods of weathering make recognizing the inscription difficult. In this paper, we propose a machine learning-based method to extract reliefs for the inscription from a rough stele surface. Relief candidate segments are initially obtained by using a curvature-based method, which include not only actual reliefs but also noises such as dents and scratches. Then, relief segments are selected using a support vector machine classifier that is trained with various features extracted from relief candidate segments. While conventional methods using a single geometric feature easily fail to detect reliefs from the rough surface, the proposed method utilizes 79-dimensional features consisting of appearance-based, cross section-based, and local extrema-based characteristics of each candidate segment to determine whether the segment is relief or not. Using the proposed method, the inscription of the stele Musul-ojakbi made during the Silla Dynasty AD578 were completely recognized. The experimental results demonstrate that the proposed method accurately extracts reliefs and achieves the highest performance on the rough stele data. The performance of the proposed method is about 8.95% and 10.4% higher than the best of the conventional methods in terms of the F1-score and the SIRI, respectively.
ANALYSIS OF THE IMPACT OF ANCHORING, HERDING BIAS, OVERCONFIDENCE AND ETHICAL CONSIDERATION TOWARDS INVESTMENT DECISION

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Learning Efficient Task-Specific Meta-Embeddings with Word Prisms

Word embeddings are trained to predict word cooccurrence statistics, which leads them to possess different lexical properties (syntactic, semantic, etc.) depending on the notion of context defined at training time.These properties manifest when querying the embedding space for the most similar vectors, and when used at the input layer of deep neural networks trained to solve downstream NLP problems.Meta-embeddings combine multiple sets of differently trained word embeddings, and have been shown to successfully improve intrinsic and extrinsic performance over equivalent models which use just one set of source embeddings.We introduce word prisms: a simple and efficient meta-embedding method that learns to combine source embeddings according to the task at hand.Word prisms learn orthogonal transformations to linearly combine the input source embeddings, which allows them to be very efficient at inference time.We evaluate word prisms in comparison to other meta-embedding methods on six extrinsic evaluations and observe that word prisms offer improvements in performance on all tasks.
ANDI @ CONcreTEXT: Predicting concreteness in context for English and Italian using distributional models and behavioural norms

In this paper we describe our participation in the CONcreTEXT task of EVALITA 2020, which involved predicting subjective ratings of concreteness for words presented in context. Our approach, which ranked first in both the English and Italian subtasks, relies on a combination of context-dependent and context-independent distributional models, together with behavioural norms. We show that good results can be obtained for Italian, by first automatically translating the Italian stimuli into English, and then using existing resources for both Italian and English.
Compressing Pre-trained Language Models by Matrix Decomposition.

Large pre-trained language models reach state-of-the-art results on many different NLP tasks when fine-tuned individually; They also come with a significant memory and computational requirements, calling for methods to reduce model sizes (green AI). We propose a two-stage model-compression method to reduce a model’s inference time cost. We first decompose the matrices in the model into smaller matrices and then perform feature distillation on the internal representation to recover from the decomposition. This approach has the benefit of reducing the number of parameters while preserving much of the information within the model. We experimented on BERT-base model with the GLUE benchmark dataset and show that we can reduce the number of parameters by a factor of 0.4x, and increase inference speed by a factor of 1.45x, while maintaining a minimal loss in metric performance.
A Tale of Two Linkings: Dynamically Gating between Schema Linking and Structural Linking for Text-to-SQL Parsing

In Text-to-SQL semantic parsing, selecting the correct entities (tables and columns) for the generated SQL query is both crucial and challenging; the parser is required to connect the natural language (NL) question and the SQL query to the structured knowledge in the database.We formulate two linking processes to address this challenge: schema linking which links explicit NL mentions to the database and structural linking which links the entities in the output SQL with their structural relationships in the database schema.Intuitively, the effectiveness of these two linking processes changes based on the entity being generated, thus we propose to dynamically choose between them using a gating mechanism.Integrating the proposed method with two graph neural network-based semantic parsers together with BERT representations demonstrates substantial gains in parsing accuracy on the challenging Spider dataset.Analyses show that our proposed method helps to enhance the structure of the model output when generating complicated SQL queries and offers more explainable predictions.
Reducing Non-Normative Text Generation from Language Models.

Large-scale, transformer-based language models such as GPT-2 are pretrained on diverse corpora scraped from the internet. Consequently, they are prone to generating non-normative text (i.e. in violation of social norms). We introduce a technique for fine-tuning GPT-2, using a policy gradient reinforcement learning technique and a normative text classifier to produce reward and punishment values. We evaluate our technique on five data sets using automated and human participant experiments. The normative text classifier is 81-90% accurate when compared to gold-standard human judgments of normative and non-normative generated text. Our normative fine-tuning technique is able to reduce non-normative text by 27-61%, depending on the data set.