Reviewerly/exHarmony huggingface dataset:
A Study on Behavioural Agents for StarCraft 2

With the recent trend of artificial intelligence, specifically within machine learning, there are some powerful tools that can be utilized to create video game artificial intelligence bots. Bots that can beat professional players or immerse players within the game to the point where enemies are considered intelligent and react to situations similar to how a real human would. However, some of these processes and tasks to create a bot can be an expensive and time-consuming process. In this research paper, we look at two models to building an AI bot and comparing the two, namely a simple reflex model and a recurrent neural network model. From the results, we can see that the recurrent neural network goes further into the tech tree and is able to produce a more complexed set of units as compared to the simple reflex solution. The simple reflex solution, however, is able to reach the win condition by defeating the enemy bot much quicker than the recurrent neural network solution at 5 min and 39 s and costs less in terms of production and complexity. The recurrent neural network solution was also able to get a higher food supply count and spent the most amount of resources in all areas including technology, economy and army supply.
Improvements to the Descent Step in the Number Field Sieve for Discrete Logarithms

The discrete logarithm problem (DLP) is a cornerstone in the public-key cryptography with various applications. Recently there is a new computational record for the DLP in a 795-bit prime field using the number field sieve for discrete logarithms (NFS-DL). The main technique used in the computation is choosing good parameters for each step of NFS-DL which reduces the time estimated by the asymptotic complexity significantly. In this article, we propose a new algorithm to find relations for high degree prime ideals and remove the previous restriction that only degree one prime ideals are allowed to appear in the descent step. This will increase the success probability of the descent step and reduce the actual running time. As the descent step could be done separately in some sense, improving the descent step is beneficial to find optimal parameters such as the smoothness bound which may result in better performance of the NFS-DL algorithm. We also give an experiment to demonstrate the effectiveness of our algorithm.
Logic Enhanced Commonsense Inference with Chain Transformer

We study the commonsense inference task that aims to reason and generate the causes and effects of a given event. Existing neural methods focus more on understanding and representing the event itself, but pay little attention to the relations between different commonsense dimensions (e.g. causes or effects) of the event, making the generated results logically inconsistent and unreasonable. To alleviate this issue, we propose Chain Transformer, a logic enhanced commonsense inference model that combines both direct and indirect inferences to construct a logical chain so as to reason in a more logically consistent way. First, we apply a self-attention based encoder to represent and encode the given event. Then a chain of decoders is implemented to reason and generate for different dimensions following the logical chain, where an attention module is designed to link different decoders and to make each decoder attend to the previous reasoned inferences. Experiments on two real-world datasets show that Chain Transformer outperforms previous methods on both automatic and human evaluation, and demonstrate that Chain Transformer can generate more reasonable and logically consistent inference results.
A fusion of salient and convolutional features applying healthy templates for MRI brain tumor segmentation

Abstract This paper proposes an improved brain tumor segmentation method based on visual saliency features on MRI image volumes. The proposed method introduces a novel combination of multiple MRI modalities used as pseudo-color channels for highlighting the potential tumors. The novel pseudo-color model incorporates healthy templates generated from the MRI slices without tumors. The constructed healthy templates are also used during the training of neural network models. Based on a saliency map built using the pseudo-color templates, combination models are proposed, fusing the saliency map with convolutional neural networks’ prediction maps to improve predictions and to reduce the networks’ eventual overfitting which may result in weaker predictions for previously unseen cases. By introducing the combination technique for deep learning techniques and saliency-based, handcrafted feature models, the fusion approach shows good abstraction capabilities and it is able to handle diverse cases that the networks were less trained for. The proposed methods were tested on the BRATS2015 and BRATS2018 databases, and the quantitative results show that hybrid models (including both trained and handcrafted features) can be promising alternatives for reaching higher segmentation performance. Moreover, healthy templates can provide additional information for the training process, enhancing the prediction performance of neural network models.
<scp>LGBTQ</scp>+ individuals seeking information and support from online communities to navigate unpleasant emotions

Abstract As part of an ongoing project, this paper investigates the LGBTQ+ individual's emotions they expressed when seeking information and support in online communities by analyzing online user generated posts in LGBT Chats &amp; Forums. Sixty‐one posts that contained emotional texts were coded using content analysis. Seven categories of emotions emerged from the analysis: fear, uncertainty, sadness, anger, shame, joy and others. The results show that emotions LGBTQ+ individuals expressed in their online requests are often unpleasant and evoked their needs of informational and emotional supports. The findings contribute to the existing knowledge of how LGBTQ+ individuals cope with unpleasant emotions and how online communities can better support sexual and gender minority people.
Crowdsourcing in Systematic Reviews: A Systematic Mapping and Survey

Context: Systematic reviews (SRs) have been adopted in the Software Engineering (SE) field for more than a decade to provide synthesis of evidence on various topics. However, the process in conducting an SR remains laborious-intensive and expensive, specially in terms of hours that SR researchers dedicate. It is worth exploring approaches to conduct SRs at lower costs (quicker, using less resources - time of researchers). One such approach is crowdsourcing, since conducting SRs activities among a large number of researchers is a promising alternative to reduce costs associated to SR conduction. Goal: The main goal of this study is to identify and summarize the body of knowledge on crowdsourcing to support the conduction of SRs in SE. Method: Two empirical research methods were used. Initially, we conducted a Systematic Mapping to identify the available and relevant studies on crowdsourcing in SRs in SE. Secondly, a survey was performed with 39 SE researchers aiming to identify their perception related to the value of performing SRs collaboratively. Results: Our results show that how to speed up the SR process; reduce bias through broad participation; and expand team expertise were most potential benefits linked to the use of crowdsourcing in SR. The main challenges were associated with quality control to ensure the quality of results. Conclusions: In spite of the challenges, we believe that crowdsourcing could be successfully employed in SR context. More empirical research is needed on how to use crowdsourcing to support SR conduction in SE and how to minimize the identified challenges.
The DNA of information design for charts and diagrams

Abstract A comprehensive framework is presented for analyzing and specifying an extensive range of visualizations, such as statistical charts, maps, family trees, Venn diagrams, flow charts, texts using indenting, technical drawings and scientific illustrations. This paper describes how the fundamental ‘DNA’ building blocks of visual encoding and composition can be combined into ‘visualization patterns’ that specify these and other types of visualizations. We offer different ways of specifying each visualization pattern, including through a DNA tree diagram and through a rigorously systematic natural language sentence. Using this framework, a design tool is proposed for exploring visualization design options.
Clustering for time-varying relational count data

Relational count data are often obtained from sources such as simultaneous purchase in online shops and social networking service information. Clustering such relational count data reveals the latent structure of the relationship between objects such as household items or people. When relational count data observed at multiple time points are available, it is worthwhile incorporating the time structure into the clustering result to understand how objects move between the clusters over time. In this paper, we propose two clustering methods for analyzing time-varying relational count data. The first model, the dynamic Poisson infinite relational model (dPIRM), handles time-varying relational count data. In the second model, which we call the dynamic zero-inflated Poisson infinite relational model, we further extend the dPIRM so that it can handle zero-inflated data. Proposing both two models is important as zero-inflated data are often encountered, especially when the time intervals are short. In addition, by explicitly deriving the relevant full conditional distributions, we describe the features of the estimated parameters and, in turn, the relationship between the two models. We show the effectiveness of both models through a simulation study and a real data example.
Underwater Image Enhancement by Multiscale Fusion Technique and Dehazing

Nowadays, underwater imaging has acquired considerable attention since it finds application in underwater object identification, species life monitoring, oil/gas pipeline detection, pollution monitoring etc. Attenuation of light is one of the major problems in capturing these images. Therefore, enhancement of underwater image is an important task. Visual features of the image can be improved by various image enhancement techniques. A useful method is to build up a fusion and dehazing technique which process a degraded input image. The proposed fusion- based underwater image enhancement technique aims to provide a final picture, which overcome all the insufficiencies of the input image by considering various weight maps. This can be achieved by employing white balancing on the underwater image followed by contrast stretching correction. Then sharpening, multi scale fusion process and dehazing can be performed to improve the visual appearance.
An Intelligent Storytelling System for Narrative Conflict Generation and Resolution

Conflict is essential for the development of complex plots or more in-depth character design. Therefore, many literary works have employed a conflict-centered narrative structure. However, it is challenging to incorporate conflicts in a planning-based story generation because a planning algorithm constructs a plan that avoids conflicts, which can occur when one action negates the precondition of the other character's action. To address this issue, this paper presents an intelligent storytelling system that can automatically generate conflicts and their resolutions using the interleaved causal relationships between different characters in the story. We describe an example to show that the proposed algorithm can successfully generate a story that contains inter-personal conflicts and is presented in the Virtual Environment. We conclude with a discussion and future study.
Application of an Improved Focal Loss in Vehicle Detection

Object detection is an important and fundamental task in computer vision. Recently, the emergence of deep neural network has made considerable progress in object detection. Deep neural network object detectors can be grouped in two broad categories: the two-stage detector and the one-stage detector. One-stage detectors are faster than two-stage detectors. However, they suffer from a severe foreground-backg-round class imbalance during training that causes a low accuracy performance. RetinaNet is a one-stage detector with a novel loss function named Focal Loss which can reduce the class imbalance effect. Thereby RetinaNet outperforms all the two-stage and one-stage detectors in term of accuracy. The main idea of focal loss is to add a modulating factor to rectify the cross-entropy loss, which down-weights the loss of easy examples during training and thus focuses on the hard examples. However, cross-entropy loss only focuses on the loss of the ground-truth classes and thus it can’t gain the loss feedback from the false classes. Thereby cross-entropy loss does not achieve the best convergence. In this paper, we proposed a new loss function named Dual Cross-Entropy Focal Loss, which improves on the focal loss. Dual cross-entropy focal loss adds a modulating factor to rectify the dual cross-entropy loss towards focusing on the hard samples. Dual cross-entropy loss is an improved variant of cross-entropy loss, which gains the loss feedback from both the ground-truth classes and the false classes. We changed the loss function of RetinaNet from focal loss to our dual cross-entropy focal loss and performed some experiments on a small vehicle dataset. The experimental results show that our new loss function improves the vehicle detection performance.
Towards a Multimodal and Context-Aware Framework for Human Navigational Intent Inference

A socially acceptable robot needs to make correct decisions and be able to understand human intent in order to interact with and navigate around humans safely. Although research in computer vision and robotics has made huge advance in recent years, today's robotics systems still need better understanding of human intent to be more effective and widely accepted. Currently such inference is typically done using only one mode of perception such as vision, or human movement trajectory. In this extended abstract, I describe my PhD research plan of developing a novel multimodal and context-aware framework, in which a robot infers human navigational intentions through multimodal perception comprised of human temporal facial, body pose and gaze features, human motion feature as well as environmental context. To facility this framework, a data collection experiment is designed to acquire multimodal human-robot interaction data. Our initial design of the framework is based on a temporal neural network model with human motion, body pose and head orientation features as input. And we will increase the complexity of the neural network model as well as the input features along the way. In the long term, this framework can benefit a variety of settings such as autonomous driving, service and household robots.
Ultrasound Water Leakage Image Denoising By The Iterative Mmse Filter Abstract

Ultrasound imaging finds many applications in water environments. The water leakage milieu is considered in this study. However, the ultrasound images are contaminated by speckle noise which damages its quality. It complicates diagnosis and quantitative and visual measurements. In this work, the iterative minimum mean square error (IMMSE) method has been extended for de-speckling of ultrasound images. Therefore, by optimally setting the number of iterations, the IMMSE filtering approach outperforms traditional speckle filtering methods such as the mean, median and the improved Lee filters in speckle removal and edge preservation.
Image Edge Detection Algorithm Based on Adjacent Scale Product Coefficient

Based on the wavelet domain scale edge detection algorithm, a simple and effective multi-scale edge detection method was proposed. By multiplying the wavelet coefficients on the adjacent scale product, one-dimensional adjacent scale product coefficients and two-dimensional adjacent scale product coefficients are defined. At the same time of noise suppression, the causality of edge in different scales is implied, and the edge point is considered to be the local modulus maximum point of scale product coefficient after threshold. Finally, through the performance analysis, it is found that the scale product can not only improve the detection performance, but also reduce the interaction between adjacent edges.
Sarcasm Detection Using Graph Convolutional Networks with Bidirectional LSTM

Sarcasm is a form of figurative language where the literal meaning of words does not hold, and instead, the opposite interpretation is intended. This deliberate ambiguity makes sarcasm detection an important task in sentiment analysis. Sarcasm detection is considered to be a binary classification problem. Traditional works rely heavily on feature-rich traditional models and deep learning models. However, in the model training, previous works worked with a sentence or comment as a unit, they may lose some important global features and ignore rich relational structures in the corpus. In contrast, if we use the whole sarcasm corpus to construct a graph network in the model, we can learn to get representations with global information under sarcasm background. In this work, we propose a new type of neural network model. Specifically, we use a graph convolutional neural(GCN) network to capture the features of global information in the satire context and jointly bidirectional LSTM(bi-LSTM) neural network to capture the sequence features of the comments respectively. And finally, we concatenate these two embeddings and put them into a traditional classifier for classification prediction. Experiment results include precision, accuracy, recall and F1 score on the publicly available dataset show that our model is significantly better than the standard evaluation model benchmarks.
Automatic lesion detection, segmentation and characterization via 3D multiscale morphological sifting in breast MRI

Previous studies on computer aided detection/diagnosis (CAD) in 4D breast magnetic resonance imaging (MRI) usually regard lesion detection, segmentation and characterization as separate tasks, and typically require users to manually select 2D MRI slices or regions of interest as the input. In this work, we present a breast MRI CAD system that can handle 4D multimodal breast MRI data, and integrate lesion detection, segmentation and characterization with no user intervention. The proposed CAD system consists of three major stages: region candidate generation, feature extraction and region candidate classification. Breast lesions are firstly extracted as region candidates using the novel 3D multiscale morphological sifting (MMS). The 3D MMS, which uses linear structuring elements to extract lesion-like patterns, can segment lesions from breast images accurately and efficiently. Analytical features are then extracted from all available 4D multimodal breast MRI sequences, including T1-, T2-weighted and DCE sequences, to represent the signal intensity, texture, morphological and enhancement kinetic characteristics of the region candidates. The region candidates are lastly classified as lesion or normal tissue by the random under-sampling boost (RUSboost), and as malignant or benign lesion by the random forest. Evaluated on a breast MRI dataset which contains a total of 117 cases with 141 biopsy-proven lesions (95 malignant and 46 benign lesions), the proposed system achieves a true positive rate (TPR) of 0.90 at 3.19 false positives per patient (FPP) for lesion detection and a TPR of 0.91 at a FPP of 2.95 for identifying malignant lesions without any user intervention. The average dice similarity index (DSI) is0.72±0.15for lesion segmentation. Compared with previously proposed lesion detection, detection-segmentation and detection-characterization systems evaluated on the same breast MRI dataset, the proposed CAD system achieves a favourable performance in breast lesion detection and characterization.
Semi-Supervised Max-Sum Clustering

We study max-sum clustering in a semi-supervised setting. Our objective function maximizes the pairwise within-cluster similarity with respect to some null hypothesis regarding the similarity. This is a natural objective that does not require any additional parameters, and is a generalization of the well-known modularity objective function. We show that for such an objective function in a semi-supervised setting we can compute an additive approximation of the optimal solution in the general case, and a constant-factor approximation when the optimal objective value is large. The supervision that we consider is in the form of cluster assignment queries and same-cluster queries; we also study the setting where the query responses are noisy. Our algorithm also generalizes to the min-sum objective function, for which we can achieve similar performance guarantees. We present computational experiments to show that our framework is effective for clustering text data - we are able to find clusterings that are close to the queried clustering and have a good objective value.
Self-Compression in Bayesian Neural Networks

Machine learning models have achieved human-level performance on various tasks. This success comes at a high cost of computation and storage overhead, which makes machine learning algorithms difficult to deploy on edge devices. Typically, one has to partially sacrifice accuracy in favor of an increased performance quantified in terms of reduced memory usage and energy consumption. Current methods compress the networks by reducing the precision of the parameters or by eliminating redundant ones. In this paper, we propose a new insight into network compression through the Bayesian framework. We show that Bayesian neural networks automatically discover redundancy in model parameters, thus enabling self-compression, which is linked to the propagation of uncertainty through the layers of the network. Our experimental results show that the network architecture can be successfully compressed by deleting parameters identified by the network itself while retaining the same level of accuracy.
Improving the Recognition of Sign Language from Acquired Data by Wireless Body Area Network

Accurate and fast recognition of sign language would greatly improve communications between the deaf and the hearers using hand-held devices. We used Myo armband as our wireless data measurement device, which is wearable technology equipped with on-board 3D Accelerometer, 3D Gyroscope and 8 channel Electromyogram acquisition system. The main objective of this paper is to provide a lightweight approach for American Sign Language recognition by reducing the dimensionality of inputs using a novel method. Data from each sensors are aggregated into one dimension to reduce the required time for data processing, as well as the amount of required memory for storage. Afterward, we extract features from aggregated data and we proceed to classification using Support Vector Machine (SVM). We compare the performance of SVM with and without our aggregation approach. Our experimental results prove that our proposed approach improves the speed of model derivation (four times faster than existing methods) and reduces the size of input data with the same accuracy.