Reviewerly/exHarmony huggingface dataset:
Domain-adaptive modules for stereo matching network

Convolutional neural networks (CNNs) have been widely used in end-to-end stereo matching networks in recent years. However, most stereo networks are not robust to variations in the environment and thus are difficult to be extended to practical applications. In this paper, an inherent factor that hinders the adaptive performance of stereo matching networks is first determined. Then we propose a domain-adaptive feature extractor (DAFE) that can extract the features of images on different domains and a feature normalization method to reduce the variances of features in across-domain situations. Moreover, the influence of various modules on the performance of the domain-adaptive network (DANet) is investigated. When trained on Sceneflow data and generalized to the real test sets, the method performs significantly better than state-of-the-art models and even better than some latest disparity networks fine-tuned on the target domain.
Encouraging domestic innovation by protecting foreign intellectual property

This paper examines the relationship of respect for foreign intellectual property (IP) and domestic innovation. In a global economy, countries may choose to protect the IP of their own citizens, foreigners, or both or neither. We develop a model that shows that countries will have higher levels of innovation when respecting both domestic and foreign intellectual property. We test this prediction and show that domestic innovation is positively related to respect for both foreign and domestic IP. Intuitively, respect for domestic IP encourages innovation. We demonstrate the less intuitive case that protection of foreign IP further incentivizes domestic innovation.
KnowSum: Knowledge Inclusive Approach for Text Summarization Using Semantic Allignment

Text summarization plays an important role in delivering compact, most relevant, and efficient text to the user. It is also applied on the field of community question answers. There is a large amount of data on the internet pertaining to each topic. The question needs to be analyzed properly so that optimized, most relevant, and summarized text answer is generated. This paper proposes an ontology-based text summarization technique using Semantic Alignment and information gain along with LSTM and flower pollination algorithm. Here MS Marco Data set is used. From this for classifying question and answers LSTM is used. The top half of the data is only taken. With respect to each domain term from domain ontology feature extraction is done using information scent. Community question answer data such as Yahoo answers and Quora dataset are taken and classified. Both of these are then mapped together based on semantic alignment using flower pollination algorithm. After mapping, the answers are prioritized based on semantic similarity and information gain. Top 5 answers are chosen and summarized. The architecture's performance is calculated and compared with the baseline approaches and it is clearly observed that the proposed ontology-based text summarization technique is predominant in terms of performance and attained a precision and accuracy of 99.94% and 96.54 % respectively.
Neural Network Based Integrated Model for Information Retrieval

The paper introduces an advance to connect various current information retrieval (IR) techniques with change impact analysis (CIA) and Bag of Words, trying to recognize the possible results of a replacement or manage the required changes for affecting the necessary change. In this paper a neural network-based LSTM-RNN Algorithm is proposed to find the similarity document. Also, RMSprop Optimization model is used to reduce the vibrations in the upward path and it can improve our learning rate and precision rate. The experimental result shows that the proposed method performs better accuracy compared to the current methods.
Research on Online Course Recommendation Model Based on Improved Collaborative Filtering Algorithm

Aiming at the problem of sparse data and poor recommendation effect in online course recommendation, this paper proposes an improved online course intelligent recommendation model based on user implicit behavior collaborative filtering. Through data analysis, the implicit behavior data such as user login details, learning details and course selection details are mined, and the online course intelligent recommendation is carried out combined with the collaborative filtering algorithm based on items. Based on the operation data of an education platform in recent two years, the experimental results show that the precision and recall rate of the improved collaborative filtering recommendation model are improved under different K values.
Adaptive Rule Discovery for Labeling Text Data

Creating and collecting labeled data is one of the major bottlenecks in machine learning pipelines and the emergence of automated feature generation techniques such as deep learning, which typically requires a lot of training data, has further exacerbated the problem. While weak-supervision techniques have circumvented this bottleneck, existing frameworks either require users to write a set of diverse, high-quality rules to label data (e.g., Snorkel), or require a labeled subset of the data to automatically mine rules (e.g., Snuba). The process of manually writing rules can be tedious and time consuming. At the same time, creating a labeled subset of the data can be costly and even infeasible in imbalanced settings.
Empirical Strategies in Software Engineering Research: A Literature Survey

Context: The Software Engineering (SE) research continues to gain strength and interest for researchers considering the need to apply rigor and scientific validity to research results. Objective: Establishing an overview of the topic through a classification scheme of publications and structure the field of interest. Method: We conducted a Systematic Mapping Study, including articles published until 2019, that report at least one study of empirical strategies in SE. Results: 80 initial sets of studies were selected and analyzed, identifying: i) empirical strategy type used and ii) Software Engineering hypotheses types used. Also, 20 papers of the set of studies for mapping were selected and analyzed, identifying 17 empirical strategies and 11 main characteristics to address the empirical research inception in SE. Conclusions: We corroborate that the selection of an empirical strategy in Software Engineering research depends on the nature and scope of the research and on the resources that the researcher has at that moment, in addition to the degree of scientific and methodological knowledge that he has to carry out an empirical study. It is necessary to continue studying in-depth the behavior and nature of the empirical strategies in Software Engineering research that allows strengthening the scientific taxonomy in SE, besides walking towards the automation of the experimental process.
Pedestrian instance segmentation with prior structure of semantic parts

• We propose a simple, easily interpreted semantic part branch for pedestrian instance segmentation. • We proposed a pedestrian parts annotations generation method. • Our method improves the accuracy of pedestrian instance segmentation on the baseline. • Our method does not need to introduce additional annotations. Existing pedestrian segmentation and detection methods often show a significant drop in performance when heavy occlusion and deformation happen because most approaches rely on holistic modeling. Unlike many previous deep models that directly learn a holistic detector, in this paper, we introduce a pedestrian instance segmentation method with a prior structure of semantic parts named Part Mask R-CNN. Based on pedestrian parts’ proportion structure, process the original dataset annotations and then generate parts annotations as prior. By combining the semantic part branch with other classic detection and segmentation branches, the network learns more about pedestrian instances. Besides, we get such a more accurate pedestrian instance segmentation model without any artificial annotations. By extensive evaluations on the Cityscapes dataset, the results demonstrate that the proposed method can improve approaches such as Mask R-CNN, inaccuracy on pedestrian single class instance segmentation.
Custom Face Recognition Using YOLO.V3

Face recognition technique is adopted in many applications such as surveillance systems, medical field, security, robot navigation, etc. Today because of deep learning there is a large improvement in the area of face recognition. The importance of facial recognition comes to more importance at the time of a pandemic situation, for attendance marking people monitoring, in health care, social distancing etc. Many facial recognition algorithms are available today. But the performance of these algorithms lacks many factors and which lead to poor performance in real-time applications. The most commonly used algorithms for face recognition are R-CNN, Fast R-CNN, etc. The main problems with these algorithms are they have poor processing speed; they take time to produce the output. To overcome this problem, YOLO.V3 algorithm can be used for facial recognition which produces faster output. In this paper we will study on different versions of YOLO algorithms, study on existing algorithm for face detection and recognition (R-CNN),and conclude with the result obtained for the YOLO.V3 algorithm for recognizing a custom face.
‘Alexa, I feel for you!’ Observers’ Empathetic Reactions towards a Conversational Agent

Conversational agents and smart speakers have grown in popularity offering a variety of options for use, which are available through intuitive speech operation. In contrast to the standard dyad of a single user and a device, voice-controlled operations can be observed by further attendees resulting in new, more social usage scenarios. Referring to the concept of ‘media equation’ and to research on the idea of ‘computers as social actors,’ which describes the potential of technology to trigger emotional reactions in users, this paper asks for the capacity of smart speakers to elicit empathy in observers of interactions. In a 2 × 2 online experiment, 140 participants watched a video of a man talking to an Amazon Echo either rudely or neutrally (factor 1), addressing it as ‘Alexa’ or ‘Computer’ (factor 2). Controlling for participants’ trait empathy, the rude treatment results in participants’ significantly higher ratings of empathy with the device, compared to the neutral treatment. The form of address had no significant effect. Results were independent of the participants’ gender and usage experience indicating a rather universal effect, which confirms the basic idea of the media equation. Implications for users, developers and researchers were discussed in the light of (future) omnipresent voice-based technology interaction scenarios.
Self-Attention Recurrent Summarization Network with Reinforcement Learning for Video Summarization Task

With the exponential growth of video data, video summarization techniques are urgently needed for reducing people’s efforts in the videos' content exploration by generating succinct but informative summaries from original lengthy videos. Though supervised video summarization approaches have demonstrated the state-of-the-art performance, unsupervised methods are still highly demanded due to resourcefully expensive human annotations and the subjectiveness of video summarization tasks. In this paper, a novel unsupervised-based Deep Self-attention Recurrent summarization network with Reinforcement Learning (DSR-RL) for video summarization is proposed. The model can learn the input video sequence and suggest the key-shot summary without additional human annotations by integrating self-attention, BRNN, and reinforcement learning mechanisms. The DSR-RL improves not only importance score through the attention map vector of self-attention network but also the diversity of summaries via the reward function of reinforcement learning. Our method outperforms the state-of-the-art unsupervised video summarization methods on both SumMe and TVSum datasets. The source code is available at https://github.com/phaphuang/DSR-RL.
Hybrid Feature Selection and Bayesian Optimization with Machine Learning for Breast Cancer Prediction

Breast Cancer is one of the most ubiquitous type of cancer among women and rarely found in men. According to the World Health Organization (WHO), Cancer is defined as an uncontrollable abnormal growth of cells in any organ or tissue of the body. Neoplasm or Malignant tumor are common words that describe cancer. According to a survey conducted by the World Health Organization (WHO), cancer accounts for deaths of approximately 9.6 million people globally, and is the second most prevalent cause of death in humans. Cancer is responsible for one out of every six deaths across the world. The good thing is cancer if diagnosed at an early stage, is likely to be treated successfully resulting in high survival rate and low treatment cost. However, it is quite difficult to diagnose it at an early-stage. Therefore, there is a necessity for an efficient cancer prediction model that can predict breast cancer at an early stage. The proposed model highlights the finest set of features required for the detection of breast cancer. It uses Bayesian optimization technique along with hyper parameter tuning and feature selection techniques to decrease the number of parameters by almost 40% while maintaining high accuracy. The best accuracy of 96.2% is obtained with Extra tree classifier algorithm by using feature selection technique along with bayesian optimization and hyperparameter tuning.
MidiPGAN: A Progressive GAN Approach to MIDI Generation

While recent research in music generation has mostly focused on encoder decoder architectures and self-attention mechanisms, prominent advancements regarding GANs have not yet been incorporated for the creation of music. These include solutions for major challenges when training GANs, most importantly training instability. In this work, we aim to apply this new knowledge to music generation, in order to make it more efficient and enable the automatic creation of music of higher quality. We utilize the progressive approach towards GANs, and implement it to train on symbolic music data. For best results, we process this data to obtain a new dataset, which matches the progressive approach. To achieve this, we propose a new way of downsampling fit for musical data. We furthermore conduct a user study to evaluate our results, and compute an FID score of 12.30 as objective metric.
Deepmix: Online Auto Data Augmentation for Robust Visual Object Tracking

Online updating of the object model via samples from historical frames is of great importance for accurate visual object tracking. Recent works mainly focus on constructing effective and efficient updating methods while neglecting the training samples for learning discriminative object models, which is also a key part of a learning problem. In this paper, we propose the DeepMix that takes historical samples’ embeddings as input and generates augmented embeddings online, enhancing the state-of-the-art online learning methods for visual object tracking. More specifically, we first propose the online data augmentation for tracking that online augments the historical samples through object-aware filtering. Then, we propose MixNet which is an offline trained network for performing online data augmentation within one-step, enhancing the tracking accuracy while preserving high speeds of the state-of-the-art online learning methods. The extensive experiments on three different tracking frameworks, i.e., DiMP, DSiam, and SiamRPN++, and three large-scale and challenging datasets, i.e., OTB-2015, LaSOT, and VOT, demonstrate the effectiveness and advantages of the proposed method.
Positioning the 5-DOF Robotic Arm using Single Stage Deep CNN Model

In teleoperation mechanism, the surgical robots are controlled using hand gestures from remote location. The remote location robotic arm control using hand gesture recognition is a challenging computer vision problem. The hand action recognition under complex environment (cluttered background, lighting variation, scale variation etc.) is a difficult and time consuming process. In this paper, a light weight Convolutional Neural Network (CNN) model Single Shot Detector (SSD) Lite MobileNet-V2 is proposed for real-time hand gesture recognition. SSD Lite versions tend to run hand gesture recognition applications on low-power computing devices like Raspberry Pi due to its light weight and timely recognition. The model is deployed using a Camera and two Raspberry Pi Controllers For the hand gesture recognition and data transfer to the cloud server, the Raspberry Pi controller 1 is used. The Raspberry Pi Controller 2 receives the cloud information and controls the Robotic arm operations. The performance of the proposed model is also compared with a SSD Inception-V2 model for the MITI Hand dataset-II (MITI HD-II). The average precision, average recall and F1-score for SSD Lite MobileNet-V2 and SSD Inception-V2 models are analyzed by training and testing the model with the learning rate of 0.0002 using Adam optimizer. SSD MobileNet-V2 model obtained an Average precision of 98.74% and SSD Inception-V2 model as 99.27%, The prediction time for SSD Lite MobileNet-V2 model using Raspberry Pi controller takes only 0.67s whereas, 1.2s for SSD Inception-V2 Model.
Gesture recognition of RGB-D and RGB static images using ensemble-based CNN architecture

The relationship between humans and computers has always been an exciting environment in this thriving period. Gesture-based recognition systems have always been a fascinating and distinct subject with the exponential growth in Computer Vision. It is a very complicated and daunting process to understand human expressions in the form of sign language. Different traditional approaches have increasingly been used to understand sign language, but attain high precision is still a difficult challenge and vision-based finger-spelling identification remains difficult, because of inter-class similarities and intra-class variability. The model considers two modalities, RGB and depth. Finger occlusion and hand shapes accurately detected and can be handled by depth information. A fine-tuned dual-path network is suggested compared to current strategies that process RGB-D images separately, that understands finger-spelling depiction in separate RGB and depth paths and gradually fuses the features acquired from both tracks.
Development of mobile interactive courses based on an artificial intelligence chatbot on the communication software LINE

Fighter jets are a critical national asset. Because of the high cost of their manufacture and that of their related equipment, both pilots and maintenance personnel must complete intensive training before coming into contact with a jet. Due to gradual military downsizing, one-on-one training is often impracticable, and the level of familiarization with procedures among personnel is difficult to measure. The US military introduced a chatbot as part of its digital training material to enhance training effectiveness and avoid equipment damage. In this study, the contribution of an artificial intelligence (AI) chatbot in training is explored. To evaluate the necessity of an AI chatbot, research samples were divided into two groups, namely an experimental and a control group, with 20 people in each group. A paired t test was employed for differentiation analysis of pretest and posttest average scores, revealing that the two groups exhibited a statistically significant improvement in their learning performance. In addition, the blend of analysis of variance results indicated significant differences between the two groups. The chatbot training was more effective than traditional instructor teaching in terms of trainee performance improvement.
Evaluation of Preprocessing Techniques for U-Net Based Automated Liver Segmentation

To extract liver from medical images is a challenging task due to similar intensity values of liver with adjacent organs, various contrast levels, various noise associated with medical images and irregular shape of liver. To address these issues, it is important to preprocess the medical images, i.e., computerized tomography (CT) and magnetic resonance imaging (MRI) data prior to liver analysis and quantification. This paper investigates the impact of permutation of various preprocessing techniques for CT images, on the automated liver segmentation using deep learning, i.e., U-Net architecture. The study focuses on Hounsfield Unit (HU) windowing, contrast limited adaptive histogram equalization (CLAHE), z-score normalization, median filtering and Block-Matching and 3D (BM3D) filtering. The segmented results show that combination of three techniques; HU-windowing, median filtering and z-score normalization achieve optimal performance with Dice coefficient of 96.93%, 90.77% and 90.84% for training, validation and testing respectively.
Pedestrian Detection from Thermal Images Incorporating Saliency Features

Methods relying entirely on visible-range/color images start to have problems in detection tasks when there is not enough light to illuminate the scene. Thermal cameras, which operate based on the infrared radiation emitted by objects, can provide detectable information in low- or no-light conditions. In this paper, we propose a method to improve the performance of a pedestrian detection algorithm on thermal images by incorporating features from saliency maps to enrich the thermal image features. We employ a modified version of a state-of-the-art object detection network, and feed the thermal images and their saliency maps to two parallel networks. Experimental results on five different datasets show that our proposed approach performs better at detecting pedestrians in thermal images compared to its vanilla version and a baseline model.
Multi-Domain Aspect Extraction Using Bidirectional Encoder Representations From Transformers

Deep learning and neural language models have obtained state-of-the-art results in aspects extraction tasks, in which the objective is to automatically extract characteristics of products and services that are the target of consumer opinion. However, these methods require a large amount of labeled data to achieve such results. Since data labeling is a costly task, there are no labeled data available for all domains. In this paper, we propose an approach for aspect extraction in a multi-domain transfer learning scenario, thereby leveraging labeled data from different source domains to extract aspects of a new unlabeled target domain. Our approach, called MDAE-BERT (Multi-Domain Aspect Extraction using Bidirectional Encoder Representations from Transformers), explores neural language models to deal with two major challenges in multi-domain learning: (1) inconsistency of aspects from target and source domains and (2) context-based semantic distance between ambiguous aspects. We evaluated our MDAE-BERT considering two perspectives (1) the aspect extraction performance using F1-Macro and Accuracy measures; and (2) by comparing the multi-domain aspect extraction models and single-domain models for aspect extraction. In the first perspective, our method outperforms the LSTM-based approach. In the second perspective, our approach proved to be a competitive alternative compared to the single-domain model trained in a specific domain, even in the absence of labeled data from the target domain.