Reviewerly/exHarmony huggingface dataset:
Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline

With the popularity of multi-modal sensors, visible-thermal (RGB-T) object tracking is to achieve robust performance and wider application scenarios with the guidance of objects' temperature information. However, the lack of paired training samples is the main bottleneck for unlocking the power of RGB-T tracking. Since it is laborious to collect high-quality RGB-T sequences, recent benchmarks only provide test sequences. In this paper, we construct a large-scale benchmark with high diversity for visible-thermal UAV tracking (VTUAV), including 500 sequences with 1.7 million high-resolution (1920* 1080 pixels) frame pairs. In addition, comprehensive applications (short-term tracking, long-term tracking and segmentation mask prediction) with diverse categories and scenes are considered for exhaustive evaluation. Moreover, we provide a coarse-to-fine attribute annotation, where frame-level attributes are provided to exploit the potential of challenge-specific trackers. In addition, we design a new RGB-T baseline, named Hierarchical Multi-modal Fusion Tracker (HMFT), which fuses RGB-T data in various levels. Numerous experiments on several datasets are conducted to reveal the effectiveness of HMFT and the complement of different fusion types. The project is available at here.
A Radical-Aware Attention-Based Model for Chinese Text Classification

Recent years, Chinese text classification has attracted more and more research attention. However, most existing techniques which specifically aim at English materials may lose effectiveness on this task due to the huge difference between Chinese and English. Actually, as a special kind of hieroglyphics, Chinese characters and radicals are semantically useful but still unexplored in the task of text classification. To that end, in this paper, we first analyze the motives of using multiple granularity features to represent a Chinese text by inspecting the characteristics of radicals, characters and words. For better representing the Chinese text and then implementing Chinese text classification, we propose a novel Radicalaware Attention-based Four-Granularity (RAFG) model to take full advantages of Chinese characters, words, characterlevel radicals, word-level radicals simultaneously. Specifically, RAFG applies a serialized BLSTM structure which is context-aware and able to capture the long-range information to model the character sharing property of Chinese and sequence characteristics in texts. Further, we design an attention mechanism to enhance the effects of radicals thus model the radical sharing property when integrating granularities. Finally, we conduct extensive experiments, where the experimental results not only show the superiority of our model, but also validate the effectiveness of radicals in the task of Chinese text classification.
Using Imbalanced Triangle Synthetic Data for Machine Learning Anomaly Detection

The extreme imbalanced data problem is the core issue in anomaly detection. The amount of abnormal data is so small that we cannot get adequate information to analyze it. The mainstream methods focus on taking fully advantages of the normal data, of which the discrimination method is that the data not belonging to normal data distribution is the anomaly. From the view of data science, we concentrate on the abnormal data and generate artificial abnormal samples by machine learning method. In this kind of technologies, Synthetic Minority Over-sampling Technique and its improved algorithms are representative milestones, which generate synthetic examples randomly in selected line segments. In our work, we break the limitation of line segment and propose an Imbalanced Triangle Synthetic Data method. In theory, our method covers a wider range. In experiment with real world data, our method performs better than the SMOTE and its meliorations.
Leveraging Long and Short-Term Information in Content-Aware Movie Recommendation via Adversarial Training

Movie recommendation systems provide users with ranked lists of movies based on individual's preferences and constraints. Two types of models are commonly used to generate ranking results: 1) long-term models and 2) session-based models. The long-term-based models represent the interactions between users and movies that are supposed to change slowly across time, while the session-based models encode the information of users' interests and changing dynamics of movies' attributes in short terms. In this paper, we propose the LSIC model, leveraging long and short-term information for content-aware movie recommendation using adversarial training. In the adversarial process, we train a generator as an agent of reinforcement learning which recommends the next movie to a user sequentially. We also train a discriminator which attempts to distinguish the generated list of movies from the real records. The poster information of movies is integrated to further improve the performance of movie recommendation, which is specifically essential when few ratings are available. The experiments demonstrate that the proposed model has robust superiority over competitors and achieves the state-of-the-art results.
Improved Facial Expression Recognition Based on DWT Feature for Deep CNN

Facial expression recognition (FER) has become one of the most important fields of research in pattern recognition. In this paper, we propose a method for the identification of facial expressions of people through their emotions. Being robust against illumination changes, this method combines four steps: Viola–Jones face detection algorithm, facial image enhancement using contrast limited adaptive histogram equalization (CLAHE) algorithm, the discrete wavelet transform (DWT), and deep convolutional neural network (CNN). We have used Viola–Jones to locate the face and facial parts; the facial image is enhanced using CLAHE; then facial features extraction is done using DWT; and finally, the extracted features are used directly to train the CNN network, for the purpose of classifying the facial expressions. Our experimental work was performed on the CK+ database and JAFFE face database. The results obtained using this network were 96.46% and 98.43%, respectively.
A Novel Capsule Based Hybrid Neural Network for Sentiment Classification

Sentiment classification of short text is a challenging task because of limited contextual information.We propose a capsule-based hybrid neural network model which can obtain the implicit semantic information effectively.Bidirectional gated recurrent unit (BGRU) is applied in this model to achieve the interdependent features with long distance.Moreover, the capsule network can extract richer textual information to improve expression ability.Compared with the attention-based model which combines self-attention mechanisms and convolutional neural networks (CNN), the capsule-based hybrid model has the advantage of less training time and simple network structure to achieve better performance.The performance is evaluated on two short text review datasets.Our capsule-based model outperforms other related models on movie review data and gets the highest accuracy of 0.8255.Meanwhile, it performs better than most of the systems in NLPCC2014 Task II and, especially achieves the best result on negative data.
On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference

Popular Natural Language Inference (NLI) datasets have been shown to be tainted by hypothesis-only biases.Adversarial learning may help models ignore sensitive biases and spurious correlations in data.We evaluate whether adversarial learning can be used in NLI to encourage models to learn representations free of hypothesis-only biases.Our analyses indicate that the representations learned via adversarial learning may be less biased, with only small drops in NLI accuracy.
UAV-Based Situational Awareness System Using Deep Learning

Situational awareness by Unmanned Aerial Vehicles (UAVs) is important for many applications such as surveillance, search and rescue, and disaster response. In those applications, detecting and locating people and recognizing their actions in near real-time can play a crucial role for preparing an effective response. However, there are currently three main limitations to perform this task efficiently. First, it is currently often not possible to access the live video feed from a UAV's camera due to limited bandwidth. Second, even if the video feed is available, monitoring and analyzing video over prolonged time is a tedious task for humans. Third, it is typically not possible to locate random people via their cellphones. Therefore, we developed the Person-Action-Locator (PAL), a novel UAV-based situational awareness system. The PAL system addresses the first issue by analyzing the video feed onboard the UAV, powered by a supercomputer-on-a-module. Specifically, as a support for human operators, the PAL system relies on Deep Learning models to automatically detect people and recognize their actions in near real-time. To address the third issue, we developed a Pixel2GPS converter that estimates the location of people from the video feed. The result - icons representing detected people labeled by their actions - is visualized on the map interface of the PAL system. The Deep Learning models were first tested in the lab and demonstrated promising results. The fully integrated PAL system was successfully tested in the field. We also performed another collection of surveillance data to complement the lab results.
Robust Vehicle Detection and Counting Algorithm Employing a Convolution Neural Network and Optical Flow

Automatic vehicle detection and counting are considered vital in improving traffic control and management. This work presents an effective algorithm for vehicle detection and counting in complex traffic scenes by combining both convolution neural network (CNN) and the optical flow feature tracking-based methods. In this algorithm, both the detection and tracking procedures have been linked together to get robust feature points that are updated regularly every fixed number of frames. The proposed algorithm detects moving vehicles based on a background subtraction method using CNN. Then, the vehicle's robust features are refined and clustered by motion feature points analysis using a combined technique between KLT tracker and K-means clustering. Finally, an efficient strategy is presented using the detected and tracked points information to assign each vehicle label with its corresponding one in the vehicle's trajectories and truly counted it. The proposed method is evaluated on videos representing challenging environments, and the experimental results showed an average detection and counting precision of 96.3% and 96.8%, respectively, which outperforms other existing approaches.
Summary Level Training of Sentence Rewriting for Abstractive Summarization

As an attempt to combine extractive and abstractive summarization, Sentence Rewriting models adopt the strategy of extracting salient sentences from a document first and then paraphrasing the selected ones to generate a summary.However, the existing models in this framework mostly rely on sentence-level rewards or suboptimal labels, causing a mismatch between a training objective and evaluation metric.In this paper, we present a novel training signal that directly maximizes summary-level ROUGE scores through reinforcement learning.In addition, we incorporate BERT into our model, making good use of its ability on natural language understanding.In extensive experiments, we show that a combination of our proposed model and training procedure obtains new state-of-the-art performance on both CNN/Daily Mail and New York Times datasets.We also demonstrate that it generalizes better on DUC-2002 test set.
Query2box: Reasoning over Knowledge Graphs in Vector Space Using Box Embeddings

Answering complex logical queries on large-scale incomplete knowledge graphs (KGs) is a fundamental yet challenging task. Recently, a promising approach to this problem has been to embed KG entities as well as the query into a vector space such that entities that answer the query are embedded close to the query. However, prior work models queries as single points in the vector space, which is problematic because a complex query represents a potentially large set of its answer entities, but it is unclear how such a set can be represented as a single point. Furthermore, prior work can only handle queries that use conjunctions ($\wedge$) and existential quantifiers ($\exists$). Handling queries with logical disjunctions ($\vee$) remains an open problem. Here we propose query2box, an embedding-based framework for reasoning over arbitrary queries with $\wedge$, $\vee$, and $\exists$ operators in massive and incomplete KGs. Our main insight is that queries can be embedded as boxes (i.e., hyper-rectangles), where a set of points inside the box corresponds to a set of answer entities of the query. We show that conjunctions can be naturally represented as intersections of boxes and also prove a negative result that handling disjunctions would require embedding with dimension proportional to the number of KG entities. However, we show that by transforming queries into a Disjunctive Normal Form, query2box is capable of handling arbitrary logical queries with $\wedge$, $\vee$, $\exists$ in a scalable manner. We demonstrate the effectiveness of query2box on two large KGs and show that query2box achieves up to 25% relative improvement over the state of the art.
Federated Learning Based Mobile Edge Computing for Augmented Reality Applications

The past decade has witnessed the prosperous growth of augmented reality (AR) devices, as they provide immersive and interactive experience for customers. AR applications have the properties of high data rate and latency sensitivity. Currently, the available bandwidth is relatively limited to transmit and process enormous generated data. Meanwhile, it is challenging for AR to accurately detect and classify the object in order to perfectly combine the corresponding virtual contents with the real world. In this work, we focus on how to solve the computation efficiency, low-latency object detection and classification problems of AR applications. Firstly, we introduce and analyze the practical mathematical model of AR, and connect the AR operating principles with the object detection and classification problem. To address this problem and reduce the executing latency simultaneously, we propose a framework collaborating mobile edge computing paradigm with federated learning, both of which are decentralized configurations. To evaluate our method, numerical results are calculated based on the open source data CIFAR-10. Compared to centralized learning, our proposed framework requires significantly fewer training iterations.
Towards Artificial Intelligence Driven Emotion Aware Fall Monitoring Framework Suitable for Elderly People with Neurological Disorder

The contemporary world's emerging issue is how the mental health and falling of a senior citizen with a neurological disorder can be maintained living at their homes as the number of aged people is increasing with the rising of life expectancy. With the advancement of the Internet of Things (IoT) and big data analytics, several works had been done on smart home health care systems that deal with in house monitoring for fall detection. Despite so much work, the challenges remain for not considering emotional care in the fall detection system for the old ones. As a remedy to the problems mentioned above, we propose an emotion aware fall monitoring framework using IoT, Artificial Intelligence (AI) Algorithms, and Big data analytics, which will deal with emotion recognition of the aged people, predictions about health conditions, and real-time fall monitoring. In the case of an emergency, the proposed framework alerts about a situation of urgency to the predefined caregiver. A smart ambulance or mobile clinic will reach the older adult's location at minimum time.
Autonomous Vision-Based Primary Distribution Systems Porcelain Insulators Inspection Using UAVs

The early detection of damaged (partially broken) outdoor insulators in primary distribution systems is of paramount importance for continuous electricity supply and public safety. Unmanned aerial vehicles (UAVs) present a safer, autonomous, and efficient way to examine the power system components without closing the power distribution system. In this work, a novel dataset is designed by capturing real images using UAVs and manually generated images collected to overcome the data insufficiency problem. A deep Laplacian pyramid-based super-resolution network is implemented to reconstruct high-resolution training images. To improve the visibility of low-light images, a low-light image enhancement technique is used for the robust exposure correction of the training images. A different fine-tuning strategy is implemented for fine-tuning the object detection model to increase detection accuracy for the specific faulty insulators. Several flight path strategies are proposed to overcome the shuttering effect of insulators, along with providing a less complex and time- and energy-efficient approach for capturing a video stream of the power system components. The performance of different object detection models is presented for selecting the most suitable one for fine-tuning on the specific faulty insulator dataset. For the detection of damaged insulators, our proposed method achieved an F1-score of 0.81 and 0.77 on two different datasets and presents a simple and more efficient flight strategy. Our approach is based on real aerial inspection of in-service porcelain insulators by extensive evaluation of several video sequences showing robust fault recognition and diagnostic capabilities. Our approach is demonstrated on data acquired by a drone in Swat, Pakistan.
Correlation Coefficient-Based Recombinative Guidance for Genetic Programming Hyperheuristics in Dynamic Flexible Job Shop Scheduling

Dynamic flexible job shop scheduling (JSS) is a challenging combinatorial optimization problem due to its complex environment. In this problem, machine assignment and operation sequencing decisions need to be made simultaneously under the dynamic environments. Genetic programming (GP), as a hyperheuristic approach, has been successfully used to evolve scheduling heuristics for dynamic flexible JSS. However, in traditional GP, recombination between parents may disrupt the beneficial building blocks by choosing the crossover points randomly. This article proposes a recombinative mechanism to provide guidance for GP to realize effective and adaptive recombination for parents to produce offspring. Specifically, we define a novel measure for the importance of each subtree of an individual, and the importance information is utilized to decide the crossover points. The proposed recombinative guidance mechanism attempts to improve the quality of offspring by preserving the promising building blocks of one parent and incorporating good building blocks from the other. The proposed algorithm is examined on six scenarios with different configurations. The results show that the proposed algorithm significantly outperforms the state-of-the-art algorithms on most tested scenarios, in terms of both final test performance and convergence speed. In addition, the rules obtained by the proposed algorithm have good interpretability.
IDENTIFY AND RANK THE CHALLENGES OF IMPLEMENTING SUSTAINABLE SUPPLY CHAIN BLOCKCHAIN TECHNOLOGY USING THE BAYESIAN BEST WORST METHOD

Globalization initiated the challenges in Supply Chain (SC) such as management and control. In this situation, Blockchain as a digital distributed ledger can guarantee clarity, tractability, and safety. Many case studies proved that we can use Blockchain Technology (BT) to solve global supply chain problems especially in smart contracts with their potential applications. BT is in its early period and it is hard to find supply chains that have successfully implemented this technology to track their sustainable actions. Therefore, it is worth studying about the role of customers, members, domestic, national, and international challenges that could resist implementing Blockchain and may affect SC sustainability. Accordingly, four categories of barriers to the use of BT are introduced which are inter-organizational, intra-organizational, technical, and external barriers. Then with Bayesian Best Worst Method, we ranked the BT barriers and the sub-barriers. The study illustrates the interconnection of these barriers and the priority of each element. The lack of business models and the best practices in implementing Blockchain technology is a challenge and it is important that practitioners acknowledge these barriers in the first steps.
Investigating students' behavioral intention to use mobile learning in higher education in UAE during Coronavirus-19 pandemic

The study explores the impacts of fear emotions on technology adoption by teachers and students during the COVID-19 pandemic. Mobile learning (ML) has been considered an educational, social platform in private and public higher education institutes. Since several fears are connected with COVID-19, this study's key hypotheses are related to how COVID-19 influences Mobile Learning (ML) adoption. Educators, teachers, and students may face some common types of fear in the course of the coronavirus pandemic, such as fear of losing social relationships, fear of educational loss and failure, and fear because of the lockdown of the family in the prevailing circumstances. Different theoretical models, named Expectation-Confirmation Model (ECM) and Technology Acceptance Model (TAM), are combined to develop an integrated model for this study. The proposed model was analyzed with the development of a questionnaire survey. The survey served as a data collection instrument to collect data from students of the University of Sharjah in Sharjah city in the United Arab Emirates (UAE). Three hundred twenty undergraduate students participated in the study. The collected data was evaluated using the partial least squares-structural equation modeling (PLS-SEM). The significant predictors revealed by experimental results included perceived fear, perceived ease of use, expectation confirmation, satisfaction, and perceived usefulness, explaining the intention to use the mobile learning platform. According to our study, teaching and learning can be benefitted to a great extent by the adoption of mobile learning (ML) during this pandemic for educational purposes; however, this process may be negatively affected by the fear of future educational results, fear of losing social relations and fear of stressful family situations. Therefore, appropriate student evaluation may be conducted to overcome the emotional distress caused by the pandemic effectively.