Reviewerly/exHarmony huggingface dataset:
An Optimized Architecture of Image Classification Using Convolutional Neural Network

The convolutional neural network (CNN) is the type of deep neural networks which has been widely used in visual recognition.Over the years, CNN has gained lots of attention due to its high capability to appropriately classifying the images and feature learning.However, there are many factors such as the number of layers and their depth, number of features map, kernel size, batch size, etc.They must be analyzed to determine how they influence the performance of network.In this paper, the performance evaluation of CNN is conducted by designing a simple architecture for image classification.We evaluated the performance of our proposed network on the most famous image repository name CIFAR-10 used for the detection and classification task.The experiment results show that the proposed network yields the best classification accuracy as compared to existing techniques.Besides, this paper will help the researchers to better understand the CNN models for a variety of image classification task.Moreover, this paper provides a brief introduction to CNN, their applications in image processing, and discuss recent advances in region-based CNN for the past few years.
LeDeepChef Deep Reinforcement Learning Agent for Families of Text-Based Games

While Reinforcement Learning (RL) approaches lead to significant achievements in a variety of areas in recent history, natural language tasks remained mostly unaffected, due to the compositional and combinatorial nature that makes them notoriously hard to optimize. With the emerging field of Text-Based Games (TBGs), researchers try to bridge this gap. Inspired by the success of RL algorithms on Atari games, the idea is to develop new methods in a restricted game world and then gradually move to more complex environments. Previous work in the area of TBGs has mainly focused on solving individual games. We, however, consider the task of designing an agent that not just succeeds in a single game, but performs well across a whole family of games, sharing the same theme. In this work, we present our deep RL agent—LeDeepChef—that shows generalization capabilities to never-before-seen games of the same family with different environments and task descriptions. The agent participated in Microsoft Research's First TextWorld Problems: A Language and Reinforcement Learning Challenge and outperformed all but one competitor on the final test set. The games from the challenge all share the same theme, namely cooking in a modern house environment, but differ significantly in the arrangement of the rooms, the presented objects, and the specific goal (recipe to cook). To build an agent that achieves high scores across a whole family of games, we use an actor-critic framework and prune the action-space by using ideas from hierarchical reinforcement learning and a specialized module trained on a recipe database.
Relation Extraction Exploiting Full Dependency Forests

Dependency syntax has long been recognized as a crucial source of features for relation extraction. Previous work considers 1-best trees produced by a parser during preprocessing. However, error propagation from the out-of-domain parser may impact the relation extraction performance. We propose to leverage full dependency forests for this task, where a full dependency forest encodes all possible trees. Such representations of full dependency forests provide a differentiable connection between a parser and a relation extraction model, and thus we are also able to study adjusting the parser parameters based on end-task loss. Experiments on three datasets show that full dependency forests and parser adjustment give significant improvements over carefully designed baselines, showing state-of-the-art or competitive performances on biomedical or newswire benchmarks.
A Hybrid Grasshopper Optimization Algorithm With Invasive Weed for Global Optimization

The grasshopper optimization algorithm (GOA) is a promising metaheuristic algorithm for optimization.In the current study, a hybrid grasshopper optimization algorithm with invasive weed optimization (IWGOA) is proposed.The invasive weed optimization (IWO) and random walk strategy are helpful for improving the search precision and accelerating the convergence rate.In addition, the exploration and exploitation capability of the IWGOA algorithm are further enhanced by the grouping strategy.The IWGOA algorithm is compared with some typical and latest optimization algorithms including genetic algorithm (GA), moth-flame optimization algorithm (MFO), particle swarm optimization and gravitational search algorithm (PSOGSA), ant lion optimizer (ALO), conventional GOA algorithm, chaotic GOA algorithm (CGOA) and opposition-based learning GOA algorithm (OBLGOA) on 23 benchmark functions and 30 CEC 2014 benchmark functions.The results show that the IWGOA algorithm is able to provide better outcomes than the other algorithms on the majority of the benchmark functions.Additionally, the IWGOA algorithm is applied to multi-level image segmentation, and obtains promising results.All of these findings demonstrate the superiority of the IWGOA algorithm.
MeMu: Metric correlation Siamese network and multi-class negative sampling for visual tracking

Despite the great success in the computer vision field, visual tracking is still a challenging task. The main obstacle is that the target object often suffers from interference, such as occlusion. As most Siamese network-based trackers mainly sample image patches of target objects for training, the tracking algorithm lacks sufficient information about the surrounding environment. Besides, many Siamese network-based tracking algorithms build a regression only with the target object samples without considering the relationship between target and background, which may deteriorate the performance of trackers. In this paper, we propose a metric correlation Siamese network and multi-class negative sampling tracking method. For the first time, we explore a sampling approach that includes three different kinds of negative samples: virtual negative samples for pre-learning the potential occlusion situation, boundary negative samples to cope with potential tracking drift, and context negative samples to cope with potential incorrect positioning. With the three kinds of negative samples, we also propose a metric correlation method to train a correlation filter that contains metric information for better discrimination. Furthermore, we design a Siamese network-based architecture to embed the metric correlation filter module mentioned above in order to benefit from the powerful representation ability of deep learning. Extensive experiments on challenging OTB100 and VOT2017 datasets demonstrate the competitive performance of the proposed algorithm performs favorably compared with state-of-the-art approaches.
A Causal Inference Method for Reducing Gender Bias in Word Embedding Relations

Word embedding has become essential for natural language processing as it boosts empirical performances of various tasks. However, recent research discovers that gender bias is incorporated in neural word embeddings, and downstream tasks that rely on these biased word vectors also produce gender-biased results. While some word-embedding gender-debiasing methods have been developed, these methods mainly focus on reducing gender bias associated with gender direction and fail to reduce the gender bias presented in word embedding relations. In this paper, we design a causal and simple approach for mitigating gender bias in word vector relation by utilizing the statistical dependency between gender-definition word embeddings and gender-biased word embeddings. Our method attains state-of-the-art results on gender-debiasing tasks, lexical- and sentence-level evaluation tasks, and downstream coreference resolution tasks.
A Secure and Lightweight Data Sharing Scheme for Internet of Medical Things

As cloud computing has many advantages such as large storage capacity, low cost and scalability, more and more patients prefer to store their health data in cloud to share with physicians, researchers or other users. However, storing shared data in remote cloud is out of patient's control and exposes to lots of security problems such as privacy and data integrity. So far, more and more data sharing schemes to preserve data security in health field have been put forward, but in most of them, data encryption and decryption are completely implemented by terminal devices, which increases the communication and computation burden of patient and user. Furthermore, most sharing schemes have no integrity verification mechanism, resulting in incomplete data for users to share. To solve the problems, we propose a secure and lightweight data sharing scheme for Internet of Medical Things. Firstly, the scheme guarantees the privacy and authorized access of shared data. Secondly, the scheme realizes efficient integrity verification before user downloads shared data to avoid incorrect query or computation result. Finally, the scheme achieves lightweight operations of patient and user.
Robust Federated Learning via Collaborative Machine Teaching

For federated learning systems deployed in the wild, data flaws hosted on local agents are widely witnessed. On one hand, given a large amount (e.g. over 60%) of training data are corrupted by systematic sensor noise and environmental perturbations, the performances of federated model training can be degraded significantly. On the other hand, it is prohibitively expensive for either clients or service providers to set up manual sanitary checks to verify the quality of data instances. In our study, we echo this challenge by proposing a collaborative and privacy-preserving machine teaching method. Specifically, we use a few trusted instances provided by teachers as benign examples in the teaching process. Our collaborative teaching approach seeks jointly the optimal tuning on the distributed training set, such that the model learned from the tuned training set predicts labels of the trusted items correctly. The proposed method couples the process of teaching and learning and thus produces directly a robust prediction model despite the extremely pervasive systematic data corruption. The experimental study on real benchmark data sets demonstrates the validity of our method.
Research on Inception Module Incorporated Siamese Convolutional Neural Networks to Realize Face Recognition

Face recognition is an active research subject of biometrics due to its significant research and application prospects.The performance of face recognition can be affected by a series of uncontrollable factors, such as illumination, expression, posture and occlusion, which restricts its real-world applications.Therefore, improving the robustness of face recognition to environmental changes became an urgent problem.In this paper, a simplified deep convolutional neural network structure having high robustness under unlimited conditions is designed for face recognition.This structure can improve training speed and face recognition accuracy, and be suitable for small-scale data sets.Inception Module Incorporated Siamese Convolutional Neural Networks (IMISCNN) is developed based on effective reduction of external interference and better features extraction by adopting the Siamese network structure.A cyclical learning rate strategy is also introduced in IMISCNN for better model convergence.Compared to classical face recognition algorithms, such as PCA, PCA and SVM, CNN, PCANet, and the original SNN et al.The accuracy of IMISCNN in CASIA-webface and Extended Yale B standard face database is 99.36% and 99.21%, respectively.Its feasibility and effectiveness have been verified in our experiments.
Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising Autoencoders

Text simplification (TS) rephrases long sentences into simplified variants while preserving inherent semantics. Traditional sequence-to-sequence models heavily rely on the quantity and quality of parallel sentences, which limits their applicability in different languages and domains. This work investigates how to leverage large amounts of unpaired corpora in TS task. We adopt the back-translation architecture in unsupervised machine translation (NMT), including denoising autoencoders for language modeling and automatic generation of parallel data by iterative back-translation. However, it is non-trivial to generate appropriate complex-simple pair if we directly treat the set of simple and complex corpora as two different languages, since the two types of sentences are quite similar and it is hard for the model to capture the characteristics in different types of sentences. To tackle this problem, we propose asymmetric denoising methods for sentences with separate complexity. When modeling simple and complex sentences with autoencoders, we introduce different types of noise into the training process. Such a method can significantly improve the simplification performance. Our model can be trained in both unsupervised and semi-supervised manner. Automatic and human evaluations show that our unsupervised model outperforms the previous systems, and with limited supervision, our model can perform competitively with multiple state-of-the-art simplification systems.
Further Understanding Videos through Adverbs: A New Video Task

Video understanding is a research hotspot of computer vision and significant progress has been made on video action recognition recently. However, the semantics information contained in actions is not rich enough to build powerful video understanding models. This paper first introduces a new video semantics: the Behavior Adverb (BA), which is a more expressive and difficult one covering subtle and inherent characteristics of human action behavior. To exhaustively decode this semantics, we construct the Videos with Action and Adverb Dataset (VAAD), which is a large-scale dataset with a semantically complete set of BAs. The dataset will be released to the public with this paper. We benchmark several representative video understanding methods (originally for action recognition) on BA and action recognition. The results show that BA recognition task is more challenging than conventional action recognition. Accordingly, we propose the BA Understanding Network (BAUN) to solve this problem and the experiments reveal that our BAUN is more suitable for BA recognition (11% better than I3D). Furthermore, we find these two semantics (action and BA) can propel each other forward to better performance: promoting action recognition results by 3.4% averagely on three standard action recognition datasets (UCF-101, HMDB-51, Kinetics).
Complementary-View Multiple Human Tracking

The global trajectories of targets on ground can be well captured from a top view in a high altitude, e.g., by a drone-mounted camera, while their local detailed appearances can be better recorded from horizontal views, e.g., by a helmet camera worn by a person. This paper studies a new problem of multiple human tracking from a pair of top- and horizontal-view videos taken at the same time. Our goal is to track the humans in both views and identify the same person across the two complementary views frame by frame, which is very challenging due to very large field of view difference. In this paper, we model the data similarity in each view using appearance and motion reasoning and across views using appearance and spatial reasoning. Combing them, we formulate the proposed multiple human tracking as a joint optimization problem, which can be solved by constrained integer programming. We collect a new dataset consisting of top- and horizontal-view video pairs for performance evaluation and the experimental results show the effectiveness of the proposed method.
An Efficient Human Activity Recognition Technique Based on Deep Learning

In this paper, we present a new deep learning-based human activity recognition technique. First, we track and extract human body from each frame of the video stream. Next, we abstract human silhouettes and use them to create binary space-time maps (BSTMs) which summarize human activity within a defined time interval. Finally, we use convolutional neural network (CNN) to extract features from BSTMs and classify the activities. To evaluate our approach, we carried out several tests using three public datasets: Weizmann, Keck Gesture and KTH Database. Experimental results show that our technique outperforms conventional state-of-the-art methods in term of recognition accuracy and provides comparable performance against recent deep learning techniques. It’s simple to implement, requires less computing power, and can be used for multi-subject activity recognition.
Moving target tracking method for unmanned aerial vehicle/unmanned ground vehicle heterogeneous system based on AprilTags

Using the characteristics of unmanned aerial vehicle/unmanned ground vehicle, heterogeneous systems can accomplish many complex tasks cooperatively. Moving target tracking is an important basis for the relative positioning and formation maintenance of heterogeneous cooperative systems. This paper first introduces the unmanned aerial vehicle/unmanned ground vehicle collaborative tracking task and heterogeneous system. In order to maintain the original stability of unmanned aerial vehicle, a control method based on SBUS protocol to simulate remote control is proposed. About unmanned ground vehicle with Mecanum wheel, a detailed description of control method is designed. For the problems of real-time performance and occlusion, a tracking scheme based on AprilTag identification is studied. The scheme tracks the Tag target in the case of no occlusion. When occlusion occurs, the scheme tracks the color feature around the Tag. The accuracy of the tracking algorithm and the problem of occlusion are greatly improved. Finally, the scheme is applied to the heterogeneous systems. Simulation and experimental results show that the proposed method is suitable for unmanned aerial vehicle/unmanned ground vehicle heterogeneous system to perform the collaborative tracking task.
Tamper-Proofing Video With Hierarchical Attention Autoencoder Hashing on Blockchain

We present ARCHANGEL; a novel distributed ledger based system for assuring the long-term integrity of digital video archives. First, we introduce a novel deep network architecture using a hierarchical attention autoencoder (HAAE) to compute temporal content hashes (TCHs) from minutes or hour-long audio-visual streams. Our TCHs are sensitive to accidental or malicious content modification (tampering). The focus of our self-supervised HAAE is to guard against content modification such as frame truncation or corruption but ensure invariance against format shift (i.e. codec change). This is necessary due to the curatorial requirement for archives to format shift video over time to ensure future accessibility. Second, we describe how the TCHs (and the models used to derive them) are secured via a proof-of-authority blockchain distributed across multiple independent archives. We report on the efficacy of ARCHANGEL within the context of a trial deployment in which the national government archives of the United Kingdom, United States of America, Estonia, Australia and Norway participated.
A Deep Learning Model for Estimation of Patients with Undiagnosed Diabetes

A screening model for undiagnosed diabetes mellitus (DM) is important for early medical care. Insufficient research has been carried out developing a screening model for undiagnosed DM using machine learning techniques. Thus, the primary objective of this study was to develop a screening model for patients with undiagnosed DM using a deep neural network. We conducted a cross-sectional study using data from the Korean National Health and Nutrition Examination Survey (KNHANES) 2013–2016. A total of 11,456 participants were selected, excluding those with diagnosed DM, an age &lt; 20 years, or missing data. KNHANES 2013–2015 was used as a training dataset and analyzed to develop a deep learning model (DLM) for undiagnosed DM. The DLM was evaluated with 4444 participants who were surveyed in the 2016 KNHANES. The DLM was constructed using seven non-invasive variables (NIV): age, waist circumference, body mass index, gender, smoking status, hypertension, and family history of diabetes. The model showed an appropriate performance (area under curve (AUC): 80.11) compared with existing previous screening models. The DLM developed in this study for patients with undiagnosed diabetes could contribute to early medical care.
Academic E-Mail Overload and the Burden of “Academic Spam”

This article presents an editorial perspective on the challenges associated with e-mail management for academic physicians. We include 2-week analysis of our own e-mails as illustrations of the e-mail volume and content. We discuss the contributors to high e-mail volumes, focusing especially on unsolicited e-mails from medical/scientific conferences and open-access journals (sometimes termed "academic spam emails"), as these e-mails comprise a significant volume and are targeted to physicians and scientists. Our 2-person sample is consistent with studies showing that journals that use mass e-mail advertising have low rates of inclusion in recognized journal databases/resources. Strategies for managing e-mail are discussed and include unsubscribing, blocking senders or domains, filtering e-mails, managing one's inbox, limiting e-mail access, and e-mail etiquette. Academic institutions should focus on decreasing the volume of unsolicited e-mails, fostering tools to manage e-mail overload, and educating physicians including trainees about e-mail practices, predatory journals, and scholarly database/resources.
Digital Video Source Identification Based on Container’s Structure Analysis

The mobile device ecosystem has dramatically evolved over the last few years, since users have openly embraced a massive use of mobile phones for different purposes: professional use, personal use, etc.Digital videos can be used to define legal responsibilities or as part of the evidence in trials.The forensic analysis of digital videos becomes very relevant to determine the origin and authenticity of a video in order to link an individual with a device, place or event.The field of forensic analysis of digital videos is constantly facing new and direct challenges.Even though the basic principles of this discipline remain unchanged, numerous issues appear every year that require new procedures and tools.Therefore, it is necessary to provide forensic analysts with techniques to identify the origin of multimedia content.In this paper, the topic of source identification in open scenarios will be discussed, since analysts do not know in advance the set of cameras to which a video belongs so they find it difficult to identify its source.This approach is similar to real-life situations since in most cases, analysts are unaware of the set of video cameras.This paper aims to create a technique that identifies the source of digital videos generated by digital devices through the use of unsupervised algorithms based on the analysis of the structure of multimedia video devices.