Reviewerly/exHarmony huggingface dataset:
DREAMT: Diversity Enlarged Mutual Teaching for Unsupervised Domain Adaptive Person Re-Identification

Pseudo-label-based methods of unsupervised domain adaption (UDA) can transfer the knowledge learned from a labeled source domain to an unlabeled target domain and have recently achieved significant progress in the application of person reidentification (re-ID). However, these methods suffer from serious label noise problems that downgrade the retrieval performance in UDA person re-ID. The mutual teaching framework (MTF) with dual networks attempts to tackle this problem by generating reliable soft pseudo labels but results in a mutual convergence problem. In this paper, a novel DiveRsity EnlArged Mutual Teaching framework (DREAMT) is proposed to solve the problem mentioned above. Based on the primary mutual-mean-teaching mechanism two strategies are developed in DREAMT, that is, GAN-based source domain augmentation (GSDA) and cross-branch mutual supervision (CBMS) for dual networks. Specifically, GSDA exploits two GANs to augment source domain datasets in different ways for pre-training to improve the pre-trained models’ performance and enlarge the diversity at the beginning of target domain adaption. During target adaption, each network in MTF adopts two branches to extract different features. CBMS based on hard and soft pseudo labels is across branches and networks and can help to maintain the diversity between training peers in the whole training process. Extensive experiments have demonstrated that our proposed DREAMT framework achieves better mAP and CMC performance than the existing mutual teaching methods and outperforms various state-of-the-art methods in UDA person re-ID tasks.
Blockchain Based Electronic Healthcare Record (EHR)

The nature of medical system is viewed as perhaps the main factor of a nation. Currently Electronic Health Record (EHR) is being used to store the patient's medical data. However, EHR faces some issues like interoperability, security, privacy. All such issues leads to the decreased quality of medical system and also to an increase in healthcare costs because data is being shared between different medical facilities over different places. The management of medical data also include some different challenges such as access to data and the way that data is often accessed at different places other than the medical facility. Blockchain technology can help in improving current healthcare system. The objective of this research paper is to show how EHR can be implemented into healthcare sector for maintaining patient's data using blockchain technology i.e. how the data can be gathered, uploaded and accessed. Blockchain provide the way to efficiently handle the medical data.
Computer-assisted interpreting systems in the education of simultaneous interpreters

The article deals with the analysis of the potential of existing terminology support systems for simultaneous interpreting and the experience of their use in universities. Based on the study the possibilities of their use in the educational process of domestic institutions of higher education in the training of interpreters were identified. It is proposed to choose the software products InterpretBank, Interplex, Interpreter’s Help for training simultaneous interpreters. It has been found that the proposed programmes contribute to the students’ ability to create their own glossaries on specific subjects, fill them in in different ways and use them directly when interpreting. The feasibility of practising CAI tools in a specialised simultaneous interpreting laboratory has been proved.
Building Knowledge Subgraphs in Question Answering over Knowledge Graphs

Question answering over knowledge graphs targets to leverage facts in knowledge graphs to answer natural language questions. The presence of large number of facts, particularly in huge and well-known knowledge graphs such as DBpedia, makes it difficult to access the knowledge graph for each given question. This paper describes a generic solution based on Personal Page Rank for extracting a small subset from the knowledge graph as a knowledge subgraph which is likely to capture the answer of the question. Given a natural language question, relevant facts are determined by a bi-directed propagation process based on Personal Page Rank. Experiments are conducted over FreeBase, DBPedia and WikiMovie to demonstrate the effectiveness of the approach in terms of recall and size of the extracted knowledge subgraphs.
Multilingual Sentiment Analysis on Twitter Data Towards Enhanced Policy Making

The great expansion in the usage and popularity of social media platforms during the last decades has led to the production of an enormous real-time volume of social texts and posts, including tweets, that are being produced by users. These collections of social data can be potentially useful and provide useful insights to policymakers to adjust new user-centric policies and regulations. However, extracting and analyzing valuable information and knowledge out of these data is a challenging task as concerns the high multilingualism that describes these data. Thus, both the research and the business communities focus on the utilization of multilingual approaches and solutions to enhance the policy making procedures. To investigate a portion of these challenges this research work performs a comparative analysis of two multilingual sentiment analysis approaches. In this context, three multilingual deep learning classifiers and a zero-shot classification approach were utilized and compared. Their comparison has unveiled insightful outcomes and has a two-fold interpretation. Multilingual deep learning classifiers that have pre-trained and evaluated in monolingual data achieve high performances and transfer inference when applied afterwards in multilingual data. However, the zero-shot classification approach fails to achieve high accuracies in monolingual data as in contrary to when applied on multilingual data.
Invisible Geolocation Signature Extraction From a Single Image

Geotagging images of interest are increasingly important to law enforcement, national security, and journalism. Today, many images do not carry location tags that are trustworthy and resilient to tampering; and landmark-based visual clues may not be readily present in every image, especially in those taken indoors. In this paper, we exploit an environmental signature from the power grid, the electric network frequency (ENF) signal, which can be inherently captured in a sensing stream at the time of recording and carries useful time&#x2013;location information. Compared to the recent art of extracting ENF traces from audio and video recordings, it is very challenging to extract an ENF trace from a single image. We address this challenge by first mathematically examining the impact of the ENF embedding steps such as electricity to light conversion, scene geometry dilution of radiation, and image sensing. We then incorporate the verified parametric models of the physical embedding process into our proposed entropy minimization method. The optimized results of the entropy minimization are used for creating a two-level ENF presence&#x2013;classification test for region-of-capturing localization. It identifies whether a single image has an ENF trace; if yes, whether it is at 50 or 60 Hz. We quantitatively study the relationship between the ENF strength and its detectability from a single image. This paper is the first comprehensive work to bring out a unique forensic capability of environmental traces that shed light on an image&#x2019;s capturing location.
Impartial Differentiable Automatic Data Augmentation Based on Finite Difference Approximation for Pedestrian Detection

Pedestrian detection is a challenging task in practical scenarios. Data augmentation is an effective way to deal with this problem, but designing a suitable data augmentation policy requires a great deal of manual experience and multiple adjustments of parameters. To address this problem, we propose an impartial differentiable automatic data augmentation (IDADA) that automatically searches a suitable augmentation policy for pedestrian detection in a more practical scenario. First, an impartiality loss is proposed to solve the bias of the operations probabilities distribution, so as to improve the impartiality for each operation. Second, the impartiality loss and the original validation loss of the detection model are combined as a target function to update the augmentation policy parameters. Since the target function is not differentiable, the finite difference approximation is applied to provide accurate gradient estimation for the augmentation policy optimization process. Comprehensive experiments performed on two typical datasets of CrowdHuman and CityPersons verify the effectiveness of the proposed approach. Specifically, our approach achieves 40.99% in MR on CrowdHuman, yielding the new state-of-the-art result on this dataset.
Hierarchical Hybrid Neural Networks With Multi-Head Attention for Document Classification

Document classification is a research topic aiming to predict the overall text sentiment polarity with the advent of deep neural networks. Various deep learning algorithms have been employed in the current studies to improve classification performance. To this end, this paper proposes a hierarchical hybrid neural network with multi-head attention (HHNN-MHA) model on the task of document classification. The proposed model contains two layers to deal with the word-sentence level and sentence-document level classification respectively. In the first layer, CNN is integrated into Bi-GRU and a multi-head attention mechanism is employed, in order to exploit local and global features. Then, both Bi-GRU and attention mechanism are applied to document processing and classification in the second layer. Experiments on four datasets demonstrate the effectiveness of the proposed method. Compared to the state-of-art methods, our model achieves competitive results in document classification in terms of experimental performance.
A Survey of Multi-label Text Classification Based on Deep Learning

Text classification (TC) is an important basic task in the field of Natural Language Processing (NLP), and multi-label text classification (MLTC) is an important branch of TC. MLTC has undergone a transformation from traditional machine learning to deep learning, and various models with excellent performance have emerged one after another. But at present, the focus of various related researches is also varied, so we combed the excellent research results in the field of MLTC in recent years, and classified them according to the focus of their research. At the same time, we also summarized the relevant data sets and evaluation indicators in the field of multi-label text classification, and made a prospect for the future of the field of MLTC.
S$^{2}$-Net:Semantic and Saliency Attention Network for Person Re-Identification

Person re-identification is still a challenging task when moving objects or another person occludes the probe person. Mainstream methods based on even partitioning apply an off-the-shelf human semantic parsing to highlight the non-collusion part. In this paper, we apply an attention branch to learn the human semantic partition to avoid misalignment introduced by even partitioning. In detail, we propose a semantic attention branch to learn 5 human semantic maps. We also note that some accessories or belongings, such as a hat, bag, may provide more informative clues to improve the person Re-ID. Human semantic parsing, however, usually treats non-human parts as distractions and discards them. To fetch the missing clues, we design a branch to capture the salient non-human parts. Finally, we merge the semantic and saliency attention to build an end-to-end network, named as S <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> -Net. Specifically, to further improve Re-ID, we develop a trade-off weighting scheme between semantic and saliency attention and set the right weight with the actual scene. The extensive experiments show that S <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> -Net gets the competitive performance. S <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> -Net achieves 87.4% mAP on Market1501 and obtains 79.3%/56.1% rank-1/mAP on MSMT17 without semantic supervision. The source codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/upgirlnana/S2Net</uri> .
Adaptive Federated Learning for Non-Convex Optimization Problems in Edge Computing Environment

Federated learning (FL), as one of novel machine learning frameworks, has been widely applied in edge computing (EC) fields. Most of the existing FL frameworks are implemented based on gradient descent (GD), and the training resources are assumed to be sufficient. However, in practical training, not all optimization problem can be well addressed by GD under FL framework in edge computing environment, and especially the edge resources are also limited. In this paper, based on the limited training resource and the FL framework, we study a kind of non-convex optimization problems, namely the gaussian mixture model (GMM), and propose an adaptive distributed expectation maximization (DEM) algorithm of GMM, which are not well addressed by using gradient descent. In particular, we analyze and give the convergence bound of the adaptive DEM. With the bound of adaptive DEM, we propose a local update control algorithm, which can achieve the best compromise between local update and global parameter aggregation of FL to achieve the maximization utilization of given resource budget. The experimental results demonstrate that adaptive DEM can well work out a kind of non-convex optimization problem under the FL framework and edge resource constrained environment.
How to Deal with Toxic People Using a Fuzzy Cognitive Map: Improving the Health and Wellbeing of the Human System

In this chapter, the fuzzy cognitive map (FCM) method is utilized to improve human system health and wellbeing in front of toxic people over time as a system safety decision-making problem. The FCM tool is systematically constructed to map the relationships between toxic people (targeted persons), toxic behaviors, toxic terrors, and toxic handling behaviors. Then, the non-linear Hebbian learning algorithm is used for refining the defined system safety decision-making problem. This study found that the FCM tool has the ability and efficiency to simulate the human system's health and wellbeing dynamically. This research provides insight into the decision-making problems to monitor human health and wellbeing status and control the trend prediction timeline. Finally, the sensitivity analysis shows the validation, robustness, and inevitability of the FCM in such decision-making problems according to the natural mode of human thinking.
A Target Detection Algorithm for Remote Sensing Images Based on a Combination of Feature Fusion and Improved Anchor

Aiming at the low average target accuracy in remote sensing images with complex background and multiple small targets, we propose a new algorithm for small target detection based on a combination of feature fusion and improved anchor. Firstly, a data enhancement method is presented to increase the number of small targets in remote sensing images. Secondly, the high level features and low level features are efficiently fused using pixel-by-pixel summation and lightweight feature extraction, which can extract features that are more favorable for small target detection. Finally, by adjusting the aspect ratio of the anchor, the misdetection rate of small targets is reduced. The algorithm proposed in this paper is applied on the PASCAL VOC data set and UCAS-AOD data set, with the mean average accuracy (mAP) of 82.5% and 87.9%, respectively. The test results demonstrate that the proposed method can not only achieve a comparable detection speed but also demonstrates considerable accuracy superiority over traditional algorithms.
EBSF: Node Characteristics-Based Block Allocation Plans for Efficient Blockchain Storage

The heavy storage problem has become a key obstacle to the application of blockchain to the actual business environments, because each node needs to keep a complete replica of blockchain data. The data volume grows undesirably large in practice. It prevents the widely used devices, e.g., tablets and mobile phones, to join blockchain systems due to their limited storage and computing resource. Previous work addressed the storage issue by allowing participating nodes to only keep a fraction of the entire transaction set, e.g., sharding. However, existing studies focus on transaction placement with the minimum cross-shard communications. These studies neglect the node characteristics (e.g., storage capacity, cost, and response capability), which impacts the storage performance adversely. In this paper, we propose EBSF, a block storage framework that achieves efficient block storage by constructing a block allocation plan based on node characteristics. Blockchain nodes are organized into committees such that nodes in a committee work together to maintain the entire blockchain data. We formulate the block allocation plan problem that assigns each block to at least one node in a committee. The goal is to minimize the total cost while reaching the threshold of the response capability of each block. We prove the NP-hardness of the problem and propose heuristic algorithms. We also propose two strategies to handle the dynamic scenario of new blocks. Extensive evaluation shows the efficiency and effectiveness of the proposed framework.
Examining the User Experience of a Digital Camera Virtual Reality Lab with Attention Guidance

In recent years, virtual reality labs have been widely used in teaching. Virtual reality labs built by virtual reality technology have been applied in different disciplines. Many studies have also shown that virtual reality experiments can replace real experiments, and some even have better learning effects than real experiments. For current digital camera experiment courses in China, it is necessary to use virtual reality labs. Attention guidance is added to the design of experimental courses. However, questions arise about learners’ acceptance and experience of digital camera virtual reality labs. Another potential problem is learners’ acceptance and experience of labs after attentional guidance is added. Therefore, the purpose of this research is to design and develop a digital camera virtual reality lab and study various aspects of the user experience (UX). The User Experience Questionnaire (UEQ) was used to obtain students' responses after completing digital camera virtual reality experimental courses. The subjects were 30 college students (N = 30). Participants were divided into two groups. In the first group, 15 students participate in the test of the virtual reality lab with attention guidance, and in the other group, 15 students participate in the test of the virtual reality lab without attention guidance. From the results, it is concluded that the UX level of the digital camera virtual reality lab is promising and positive. The subjects were satisfied with the virtual reality lab with attention guidance and without attention guidance, though the subjects with attention guidance were more satisfied.
A Chatbot for Promoting Cybersecurity Awareness

Cybersecurity is one of the hot topics nowadays. However, not many Internet users know the cyber risks around them. To promote cybersecurity awareness, this paper presents a chatbot that is a cybersecurity expert. It aims to let its users learn more about cybersecurity. It relies on Google Dialogflow, which is a natural language understanding platform. Our chatbot contains a knowledge base on cybersecurity knowledge. Users can make queries to the chatbot to learn definitions and concepts of different cybersecurity terms. Our chatbot also provides self-quizzes for users to test their knowledge on different cybersecurity topics. It also provides suggestions to users on cybersecurity issues such as how to identify and handle phishing emails. In a survey of twenty users, the majority of the users agreed that our chatbot is easy to use and can increase their awareness of cybersecurity issues.
Deep Multi-Attributed-View Graph Representation Learning

Graph representation learning aims at mapping a graph into a lower-dimensional feature space. Deep attributed graph representation, utilizing deep learning models on the graph structure and attributes, shows its significance in mining complex relational data. Most existing deep attributed graph representation models assume graph attributes in a single-attributed view. However, rich information in real-world applications demands the ability to handle multiple attributed views. For example, in social network users’ profiles and posts represent two distinct attributed views. A single-attributed view or a simple ensemble of them fails to represent the rich information and complex relations therein. To confront this challenge, this paper proposes a novel deep unsupervised graph representation learning model, called Multi-attributed-view graph Convolutional AutoEncoder (MagCAE). MagCAE learns the node-pairwise proximity among multi-attributed views and node embeddings, across which a novel loss function is designed to preserve the node-pairwise likelihood. An aggregation layer is specially developed in MagCAE to optimize the weights of embeddings on multi-attributed views. The extensive experiments on four datasets demonstrate the superiority of MagCAE over twelve baselines.
Image Dynamics-Based Visual Servo Control for Unmanned Aerial Manipulatorl With a Virtual Camera

This article proposes a visual servo control method based on image dynamics for an unmanned aerial manipulator (UAM) combining an unmanned aerial vehicle (UAV) with a multidegree-of-freedom onboard manipulator. First, taking into account the dynamic characteristics of the coupled UAM, the dynamic model of the coupled system is derived. Then, based on the perspective projection model, the appropriate image features are defined on the virtual image plane to obtain decoupled image feature dynamics. Combining image features dynamics and UAM dynamics, the position and attitude controller of the UAM flying platform with the center of gravity compensation is derived. Furthermore, the image feature motion is further applied to the positioning control of the end-effector of the onboard manipulator. In addition, the UAM motion distribution strategy is designed to coordinate the movement between the UAM flying platform and the onboard manipulator. The simulation results illustrate the performance of the proposed visual servo control based on image dynamics. As a result, the trajectories of the feature points are smooth in the real image plane, which means the UAM movement is stable. Finally, the experimental results in an outdoor environment verify the practicability and effectiveness of the proposed visual servo control method for autonomous operations. The proposed control method manages the UAM from an unknown initial position to the desired position, which can be applied to various types of aerial operations missions such as object transmission.