Reviewerly/exHarmony huggingface dataset:
Small-Task Incremental Learning.

Lifelong learning has attracted much attention, but existing works still struggle to fight catastrophic forgetting and accumulate knowledge over long stretches of incremental learning. In this work, we propose PODNet, a model inspired by representation learning. By carefully balancing the compromise between remembering the old classes and learning new ones, PODNet fights catastrophic forgetting, even over very long runs of small incremental tasks --a setting so far unexplored by current works. PODNet innovates on existing art with an efficient spatial-based distillation-loss applied throughout the model and a representation comprising multiple proxy vectors for each class. We validate those innovations thoroughly, comparing PODNet with three state-of-the-art models on three datasets: CIFAR100, ImageNet100, and ImageNet1000. Our results showcase a significant advantage of PODNet over existing art, with accuracy gains of 12.10, 6.51, and 2.85 percentage points, respectively. Code is available at this https URL
Robust Device-Independent Quantum Key Distribution

Device-independent quantum key distribution (DIQKD) is the art of using untrusted devices to distribute secret keys in an insecure network. It thus represents the ultimate form of cryptography, offering not only information-theoretic security against channel attacks, but also against attacks exploiting implementation loopholes. In recent years, much progress has been made towards realising the first DIQKD experiments, but current proposals are just out of reach of today's loophole-free Bell experiments. In this Letter, we close the gap between the theory and practice of DIQKD with a simple variant of the original protocol based on the celebrated Clauser-Horne-Shimony-Holt (CHSH) Bell inequality. In using two randomly chosen key generating bases instead of one, we show that the noise tolerance of DIQKD can be significantly improved. In particular, the extended feasibility region now covers some of the most recent loophole-free CHSH experiments, hence indicating that the first realisation of DIQKD already lies within the range of these experiments.
Flight and Interaction Control of an Innovative Ducted Fan Aerial Manipulator

An innovative aerial manipulator with ducted fans is proposed to achieve side-on aerial manipulation tasks in a confined environment, such as canopy sampling in dense forests. The dynamic model of the novel design is studied, and on this basis a composite controller is proposed to address the challenges of arm extension and physical interaction during the manipulation process. An adaptive controller is proposed for the aerial platform to achieve good stability and tracking performance under the manipulator motion, and an impedance controller is designed for the manipulator to ensure compliance and stability during physical contact. The experimental tests validate the effectiveness of the proposed prototype structure and controller design.
Revised Grey Wolf Optimized SVM-KNN Ensemble Based Automated Diagnosis of Breast Cancer

Breast cancer causes a high percentage of mortality among women throughout the world.For improving breast cancer survival and results, early detection is important.The primary aim of the given study is to enhance the results of automated expert system (ES) in the field of medical diagnosis and come up with more accurate diagnosis system for breast cancer detection at early stage.This research introduces a new ensemble approach that accurately diagnose the breast cancer at a pre-developed stage.The recognized Wisconsin Diagnosis Breast Cancer (WDBC) dataset is used to provide raw breast cancer data in our study.Our proposed model rGWO-KSE (revised Grey Wolf Optimized SVM KNN Ensemble) implements an SVM-KNN ensemble and optimizes it with our newly introduced rGWO (revised Grey Wolf Optimization) technique.The performance of the proposed algorithm is evaluated based on several measures, e.g.accuracy, sensitivity, specificity, f-score and precision.Our model achieves 98.83 % accuracy in detection of breast cancer on WDBC dataset.The comparison with the previous studies for breast cancer detection proves that the proposed study provides better values for performance measures.
A fast matching algorithm for the images with large scale disparity

With the expansion of application areas of unmanned aerial vehicle (UAV) applications, there is a rising demand to realize UAV navigation by means of computer vision. Speeded-Up Robust Features (SURF) is an ideal image matching algorithm to be applied to solve the location for UAV. However, if there is a large scale difference between two images with the same scene taken by UAV and satellite respectively, it is difficult to apply SURF to complete the accurate image matching directly. In this paper, a fast image matching algorithm which can bridge the huge scale gap is proposed. The fast matching algorithm searches an optimal scaling ratio based on the ground distance represented by pixel. Meanwhile, a validity index for validating the performance of matching is given. The experimental results illustrate that the proposed algorithm performs better performance both on speed and accuracy. What's more, the proposed algorithm can also obtain the correct matching results on the images with rotation. Therefore, the proposed algorithm could be applied to location and navigation for UAV in future.
Smemo

Recent improvements in natural language processing pushed the development of a wide range of products and applications that allow natural spoken interaction for children. Previous studies explored conversational agents for children and found out that they are valuable both for playing and learning. In addition, recent researches highlighted that, when we want to develop a conversational tool for children, we must consider that they have different modes from adults and they approach conversational technologies in a different way because of their linguistic skills, their way of conducting dialogues and, more generally, their interaction attitude. In this paper, we describe Smemo, a multi-modal interface for children aged 6 to 9 years old. This tool permits to approach conversational technologies in an original and multi-modal way giving the user the possibility to customize and personalize the agent. The main goal of Smemo is to promote an innovative learning method by putting children at the center of the process.
Ecological Risk Assessment of Mineral and Heavy Metals Levels of Soil Around Auto Mechanic Village Wukari, Nigeria

Environmental contamination is one of the serious challenge facing humanity and other life forms on our planet today. contamination occurs when contaminating substances exceeds their natural levels or when natural resources are use at a rate higher than nature’s capacity to restore itself. This study determines the levels of mineral element and potential toxic metals namely: P, K, Mg, Mn, Si, Fe, Zn, Cd, Pb and Al in agricultural soil around mechanic village Wukari-Nigeria. by means of MP-AES (4210 MP-AES Agilent technologies) while their pollution indices by means of geo-accumulation index and contamination factor. The results reveals that the mean ± standard deviation abundance of mineral and heavy metals in the soil was: Fe (20723.64±153.71), Al (3753.80±30.54) K (368.13±2.17), Mg (298.05±3.88), Mn (231.97±0.74), P (221.22±5.47), Zn (184.83±0.47), Pb (86.29±0.31), Si (64.27±0.43) and Cd (1.33±0.06). There was generally a significant difference between the concentration in the test and control sample (P &lt; 0.05). While pollution indices show moderate to very high contamination of the soil by zinc, lead and cadmium signalling higher potential risk in terms of mobility on acidification since the organic content is moderate and soil texture being sandy-loam usually characterise with moderate capacity to immobilize heavy metals. Lead contamination can result in neurological and hematological dysfunctions, renal and hepatic damages as well as reproductive disorders in the humans while cadmium is known to have effect on kidney.
Improving of steering and nonlocality via local filtering operation in Heisenberg XY model

In this work, we mainly study quantum steering and nonlocality of two-qubit Heisenberg [Formula: see text] spin-1/2 chain via local filtering operation. Our analytical results show that quantum steering and nonlocality can be affected by coupling constant [Formula: see text], temperature [Formula: see text], external magnetic field [Formula: see text] and anisotropy constant [Formula: see text]. Quantum steering and nonlocality would degrade with the increase of temperature [Formula: see text] and anisotropy constant [Formula: see text]. When [Formula: see text] is small, we can observe quantum steering and nonlocality increase with [Formula: see text]. When [Formula: see text] getting bigger, what we will see is that steering goes down first, then grows up. We can improve quantum steering and nonlocality via local filtering operation. We chose an appropriate parameter [Formula: see text], the steering and nonlocality can be improved when we fix three in those four parameters. There is a queer phenomenon in some situations that the range of one side for steering and nonlocality can extend greatly by losing them in another side. Therefore, our investigations might shed light on steering and nonlocality under the Heisenberg [Formula: see text] spin chain model and make a little step in the progress of quantum information.
Vision to Language: Methods, Metrics and Datasets

Alan Turing’s pioneering vision of machines in the 1950s, that are capable of thinking like humans is still what Artificial Intelligence (AI) and Deep Learning research aspires to manifest, 70 years on. With replicating or modeling human intelligence as the ultimate goal, AI’s Holy Grail is to create systems that can perceive and reason about the world like humans and perform tasks such as visual interpretation/processing, speech recognition, decision-making and language understanding. In this quest, two of the dominant subfields of AI, Computer Vision and Natural Language Processing, attempt to create systems that can fully understand the visual world and achieve human-like language processing, respectively. To be able to interpret and describe visual content in natural language is one of the most distinguished capabilities of a human. While humans find it rather easy to accomplish, it is very hard for a machine to mimic this complex process. The past decade has seen significant research effort on the computational tasks that involve translation between and fusion of the two modalities of human communication, namely Vision and Language. Moreover, the unprecedented success of deep learning has further propelled research on tasks that link images to sentences, instead of just tags (as done in Object Recognition and Classification). This chapter discusses the fundamentals of generating natural language description of images as well as the prominent and the state-of-the-art methods, their limitations, various challenges in image captioning and future directions to push this technology further for practical real-world applications. It also serves as a reference to a comprehensive list of data resources for training deep captioning models and metrics that are currently in use for model evaluation.
Density and Time based Traffic Control System using Video Processing

Traffic is the serious issue which each nation faces due to the expansion in number of vehicles. One of the strategies to beat the traffic issue is to build up a smart traffic control framework which depends on computing the traffic density and about utilizing constant video and picture preparing procedures. The topic is to control the traffic by deciding the traffic density on each roadside and control the traffic signal smartly by utilizing the density data. In this paper, an automated system based on processing of real time videos is proposed for detection of vehicles and recording count of them. The System will consist of various stages which includes Object Car Detection and Signal variation based on density. Captured video will be converted into frames and which will be pre-processed for object detection using Haar-Cascade than detected object count will be used to obtain the density and manipulate the signal accordingly. The density count algorithm works by contrasting the ongoing edge of live video by the reference picture and via looking through vehicles just in the district of intrigue (for example street region). The figured vehicle thickness can be contrasted and other course of the traffic so as to perform control of the traffic flags in more smart and proficient manner.
A Feature Based Approach on Behavior Analysis of the Users on Twitter: A Case Study of AusOpen Tennis Championship

Due to the advancement of technology, and the promotion of smartphones, using social media got more and more popular. Nowadays, it has become an undeniable part of people’s lives. So, they will create a flow of information by the content they share every single moment. Analyzing this information helps us to have a better understanding of users, their needs, their tendencies and classify them into different groups based on their behavior. These behaviors are various and due to some extracted features, it is possible to categorize the users into different categories. In this paper, we are going to focus on Twitter users and the AusOpen Tennis championship event as a case study. We define the attributions describing each class and then extract data and identify features that are more correlated to each type of user and then label user type based on the reasoning model. The results contain 4 groups of users; Verified accounts, Influencers, Regular profiles, and Fake profiles.
Symmetric Asynchronous Ratcheted Communication with Associated Data

Following up mass surveillance and privacy issues, modern secure communication protocols now seek strong security, such as forward secrecy and post-compromise security, in the face of state exposures. To address this problem, ratcheting was thereby introduced, widely used in real-world messaging protocols like Signal. However, ratcheting comes with a high cost. Recently, Caforio et al. proposed pragmatic constructions which compose a weakly secure “light” protocol and a strongly secure “heavy” protocol, in order to achieve the so-called ratcheting on demand. The light protocol they proposed has still a high complexity. In this paper, we prove the security of the lightest possible protocol we could imagine, which essentially encrypts then hashes the secret key. We prove it without any random oracle by introducing a new security notion in the standard model. Our protocol composes well with the generic transformation techniques by Caforio et al. to offer high security and performance at the same time.
Evolutionary Multimodal Optimization Based on Bi-Population and Multi-Mutation Differential Evolution

The most critical issue of multimodal evolutionary algorithms (EAs) is to find multiple distinct global optimal solutions in a run.EAs have been considered as suitable tools for multimodal optimization because of their population-based structure.However, EAs tend to converge toward one of the optimal solutions due to the difficulty of population diversity preservation.In this paper, we propose a bi-population and multi-mutation differential evolution (BMDE) algorithm for multimodal optimization problems.The novelties and contribution of BMDE include the following three aspects: First, bi-population evolution strategy is employed to perform multimodal optimization in parallel.The difference between inferior solutions and the current population can be considered as a promising direction toward the optimum.Second, multi-mutation strategy is introduced to balance exploration and exploitation in generating offspring.Third, the update strategy is applied to individuals with high similarity, which can improve the population diversity.Experimental results on CEC2013 benchmark problems show that the proposed BMDE algorithm is better than or at least comparable to the state-of-the-art multimodal algorithms in terms of the quantity and quality of the optimal solutions.
Quantum phase transition and Berry phase in an extended Dicke model

We investigate quantum phase transitions, quantum criticality, and Berry phase for the ground state of an ensemble of non-interacting two-level atoms embedded in a non-linear optical medium, coupled to a single-mode quantized electromagnetic field. The optical medium is pumped externally through a classical electric field, so that there is a degenerate parametric amplification effect, which strongly modifies the field dynamics without affecting the atomic sector. Through a semiclassical description the different phases of this extended Dicke model are described. The quantum phase transition is characterized with the expectation values of some observables of the system as well as the Berry phase and its first derivative, where such quantities serve as order parameters. It is remarkable that the model allows the control of the quantum criticality through a suitable choice of the parameters of the non-linear optical medium, which could make possible the use of a low intensity laser to access the superradiant region experimentally.
Trajectory Planning with Safety Guaranty for a Multirotor based on the Forward and Backward Reachability Analysis

Planning a trajectory with guaranteed safety is a core part for a risk-free flight of a multirotor. If a trajectory planner only aims to ensure safety, it may generate trajectories which overly bypass risky regions and prevent the system from achieving specific missions. This work presents a robust trajectory planning algorithm which simultaneously guarantees the safety and reachability to the target state in the presence of unknown disturbances. We first characterize how the forward and backward reachable sets (FRSs and BRSs) are constructed by using Hamilton-Jacobi reachability analysis. Based on the analysis, we present analytic expressions for the reachable sets and then propose minimal ellipsoids which closely approximate the reachable sets. In the planning process, we optimize the reference trajectory to connect the FRSs and BRSs, while avoiding obstacles. By combining the FRSs and BRSs, we can guarantee that any state inside of the initial set reaches the target set. We validate the proposed algorithm through a simulation of traversing a narrow gap.
Negation Handling in Machine Learning-Based Sentiment Classification for Colloquial Arabic

One crucial aspect of sentiment analysis is negation handling, where the occurrence of negation can flip the sentiment of a review and negatively affects the machine learning-based sentiment classification. The role of negation in Arabic sentiment analysis has been explored only to a limited extent, especially for colloquial Arabic. In this paper, the authors address the negation problem in colloquial Arabic sentiment classification using the machine learning approach. To this end, they propose a simple rule-based algorithm for handling the problem that affects the performance of a machine learning classifier. The rules were crafted based on observing many cases of negation, simple linguistic knowledge, and sentiment lexicon. They also examine the impact of the proposed algorithm on the performance of different machine learning algorithms. Furthermore, they compare the performance of the classifiers when their algorithm is used against three baselines. The experimental results show that there is a positive impact on the classifiers when the proposed algorithm is used compared to the baselines.
Tensor Fields for Data Extraction from Chart Images: Bar Charts and Scatter Plots

Charts are an essential part of both graphicacy (graphical literacy), and statistical literacy. As chart understanding has become increasingly relevant in data science, automating chart analysis by processing raster images of the charts has become a significant problem. Automated chart reading involves data extraction and contextual understanding of the data from chart images. In this paper, we perform the first step of determining the computational model of chart images for data extraction for selected chart types, namely, bar charts, and scatter plots. We demonstrate the use of positive semidefinite second-order tensor fields as an effective model. We identify an appropriate tensor field as the model and propose a methodology for the use of its degenerate point extraction for data extraction from chart images. Our results show that tensor voting is effective for data extraction from bar charts and scatter plots, and histograms, as a special case of bar charts.
Application of Text Mining to Nursing Texts

Hyun, Sookyung RN, PhD; Cooper, Cheryl APRN-CNS, MSN, RNEditor(s): Alexander, Susan DNP, ANP-BC, ADM-BC Author Information
Overview of the NLPCC 2020 Shared Task: Multi-Aspect-Based Multi-Sentiment Analysis (MAMS)

In this paper, we present an overview of the NLPCC 2020 shared task on Multi-Aspect-based Multi-Sentiment Analysis (MAMS). The evaluation consists of two sub-tasks: (1) aspect term sentiment analysis (ATSA) and (2) aspect category sentiment analysis (ACSA). We manually annotated a large-scale restaurant reviews corpus for MAMS, in which each sentence contains at least two different aspects with different sentiment polarities. Thus, the provided MAMS dataset is more challenging than the existing aspect-based sentiment analysis (ABSA) datasets. MAMS attracted a total of 50 teams to participate in the evaluation task. We believe that MAMS will push forward the research in the field of aspect-based sentiment analysis.
DoF-Dependent and Equal-Partition Based Lens Distortion Modeling and Calibration Method for Close-Range Photogrammetry

Lens distortion is closely related to the spatial position of depth of field (DoF), especially in close-range photography. The accurate characterization and precise calibration of DoF-dependent distortion are very important to improve the accuracy of close-range vision measurements. In this paper, to meet the need of short-distance and small-focal-length photography, a DoF-dependent and equal-partition based lens distortion modeling and calibration method is proposed. Firstly, considering the direction along the optical axis, a DoF-dependent yet focusing-state-independent distortion model is proposed. By this method, manual adjustment of the focus and zoom rings is avoided, thus eliminating human errors. Secondly, considering the direction perpendicular to the optical axis, to solve the problem of insufficient distortion representations caused by using only one set of coefficients, a 2D-to-3D equal-increment partitioning method for lens distortion is proposed. Accurate characterization of DoF-dependent distortion is thus realized by fusing the distortion partitioning method and the DoF distortion model. Lastly, a calibration control field is designed. After extracting line segments within a partition, the de-coupling calibration of distortion parameters and other camera model parameters is realized. Experiment results shows that the maximum/average projection and angular reconstruction errors of equal-increment partition based DoF distortion model are 0.11 pixels/0.05 pixels and 0.013°/0.011°, respectively. This demonstrates the validity of the lens distortion model and calibration method proposed in this paper.