Reviewerly/exHarmony huggingface dataset:
Crowd Behavior Analysis based on Convolutional Neural Network: Social Distancing Control COVID-19

The outbreak of the COVID-19 and the lack of pharmaceutical intervention increase the spread of COVID-19. Since no vaccine or treatment are yet available, social distancing represents a good strategy to control the propagation of this pandemic and learn to live with it. In this context, we introduce a new approach for crowd behavior analysis from UAV-captured video sequences in order to monitor social distancing. The proposed approach involves two methods: a macroscopic method and a microscopic method. The macroscopic method aims to estimate the crowd density by classifying the aerial frame patches into four categories: Dense, Sparse, Medium and None. However, the microscopic method allows to detect and track humans and then compute the distance between them. The quantitative and qualitative results validate the performance of our methods compared to the state-of-the-art references.
Few-Shot Object Detection via Sample Processing

Few-shot object detection (FSOD) eliminates the dependence on tremendous instances with manual annotations in conventional object detection. We deem that the scarcity of positive samples is the main reason that restricts the performance of FSOD detectors. In this paper, a novel FSOD model via sample processing, namely, FSSP, is proposed to detect objects accurately with only a few annotated samples, which is based on the structural design of the Siamese network and uses YOLOv3-SPP as the baseline. Central to FSSP are our designed self-attention (SAM) and positive-sample augmentation (PSA) modules. The former attempts to better extract the representative features of hard samples, and the latter expands the number and enriches the scale distribution of positive samples, inhibiting the growth of negative samples. For the fine-tuning phase, we modify the classification loss function to increase the punishment for hard samples. Experiments conducted on the PASCAL VOC and MS COCO datasets confirm that the proposed FSSP achieves competitive detection performance compared with state-of-the-art detectors.
Library Marketing: Sending Text Messages and Emails to Online Library Users During COVID-19

Before the start of the COVID-19 pandemic, the College’s Hirons Library & Learning Center (HLLC) library marketing efforts included word of mouth, information literacy instruction session, physical and electronic displays, and announcement on the library webpage to library users. In March 2020, due to the COVID-19 pandemic, the HLLC, in collaboration with the Office of Institutional Research & Training (OIRT), designed two new library marketing campaigns using the project management app, Notion. Both campaigns were designed to meet the new and changing needs of our new, predominately remote, users. In the first library marketing campaign, the HLLC’s marketing goal was very focused and deliberate in purpose. The library emailed the College community messages to increase awareness and usage of the library’s eBook collections. In the second library marketing campaign, the library broadened the purpose of the marketing campaigned to promote online resources, tools, and virtual programs. The library sent text messages to the students. In this edition of the column, we describe how our marketing efforts have changed due to the pandemic, including which strategies we employed, and the extent to which we recognized success with these new efforts.
Improving the Decision-Making Process in the Higher Learning Institutions via Electronic Records Management System Adoption

Electronic Records Management System (ERMS) is a computer program or set of applications that is utilized for keeping up to date records along with their storage.ERMS has been extensively utilized for enhancing the performance of academic institutions.The system assists in the planning and decision-making processes, which in turn enhances the competencies.However, although ERMS is significant in supporting the process of decision-making, the majority of organizations have failed to take an initiative to implement it, taking into account that are some implementing it without an appropriate framework, and thus resulted in the practice which does not meet the accepted standard.Therefore, this study identifies the factors influencing the adoption of ERMS among employees of HLI in Yemen and the role of such adoption in the decision-making process, using the Unified Theory of Acceptance and Use of Technology (UTAUT) along with Technology, Organization and Environment (TOE) as the underpinning theories.The study conducts a cross-sectional survey with a questionnaire as the technique for data collection, distributed to 364 participants in various Yemeni public Higher Learning Institutions (HLI).Using AMOS as a statistical method, the findings revealed there are significant and positive relationships between technology factors (effort expectancy, performance expectancy, IT infrastructure and security), organizational factors (top management support, financial support, training, and policy),environmental factors (competitiveness pressure, facilitating conditions and trust) and behavioral intention to adopt ERMS, which in return has a significant relationship with the process of decision-making in HLI.The study also presents a variety of theoretical and empirical contributions that enrich the body of knowledge in the field of technology adoption and the electronic record's domain.
Generalizable control for multiparameter quantum metrology

Quantum control can be employed in quantum metrology to improve the precision limit for the estimation of unknown parameters. The optimal control, however, typically depends on the actual values of the parameters and thus needs to be designed adaptively with the updated estimations of those parameters. Traditional methods, such as gradient ascent pulse engineering (GRAPE), need to be rerun for each new set of parameters encountered, making the optimization costly, especially when many parameters are involved. Here we study the generalizability of optimal control, namely, optimal controls that can be systematically updated across a range of parameters with minimal cost. In cases where control channels can completely reverse the shift in the Hamiltonian due to a change in parameters, we provide an analytical method which efficiently generates optimal controls for any parameter starting from an initial optimal control found by either GRAPE or reinforcement learning. When the control channels are restricted, the analytical scheme is invalid, but reinforcement learning still retains a level of generalizability, albeit in a narrower range. In cases where the shift in the Hamiltonian is impossible to decompose to available control channels, no generalizability is found for either the reinforcement learning or the analytical scheme. We argue that the generalization of reinforcement learning is through a mechanism similar to the analytical scheme. Our results provide insights into when and how the optimal control in multiparameter quantum metrology can be generalized, thereby facilitating efficient implementation of optimal quantum estimation of multiple parameters, particularly for an ensemble of systems with ranges of parameters.
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation

We provide several convergence theorems for SGD for two large classes of structured non-convex functions: (i) the Quasar (Strongly) Convex functions and (ii) the functions satisfying the Polyak-Lojasiewicz condition. Our analysis relies on the Expected Residual condition which we show is a strictly weaker assumption as compared to previously used growth conditions, expected smoothness or bounded variance assumptions. We provide theoretical guarantees for the convergence of SGD for different step size selections including constant, decreasing and the recently proposed stochastic Polyak step size. In addition, all of our analysis holds for the arbitrary sampling paradigm, and as such, we are able to give insights into the complexity of minibatching and determine an optimal minibatch size. In particular we recover the best known convergence rates of full gradient descent and single element sampling SGD as a special case. Finally, we show that for models that interpolate the training data, we can dispense of our Expected Residual condition and give state-of-the-art results in this setting.
Analytical framework for quantum alternating operator ansätze

Abstract We develop a framework for analyzing layered quantum algorithms such as quantum alternating operator ansätze (QAOA). In the context of combinatorial optimization, our framework relates quantum cost gradient operators, derived from the cost and mixing Hamiltonians, to classical cost difference functions that reflect cost function neighborhood structure. By considering QAOA circuits from the Heisenberg picture, we derive exact general expressions for expectation values as series expansions in the algorithm parameters, cost gradient operators, and cost difference functions. This enables novel interpretability and insight into QAOA behavior in various parameter regimes. For single-level QAOA 1 we show the leading-order changes in the output probabilities and cost expectation value explicitly in terms of classical cost differences, for arbitrary cost functions. This demonstrates that, for sufficiently small positive parameters, probability flows from lower to higher cost states on average. By selecting signs of the parameters, we can control the direction of flow. We use these results to derive a classical random algorithm emulating QAOA 1 in the small-parameter regime, i.e. that produces bitstring samples with the same probabilities as QAOA 1 up to small error. For deeper QAOA p circuits we apply our framework to derive analogous and additional results in several settings. In particular we show QAOA always beats random guessing. We describe how our framework incorporates cost Hamiltonian locality for specific problem classes, including causal cone approaches, and applies to QAOA performance analysis with arbitrary parameters. We illuminate our results with a number of examples including applications to QUBO problems, MaxCut, and variants of MaxSAT. We illustrate the generalization of our framework to QAOA circuits using mixing unitaries beyond the transverse-field mixer through two examples of constrained optimization problems, Max Independent Set and Graph Coloring. We conclude by outlining some of the further applications we envision for the framework.
Abusive content detection in transliterated Bengali-English social media corpus

Abusive text detection in low-resource languages such as Bengali is a challenging task due to the inadequacy of resources and tools.The ubiquity of transliterated Bengali comments in social media makes the task even more involved as monolingual approaches cannot capture them.Unfortunately, no transliterated Bengali corpus is publicly available yet for abusive content analysis.Therefore, in this paper, we introduce an annotated corpus of 3000 transliterated Bengali comments categorized into two classes, abusive and non-abusive, 1500 comments for each.For baseline evaluations, we employ several supervised machine learning (ML) and deep learning-based classifiers.We find support vector machine (SVM) classifier shows the highest efficacy for identifying abusive content.We make the annotated corpus publicly available for the researchers to aid abusive content detection in Bengali social media data.
An Exploratory Analysis of the Relation between Offensive Language and Mental Health

In this paper, we analyze the interplay between the use of offensive language and mental health.We acquired publicly available datasets created for offensive language identification and depression detection and we train computational models to compare the use of offensive language in social media posts written by groups of individuals with and without self-reported depression diagnosis.We also look at samples written by groups of individuals whose posts show signs of depression according to recent related studies.Our analysis indicates that offensive language is more frequently used in the samples written by individuals with self-reported depression as well as individuals showing signs of depression.The results discussed here open new avenues in research in politeness/offensiveness and mental health.
Adversarial Partial Multi-Label Learning with Label Disambiguation

Partial multi-label learning (PML), which tackles the problem of learning multi-label prediction models from instances with overcomplete noisy annotations, has recently started gaining attention from the research community. In this paper, we propose a novel adversarial learning model, PML-GAN, under a generalized encoder-decoder framework for partial multi-label learning. The PML-GAN model uses a disambiguation network to identify irrelevant labels and uses a multi-label prediction network to map the training instances to their disambiguated label vectors, while deploying a generative adversarial network as an inverse mapping from label vectors to data samples in the input feature space. The learning of the overall model corresponds to a minimax adversarial game, which enhances the correspondence of input features with the output labels in a bi-directional mapping. Extensive experiments are conducted on both synthetic and real-world partial multi-label datasets, while the proposed model demonstrates the state-of-the-art performance.
Research on path planning of three-neighbor search A* algorithm combined with artificial potential field

Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal results.
Hough Transform-Based Angular Features for Learning-Free Handwritten Keyword Spotting

Handwritten keyword spotting (KWS) is of great interest to the document image research community. In this work, we propose a learning-free keyword spotting method following query by example (QBE) setting for handwritten documents. It consists of four key processes: pre-processing, vertical zone division, feature extraction, and feature matching. The pre-processing step deals with the noise found in the word images, and the skewness of the handwritings caused by the varied writing styles of the individuals. Next, the vertical zone division splits the word image into several zones. The number of vertical zones is guided by the number of letters in the query word image. To obtain this information (i.e., number of letters in a query word image) during experimentation, we use the text encoding of the query word image. The user provides the information to the system. The feature extraction process involves the use of the Hough transform. The last step is feature matching, which first compares the features extracted from the word images and then generates a similarity score. The performance of this algorithm has been tested on three publicly available datasets: IAM, QUWI, and ICDAR KWS 2015. It is noticed that the proposed method outperforms state-of-the-art learning-free KWS methods considered here for comparison while evaluated on the present datasets. We also evaluate the performance of the present KWS model using state-of-the-art deep features and it is found that the features used in the present work perform better than the deep features extracted using InceptionV3, VGG19, and DenseNet121 models.
Automatic recognition of pulse repetition interval modulation using temporal convolutional network

Pulse Repetition Interval (PRI) modulation recognition is a key issue in radar identification process in modern electronic intelligent (ELINT) and electronic support measure (ESM) systems. In this study, a novel approach based on the intrinsic property of the temporal convolutional network (TCN) is presented for PRI modulation type recognition. Since a causal TCN is used for this purpose, the method is suitable for online ESM and ELINT analysis. The simulation results show that the method accurately classifies seven types of PRI modulation including simple, dwell and switch, stagger, jitter, agile, sliding and periodic modulations in realistic scenarios. In order to investigate the performance of the method when the problem of missing or spurious pulses occurs, the impacts of these problems are examined, separately and together, on the recognition process. It is shown that the method works effectively even with a relatively high percentage of missing and/or spurious pulses (up to 30%). The method also works effectively in the presence of unintentional jitter and large outliers, which can be due to radar antenna scan. The experimental results on real data confirm that the proposed method performs accurately and effectively in real world scenarios.
Human Gender Detection from Facial Images Using Convolution Neural Network

Human gender detection which is a part of facial recognition has received extensive attention because of it’s different kind of application. Previous research works on gender detection have been accomplished based on different static body feature for example face, eyebrow, hand-shape, body-shape, finger nail etc. In this research work, we have presented human gender classification using Convolution Neural Network (CNN) from human face images as CNN has been recognised as best algorithm in the field of image classification. To implement our system, at first a pre-processing technique has been applied on each image using image processing. The pre-processed image is passed through the Convolution, RELU and Pooling layer for feature extraction. A fully connected layer and a classifier is applied in the classification part of the image. To obtain a better result, we have implemented our system using different optimizers and also have used k fold cross-validation as deep learning approach. The whole method has been evaluated on two dataset collected from Kaggle website and Nottingham Scan Database. The experimented result shows a highest accuracy which is 97.44% using Kaggle dataset and 90% accuracy using Nottingham Scan Database.
Question Answering System for Chemistry

This paper describes the implementation and evaluation of a proof-of-concept Question Answering (QA) system for accessing chemical data from knowledge graphs (KGs) which offer data from chemical kinetics to the chemical and physical properties of species. We trained the question classification and named the entity recognition models that specialize in interpreting chemistry questions. The system has a novel design which applies a topic model to identify the question-to-ontology affiliation to handle ontologies with different structures. The topic model also helps the system to provide answers with a higher quality. Moreover, a new method that automatically generates training questions from ontologies is also implemented. The question set generated for training contains 432,989 questions under 11 types. Such a training set has been proven to be effective for training both the question classification model and the named entity recognition model. We evaluated the system using other KGQA systems as baselines. The system outperforms the chosen KGQA system answering chemistry-related questions. The QA system is also compared to the Google search engine and the WolframAlpha engine. It shows that the QA system can answer certain types of questions better than the search engines.
Automatic Detection Method of Technical and Tactical Indicators for Table Tennis Based on Trajectory Prediction Using Compensation Fuzzy Neural Network

In the system design of table tennis robot, the important influencing factors of automatic detection of technical and tactical indicators for table tennis are table tennis rotation state, trajectory, and rebound force. But the general prediction algorithm cannot process the time series data and give the corresponding rotation state. Therefore, this paper studies the automatic detection method of technical and tactical indicators for table tennis based on the trajectory prediction using the compensation fuzzy neural network. In this paper, the compensation fuzzy neural network algorithm which combines the compensation fuzzy algorithm and recurrent neural network is selected to construct the automatic detection of technical and tactical indicators for table tennis. The experimental results show that the convergence time of the compensation fuzzy neural network is shorter, the training time is shortened, and the prediction accuracy is improved. At the same time, in terms of performance testing, the model can accurately distinguish the influence of table tennis rotation state and rebound on table tennis motion estimation, so as to improve the accuracy of motion trajectory prediction. In addition, the accuracy of trajectory prediction will be improved with the increase of input data. When the number of data reaches 30, the trajectory prediction error is within the actual acceptable error range.
Adaptive Light Estimation using Dynamic Filtering for Diverse Lighting Conditions

High dynamic range (HDR) panoramic environment maps are widely used to illuminate virtual objects to blend with real-world scenes. However, in common applications for augmented and mixed-reality (AR/MR), capturing 360° surroundings to obtain an HDR environment map is often not possible using consumer-level devices. We present a novel light estimation method to predict 360° HDR environment maps from a single photograph with a limited field-of-view (FOV). We introduce the Dynamic Lighting network (DLNet), a convolutional neural network that dynamically generates the convolution filters based on the input photograph sample to adaptively learn the lighting cues within each photograph. We propose novel Spherical Multi-Scale Dynamic (SMD) convolutional modules to dynamically generate sample-specific kernels for decoding features in the spherical domain to predict 360° environment maps. Using DLNet and data augmentations with respect to FOV, an exposure multiplier, and color temperature, our model shows the capability of estimating lighting under diverse input variations. Compared with prior work that fixes the network filters once trained, our method maintains lighting consistency across different exposure multipliers and color temperature, and maintains robust light estimation accuracy as FOV increases. The surrounding lighting information estimated by our method ensures coherent illumination of 3D objects blended with the input photograph, enabling high fidelity augmented and mixed reality supporting a wide range of environmental lighting conditions and device sensors.