Reviewerly/exHarmony huggingface dataset:
An integrated theory of deciding and acting.

This article presents a theory in which motor execution in perceptual decision-making tasks is determined by the same evolving decision variable that drives response time. The theory builds upon recent insights from the neuroscience of decision-making and motor control. It is formalized as an extension of Ratcliff's diffusion model, and assumes that two thresholds operate on the evidence accumulation decision variable. The first threshold, referred to as electromyographic (EMG) threshold, marks the onset of electrical activity in the response-relevant muscle and the beginning of force production. The second threshold corresponds to the response. The theory makes several benchmark predictions. Notably, the mean duration of motor execution, as quantified by the mean latency between EMG onset and the response, should depend on the rate of evidence accumulation, and should thus increase as the perceptual difficulty of the task increases. We tested these predictions in a paradigmatic perceptual decision-making task, the random dot motion task, and recorded the EMG activity of response-relevant muscles. The behavioral and EMG data provide very strong evidence for each prediction. A final quantitative evaluation of the model showed good fits to these data. The theory resolves conflicting findings in the fields of mathematical psychology, motor control, and decision neurosciences. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Accuracy and Repeatability Tests on HoloLens 2 and HTC Vive

Augmented and virtual reality have been experiencing rapid growth in recent years, but there is still no deep knowledge regarding their capabilities and in what fields they could be explored. In that sense, this paper presents a study on the accuracy and repeatability of Microsoft’s HoloLens 2 (augmented reality device) and HTC Vive (virtual reality device) using an OptiTrack system as ground truth. For the HoloLens 2, the method used was hand tracking, whereas, in HTC Vive, the object tracked was the system’s hand controller. A series of tests in different scenarios and situations were performed to explore what could influence the measures. The HTC Vive obtained results in the millimeter range, while the HoloLens 2 revealed not very accurate measurements (around 2 cm). Although the difference can seem to be considerable, the fact that HoloLens 2 was tracking the user’s hand and not the system’s controller made a huge impact. The results are considered a significant step for the ongoing project of developing a human–robot interface by demonstrating an industrial robot using extended reality, which shows great potential to succeed based on our data.
Modeling Sequences as Distributions with Uncertainty for Sequential Recommendation

The sequential patterns within the user interactions are pivotal for representing the user's preference and capturing latent relationships among items. The recent advancements of sequence modeling by Transformers advocate the community to devise more effective encoders for the sequential recommendation. Most existing sequential methods assume users are deterministic. However, item-item transitions might fluctuate significantly in several item aspects and exhibit randomness of user interests. This stochastic characteristics brings up a solid demand to include uncertainties in representing sequences and items. Additionally, modeling sequences and items with uncertainties expands users' and items' interaction spaces, thus further alleviating cold-start problems.
AttacKG: Constructing Technique Knowledge Graph from Cyber Threat Intelligence Reports

Cyber attacks are becoming more sophisticated and diverse, making attack detection increasingly challenging. To combat these attacks, security practitioners actively summarize and exchange their knowledge about attacks across organizations in the form of cyber threat intelligence (CTI) reports. However, as CTI reports written in natural language texts are not structured for automatic analysis, the report usage requires tedious manual efforts of threat intelligence recovery. Additionally, individual reports typically cover only a limited aspect of attack patterns (e.g., techniques) and thus are insufficient to provide a comprehensive view of attacks with multiple variants. In this paper, we propose AttacKG to automatically extract structured attack behavior graphs from CTI reports and identify the associated attack techniques. We then aggregate threat intelligence across reports to collect different aspects of techniques and enhance attack behavior graphs into technique knowledge graphs (TKGs). In our evaluation against real-world CTI reports from diverse intelligence sources, AttacKG effectively identifies 28,262 attack techniques with 8,393 unique Indicators of Compromises. To further verify the accuracy of AttacKG in extracting threat intelligence, we run AttacKG on 16 manually labeled CTI reports. Experimental results show that AttacKG accurately identifies attack-relevant entities, dependencies, and techniques with F1-scores of 0.887, 0.896, and 0.789, which outperforms the state-of-the-art approaches. Moreover, our TKGs directly benefit downstream security practices built atop attack techniques, e.g., advanced persistent threat detection and cyber attack reconstruction.
Harris Hawk Optimization: A Survey onVariants and Applications

In this review, we intend to present a complete literature survey on the conception and variants of the recent successful optimization algorithm, Harris Hawk optimizer (HHO), along with an updated set of applications in well-established works. For this purpose, we first present an overview of HHO, including its logic of equations and mathematical model. Next, we focus on reviewing different variants of HHO from the available well-established literature. To provide readers a deep vision and foster the application of the HHO, we review the state-of-the-art improvements of HHO, focusing mainly on fuzzy HHO and a new intuitionistic fuzzy HHO algorithm. We also review the applications of HHO in enhancing machine learning operations and in tackling engineering optimization problems. This survey can cover different aspects of HHO and its future applications to provide a basis for future research in the development of swarm intelligence paths and the use of HHO for real-world problems.
Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network

With the increasing popularity of posting multimodal messages online, many recent studies have been carried out utilizing both textual and visual information for multi-modal sarcasm detection.In this paper, we investigate multimodal sarcasm detection from a novel perspective by constructing a cross-modal graph for each instance to explicitly draw the ironic relations between textual and visual modalities.Specifically, we first detect the objects paired with descriptions of the image modality, enabling the learning of important visual information.Then, the descriptions of the objects are served as a bridge to determine the importance of the association between the objects of image modality and the contextual words of text modality, so as to build a cross-modal graph for each multi-modal instance.Furthermore, we devise a cross-modal graph convolutional network to make sense of the incongruity relations between modalities for multi-modal sarcasm detection.Extensive experimental results and in-depth analysis show that our model achieves state-of-the-art performance in multi-modal sarcasm detection 1 .
DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition

Unconstrained handwritten text recognition is a challenging computer vision task. It is traditionally handled by a two-step approach, combining line segmentation followed by text line recognition. For the first time, we propose an end-to-end segmentation-free architecture for the task of handwritten document recognition: the Document Attention Network. In addition to text recognition, the model is trained to label text parts using begin and end tags in an XML-like fashion. This model is made up of an FCN encoder for feature extraction and a stack of transformer decoder layers for a recurrent token-by-token prediction process. It takes whole text documents as input and sequentially outputs characters, as well as logical layout tokens. Contrary to the existing segmentation-based approaches, the model is trained without using any segmentation label. We achieve competitive results on the READ 2016 dataset at page level, as well as double-page level with a CER of 3.43% and 3.70%, respectively. We also provide results for the RIMES 2009 dataset at page level, reaching 4.54% of CER. We provide all source code and pre-trained model weights at https://github.com/FactoDeepLearning/DAN.
Recommendation System Based on Singular Value Decomposition and Multi-Objective Immune Optimization

Recommendation system plays a significant role in helping people to get effective information from mass data.Traditional recommendation systems focus on the recommendation accuracy, which is not sufficient.In this paper, we also consider the various needs of users to achieve more diverse recommendation.However, accuracy and diversity are two conflicting goals for recommendation system.Hence, we model the recommendation system as a multi-objective optimization problem, and aim to find tradeoff solutions between the two goals.Because the rating matrix is rather sparser in recommendation, we first use singular value decomposition to get the recommendation list, then multi-objective immune algorithm is used to optimize it.The experimental results illustrate that the proposed algorithm can get more diverse and accurate recommendation results.
Redundant Robot Assignment on Graphs with Uncertain Edge Costs

We provide a framework for the assignment of multiple robots to goal locations, when robot travel times are uncertain. Our premise is that time is the most valuable asset in the system. Hence, we make use of redundant robots to counter the effect of uncertainty and minimize the average waiting time at destinations. We apply our framework to transport networks represented as graphs, and consider uncertainty in the edge costs (i.e., travel time). Since solving the redundant assignment problem is strongly NP-hard, we exploit structural properties of our problem to propose a polynomial-time solution with provable sub-optimality bounds. Our method uses distributive aggregate functions, which allow us to efficiently (i.e., incrementally) compute the effective cost of assigning redundant robots. Experimental results on random graphs show that the deployment of redundant robots through our method reduces waiting times at goal locations, when edge traversals are uncertain.
Generic Primitive Detection in Point Clouds Using Novel Minimal Quadric Fits

We present a novel and effective method for detecting 3D primitives in cluttered, unorganized point clouds, without axillary segmentation or type specification. We consider the quadric surfaces for encapsulating the basic building blocks of our environments - planes, spheres, ellipsoids, cones or cylinders, in a unified fashion. Moreover, quadrics allow us to model higher degree of freedom shapes, such as hyperboloids or paraboloids that could be used in non-rigid settings. We begin by contributing two novel quadric fits targeting 3D point sets that are endowed with tangent space information. Based upon the idea of aligning the quadric gradients with the surface normals, our first formulation is exact and requires as low as four oriented points. The second fit approximates the first, and reduces the computational effort. We theoretically analyze these fits with rigor, and give algebraic and geometric arguments. Next, by re-parameterizing the solution, we devise a new local Hough voting scheme on the null-space coefficients that is combined with RANSAC, reducing the complexity from O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ) to O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) (three points). To the best of our knowledge, this is the first method capable of performing a generic cross-type multi-object primitive detection in difficult scenes without segmentation. Our extensive qualitative and quantitative results show that our method is efficient and flexible, as well as being accurate.
Deep 3D perception of people and their mobility aids

Robots operating in populated environments, such as hospitals, office environments or airports, encounter a large variety of people with some of them having an advanced need for cautious interaction because of their advanced age or motion impairments. To provide appropriate assistance and support robot helpers require the ability to recognize people and their potential requirements. In this article, we present a people detection framework that distinguishes people according to the mobility aids they use. Our framework uses a deep convolutional neural network for detecting people in image data. For human-aware robots it is necessary to know where people are in a 3D world reference frame instead of only locating them in a 2D image, therefore we add a 3D centroid regression output to the network to predict the Cartesian position of people. We further use a probabilistic class, position and velocity tracker to account for false detections and occlusions. Our framework comes in two variants: The depth only variant targets high privacy demands, while the RGB only framework provides improved detection performance for non-critical applications. Both variants do not require additional geometric information about the environment. We demonstrate our approach using a dedicated dataset acquired with the support of a mobile robotic platform. The dataset contains five classes: pedestrian, person in wheelchair, pedestrian pushing a person in a wheelchair, person using crutches and person using a walking frame. Our framework achieves an mAP of 0.87 for RGB and 0.79 for depth images at a detection distance threshold of 0.5 m on our dataset, with a runtime of 53 ms per image. The annotated dataset is publicly available and our framework is made open source as a ROS people detector.
Collaborative Sensing with Interactive Learning using Dynamic Intelligent Virtual Sensors

Although the availability of sensor data is becoming prevalent across many domains, it still remains a challenge to make sense of the sensor data in an efficient and effective manner in order to provide users with relevant services. The concept of virtual sensors provides a step towards this goal, however they are often used to denote homogeneous types of data, generally retrieved from a predetermined group of sensors. The DIVS (Dynamic Intelligent Virtual Sensors) concept was introduced in previous work to extend and generalize the notion of a virtual sensor to a dynamic setting with heterogenous sensors. This paper introduces a refined version of the DIVS concept by integrating an interactive machine learning mechanism, which enables the system to take input from both the user and the physical world. The paper empirically validates some of the properties of the DIVS concept. In particular, we are concerned with the distribution of different budget allocations for labelled data, as well as proactive labelling user strategies. We report on results suggesting that a relatively good accuracy can be achieved despite a limited budget in an environment with dynamic sensor availability, while proactive labeling ensures further improvements in performance.
Adaptive synchronization control with optimization policy for fractional-order chaotic systems between<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e671" altimg="si5.gif"><mml:mi mathvariant="bold">0</mml:mi></mml:math>and<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e676" altimg="si6.gif"><mml:mi mathvariant="bold">1</mml:mi></mml:math>and its application in secret communication

For fractional-order chaotic systems with unequal orders between 0 and 1, a synchronization based on them and its application in secret communication are investigated in the presence of parameter uncertainty and unknown external disturbance. Firstly, through using fractional calculus theory and adaptive control method, we design a feedback controller to achieve the synchronization between the systems. Next, in order to make the controller better apply to secret communication, an optimization policy is provided for it by some performance indicator about state variables in the error system. Meanwhile, to further enhance the performance of the controller, we introduce an interval estimation (IE) strategy into quantum particle swarm optimization (QPSO) algorithm to improve it, and then this algorithm is used to optimize the controller parameters. In addition, for the security of signal transmission, a feedback mechanism with encrypted signal is offered in secret communication. Finally, by comparison, simulation experiments verify the validity and applicability of the proposed method.
Meta-analysis of Student Performance Assessment Using Fuzzy Logic

The assessment system generally requires transparency and objectivity to assess student performance in terms of abstraction. Fuzzy logic method has been used as one of the best methods to reduce this uncertainty. Therefore, we have conducted a literature review to examine the application of fuzzy logic in assessing the performance of different students. The Preferred Reporting Items for the Systematics Review and Meta-Analysis Method (PRISMA) were used as the basic method for conducting systematic reviews and meta-analyses. The articles reviewed were 38 articles from 2008 until 2018. All articles were classified based on the author, year of publication, type of journal or conference, sample size, context, data type, fuzzy technique, and basic findings. The results of this review show the positive effects of using fuzzy logic on student performance assessment. Overall, this review provides an appropriate reference for further research by identifying research needs in aspects of student performance assessment.
Highly Effective

Hamdy Mubarak, Ahmed Abdelali, Hassan Sajjad, Younes Samih, Kareem Darwish. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Improved Disease Classification in Chest X-Rays with Transferred Features from Report Generation

Radiology includes using medical images for detection and diagnosis of diseases as well as guiding further interventions. Chest X-rays are commonly used radiological examinations to help spot thoracic abnormalities or diseases, especially lung-related diseases. However, the reporting of chest x-rays requires experienced radiologists who are often in shortage in many regions of the world. In this paper, we first develop an automatic radiology report generation system. Due to the lack of large annotated radiology report datasets and the difficulty of evaluating the generated reports, the clinical value of such systems is often limited. To this end, we train our report generation network on the small IU Chest X-ray dataset then transfer the learned visual features to classification networks trained on the large ChestX-ray14 dataset and use a novel attention guided feature fusion strategy to improve the detection performance of 14 common thoracic diseases. Through learning the correspondences between different types of feature representations, common features learned by both the report generation and the classification model are assigned with higher attention weights and the weighted visual features boost the performance of state-of-the-art baseline thoracic disease classification networks without altering any learned features. Our work not only offers a new way to evaluate the effectiveness of the learned radiology report generation network, but also proves the possibility of transferring different types of visual representations learned on a small dataset for one task to complement features learned on another large dataset for a different task and improve the model performance.
A Kalman-filtering derivation of simultaneous input and state estimation

Simultaneous input and state estimation algorithms are studied as particular limits of Kalman filtering problems. This admits interpretation of the algorithm properties and critical analysis of their claims to being partly model-free and to providing unbiased estimates. A disturbance model, white noise of unbounded variance, is provided and the bias feature is shown to be a geometric projection property rather than probabilistic in nature. As a consequence of this analysis, the algorithm is connected, in the stationary case, to Algebraic Riccati equation computations for the gains, estimate covariances and filter frequency response.
Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System

Developing effective and efficient recommendation methods is very challenging for modern e-commerce platforms. Generally speaking, two essential modules named “ClickThrough Rate Prediction” (CTR) and “Conversion Rate Prediction” (CVR) are included, where CVR module is a crucial factor that affects the final purchasing volume directly. However, it is indeed very challenging due to its sparseness nature. In this paper, we tackle this problem by proposing multiLevel Deep Cascade Trees (ldcTree), which is a novel decision tree ensemble approach. It leverages deep cascade structures by stacking Gradient Boosting Decision Trees (GBDT) to effectively learn feature representation. In addition, we propose to utilize the cross-entropy in each tree of the preceding GBDT as the input feature representation for next level GBDT, which has a clear explanation, i.e., a traversal from root to leaf nodes in the next level GBDT corresponds to the combination of certain traversals in the preceding GBDT. The deep cascade structure and the combination rule enable the proposed ldcTree to have a stronger distributed feature representation ability. Moreover, inspired by ensemble learning, we propose an Ensemble ldcTree (E-ldcTree) to encourage the model’s diversity and enhance the representation ability further. Finally, we propose an improved Feature learning method based on EldcTree (F-EldcTree) for taking adequate use of weak and strong correlation features identified by pretrained GBDT models. Experimental results on off-line data set and online deployment demonstrate the effectiveness of the proposed methods.