Reviewerly/exHarmony huggingface dataset:
Blending kinematic and software models for tighter reachability analysis

Reachable sets are critical for path planning and navigation of mobile autonomous systems. Traditionally, these sets are computed using system models instantiated with their physical bounds. This exclusive focus on the physical bounds belies the fact that these systems are increasingly driven by sophisticated software components that can also bound the variables in the system models. This work explores the degree to which bounds manifested in the software can affect the computation of reachable sets, introduces an analysis approach to discover such bounds in code, and illustrates the potential of that approach on two systems. The preliminary results reveal that taking into consideration software bounds can reduce traditionally computed reachable sets by up to 91%.
Trajectory optimization for a class of robots belonging to Constrained Collaborative Mobile Agents (CCMA) family

We present a novel class of robots belonging to Constrained Collaborative Mobile Agents (CCMA) family which consists of ground mobile bases with non-holonomic constraints. Moreover, these mobile robots are constrained by closed-loop kinematic chains consisting of revolute joints which can be either passive or actuated. We also describe a novel trajectory optimization method which is general with respect to number of mobile robots, topology of the closed- loop kinematic chains and placement of the actuators at the revolute joints. We also extend the standalone trajectory optimization method to optimize concurrently the design parameters and the control policy. We describe various CCMA system examples, in simulation, differing in design, topology, number of mobile robots and actuation space. The simulation results for standalone trajectory optimization with fixed design parameters is presented for CCMA system examples. We also show how this method can be used for tasks other than end-effector positioning such as internal collision avoidance and external obstacle avoidance. The concurrent design and control policy optimization is demonstrated, in simulations, to increase the CCMA system workspace and manipulation capabilities. Finally, the trajectory optimization method is validated in experiments through two 4-DOF prototypes consisting of 3 tracked mobile bases.
Complex Network Construction for Interactive Image Segmentation Using Particle Competition and Cooperation: A New Approach

In the interactive image segmentation task, the Particle Competition and Cooperation (PCC) model is fed with a complex network, which is built from the input image. In the network construction phase, a weight vector is needed to define the importance of each element in the feature set, which consists of color and location information of the corresponding pixels, thus demanding a specialist’s intervention. The present paper proposes the elimination of the weight vector through modifications in the network construction phase. The proposed model and the reference model, without the use of a weight vector, were compared using 151 images extracted from the Grabcut dataset, the PASCAL VOC dataset and the Alpha matting dataset. Each model was applied 30 times to each image to obtain an error average. These simulations resulted in an error rate of only 0.49% when classifying pixels with the proposed model while the reference model had an error rate of 3.14%. The proposed method also presented less error variation in the diversity of the evaluated images, when compared to the reference model.
Error Analysis for Visual Question Answering

In recent years, the task of visual question answering (VQA) at the intersection of computer vision and natural language processing is gaining interest in the scientific community. Even though modern systems achieve good results on standard datasets, these results are far from what is achieved in Computer Vision or Natural Language Processing separately, for example, in tasks of image classification or machine translation. One of the reasons for this phenomenon is the problem of modelling the interaction between modalities, which is partially solved by using the attention mechanism, as, for example, in the models used in this paper. Another reason lies in the statement of the problem itself. In addition to the problems inherited from CV and NLP, there are problems associated with the variety of situations shown in the picture and the possible questions for them. In this paper, we analyze errors for the state-of-the-art approaches and separate them into several classes: text recognition errors, answer structure, entity counting, type of the answer, and ambiguity of an answer. Text recognition errors occur when answering a question like “what is written in ..?” and associated with the representation of the image. Errors in the answer structure are associated with the reduction of the VQA to the classification task. Entity counting is a known weakness of current models. A typical situation of errors in the type of answer is when the model answers the “Yes/No” question in a different way. Errors from the ambiguity of an answer class occur when the model produces an answer that is correct in meaning but does not coincide with the formulation of the ground truth. Addressing these types of errors will lead to the overall improvement of VQA systems.
Classification of adeno carcinoma, high squamous intraephithelial lesion, and squamous cell carcinoma in Pap smear images based on extreme learning machine

Cervical cancer is a malignant tumour that attacks the female genital area originating from epithelial metaplasia in the squamous protocol junction area. One method of diagnosis of cervical cancer is to do a Pap smear examination by taking a cervical cell smear from the woman’s cervix and observing its cell development. However, examination of cervical cancer from Pap smear results usually takes a long time. This is because medical practitioners still rely on visual observations in the analysis of the results of Pap smear so that the results are subjective. Therefore, we need a programme that can help the classification process in establishing a diagnosis of cervical cancer with high accuracy results. In this study, a cervical cancer classification program was developed using a combination of the Grey Level Co-occurrence Matrix (GLCM) and Extreme Learning Machine (ELM) methods. There are three classes of cervical cell images classified, namely adenocarcinoma, High Squamous Intraepithelial Lesion (HSIL) and Squamous Cell Carcinoma (SCC). From the results of the training program obtained an accuracy 100% and from the testing program obtained an accuracy of 80%.
Bubblenet: A Disperse Recurrent Structure To Recognize Activities

This paper presents an approach to perform human activity recognition in videos through the employment of a deep recurrent network, taking as inputs appearance and optical flow information. Our method proposes a novel architecture named BubbleNET, which is based on a recurrent layer dispersed into several modules (referred to as bubbles) along with an attention mechanism based on squeeze-and-excitation strategy, responsible to modulate each bubble contribution. Thereby, we intend to gather information from fundamentally correlated segments of the input data, creating a signature of components that characterize each activity. Our experiments, conducted on widely employed activity recognition datasets, support the existence of these signatures, evidenced by maps of bubble activations for every class of the datasets. To compare the approach to literature methods, mean accuracy is taken into account, for which BubbleNET obtained 97.62%, 91.70% and 82.60% on UCF-101, YUP++ and HMDB-51 datasets, respectively, being placed among state-of-the-art methods.
Learning Discriminative Part Features Through Attentions For Effective And Scalable Person Search

This paper proposes a new method for person search, the task of detecting a specific person exemplified by a query image from a gallery of scene images. Current state-of-the-art techniques in person search demonstrate impressive performance, but are limited in terms of efficiency and scalability since they require multiple models and/or have to re-process gallery images per query. We argue that a concise framework with a single neural network can achieve both of scalability and performance at once. In our framework, the network detects people and extracts their appearance features so that person search is done by finding the person closest to the query in the feature space. For performance, we focus on the quality of the person appearance features: Our network is designed and trained to produce person features that are discriminative, fine-grained, adaptive to appearance variations, and robust against person localization errors. To this end, we design channel attention and part-wise spatial attention modules as well as a loss for learning discriminative features. Our framework outperforms current state of the art on the PRW benchmark even with the concise pipeline based on a single network.
Personalized approach to developing image processing and analysis methods for medical video systems.

The paper describes the new approach to developing image processing methods for medical video systems. The core idea is the personalized workflow of video data processing and analysis. It allows to increase the sensitivity and specificity of the physician - the user of system. At the visual analysis by the physician, personalization should provide the image representation as much as possible corresponding to physiological and psycho-visual features of the physician - the image should be comfortable for the analysis. In automatic analysis, the personalized approach allows the physician to set the optimal level of interaction with the system. An experienced specialist will need the results of diagnostic features evaluation and statistical analysis, while in the training mode one will need more AI diagnostic functions of the system. The paper provides specific examples of the personalized approach implementation in the visualization, enhancement and analysis of medical video data.
Cost-aware Dynamic Data Center Federation Formation for Short-term Service Requests

In current environment, many web services map to the distributed data centers, to respond to variable and increasing workloads. It is not cost efficient for one data center to increase number of servers in order to fulfill all requests coming to it. For this reason, federated data center is a key solution for sharing unused resources and gain some profit. In this paper, we have used demand multiplexing and exploits the spatio-temporal variation of electricity price of distributed data center across locations and also consider the power consumption cost of servers at data center. We develop an Integer Linear Programming Problem (ILPP) for Profit Maximization Problem of federated data centers and proposes Greedy algorithm for computing an optimal federation formation of data centers. The performance of the proposed Greedy federation formation algorithm (G-FFA) compares with two other algorithms which are Optimal Federation Formation Algorithm (OFFA) and Random Federation Formation Algorithm (RFFA). Experiments are performed on real world traffic demands at data center in which the proposed (G-FFA) algorithm gives an optimize solution to the optimization problem.
Improving the Performance and Explainability of Mammogram Classifiers with Local Annotations

Cancer prediction models, which deeply impact human lives, must provide explanations for their predictions. We study a simple extension of a cancer mammogram classifier, trained with image-level annotations, to facilitate the built-in generation of prediction explanations. This extension also enables the classifier to learn from local annotations of malignant findings, if such are available. We tested this extended classifier for different percentages of local annotations in the training data. We evaluated the generated explanations by their level of agreement with (i) local annotations of malignant findings, and (ii) perturbation-based explanations, produced by the LIME method, which estimates the effect of each image segment on the classification score. Our results demonstrate an improvement in classification performance and explainability when local annotations are added to the training data. We observe that training with only 20–40% of the local annotations is sufficient to achieve improved performance and explainability comparable to a classifier trained with the entire set of local annotations.
An Effective Fusion of a Color and Texture Descriptor for an Image Retrieval System: An Exploratory Analysis

Content-based image retrieval (CBIR) is one of the most prominent systems by which the desired and relevant images are recovered from a massive database by using the basic image features like color, shape, texture, spatial information, and edge. In this paper, an experimental analysis is being done to determine the most efficient combination using color and texture descriptors. Here, color moment (CM) is used for color feature extraction and is used individually in a combination with different texture descriptors, namely discrete wavelet transform (DWT), Gabor transform, Curvelet transform, graylevel co-occurrence matrix (GLCM), and local binary pattern (LBP). These color and texture features can be combined using two different levels of the system: feature-level fusion and score-level fusion. Both the feature-level and score-level combination techniques are used in this paper. The results of this analytical experimentation depict that the framework of CM, and GLCM attains the highest results among all the other combinations using feature-level fusion technique on a benchmark CBIR dataset, particularly WANG. Precision, recall, f-score are some of the evaluation parameters which are utilized in this paper to measure the effectiveness of the fused descriptors.
Real-Time Gesture Recognition Using Deep Learning Towards Alzheimer’s Disease Applications

There have been significant efforts in the direction of improving accuracy in detecting human action using skeleton joints. Determining actions in a noisy environment is still challenging since the Cartesian coordinate of the skeleton joints provided by depth sense camera depends on camera position and skeleton position. In a few of the human-computer interaction applications, skeleton position and camera position keep changing. The proposed method recommends using relative positional values instead of actual Cartesian coordinate values. Recent advancements in the Convolution Neural Network (CNN) help us achieve higher prediction accuracy using image format input. To represent skeleton joints in image format, we need to represent skeleton information in matrix form with equal height and width. With some depth sense cameras, the number of skeleton joints provided is limited, and we need to depend on relative positional values to have a matrix representation of skeleton joints. We can show near the state-of-the-art performance on MSR 3-Dimensional (3D) data and the new representation of skeleton joints. We have used image shifting instead of interpolation between frames, which helps us have state-of-the-art performance.
A Scalable Simulation of the BB84 Protocol Involving Eavesdropping

In this article we present the BB84 quantum key distribution scheme from two perspectives. First, we provide a theoretical discussion of the steps Alice and Bob take to reach a shared secret using this protocol, while an eavesdropper Eve is either involved or not. Then, we offer and discuss two distinct implementations that simulate BB84 using IBM’s Qiskit framework, the first being an exercise solved during the “IBM Quantum Challenge” event in early May 2020, while the other was developed independently to showcase the intercept-resend attack strategy in detail. We note the latter’s scalability and increased output verbosity, which allow for a statistical analysis to determine the probability of detecting the act of eavesdropping.
Robustness And Overfitting Behavior Of Implicit Background Models

In this paper, we examine the overfitting behavior of image classification models modified with Implicit Background Estimation (SCrIBE), which transforms them into weakly supervised segmentation models that provide spatial domain visualizations without affecting performance. Using the segmentation masks, we derive an overfit detection criterion that does not require testing labels. In addition, we assess the change in model performance, calibration, and segmentation masks after applying data augmentations as overfitting reduction measures and testing on various types of distorted images.
Eliciting tangible and gestural user interactions with and on a cooking pan

Embedding computational capabilities in everyday objects enables novel interaction concepts that are seamlessly integrated in users' everyday tasks. We conducted an elicitation study to investigate how subjects use a pan to control functions related to cooking. The primary focus of the study was to identify whether the elicited proposals tend towards tangible (i.e. moving or rotating the pan) or gestural (i.e. tapping or swiping on the pan handle) interactions. We present an analysis of over 500 interaction proposals from 20 subjects. While priming and used pan handle did not affect the number or type of elicited interactions, we found statistically significant differences for different types of task. While pan interaction is suitable for controlling cook top temperature, subjects have a rejecting attitude towards using a pan to interact with the cooker hood or a digital cookbook. We derive recommendations for when and how to interact with a pan during the cooking process.
Reducing the Collision Checking Time in Cluttered Environment for Sampling-Based Motion Planning

In the Sampling-based motion planning, the collision checking procedure called many times and this operation takes the most planning time. The time spend in the collision checking increases with the number of obstacles. This paper aims to reduce the number of calls of the collision checking procedure in environment containing high number of obstacles. Our approach shows that the complexity of collision checking does not depend on the number of obstacles. The idea is to perform a pre-procedure that localizes the obstacles by segmenting the workspace into a grid and save it in a matrix form. This information is used in the collision checking procedure. We have used the Probabilistic Roadmaps (PRM) motion planner with a punctual robot and polygonal obstacles in 2D environment. Simulation results show the efficiency of our algorithm in reducing the complexity of collision checking and minimizing the planning time.
Scene-Unified Image Translation For Visual Localization

Visual localization is a key technology in the field of 3D robot vision. One of its major difficulties lies in how to deal with the challenges brought by the appearance changes of query images and database images caused by large time spans. Many methods focus on extracting more robust features from images to deal with the impact of complex scenes. In this paper, we explore the impact of image translation on visual localization tasks in complex scenes. We propose UniGAN - a modified image translation model, fusing semantic label constraints and finer reconstruction losses, to unify images captured under different environmental conditions to a standard scene more suitable for localization tasks. To estimate the 6-DOF camera pose, a two-stage localization framework composed of image retrieval and local matching is utilized. Experiments show that our method outperforms the state-of-the-art in terms of both accuracy and robustness to environmentally sensitive scenes.
The hardest halfspace

Abstract We study the approximation of halfspaces $$h:\{0,1\}^n\to\{0,1\}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>h</mml:mi> <mml:mo>:</mml:mo> <mml:msup> <mml:mrow> <mml:mo>{</mml:mo> <mml:mn>0</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> <mml:mo>}</mml:mo> </mml:mrow> <mml:mi>n</mml:mi> </mml:msup> <mml:mo>→</mml:mo> <mml:mrow> <mml:mo>{</mml:mo> <mml:mn>0</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> <mml:mo>}</mml:mo> </mml:mrow> </mml:mrow> </mml:math> in the infinity norm by polynomials and rational functions of any given degree. Our main result is an explicit construction of the “hardest” halfspace, for which we prove polynomial and rational approximation lower bounds that match the trivial upper bounds achievable for all halfspaces. This completes a lengthy line of work started by Myhill and Kautz (1961). As an application, we construct a communication problem that achieves essentially the largest possible separation, of O(n) versus $$2^{-\Omega(n)}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>2</mml:mn> <mml:mrow> <mml:mo>-</mml:mo> <mml:mi>Ω</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:msup> </mml:math> , between the sign-rank and discrepancy. Equivalently, our problem exhibits a gap of log n versus $$\Omega(n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Ω</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> between the communication complexity with unbounded versus weakly unbounded error, improving quadratically on previous constructions and completing a line of work started by Babai, Frankl, and Simon (FOCS 1986). Our results further generalize to the k -party number-on-the-forehead model, where we obtain an explicit separation of log n versus $$\Omega(n/4^{n})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Ω</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>/</mml:mo> <mml:msup> <mml:mn>4</mml:mn> <mml:mi>n</mml:mi> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> for communication with unbounded versus weakly unbounded error.
Analysis of Multiagent System for Data Analysis

Agents are an important part of solving many current problems in distributed computing, including complexity and information overload. There is a need to integrate multiagent technology and data mining to counter the growth of heterogeneous and distributed data sources. This article describes the main concepts: agents, data mining, and analyzes the possibilities of their integration. A comparative analysis of existing data mining systems based on multiagent systems is also provided.
Image Inpainting Using Parallel Network

Due to the lack of contextual information and the difficulty to directly learn the distribution of a complete image, existing image inpainting methods always use a two-stages approach to make plausible prediction for missing pixels in a coarse-to-fine manner. In this paper, we propose a novel inpainting method with two parallel pipelines. The first pipeline is a standard image completion path that takes the corrupted image as input and outputs the predicted complete image. The second pipeline exists only during the training phase that inputs a complementary image of the corrupted one and still outputs the same complete image. The two pipelines operate simultaneously, and they share identical encoder and most parameters in the decoder. Furthermore, inspired by VAE, random Gaussian noise are added to the features not only to improve the robustness of the model but also to enable generating diverse and plausible results. We evaluated our model on several public datasets and demonstrated that the proposed method outperforms several state-of-the-arts approaches.