Reviewerly/exHarmony huggingface dataset:
Search-based framework for transparent non-overlapping ensemble models

Due to their generalizing ability, classifier ensembles are considered very powerful predictive models. A typical ensemble consists of a static or dynamic pool of classifiers and a combination method, which translates predictions of many models into one. The combination step is often complex and renders the inner behavior of the whole ensemble incomprehensible to a typical user. In this work, in the light of recent interest in Explainable AI (XAI) research, we are proposing a novel approach to building an interpretable ensemble model. It is based on decision space splitting into non-overlapping regions. Every area has an assigned interpretable classifier and its boundaries are selected using the genetic programming approach. We experimentally evaluate the proposed method and compare it to Decision Tree and Random Forest. The results show that the proposed approach is competitive with the state-of-the-art techniques and prone to further expansion.
Newfangled 3d human pose estimation using MediaPipe with foreground object detection

Human pose assessment from video plays an important role in various applications such as measuring physical exercises, sign language recognition and full body gesture control. For example, it can be the basis for yoga, dance, and exercise applications. It can also enable the overview of digital content and information at the top of the physics world in augmented reality. Here we propose, Newfangled 3d Human pose estimation using MediaPipe with foreground object detection (HPEM), model uses MediaPipe library. MediaPipe pose is applied for faster pose estimation of humans, prior in which the humans and other objects are detected and classified in foreground object detection. This paper conducted various experiments, and the results proved superior and the success of this algorithm.
Improved Ice Cream Detection Algorithm Based on YOLOv5

Manufacturers make production decisions by analyzing the sales of different types of ice cream at each retail store from user purchase videos. The color difference in the appearance of ice cream in the video can cause missed detection of objects, therefore, the instance normalization and batch normalization modules is introduced into YOLOV5-x; and then, to reduce the semantic feature loss caused by up-sampling, the flow alignment module is used to improve detection performance. The experimental results indicate that, compared with the original YOLOv5-x, our method increases by 1.2% on mAP@0.5 and increases by 0.7% on mAP@0.5-0.95.
Predicting Potentially Undetectable Flows in DPI System using Machine Learning

A Deep Packet Inspection (DPI) system examines packet payload to identify traffic flow. For flows that are eventually detected, DPI system processes average 1 to 2 packets while for eventually undetected flows, DPI system processes 10+ packets on average before concluding that detection is not possible. For such flows, costly packet processing operation is done without any eventual detection result. Our proposed algorithm adds intelligence in DPI system, using classifier and regressor Machine Learning models, to predict that given flow has very low chance of eventually being detected by the given DPI system. With that, DPI engine can stop processing that flow for subsequent packets and save a lot of unnecessary processing to enhance DPI system throughput. Our initial test results show that a competitively fast solution achieving above 98% accuracy can be derived using this method.
Composition of Fuzzy Numbers with Chaotic Maps

In the research on chaotic systems, which are used for cryptography and secure communication schemes, a key characteristic is the introduction of novel chaotic systems, usually by adding complexity to the existing ones in order to ensure security. In this direction, this chapter proposes novel chaotic systems, which are the composition of fuzzy numbers with the existing chaotic maps. In particular, the composition of the Gaussian fuzzy number with the logistic, sine, and Chebyshev chaotic maps is introduced. The resulting maps exhibit more complex behaviours compared to their classic counterparts, presenting interesting chaos-related phenomena, such as antimonotonicity, crisis, coexisting attractors, and period doubling route to chaos. To showcase the aforementioned results, the well-known tools for studying the behaviour of chaotic systems are being used, namely bifurcation diagrams and Lyapunov exponent diagrams.
Graph iForest: Isolation of anomalous and outlier graphs

We present an anomaly and outlier detection method for graph data. The method relies on the consideration that anomalies and outliers are more easily isolated by certain incremental partitionings of the data space. Specifically, we build upon the isolation forest method and introduce a new incremental partitioning of the space of graphs that makes the isolation forest method applicable to generic attributed graphs, i.e., graphs where both nodes and edges can be associated with attributes. Within the considered general setup, the topology and the number of nodes can change from graph to graph, and a node correspondence between different graphs can be absent or unknown. Examples of applications of what proposed include the identification of frauds and fake news in communication networks, and breakage of systems monitored by sensor networks. The main novel contribution of the paper is a graph space partitioning which we prove to be expressive enough to identify anomalies and outlier graphs in a given dataset. An empirical analysis on synthetic and real-world graphs validates the effectiveness of the proposed method.
Helpful or Harmful: Inter-task Association in Continual Learning

When optimizing sequentially incoming tasks, deep neural networks generally suffer from catastrophic forgetting due to their lack of ability to maintain knowledge from old tasks. This may lead to a significant performance drop of the previously learned tasks. To alleviate this problem, studies on continual learning have been conducted as a countermeasure. Nevertheless, it suffers from an increase in computational cost due to the expansion of the network size or a change in knowledge that is favorably linked to previous tasks. In this work, we propose a novel approach to differentiate helpful and harmful information for old tasks using a model search to learn a current task effectively. Given a new task, the proposed method discovers an underlying association knowledge from old tasks, which can provide additional support in acquiring the new task knowledge. In addition, by introducing a sensitivity measure to the loss of the current task from the associated tasks, we find cooperative relations between tasks while alleviating harmful interference. We apply the proposed approach to both task- and class-incremental scenarios in continual learning, using a wide range of datasets from small to large scales. Experimental results show that the proposed method outperforms a large variety of continual learning approaches for the experiments while effectively alleviating catastrophic forgetting.
Gaze Buttons Revisited.Looking for an Effective Way of "Clicking" without the Mouse

The fundamental problem for designing a gaze-based human-computer interaction is related to development of an effective method for activating graphical user interface elements by means of gaze only. Such a method should be easy for the user to apply, however at the same time, it requires eye movements that are clearly different from the natural behavior of the eye. We examined three methods of button activation by gaze, looking for the most effective way of gaze "clicking". These were: 1) the most standard method based on the use of dwell-time, 2) its modification based on detection of fixation located inside the buttons area and 3) and the most novel method based on gaze gestures consisting of movement into the button area and outward movement in the approximately opposite direction. We compared these gaze control methods under homogeneous conditions, which allows for a more reliable assessment of their relative usefulness. Two layouts of buttons were used: arranged on a grid, like on a telephone pad, and on a circle with an empty center. The experimental task was to enter a set of four-digit PINs using a set of gaze buttons corresponding to ten digits. A group of novices were instructed to use all the three methods and both button layouts (six experimental conditions). The activation methods were compared in terms of system usability, objectively measured by the PIN entry speed and the number of errors, as well as using a subjective SUS questionnaire. The system based on gaze gestures was worse in both measures; however, it had its followers. The method based on fixation detection instead of dwell-time did not significantly increase the entry speed due to the greater number of errors caused by non-intentional buttons activation. The circle layout turned out to be generally more convenient than the telephone pad layout.
Bridging the Domain Gap Towards Generalization in Automatic Colorization

We propose a novel automatic colorization technique that learns domain-invariance across multiple source domains and is able to leverage such invariance to colorize grayscale images in unseen target domains. This would be particularly useful for colorizing sketches, line arts, or line drawings, which are generally difficult to colorize due to a lack of data. To address this issue, we first apply existing domain generalization (DG) techniques, which, however, produce less compelling desaturated images due to the network's over-emphasis on learning domain-invariant contents (or shapes). Thus, we propose a new domain generalizable colorization model, which consists of two modules: (i) a domain-invariant content-biased feature encoder and (ii) a source-domain-specific color generator. To mitigate the issue of insufficient source domain-specific color information in domain-invariant features, we propose a skip connection that can transfer content feature statistics via adaptive instance normalization. Our experiments with publicly available PACS and Office-Home DG benchmarks confirm that our model is indeed able to produce perceptually reasonable colorized images. Further, we conduct a user study where human evaluators are asked to (1) answer whether the generated image looks naturally colored and to (2) choose the best-generated images against alternatives. Our model significantly outperforms the alternatives, confirming the effectiveness of the proposed method. The code is available at https://github.com/Lhyejin/DG-Colorization .
Improved Model Based on GoogLeNet and Residual Neural Network ResNet

To improve the accuracy of image classification, a kind of improved model is proposed. The shortcut is added to GoogLeNet inception v1 and several other ways of shortcut are given, and they are GRSN1_2, GRSN1_3, GRSN1_4. Among them, the information of the input layer is directly output to each subsequent layer in the form of shortcut. The new improved model has the advantages of multi-size and small convolution kernel in the same layer in the network and the advantages of shortcut to reduce information loss. Meanwhile, as the number of inception blocks increases, the number of channels is increased to deepen the extraction of information. The GRSN, GRSN1_2, GRSN1_3, GRSN1_4, GoogLeNet, and ResNet models were compared on cifar10, cifar100, and mnist datasets. The experimental results show that the proposed model has 3.07% improved to ResNet on data set cifar10, 2.08% on data set cifar100, 17.69% improved to GoogLeNet on data set cifar10, 28.47% on data set cifar100.
Design and Implementation of Web Page Classification Method Based on XLNet Fusing Hierarchical Attention and CNN

The explosive growth in the number of web pages makes web page classification crucial for web information retrieval, content filtering, and topic crawling, etc. To address the problems that traditional web page classification models have incomplete web text feature extraction and difficulty in capturing and utilizing both global semantic features and local semantic features, we propose a web page classification model XLNet-HAC based on the pre-trained model XLNet fusing Hierarchical Attention and CNN. For web page text extracted using URLs, the pre-trained model XLNet is used as an embedding layer to obtain a feature matrix representation with rich contextual relationships, using word attention and sentence attention mechanisms of Hierarchical Attention to capture the words and sentences that highly contribute to the classification, thus generates a global feature representation of the web page text, and the multi-channel CNN with convolutional kernels of different sizes is used to extract local features at multiple granularities of web page text. Finally, the outputs of Hierarchical Attention and CNN are applied to the softmax classifier respectively, and the classification results are fused to obtain the final classification result. The comparison experimental results on THUCNews dataset and DMOZ dataset show that the XLNet-HAC model proposed in this study outperforms the other comparison models in terms of classification accuracy and F1-score.
Leveraging Knowledge Graph Technologies to Assess Journals and Conferences at Springer Nature

Research publishing companies need to constantly monitor and compare scientific journals and conferences in order to inform critical business and editorial decisions. Semantic Web and Knowledge Graph technologies are natural solutions since they allow these companies to integrate, represent, and analyse a large quantity of information from heterogeneous sources. In this paper, we present the AIDA Dashboard 2.0, an innovative system developed in collaboration with Springer Nature to analyse and compare scientific venues, now also available to the public. This tool builds on a knowledge graph which includes over 1.5B RDF triples and was produced by integrating information about 25M research articles from Microsoft Academic Graph, Dimensions, DBpedia, GRID, CSO, and INDUSO. It can produce sophisticated analytics and rankings that are not available in alternative systems. We discuss the advantages of this solution for the Springer Nature editorial process and present a user study involving 5 editors and 5 researchers, which yielded excellent results in terms of quality of the analytics and usability.
FPGA-Based Accelerator for AI-Toolbox Reinforcement Learning Library

In reinforcement learning (RL) an agent interacts with the environment based on sequential decisions. This agent receives a reward from the environment according to decisions and tries to maximize the reward. RL is used in several domains, such as production, autonomous driving, business management, education, games, healthcare, natural language processing, robotics, and among others. RL methodologies require processing large volumes of data and computational power. To speed up these applications, field-programmable gate array (FPGA) are widely employed in the literature. This letter proposes an accelerator for the Markov decision process (MDP) implemented in the AI-Toolbox public library using high-level synthesis tools, using the tiger-antelope problem as use case. Our approach shows an acceleration greater than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7\times $ </tex-math></inline-formula> compared to the original version.
Bug-Fix Variants: Visualizing Unique Source Code Changes across GitHub Forks

Forking is a common practice for developers when building upon on already existing projects. These forks create variants, which have a common code base but then evolve the code in different directions, which is specific to that forked project requirements. An interesting side-effect of having multiple forks is the ability to select between different evolution directions of the code which is based on developers fixing bugs in the code base. However, the key issue that this decentralized form of information is difficult to analyze. In this study, we propose a visualization to analyze active changes in fork repositories that have not been merged back to the original project. Our visualization shows code commit activities in multiple forks with highlight on bug fix commits in the history of forks. While the commit activity of each repository is visualized similarly to the code frequency view of GitHub, our view shows only commits unique to fork repositories. To illustrate the effectiveness of our visualization, we have applied our view to two use cases: identifying forks from a repository no longer maintained, and identifying a bug fix among forks. In the first case, we identify a fork of a suspended project named Obfuscator-LLVM. Our view shows the original repository and its most active fork that continue the development on the top. In the second case, we identify a bug fix in a fork of Clipy project. Our view shows that the most active fork has its own bug fixes; we could easily identify a patch for the bug highlighted in the view. As a new ideas paper, we then present our outline of three research questions to spark real world use-cases and goals for our visualization has the potential to uncover. A prototype of our visualization is available at https://naist-se.github.io/vissoft2022/
Ensemble Optimization for Invasive Ductal Carcinoma (IDC) Classification Using Differential Cartesian Genetic Programming

The high cost of acquiring annotated histological slides for breast specimens entails exploiting an ensemble of models appropriately trained on small datasets. Histological Image Classification ensembles strive to accurately detect abnormal tissues in the breast samples by determining the correlation between the predictions of its weak learners. Nonetheless, the state-of-the-art ensemble methods, such as boosting and bagging, count merely on manipulating the dataset and lack intelligent ensemble decision making. Furthermore, the methods mentioned above are short of the diversity of the weak models of the ensemble. Likewise, other commonly used voting strategies, such as weighted averaging, are limited to how the classifiers’ diversity and accuracy are balanced. Hence, In this paper, we assemble a Neural Network ensemble that integrates the models trained on small datasets by employing biologically-inspired methods. Our procedure is comprised of two stages. First, we train multiple heterogeneous pre-trained models on the benchmark Breast Histopathology Images for Invasive Ductal Carcinoma (IDC) classification dataset. In the second meta-training phase, we utilize the differential Cartesian Genetic Programming (dCGP) to generate a Neural Network that merges the trained models optimally. We compared our empirical outcomes with other state-of-the-art techniques. Our results demonstrate that improvising a Neural Network ensemble using Cartesian Genetic Programming transcended formerly published algorithms on slim datasets.
Marine Snow Detection for Real Time Feature Detection

Underwater images are often degraded due to backscatter, light attenuation and light artifacts. One important aspect of it is marine snow, which are particles of varying shape and size. Computer vision technologies can be strongly affected by them and may therefore provide incorrect and biased results. In robotic applications, there is limited computational power for online processing. A method for real time marine snow detection is proposed in this paper based on a multi-step process of spatial-temporal data. The RGB colored images are converted to the YCbCr color space before they are decomposed to isolate the high frequency information using a guided filter for a first selection of candidates. Convolution with an uniform kernel is then applied for further analysis of the candidates. The method is demonstrated in two use cases, underwater feature detection and image enhancement.
A Semi-Supervised Image-to-Image Translation Framework for SAR–Optical Image Matching

Synthetic Aperture Radar (SAR) and optical image matching aims to acquire correspondences from a certain pair of SAR and optical images. Recent advances in the image-to-image translation provided a way to simplify the SAR-optical image matching into the SAR-SAR or optical-optical image matchings. Existing image-to-image translations mainly focus on supervised or unsupervised learning. However, gathering sufficient amounts of aligned training data for supervised learning is challenging, while unsupervised learning cannot guarantee enough correct correspondences. In this work, we investigate the applicability of semi-supervised image-to-image translation for SAR-optical image matching such that both aligned and unaligned SAR-optical images could be used. To this end, we combine the benefits of both supervised and unsupervised well-known image-to-image translation methods, i.e., Pix2pix and CycleGAN, and propose a simple yet effective semi-supervised image-to-image translation framework. Through extensive experimental comparisons to baseline methods, we verify the effectiveness of the proposed framework in both semi-supervised and fully-supervised settings. Our codes are available at https://github.com/WenliangDu/Semi-I2I.
Trajectory Optimization for Truck-Trailer Systems Based on Predictive Path-Following Control

Autonomous maneuvering of truck-trailer systems is a challenging task, especially if collisions with obstacles must be avoided. Due to the complexity, global path planners typically focus on the efficient computation of feasible but sub-optimal solutions. In this context, this paper presents a model predictive path-following controller for local optimization of the globally planned paths. This allows to eliminate redundant path elements, to increase obstacle clearance and to compensate initial path deviations. Furthermore, it is proposed to constrain the terminal state to the reference path and to formulate the terminal costs in terms of a precomputed reference trajectory, both of which improve the numerical robustness of the optimization. Simulation results show the benefits of the optimized solution in a difficult planning scenario.
PourNet: Robust Robotic Pouring Through Curriculum and Curiosity-based Reinforcement Learning

Pouring liquids accurately into containers is one of the most challenging tasks for robots as they are unaware of the complex fluid dynamics and the behavior of liquids when pouring. Therefore, it is not possible to formulate a generic pouring policy for real-time applications. In this paper, we propose PourNet, as a generalized solution to pouring different liquids into containers. PourNet is a hybrid planner that uses deep reinforcement learning, for end-effector planning, and Nonlinear Model Predictive Control, for joint planning. In this work, we introduce a novel simulation environment using Unity3D and NVIDIA-Flex to train our agents. By effective choice of the state space, action space and the reward functions, we allow for a direct sim-to-real transfer of the learned skills without additional training. In the simulation, PourNet outperforms state-of-the-art by an average of 4.9g deviation for water-like, and 9.2g deviation for honey-like liquids. In the real-world scenario using Kinova Movo Platform, PourNet achieves an average pouring deviation of 2.3g for dish soap when using a novel pouring container. The average pouring deviation measured for water was 5.5g. All comprehensive experiments and the simulation environment is available at: http://cxdcxd.github.io/RRS/.