Reviewerly/exHarmony huggingface dataset:
Trajectory Planning in Unknown 2D Workspaces: A Smooth, Reactive, Harmonics-Based Approach

A novel reactive method for robot trajectory planning within unknown 2D workspaces is presented in this letter. The trajectories provided by this method stem from an underlying potential field and are provably safe, with asymptotic convergence to the desired position. Given an initially unknown workspace, and a sensing process for obtaining boundary information, the scope of this work is to provide an underlying vector field that can be used as a reference signal for higher order, non-linear dynamical systems (robots). This can be achieved by tracking, either the field itself, or the trajectories that result from the latter. In this spirit, our method is advantageous due to: a) its reactivity, as open-loop approaches may exhibit large errors during the transient phase within unknown workspaces, whereas our method provides a velocity profile for each position of the robot within the workspace, b) its smoothness, and c) its safety and convergence guarantees. We prove the asserted claims and provide rigorous comparative simulations in various benchmark workspaces, as well as in a high-fidelity ROS-Gazebo implementation.
PeMNet for Pectoral Muscle Segmentation

As an important imaging modality, mammography is considered to be the global gold standard for early detection of breast cancer. Computer-Aided (CAD) systems have played a crucial role in facilitating quicker diagnostic procedures, which otherwise could take weeks if only radiologists were involved. In some of these CAD systems, breast pectoral segmentation is required for breast region partition from breast pectoral muscle for specific analysis tasks. Therefore, accurate and efficient breast pectoral muscle segmentation frameworks are in high demand. Here, we proposed a novel deep learning framework, which we code-named PeMNet, for breast pectoral muscle segmentation in mammography images. In the proposed PeMNet, we integrated a novel attention module called the Global Channel Attention Module (GCAM), which can effectively improve the segmentation performance of Deeplabv3+ using minimal parameter overheads. In GCAM, channel attention maps (CAMs) are first extracted by concatenating feature maps after paralleled global average pooling and global maximum pooling operation. CAMs are then refined and scaled up by multi-layer perceptron (MLP) for elementwise multiplication with CAMs in next feature level. By iteratively repeating this procedure, the global CAMs (GCAMs) are then formed and multiplied elementwise with final feature maps to lead to final segmentation. By doing so, CAMs in early stages of a deep convolution network can be effectively passed on to later stages of the network and therefore leads to better information usage. The experiments on a merged dataset derived from two datasets, INbreast and OPTIMAM, showed that PeMNet greatly outperformed state-of-the-art methods by achieving an IoU of 97.46%, global pixel accuracy of 99.48%, Dice similarity coefficient of 96.30%, and Jaccard of 93.33%, respectively.
Profiling Web Archival Voids for Memento Routing

Prior work on web archive profiling were focused on Archival Holdings to describe what is present in an archive. This work defines and explores Archival Voids to establish a means to represent portions of URI spaces that are not present in a web archive. Archival Holdings and Archival Voids profiles can work independently or as complements to each other to maximize the Accuracy of Memento Aggregators. We discuss various sources of truth that can be used to create Archival Voids profiles. We use access logs from Arquivo.pt to create various Archival Voids profiles and analyze them against our MemGator access logs for evaluation. We find that we could have avoided more than 8&#x0025; of additional False Positives on top of the 60&#x0025; Accuracy we got from profiling Archival Holdings in our prior work, if Arquivo.pt were to provide an Archival Voids profile based on URIs that were requested hundreds of times and never returned any success responses,
Predicting Victories in Video Games: Using Single XGBoost with Feature Engineering : IEEE BigData 2021 Cup - Predicting Victories in Video Games

Prediction of who is going to win in video games is an important machine learning application [1] [2]. Accordingly, one of the BigData Cup competitions at the 2021 IEEE International Conference on Big Data [3] <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> was related to this topic. The goal of this challenge is to find a way to predict victories of a video game named Tactical Troops: Anthracite Shift [4] <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> by game logs. In this kind of challenge, ensemble models are often to be used to get a more accurate result. In this paper, we use a single extreme gradient boosting model (XGBoost [5]) with a wide variety of features from game logs and metadata. The proposed model and features achieved 3rd place with an AUC score of 0.8978 in the final result.
A cyber warfare perspective on risks related to health IoT devices and contact tracing

The wide use of IT resources to assess and manage the recent COVID-19 pandemic allows to increase the effectiveness of the countermeasures and the pervasiveness of monitoring and prevention. Unfortunately, the literature reports that IoT devices, a widely adopted technology for these applications, are characterized by security vulnerabilities that are difficult to manage at the state level. Comparable problems exist for related technologies that leverage smartphones, such as contact tracing applications, and non-medical health monitoring devices. In analogous situations, these vulnerabilities may be exploited in the cyber domain to overload the crisis management systems with false alarms and to interfere with the interests of target countries, with consequences on their economy and their political equilibria. In this paper we analyze the potential threat to an example subsystem to show how these influences may impact it and evaluate a possible consequence.
Focal learning on stranger for imbalanced image segmentation

It is an open issue to train effective deep network models on class imbalance datasets. In the widely used cost-sensitive imbalanced learning methods, the costs are based on the losses or class probabilities of samples. In this paper, it is discovered that these traditional cost-sensitive methods discard the clustering feature, and introduce the errors of annotations into costs, leading to sub-optimal models. It is further investigated that the feature magnitude of sample, which is computed before probability and loss, not only is independent of the annotation, but also represents the familiarity degree of model with the sample. These characteristics of feature magnitude are used to guide the training and inference of model. First, the concept of stranger is proposed, which is the sample with small feature magnitude value, and the idea of focal learning on strangers (FLS) is proposed. By adding the idea of FLS into two existing cost-sensitive methods, two novel losses are put forward: instance-level focal stranger loss (IFSL) and class-level focal stranger loss (CFSL). The losses can improve the aggregation features of samples within class, and reduce the negative influences of annotation errors on imbalanced learning. Second, considering the large difference of feature magnitude means between minority class and majority class in case of extreme class-imbalance dataset, a bias determination (BD) strategy is put forward to improve classification performance during inference. The methods are applied to the tasks of image semantic segmentation and salient-instance segmentation. The experimental results on four public semantic segmentation datasets demonstrate that IFSL can reduce the over-fitting of model, improve the classification accuracy of rare samples, and alleviate the reliance of performance on the annotation quality. The experimental results on two public salient-instance segmentation datasets show that CFSL makes the model have better scoring ability for salient object. Besides, the BD strategy can reduce the wrong classification caused by bias model. Therefore, the proposed methods can significantly advance image segmentation.
A Denotational Semantics of Solidity in Isabelle/HOL

Smart contracts are programs, usually automating legal agreements such as financial transactions. Thus, bugs in smart contracts can lead to large financial losses. For example, an incorrectly initialized contract was the root cause of the Parity Wallet bug that made USD 280mil worth of Ether inaccessible. Ether is the cryptocurrency of the Ethereum blockchain that uses Solidity for expressing smart contracts.In this paper, we address this problem by presenting an executable denotational semantics for Solidity in the interactive theorem prover Isabelle/HOL. This formal semantics builds the foundation of an interactive program verification environment for Solidity programs and allows for inspecting Solidity programs by (symbolic) execution. We combine the latter with grammar-based fuzzing to ensure that our formal semantics complies to the Solidity implementation on the Ethereum Blockchain. Finally, we demonstrate the formal verification of Solidity programs by two examples: constant folding and memory optimization.
On Entanglement Assisted Classical Optical Communication with Transmitter Side Optical Phase-Conjugation

Entanglement assisted (EA) communication has been advocated by numerous authors as a potential alternative to classical communications, in particular in noisy and low-brightness regime.In this paper, we propose an EA scheme employing the optical phase-conjugation (OPC) on transmitter side.We show that the proposed EA communication system, with the transmitter side OPC, significantly outperforms its classical counterpart in low-brightness and highly noisy regime, while employing a classical coherent detection scheme for two-dimensional demodulation.Moreover, the capacity of the proposed EA scheme is significantly higher than EA repetition-BPSK, homodyne, heterodyne, and Holevo capacities.Finally, the proposed EA scheme outperforms the corresponding EA scheme with OPC placed on a receiver side.
Performance Evaluation of Adversarial Attacks on Whole-Graph Embedding Models

Graph embedding techniques are becoming increasingly common in many fields ranging from scientific computing to biomedical applications and finance. These techniques aim to automatically learn low-dimensional representations for a variety of network analysis tasks. In literature, several methods (e.g., random walk-based, factorization-based, and neural network-based) show very promising results in terms of their usability and potential. Despite their spreading diffusion, little is known about their reliability and robustness, particularly when applied to the real world of data, where adversaries or malfunctioning/noisy data sources may supply deceptive data. The vulnerability emerges mainly by inserting limited perturbations in the input data when these lead to a dramatic deterioration in performance. In this work, we propose an analysis of different adversarial attacks in the context of whole-graph embedding. The attack strategies involve a limited number of nodes based on the role they play in the graph. The study aims to measure the robustness of different whole-graph embedding approaches to those types of attacks, when the network analysis task consists in the supervised classification of whole-graphs. Extensive experiments carried out on synthetic and real data provide empirical insights on the vulnerability of whole-graph embedding models to node-level attacks in supervised classification tasks.
Data-driven approach to predict the sequence of component failures: a framework and a case study on a process industry

Purpose The research approach is based on the concept that a failure event is rarely random and is often generated by a chain of previous events connected by a sort of domino effect. Thus, the purpose of this study is the optimal selection of the components to predictively maintain on the basis of their failure probability, under budget and time constraints. Design/methodology/approach Assets maintenance is a major challenge for any process industry. Thanks to the development of Big Data Analytics techniques and tools, data produced by such systems can be analyzed in order to predict their behavior. Considering the asset as a social system composed of several interacting components, in this work, a framework is developed to identify the relationships between component failures and to avoid them through the predictive replacement of critical ones: such relationships are identified through the Association Rule Mining (ARM), while their interaction is studied through the Social Network Analysis (SNA). Findings A case example of a process industry is presented to explain and test the proposed model and to discuss its applicability. The proposed framework provides an approach to expand upon previous work in the areas of prediction of fault events and monitoring strategy of critical components. Originality/value The novel combined adoption of ARM and SNA is proposed to identify the hidden interaction among events and to define the nature of such interactions and communities of nodes in order to analyze local and global paths and define the most influential entities.
Context-Aware End-User Development Review

Context-aware application development frameworks enable context management and environment adaptation to automatize people’s activities. New technologies such as 5G and the Internet of Things (IoT) increase environment context (from devices/services), making functionalities available to augment context-aware applications. The result is an increased deployment of context-aware applications to support end-users in everyday activities. However, developing applications in context-aware frameworks involve diverse technologies, so that it traditionally involves software experts. In general, context-aware applications are limited in terms of personalization for end-users. They include configurations to personalize applications, but non-software experts can only change some of these configurations. Nowadays, advances in human–computer interaction provide techniques/metaphors to approach non-software experts. One approach is end-user development (EUD)—a set of activities and development tools that considers non-software experts as application builders. In this paper, we present our analysis of existing EUD approaches for building context-aware applications. We present a literature review of 37 screened papers obtained from research databases. This review aims to identify the methods, techniques, and tools proposed to build context-aware applications. Specifically, we reviewed EUD building techniques and implementations. Building techniques include metaphors/interaction styles proposed for application specification, composition, and testing. The implementations include a specification method to integrate and process context on the target application platforms. We also present the adoption trend and challenges of context-aware end-user development.
SBRAC: Blockchain-based sealed-bid auction with bidding price privacy and public verifiability

Electronic auction (e-auction) has become one of the most popular approaches to allocate resources or services in contemporary society because of removing physical limitations of traditional auctions. The rapid expansion of e-auction enhances work efficiency but incurs some new and distinctive challenges such as low-robustness, data privacy, and lack of trust. To address the above concerns, various auction designs have been proposed by researchers from the academic and industrial circles. However, most of them fail in simultaneously achieving decentralization (i.e. auctioneer-free), privacy-preserving (i.e. bids privacy), collusion-resistance, and secure-channel free. In this paper, we present an effective smart contract-based verifiable privacy-preserving sealed-bid reverse auction scheme, which not only has no need for trusted third parties, but also achieves robustness and privacy protection of losing bids even in the face of overwhelming collusion. In this scheme, the central auctioneer is replaced with some carefully designed smart contracts for decentralization, while zero-knowledge proof and anonymous veto networks are used to avoid the secret communication channel, support the public verifiability and detect the malicious behaviors of bidders. More practically, the complexity of the designed scheme is independent of the number of bidders. Finally, we implement a prototype of our design based on a locally simulated Ethereum test network. The extensive experimental results demonstrate the scheme is the high efficiency and practicality.
Automatic Generation of Chest X-Ray Medical Imaging Reports using LSTM-CNN

Generating medical reports manually is a difficult task, especially in rural areas and in urgent medical cases, where there is an emergency. It can also be error-prone for inexperienced physicians to generate a medical report. There are various deep learning methodologies such as Image captioning, image classification that has been implemented earlier to solve this problem. Generating a medical report automatically is a difficult task, considering the less amount of open-source data available and the paired data which contains medical Images and the report is also limited. One of the challenging tasks is data bias in medical Imaging. A generative encoder-decoder model is suggested to solve this problem in an efficient way. There are various other challenges. First, the medical report itself contains various heterogeneous information such as paragraphs, tags, keywords. Secondly, it is also difficult to identify the abnormal regions in medical images. To solve this problem, a multi-task framework is built, which can perform tag generation and paragraph generation. LSTM (Long Short Term Memory) is built to generate long heterogeneous paragraphs in the medical report. The model working is demonstrated on Chest X-Ray dataset and also on pathology dataset.
Leveraging MoCap Data for Human Mesh Recovery

Training state-of-the-art models for human body pose and shape recovery from images or videos requires datasets with corresponding annotations that are really hard and expensive to obtain. Our goal in this paper is to study whether poses from 3D Motion Capture (MoCap) data can be used to improve image-based and video-based human mesh recovery methods. We find that fine-tune image-based models with synthetic renderings from MoCap data can increase their performance, by providing them with a wider variety of poses, textures and backgrounds. In fact, we show that simply fine-tuning the batch normalization layers of the model is enough to achieve large gains. We further study the use of MoCap data for video, and introduce PoseBERT, a transformer module that directly regresses the pose parameters and is trained via masked modeling. It is simple, generic and can be plugged on top of any state-of-the-art image-based model in order to transform it in a video-based model leveraging temporal information. Our experimental results show that the proposed approaches reach state-of-the-art performance on various datasets including 3DPW, MPI-INF-3DHP, MuPoTS-3D, MCB and AIST.
Learn more from less: Generalized zero-shot learning with severely limited labeled data

Generalized Zero-Shot Learning (GZSL) aims to recognize samples from both seen (base) and unseen (novel) categories by learning a classification model with assistance of some class-level semantic information. Despite promising results have been achieved, most existing approaches still require a large amount of labeled data for training a basic transfer model. To further alleviate the annotation burden, we present a low-data training setting for zero-shot task, where only a few samples are available for each training class. To compensate the scarcity of the training data, we introduce a novel meta-learning paradigm to train a model-agnostic classifier conditioned on the class semantics with only a few training samples. By mimicking a collection of zero-shot tasks under the training data scarcity scenario, the semantic knowledge can be transferred explicitly. To address the model bias, we introduce a simple but effective strategy via a domain detector called entropy gate to assist the model to search optimal weights respectively for both seen and unseen classes. Experiments on four standard benchmarks, i.e., AWA2, APY, CUB, and FLO demonstrate that our proposed model significantly outperforms state-of-the-art methods under both traditional GZSL and the proposed low-data regime settings.
Robust Federated Averaging via Outlier Pruning

Federated Averaging (FedAvg) is the baseline Federated Learning (FL) algorithm that applies the stochastic gradient descent for local model training and the arithmetic averaging of the local models’ parameters for global model aggregation. Succeeding FL works commonly utilize the arithmetic averaging scheme of FedAvg for the aggregation. However, such arithmetic averaging is prone to the outlier model-updates, especially when the clients’ data are non-Independent and Identically Distributed (non-IID). As such, the classical aggregation approach suffers from the dominance of the outlier updates and, consequently, causes high communication costs towards producing a decent global model. In this letter, we propose a robust aggregation strategy to alleviate the above issues. In particular, we propose first pruning the node-wise outlier updates (weights) from the local trained models and then performing the aggregation on the selected effective weights-set at each node. We provide the theoretical result of our method and conduct extensive experiments on the MNIST, CIFAR-10, and Shakespeare datasets with IID and non-IID settings, which demonstrate that our aggregation approach outperforms the state-of-the-art methods in terms of communication speedup, test-set performance and training convergence.
A tutorial on generative adversarial networks with application to classification of imbalanced data

A challenge unique to classification model development is imbalanced data. In a binary classification problem, class imbalance occurs when one class, the minority group, contains significantly fewer samples than the other class, the majority group. In imbalanced data, the minority class is often the class of interest (e.g., patients with disease). However, when training a classifier on imbalanced data, the model will exhibit bias towards the majority class and, in extreme cases, may ignore the minority class completely. A common strategy for addressing class imbalance is data augmentation. However, traditional data augmentation methods are associated with overfitting, where the model is fit to the noise in the data. In this tutorial we introduce an advanced method for data augmentation: Generative Adversarial Networks (GANs). The advantages of GANs over traditional data augmentation methods are illustrated using the Breast Cancer Wisconsin study. To promote the adoption of GANs for data augmentation, we present an end-to-end pipeline that encompasses the complete life cycle of a machine learning project along with alternatives and good practices both in the paper and in a separate video. Our code, data, full results and video tutorial are publicly available in the paper's github repository.