Reviewerly/exHarmony huggingface dataset:
MSF-GAN: Multi-Scale Fuzzy Generative Adversarial Network for Breast Ultrasound Image Segmentation

Automatic breast ultrasound image (BUS) segmentation is still a challenging task due to poor image quality and inherent speckle noise. In this paper, we propose a novel multi-scale fuzzy generative adversarial network (MSF-GAN) for breast ultrasound image segmentation. The proposed MSF-GAN consists of two networks: a generative network to generate segmentation maps for input BUS images, and a discriminative network that employs a multi-scale fuzzy (MSF) entropy module for discrimination. The major contribution of this paper is applying fuzzy logic and fuzzy entropy in the discriminative network which can distinguish the uncertainty of segmentation maps and groundtruth maps and forces the generative network to achieve better segmentation performance. We evaluate the performance of MSF-GAN on three BUS datasets and compare it with six state-of-the-art deep neural network-based methods in terms of five metrics. MSF-GAN achieves the highest mean IoU of 78.75%, 73.30%, and 71.12% on three datasets, respectively.
Evaluating Augmented Reality for Wireless Network Security Education

In this innovative practice full paper, we investigate the benefits of using an augmented reality (AR) application in wireless network security education. For this purpose, we design and implement an AR application which passively captures wireless network traffic and superimposes the otherwise imperceptible network packets on the camera image of sending and receiving devices. We perform a user-study with 25 participants, who are provided with a laptop which runs our application. Students are tasked to spot Evil Twin, Address-Resolution Protocol (ARP) Poisoning and Denial of Service (DoS) attacks launched on a 802.11 Wi-Fi network by observing it with the AR application. We find that students were able to spot and describe those attacks intuitively. From those results we conclude that an AR-based approach is a valuable addition to wireless network security laboratory courses. Additionally, our results indicate, that our application provides access to this complex topic even for users without a technical background. To the best of our knowledge, this is the first experimental study, which shows the beneficial effects of using AR in wireless network security education.
Reconnection of fragmented parts of coronary arteries using local geometric features in X-ray angiography images

The segmentation of coronary arteries in X-ray images is essential for image-based guiding procedures and the diagnosis of cardiovascular disease. However, owing to the complex and thin structures of the coronary arteries, it is challenging to accurately segment arteries in X-ray images using only a single neural network model. Consequently, coronary artery images obtained by segmentation with a single model are often fragmented, with parts of the arteries missing. Sophisticated post-processing is then required to identify and reconnect the fragmented regions. In this paper, we propose a method to reconstruct the missing regions of coronary arteries using X-ray angiography images.We apply an independent convolutional neural network model considering local details, as well as a local geometric prior, for reconnecting the disconnected fragments. We implemented and compared the proposed method with several convolutional neural networks with customized encoding backbones as baseline models.When integrated with our method, existing models improved considerably in terms of similarity with ground truth, with a mean increase of 0.330 of the Dice similarity coefficient in local regions of disconnected arteries. The method is efficient and is able to recover missing fragments in a short number of iterations.Owing to the restoration of missing fragments of coronary arteries, the proposed method enables a significant enhancement of clinical impact. The method is general and can simply be integrated into other existing methods for coronary artery segmentation.
Kohonen Self-Organizing Map based Route Planning: A Revisit

In this paper, we revisit the long-standing Traveling Salesman Problem (TSP) and focus on the challenging, yet practical route planning problem with limited computational resources. We make contributions to TSP, one of the most famous NP-hard problems by providing a new improved approximate solution, which we term TOpology Preserving Self-Organizing Map (TOPSOM). TOPSOM well preserves the topology of the node map to be traversed by maintaining the continuity of nodes and the distances between them. In addition, to satisfy the requirements of convex hull, we design an elastic competitive Hebbian learning rule. TOPSOM can solve large-scale TSPs with high precision and high efficiency with limited computational costs. Extensive experimental results on mainstream route planning benchmarks including TSPLIB and National TSP’s show that our method consistently outperforms baseline methods, by up to 7.7% in terms of the Percent Deviation of Mean solution to best known solution.
An extended artificial bee colony with local search for solving the Skyline-based web services composition under interval QoS properties

In this paper, we introduce an extended version of artificial bee colony with a local search method (EABC) for solving the QoS uncertainty-aware web service composition (IQSC) problem, where the ambiguity of the QoS properties are represented using the interval-number model. At first, we formulate the addressed problem as an interval constrained single-objective optimization model. Then, we use the skyline operator to prune the redundant and dominated web services from their sets of functionally equivalent ones. Whereas, EABC is employed to solve the IQSC problem in a reduced search space more effectively and more efficiently. For the purpose of validation of the performance and the efficiency of the proposed approach, we present the experimental comparisons to an existing skyline-based PSO, an efficient discrete gbest-guided artificial bee colony and a recently provided Harris Hawks optimization with an elite evolutionary strategy algorithms on an interval extended version of the public QWS dataset.
A Topological Approach to Finding Coarsely Diverse Paths

We present a topological method for finding coarsely diverse pathways. The use of pre-computed paths for online planning in a dynamic context reduces the overhead of re-planning alternate routes. Our algorithm applied the notion of discrete Morse theory to identify critical points incident on the obstacles and used this information to identify and return a diverse set of coarse paths. Three sampling-based planning approaches are converted to topology-aware planners and compared to another that employs the SPARS2 path planning algorithm. We report on the number of coarse pathways found, computation time, and average path length and show that our approach outperformed previously published path diversity algorithms.
REBORN: Transfer learning based social network alignment

Social network alignment (SNA) aims to identify the same person across different social networks. Existing SNA methods can be categorized as supervised, semi-supervised and unsupervised. However, for supervised and semi-supervised methods, obtaining supervisions is expensive and time-consuming; for unsupervised methods, their performances will reach to a bottleneck due to the lack of supervisions. In the real world, there exist many social networks which have already been aligned spending money and time, and many social networks to be aligned. To bridge this gap, we study the SNA problem from the transfer learning perspective, and propose to leverage the knowledge of two aligned social networks (source domain) to facilitate the alignment of two unaligned social networks (target domain). Specifically, we first propose Ego-Transformer to align two social networks in the same domain. Then, we propose WWGAN to eliminate the shift between different domains. Finally, we propose REBORN which tactfully incorporates Ego-Transformer and WWGAN together. We conduct extensive experiments on the datasets of Facebook-Twitter and Weibo-Douban, and compare REBORN with 8 state-of-the-art SNA methods including SNNA, MSUIL, BRIGHT, DeepLink, IONE, PALE, WAlign, and UUILgan. The results demonstrate that REBORN can averagely achieve 20.02% higher [email protected]k and 23.23% higher [email protected]k compared with these methods.
Group Signatures with Designated Traceability

We propose a group signature scheme with a function of designated traceability; each opener has attributes and a signer of a group signature can be traced by only the openers whose attributes satisfy the boolean formula designated by the signer. We describe syntax and security definitions of the scheme. Then we give a generic construction of the scheme by employing a ciphertext-policy attribute-based encryption scheme.
Measurement of out-of-plane displacement in time series using laser lines and a camera with a diffraction grating

The inspection of infrastructure such as road bridges requires an efficient method for measuring the out-of-plane displacement. In this study, a method was proposed that uses laser lines to measure the time series of the out-of-plane displacement. An image of parallel laser lines with the same spacing is easily generated by installing a diffraction grating in front of the imaging lens of a camera. The displacement can then be obtained from phase analysis of the parallel laser lines. Laboratory experiments were performed to confirm the principle of the proposed method. Then, using the proposed method, field experiments of measuring the displacement time series of an actual road bridge were also done. The results of laboratory experiment confirmed that the principle of the proposed method was correct, and results of field experiment shown that displacement was measured at a very high resolution.
Two Unorthodox Aspects in Handcrafted-feature Extraction for Human Activity Recognition Datasets

Human Activity Recognition (HAR) is one of the underlying research areas in the field of biomedical data science. One of the major tasks of researching with HAR datasets is extracting features from the raw datasets. Many methods have been proposed so far as an approach. In this paper, we have used two unorthodox methods (in the HAR field) for feature extracting, namely- ‘Vector Point’ (VP) and’ Absolute Distance’ (AD). By using these techniques, the data size gets decreased 3 and 6 times consecutively and the time complexity also gets decreased considerably. The extracted features are utilized as inputs to a Random Forest classifier. We have got an AVC score of ‘1’ for our merged dataset with both the VP and AD methods which manifests the success of our work.
Efficient Quantum Implementation of Majority-Based Full Adder Circuit Using Clifford+T-Group

Biswal, Laxmidhar Mondal, Bappaditya Chakraborty, Anindita Rahaman, HafizurThe rapid progress in quantum technologies, as well as quantum algorithms, has paved the way for general-purpose, scalable quantum computers. The single most important challenge to that ambitious goal is the handling of noise. Literally, quantum states are fragile; highly sensitive toward noise which requires regular encoding and decoding of quantum information through quantum error-correcting code (QECC) so as to accomplish fault tolerance. Due to the efficient physical implementation of promising QECC on multiple quantum technologies, the Clifford+T group of Quantum operators is nowadays commonly used for the fault-tolerant Quantum circuit realizations. On the other hand, the majority-based data structure converts a ripple-carry adder into a carry-look ahead adder. In this work, we focus on the implementation of Majority-based 1-bit and n-bit Full Adder (FA) in the fault-tolerant quantum logic circuits. We have also calculated the performance parameters viz. T-count, ancillary cost, and T-depth that connected with the quantum circuit of full adder circuit in fault-tolerant logic.
The athletic heart syndrome in web service evolution

Abstract Despite the particular standards, technologies, and trends (W3C, RESTful, microservices, etc.) that a team decides to follow for the development of a service‐oriented system, most likely the team members will have to use one or more services that solve general‐purpose problems like cloud computing, networking and content delivery, storage and database, management and governance, and application integration. Typically, general‐purpose services are long‐lived, they have several responsibilities, their interfaces are complex, and they grow over time. The way that these services evolve also affects the evolution of any system that will depend on them. Consequently, the selection of the particular services that will be used is a main concern for the team. In this paper, we report a pattern, called the athletic heart syndrome , which facilitates the selection of services that evolve properly. Patterns specify best practices that emerge from multiple real‐world cases. In our context, the athletic heart syndrome comes out from a study that concerns the evolution of a set of popular, long‐lived Amazon services that cover different domains. According to the athletic heart syndrome , the developers should select services whose heartbeat of changes looks like the heartbeat of an athlete when he is at rest. Specifically, the heartbeat of changes should consist mostly of calm periods, interrupted by few spikes of change. Similarly, the incremental growth of the services should involve mainly calm periods of maintenance, separated by spikes of growth. Selecting services that adhere to the pattern signifies high chances that the services evolve to deal with changing requirements. The pattern further guarantees that the service evolution involves both the expansion of the services with new functionalities and the maintenance of existing ones. The pattern also assures that the complexity increase in the service interfaces will be smooth and tolerable. Finally, conformance with the pattern implies that the growth of the services will be predictable.
Introduction: Digitalization as a Driver of the Contemporary Economy

Abstract Milena Ratajczak-Mrozek and Paweł Marszałek discuss how digitalization and digital technologies are impacting markets, firms and financial institutions. They stress the need for adopting the interdisciplinary (taking into account not only economic, but also legal and social dimensions) approach in research on digitalization, as such approach provides a comprehensive view of the process, its manifestations, features and impact. The authors also identify six channels through which digitalization has had an impact on the economy. They argue that digitalization may be considered in the context of both challenges and opportunities set by digital technologies for business models and business operations and markets, as well as the main driver of growth and competitiveness for markets and firms/businesses. In this context, the authors introduce 10 chapters of the book and present the links between different level of analysis—micro, mezzo and macro.
Detecting Face Touching with Dynamic Time Warping on Smartwatches: A Preliminary Study

Respiratory diseases such as the novel coronavirus (COVID-19) can be transmitted through people's face-touching behaviors. One of the official recommendations for protecting ourselves from such viruses is to avoid touching our eyes, nose, or mouth with unwashed hands. However, prior work has found that people touch their face 23 times per hour on average without realizing it. Therefore, in this Late-Breaking Work, we explore a possible approach to help users avoid touching their face in daily life by alerting them through a smartwatch application every time a face-touching behavior occurs. We selected 10 everyday activities including several that should be easy to distinguish from face touching and several that should be more challenging. We recruited 10 participants and asked them to perform each activity repeatedly for 3 minutes at their own pace while wearing a Samsung smartwatch. Based on the collected accelerometer data, we used dynamic time warping (DTW) to distinguish between the two groups of activities (i.e., face-touching and non-face-touching), which is a method well-suited for small datasets. Our findings show that the DTW-based classifier is capable of classifying the activities into two groups with high accuracy (i.e., 99.07% for the user-dependent scenario). We demonstrated that smartwatches have the potential to detect face-touching behaviors with the proposed methodology. Future work can explore other classification approaches, collect larger datasets, and consider other sensors to increase the robustness of our results.
TLB Poisoning Attacks on AMD Secure Encrypted Virtualization

AMD's Secure Encrypted Virtualization (SEV) is an emerging technology of AMD server processors, which provides transparent memory encryption and key management for virtual machines (VM) without trusting the underlying hypervisor. Like Intel Software Guard Extension (SGX), SEV forms a foundation for confidential computing on untrusted machines; unlike SGX, SEV supports full VM encryption and thus makes porting applications straightforward. To date, many mainstream cloud service providers, including Microsoft Azure and Google Cloud, have already adopted (or are planning to adopt) SEV for confidential cloud services.
Exploring the relationships between teacher-led and learner-led mobile learning activities and their impacts on teacher evaluation results

The impact of teacher-led and learner-led mobile learning activities on increasing learners' motivation and learning outcomes has been reported as effective. However, the relationship between them remains unclear. In this study, the authors investigated the relationship through survey (n = 366) and interview (n = 7) data collected from students in one of the most prestigious medical schools in China. Three lessons are learned. First, confirmatory factor analysis validated a two-factor model on the use of mobile devices. The participants spend more time on studying with teacher-led than student-led activities. Second, structural equation modelling results suggest that the use of teacher-led mobile activities has a positive impact on learner-led activities and students' satisfaction with overall teaching quality. Third, students perceive mobile devices as useful learning tools when teacher-led activities are present, whereas in the absence of teachers' involvement, student-led activities are perceived as less efficient than other forms of learning.
Improving Deep Image Matting Via Local Smoothness Assumption

Natural image matting is a fundamental and challenging computer vision task. Conventionally, the problem is formulated as an underconstrained problem. Since the problem is ill-posed, further assumptions on the data distribution are required to make the problem well-posed. For classical matting methods, a commonly adopted assumption is the local smoothness assumption on foreground and background colors. However, the use of such assumptions was not systematically considered for deep learning based matting methods. In this work, we consider two local smoothness assumptions which can help improving deep image matting models. Based on the local smoothness assumptions, we propose three techniques, i.e., training set refinement, color augmentation and backpropagating refinement, which can improve the performance of the deep image matting model significantly. We conduct experiments to examine the effectiveness of the proposed algorithm. The experimental results show that the proposed method has favorable performance compared with existing matting methods.
SLUGGER: Lossless Hierarchical Summarization of Massive Graphs

Given a massive graph, how can we exploit its hierarchical structure for concisely but exactly summarizing the graph? By exploiting the structure, can we achieve better compression rates than state-of-the-art graph summarization methods? The explosive proliferation of the Web has accelerated the emergence of large graphs, such as online social networks and hyperlink networks. Consequently, graph compression has become increasingly important to process such large graphs without expensive I/O over the network or to disk. Among a number of approaches, graph summarization, which in essence combines similar nodes into a supernode and describe their connectivity concisely, protrudes with several advantages. However, we note that it fails to exploit pervasive hierarchical structures of real-world graphs as its underlying representation model enforces supernodes to be disjoint. In this work, we propose the hierarchical graph summarization model, which is an expressive graph representation model that includes the previous one proposed by Navlakha et al. as a special case. The new model represents an unweighted graph using positive and negative edges between hierarchical supernodes, each of which can contain others. Then, we propose Slugger, a scalable heuristic for concisely and exactly representing a given graph under our new model. Slugger greedily merges nodes into supernodes while maintaining and exploiting their hierarchy, which is later pruned. Slugger significantly accelerates this process by sampling, approximation, and memoization. Our experiments on 16 real-world graphs show that Slugger is (a) Effective: yielding up to 29.6% more concise summary than state-of-the-art lossless summarization methods, (b) Fast: summarizing a graph with 0.8 billion edges in a few hours, and (c) Scalable: scaling linearly with the number of edges in the input graph.
Explainability of the Implications of Supervised and Unsupervised Face Image Quality Estimations Through Activation Map Variation Analyses in Face Recognition Models

It is challenging to derive explainability for unsupervised or statistical-based face image quality assessment (FIQA) methods. In this work, we propose a novel set of explainability tools to derive reasoning for different FIQA decisions and their face recognition (FR) performance implications. We avoid limiting the deployment of our tools to certain FIQA methods by basing our analyses on the behavior of FR models when processing samples with different FIQA decisions. This leads to explainability tools that can be applied for any FIQA method with any CNN-based FR solution using activation mapping to exhibit the network’s activation derived from the face embedding. To avoid the low discrimination between the general spatial activation mapping of low and high-quality images in FR models, we build our explainability tools in a higher derivative space by analyzing the variation of the FR activation maps of image sets with different quality decisions. We demonstrate our tools and analyze the findings on four FIQA methods, by presenting inter and intra-FIQA method analyses. Our proposed tools and the analyses based on them point out, among other conclusions, that high-quality images typically cause consistent low activation on the areas outside of the central face region, while low-quality images, despite general low activation, have high variations of activation in such areas. Our explainability tools also extend to analyzing single images where we show that low-quality images tend to have an FR model spatial activation that strongly differs from what is expected from a high-quality image where this difference also tends to appear more in areas outside of the central face region and does correspond to issues like extreme poses and facial occlusions. The implementation of the proposed tools is accessible here <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/fbiying87/Explainable_FIQA_WITH_AMVA