Reviewerly/exHarmony huggingface dataset:
Identifying information security opinion leaders in organizations: Insights from the theory of social power bases and social network analysis

Organizations are increasingly prioritizing the management of information security (InfoSec) to facilitate their data-driven business. There are densely connected organizational networks in modern organizations, where both formal and informal leaders influence other employees' InfoSec behaviors. We analyzed the organizational networks in a large organization and investigated the interactions that made employees appear as influential InfoSec opinion leaders to their colleagues. The findings indicate that influential InfoSec opinion leaders were perceived by colleagues as knowledgeable in InfoSec and capable of rewarding and sanctioning InfoSec behaviors. Other traits such as formal seniority, age, tenure, and department membership were also found to increase InfoSec leadership.
Deep learning in pervasive health monitoring, design goals, applications, and architectures: An overview and a brief synthesis

The continuous growth of an aging population in some countries, and patients with chronic conditions needs the development of efficient solutions for healthcare. Pervasive Health Monitoring (PHM) is an important pervasive computing application that has the potential to provide patients with a high-quality medical service and enable quick-response alerting of critical conditions. To that end, PHM enables continuous and ubiquitous monitoring of patients' health and wellbeing using Internet of Things (IoT) technologies, such as wearables and ambient sensors. In recent years, deep learning (DL) has attracted a growing interest from the research community to improve PHM applications. In this paper, we discuss the state-of-the-art of DL-based PHM, through identifying, (1) the main PHM applications where DL is successful, (2) design goals and objectives of using DL in PHM, and (3) design notes including DL architectures and data preprocessing. Finally, main advantages, limitations and challenges of the adoption of DL in PHM are discussed.
How biased is our validation (data) for AS relationships?

The business relationships between Autonomous Systems (ASes) can provide fundamental insights into the Internet's routing ecosystem. Throughout the last two decades, many works focused on how to improve the inference of those relationships. Yet, it has proven difficult to assemble extensive ground-truth data sets for validation. Therefore, more recent works rely entirely on relationships extracted from BGP communities to serve as "best-effort" ground-truth. In this paper, we highlight the shortcomings of this trend. We show that the best-effort validation data does not cover relationships between ASes within the Latin American (LACNIC) service region even though ~14% of all inferred relationships are from that region. We further show that the overall precision of 96-98 % for peering relationships achieved by three of the most prominent algorithms can drop by 14-25 % when considering only peering relationships between Tier-1 and other transit providers. Finally, we discuss potential ways to overcome the presented challenges in the future.
BCNN: Binary complex neural network

Binarized neural networks, or BNNs, show great promise in edge-side applications with resource limited hardware, but raise the concerns of reduced accuracy. Motivated by the complex neural networks, in this paper we introduce complex representation into the BNNs and propose Binary complex neural network – a novel network design that processes binary complex inputs and weights through complex convolution, but still can harvest the extraordinary computation efficiency of BNNs. To ensure fast convergence rate, we propose novel BCNN based batch normalization and weight initialization strategies. Experimental results on image and radio signal classifications show that BCNN can achieve better accuracy compared to the original BNN models. BCNN improves BNN by strengthening its learning capability through complex representation and extending its applicability to complex-valued input data. Our code is available at https://github.com/flying-Yan/BCNN.
Multiple Object Trackers in OpenCV: A Benchmark

Object tracking is one of the most important and fundamental disciplines of Computer Vision. Many Computer Vision applications require specific object tracking capabilities, including autonomous and smart vehicles, video surveillance, medical treatments, and many others. The OpenCV as one of the most popular libraries for Computer Vision includes several hundred Computer Vision algorithms. Object tracking tasks in the library can be roughly clustered in single and multiple object trackers. The library is widely used for real-time applications, but there are a lot of unanswered questions such as when to use a specific tracker, how to evaluate its performance, and for what kind of objects will the tracker yield the best results? In this paper, we evaluate 7 trackers implemented in OpenCV against the MOT20 dataset. The results are shown based on Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP) metrics.
ZaligVinder: A generic test framework for string solvers

Abstract The increased interest in string solving in the recent years has made it very hard to identify the right tool to address a particular user's purpose. Firstly, there is a multitude of string solvers, each addressing essentially some subset of the general problem. Generally, the addressed fragments are relevant and well motivated, but the lack of comparisons between the existing tools on an equal set of benchmarks cannot go unnoticed, especially as a common framework to compare solvers seems to be missing. In this paper, we gather a set of relevant benchmarks and introduce our new benchmarking framework to address this purpose.
Named Entity Recognition in Unstructured Medical Text Documents

Physicians provide expert opinion to legal courts on the medical state of patients, including determining if a patient is likely to have permanent or non-permanent injuries or ailments. An independent medical examination (IME) report summarizes a physician&#x0027;s medical opinion about a patient&#x0027;s health status based on the physician&#x0027;s expertise. IME reports contain private and sensitive information (Personally Identifiable Information or PII) that needs to be removed or randomly encoded before further research work can be conducted. In our study the IME is an orthopedic surgeon from a private practice in the United States. The goal of this research is to perform named entity recognition (NER) to identify and subsequently remove/encode PII information from IME reports prepared by the physician. We apply the NER toolkits of OpenNLP and spaCy, two freely available natural language processing platforms, and compare their precision, recall, and f-measure performance at identifying five categories of PII across trials of randomly selected IME reports using each model&#x0027;s common default parameters. We find that both platforms achieve high performance (f-measure &#x003E; 0.9) at de-identification and that a spaCy model trained with a 70&#x2013;30 train-test data split is most performant.
A Morphological Image Segmentation Algorithm for Circular Overlapping Cells

Cell segmentation is an important topic in medicine. A cell image segmentation algorithm based on morphology is proposed. First, some morphological operations, including top-hat transformation, bot-hat transformation, erosion operation, dilation operation, opening operation, closing operation, majority operation, skeleton operation, etc., are applied to remove noise or enhance cell images. Then the small blocks in the cell image are deleted as noise, the medium blocks are removed and saved as normal cells, and the large blocks are segmented as overlapping cells. Each point on the edge of the overlapping cell area to be divided is careful checked. If the shape of the surrounding area is a corner and its angle is smaller than the specified value, the overlapping cell will be divided along the midline of the corner. The length of each division is about a quarter of the diameter of a normal cell. Then small blocks are deleted, and medium blocks are removed and saved, after the edges of all blocks are smoothed. This step is repeated until no dividing point is found. The last remaining image, plus the saved blocks, is the final segmentation result of the cell image. The experimental results show that this algorithm has high segmentation accuracy for lightly or moderately overlapping cells.
AskMe: joint individual-level and community-level behavior interaction for question recommendation

Questions in Community Question Answering (CQA) sites are recommended to users, mainly based on users’ interest extracted from questions that users have answered or have asked. However, there is a general phenomenon that users answer fewer questions while pay more attention to follow questions and vote answers. This can impact the performance when recommending questions to users (for obtaining their answers) by using their historical answering behaviors on existing studies. To address the data sparsity issue, we propose AskMe, which aims to leverage the rich, hybrid behavior interactions in CQA to improve the question recommendation performance. On the one hand, we model the rich correlations between the user’s diverse behaviors (e.g., answer, follow, vote) to obtain the individual-level behavior interaction. On the other hand, we model the sophisticated behavioral associations between similar users to obtain the community-level behavior interaction. Finally, we propose the way of element-level fusion to mix these two kinds of interactions together to predict the ranking scores. A dataset collected from Zhihu (1126 users, 219434 questions) is utilized to evaluate the performance of the proposed model, and the experimental results show that our model has gained the best performance compared to baseline methods, especially when the historical answering behaviors data is scarce.
Research and Application of Chinese Calligraphy Character Recognition Algorithm Based on Image Analysis

Chinese characters and calligraphy are important components of Chinese traditional culture, which constitute rich and colorful Chinese history and culture. When users browse and appreciate books with Chinese characters, especially when browsing and appreciating ancient books with many traditional Chinese characters, they will encounter many unfamiliar Chinese characters, which brings obstacles to users' appreciation of works. In view of the above problems, this paper attempts to combine deep learning with traditional recognition methods to recognize calligraphy images. From the perspective of calligraphy style diversity, the deep convolution neural network is used to classify the calligraphy image in style, and then the traditional classification algorithm is used to identify the calligraphy image and get its corresponding Chinese characters. Through experimental comparison, the calligraphy character recognition algorithm proposed in this paper is more efficient. The Chinese character recognition algorithm is applied to the calligraphy scoring system to realize the interface of the Chinese character scoring system. Taking the scoring of three different fonts as an example, the calligraphy Chinese character scoring of the practitioners of three fonts is completed.
Knowledge Cross-Distillation for Membership Privacy

A membership inference attack (MIA) poses privacy risks for the training data of a machine learning model. With an MIA, an attacker guesses if the target data are a member of the training dataset. The state-of-the-art defense against MIAs, distillation for membership privacy (DMP), requires not only private data for protection but a large amount of unlabeled public data. However, in certain privacy-sensitive domains, such as medicine and finance, the availability of public data is not guaranteed. Moreover, a trivial method for generating public data by using generative adversarial networks significantly decreases the model accuracy, as reported by the authors of DMP. To overcome this problem, we propose a novel defense against MIAs that uses knowledge distillation without requiring public data. Our experiments show that the privacy protection and accuracy of our defense are comparable to those of DMP for the benchmark tabular datasets used in MIA research, Purchase100 and Texas100, and our defense has a much better privacy-utility trade-off than those of the existing defenses that also do not use public data for the image dataset CIFAR10.
iMap

Node mapping between large graphs (or network alignment) plays a key preprocessing role in joint-graph data mining applications like social link prediction, cross-platform recommendation, etc. Most existing approaches attempt to perform alignment at the granularity of entire graphs, while handling the whole graphs may lower the scalability and the noisy nodes/edges in the graphs may impact the effectiveness. From the observation that potential node mappings always appear near known corresponding nodes, we propose iMAP, a novel sub-graph expansion based alignment framework to incrementally construct meaningful sub-graphs and perform alignment on each sub-graph pair iteratively, which reduces the unnecessary computation cost in the original raw networks and improves effectiveness via excluding possible noises. Specifically, iMap builds a candidate sub-graph around known matched nodes initially. In each following iteration, iMap trains an alignment model to infer the node mapping relationship between sub-graphs, from which the sub-graphs are further extended and refined. In addition, we design a Graph Neural Network(GNN) based model named MAP on each sub-graph pair in the iMap framework. MAP utilizes trainable Multi-layer Perception (MLP) prediction heads for similarity computation and employs a mixed loss function consisting of the ranking loss for contrastive learning and the cross-entropy loss for classification. Extensive experiments conducted on real social networks demonstrate superior efficiency and effectiveness (above 12% improvement) of our proposed method compared to several state-of-the-art methods.
Scaling Blockchains Using Pipelined Execution and Sparse Peers

Large cloud providers such as AWS and IBM now provide managed blockchain platforms, showcasing an active interest in blockchains. Unfortunately, blockchains provide poor performance and scalability. This is true even for the Execute-Order-Validate (EOV) style of blockchains which improves over the traditional Order-Execute architecture. We experimentally show that EOV platforms scale poorly using both vertical and horizontal scaling approaches. We find that the throughput is bottlenecked by the Validation and Commit phases, which poorly utilize the resources, limiting performance and scalability.
Adv-Plate Attack: Adversarially Perturbed Plate for License Plate Recognition System

Deep learning technology has been used to develop improved license plate recognition (LPR) systems. In particular, deep neural networks have brought significant improvements in the LPR system. However, deep neural networks are vulnerable to adversarial examples. In the existing LPR system, adversarial examples study specific spots that are easily identifiable by humans or require human feedback. In this paper, we propose a method of generating adversarial examples in the license plate, which has no human feedback and is difficult to identify by humans. In the proposed method, adversarial noise is added only to the license plate among the entire image to create an adversarial example that is erroneously recognized by the LPR system without being identified by humans. Experiments were performed using the baza silka dataset, and TensorFlow was used as the machine learning library. When epsilon is 0.6 for the first type, and alpha and the iteration of the second type are 0.4 and 1000, respectively, the adversarial examples generated by the first and second type generation methods are reduced to 20% and 15% accuracy in the LPR system.
A Content-Based Recommender System for Hidden Cultural Heritage Sites Enhancing

The enhancement of Italian artistic and cultural heritage is a big challenge for modern technologies. Modern systems are capable of producing an information flood that, however, keeps users away from what they need. There are fascinating places that often remain hidden due to the overload of information. In this scenario, it would be interesting to introduce a recommendation methodology that brings the right information to the user according to their profile, ensuring a unique and tailored experience. This paper aims to propose a Content-Based Recommender System able to exploit Singular Value Decomposition proprieties. The Recommender System will be used to recommend hidden cultural sites through the support of the Digital Storytelling and Context-Aware approach. Numerical tests return promising results compared with some Collaborative Filtering RS methodologies.Casillo, Mario Conte, Dajana Lombardi, Marco Santaniello, Domenico Troiano, Alfredo Valentino, Carmine
Gamma ray log to estimate clay content in the layers of water boreholes

This work describes a complete procedure to estimate the clay content of water-bearing layers in boreholes from gamma-ray logs using an optimised clay index, which conventionally is the normalised gamma-ray value in each layer with respect to the maximum and minimum values throughout the borehole. In addition to the geological description of the basin, the interest of knowing the clay content of the layers lies in its importance for the estimation of the permeability of them, given the high influence that this content has on permeability, and also in its application for the determination of the real formation factor. To use the clay index, the gamma-ray log particularities require extensive processing including background radiation removal and the filtering of random oscillations in radioactivity, among other factors, to ensure reliable results; so specific algorithms are presented to carry out this processing. The direct utilisation of the maximum gamma-ray value in clay index is problematic as it depends on the presence of media with a certain radioactive emission that is independent of the clay content, and whose emissivity and abundance are not constant throughout a hydrological basin. For this reason, a statistical criterion, developed to replace the maximum gamma-ray logged by the value that corresponds to 100% of the clay content, is presented. The procedures developed in this study was applied to a twenty-seven borehole series located in the southern Duero Basin, in the Iberian Peninsula, which can be considered representative of continental basins with multi-layered detrital lithologies. Two relevant conclusions were derived from this study. First, the distribution of the layers in each borehole within a detrital basin followed a beta distribution based on their clay content. Second, the mode of this distribution was the best estimator of the sedimentary environment, which clarifies its hydrological potential.
Global trends health science libraries: Part 1

This is the first of three articles based on articles published in the Health Information and Libraries Journal's Regular Feature (International Perspectives and Initiatives). Key trends from 12 countries in Europe, North America, Africa and Asia were identified. In this issue, two trends are described: emergence of new roles and challenges for library staff; supporting researchers engaging in research data management and maintaining institutional repositories. Readers are challenged to compare these trends with their own experiences.J.M.
Comparison of Keras Optimizers for Earthquake Signal Classification Based on Deep Neural Networks

This paper presents a comparative study of the gradient descent optimization algorithm found in the Keras library. The optimizers used in this study were Adadelta, Adagrad, Adam, Adamax, Nadam, RMSprop, and SGD. Previous research on earthquake classification was used as a basis for comparing optimizers. Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) were used for feature extraction contained in earthquake signals and noise. The dataset used was Stanford Earthquake Datasets (STEAD). RMSprop optimizer continued to outperform the others in terms of accuracy. The observed performance was accuracy. The results showed that the two earthquake classification models using the RMSprop optimizer outperformed other optimizers. The best accuracy obtained with RMSprop was 99.2%, with a loss of 0.102 for model 1 and 99.0%, with a loss of 0.107 for model 2. Thus, RMSprop was found the most suitable optimizer for algorithms using a combination of CNN and LSTM.
PEDENet: Image anomaly localization via patch embedding and density estimation

A neural network targeting at unsupervised image anomaly localization, called the PEDENet, is proposed in this work. PEDENet contains a patch embedding (PE) network, a density estimation (DE) network, and an auxiliary network called the location prediction (LP) network. The PE network takes local image patches as input and performs dimension reduction to get low-dimensional patch embeddings via a deep encoder structure. Being inspired by the Gaussian Mixture Model (GMM), the DE network takes those patch embeddings, and then predicts the cluster membership of an embedded patch. The sum of membership probabilities is used as a loss term to guide the learning process. The LP network is a Multi-layer Perception (MLP), which takes embeddings from two neighboring patches as input and predicts their relative location. The performance of the proposed PEDENet is evaluated extensively and benchmarked with that of state-of-the-art methods.
The effect of visually manipulating back size and morphology on back perception, body ownership, and attitudes towards self-capacity during a lifting task

Body re-sizing illusions can profoundly alter perception of our own body. We investigated whether creating the illusion of a muscled and fit-looking back (Strong) influenced perceived back size, body ownership, and attitudes towards self-capacity during a lifting task. Twenty-four healthy male volunteers performed a standardised lifting task while viewing real-time (delay < 20 ms) video of their own back through a head-mounted display under four different conditions (Normal size, Strong, Reshaped, Large; order randomised). The MIRAGE-mediated reality system was used to modify the shape, size, and morphology of the back. Participants were poor at recognizing the correct appearance of their back, for both implicit (perceived width of shoulders and hips) and explicit (questionnaire) measures of back size. Visual distortions of body shape (Reshaped condition) altered implicit back size measures. However, viewing a muscled back (Strong condition) did not result in a sense of agency or ownership and did not update implicit perception of the back. No conditions improved perceptions/attitudes of self-capacity (perceived back strength, perceived lifting confidence, and perceived back fitness). The results lend support for the importance of the embodiment of bodily changes to induce changes in perception. Further work is warranted to determine whether increased exposure to illusory changes would alter perceptions and attitudes towards self-capacity or whether different mechanisms are involved.