Reviewerly/exHarmony huggingface dataset:
Match Feature U-Net: Dynamic Receptive Field Networks for Biomedical Image Segmentation

Medical image segmentation is a fundamental task in medical image analysis. Dynamic receptive field is very helpful for accurate medical image segmentation, which needs to be further studied and utilized. In this paper, we propose Match Feature U-Net, a novel, symmetric encoder– decoder architecture with dynamic receptive field for medical image segmentation. We modify the Selective Kernel convolution (a module proposed in Selective Kernel Networks) by inserting a newly proposed Match operation, which makes similar features in different convolution branches have corresponding positions, and then we replace the U-Net’s convolution with the redesigned Selective Kernel convolution. This network is a combination of U-Net and improved Selective Kernel convolution. It inherits the advantages of simple structure and low parameter complexity of U-Net, and enhances the efficiency of dynamic receptive field in Selective Kernel convolution, making it an ideal model for medical image segmentation tasks which often have small training data and large changes in targets size. Compared with state-of-the-art segmentation methods, the number of parameters in Match Feature U-Net (2.65 M) is 34% of U-Net (7.76 M), 29% of UNet++ (9.04 M), and 9.1% of CE-Net (29 M). We evaluated the proposed architecture in four medical image segmentation tasks: nuclei segmentation in microscopy images, breast cancer cell segmentation, gland segmentation in colon histology images, and disc/cup segmentation. Our experimental results show that Match Feature U-Net achieves an average Mean Intersection over Union (MIoU) gain of 1.8, 1.45, and 2.82 points over U-Net, UNet++, and CE-Net, respectively.
Topology Density Map for Urban Data Visualization and Analysis

Density map is an effective visualization technique for depicting the scalar field distribution in 2D space. Conventional methods for constructing density maps are mainly based on Euclidean distance, limiting their applicability in urban analysis that shall consider road network and urban traffic. In this work, we propose a new method named Topology Density Map, targeting for accurate and intuitive density maps in the context of urban environment. Based on the various constraints of road connections and traffic conditions, the method first constructs a directed acyclic graph (DAG) that propagates nonlinear scalar fields along 1D road networks. Next, the method extends the scalar fields to a 2D space by identifying key intersecting points in the DAG, dividing the underlying territory into planar regions using a weighted Voronoi diagram, and calculating the scalar fields for every point. Two case studies demonstrate that the Topology Density Map supplies accurate information to users and provides an intuitive visualization for decision making. An interview with domain experts demonstrates the feasibility, usability, and effectiveness of our method.
Mobile Money Fraud Prediction—A Cross-Case Analysis on the Efficiency of Support Vector Machines, Gradient Boosted Decision Trees, and Naïve Bayes Algorithms

The onset of COVID-19 has re-emphasized the importance of FinTech especially in developing countries as the major powers of the world are already enjoying the advantages that come with the adoption of FinTech. Handling of physical cash has been established as a means of transmitting the novel corona virus. Again, research has established that, been unbanked raises the potential of sinking one into abject poverty. Over the years, developing countries have been piloting the various forms of FinTech, but the very one that has come to stay is the Mobile Money Transactions (MMT). As mobile money transactions attempt to gain a foothold, it faces several problems, the most important of them is mobile money fraud. This paper seeks to provide a solution to this problem by looking at machine learning algorithms based on support vector machines (kernel-based), gradient boosted decision tree (tree-based) and Naïve Bayes (probabilistic based) algorithms, taking into consideration the imbalanced nature of the dataset. Our experiments showed that the use of gradient boosted decision tree holds a great potential in combating the problem of mobile money fraud as it was able to produce near perfect results.
Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant

We introduce a variational framework to learn the activation functions of deep neural networks. Our aim is to increase the capacity of the network while controlling an upper-bound of the actual Lipschitz constant of the input-output relation. To that end, we first establish a global bound for the Lipschitz constant of neural networks. Based on the obtained bound, we then formulate a variational problem for learning activation functions. Our variational problem is infinite-dimensional and is not computationally tractable. However, we prove that there always exists a solution that has continuous and piecewise-linear (linear-spline) activations. This reduces the original problem to a finite-dimensional minimization where an l1 penalty on the parameters of the activations favors the learning of sparse nonlinearities. We numerically compare our scheme with standard ReLU network and its variations, PReLU and LeakyReLU and we empirically demonstrate the practical aspects of our framework.
Ener3DMap-SolarWeb roofs: A geospatial web-based platform to compute photovoltaic potential

The effective exploitation and management of renewable energies requires knowledge not only of the energy intensity at the exploitation site but also of the influence of the geometry of the site and its surroundings. For this reason, the efficient processing and interpretation of combined geospatial and energy data is a key issue. This paper presents the development of a web-based tool for the automatic computation of photovoltaic potential on rooftops and on parcels without buildings. The tool called Ener3DMap-SolarWeb Roofs is based on Leaflet and supports WMS, GeoJSON, GeoCSV and KML formats, among others. With these data formats, base maps, geometric data from the rooftops automatically computed from LiDAR and imagery data with self-developed processing algorithms, cadastral data and a solar radiation model are integrated in the tool. These different types of data, the high level of automation and the different scales for which energy data is calculated (hourly, monthly and annually) are the main contributions of the presented tool compared to other existing solutions. The capacities of the tool are tested through its application to analyze the solar potential of rooftops with different shapes and for different solar panel configurations. The accuracy of the results is ensured through the integration of a validated methodology for the computation of geometry and a validated solar radiation model, PVGIS.
Leveraging lightweight blockchain to establish data integrity for surveillance cameras

The video footage produced by surveillance cameras is important evidence to support criminal investigations. Video evidence can be sourced from public (trusted) as well as private (untrusted) surveillance systems. This raises the issue of establishing integrity for information provided by the untrusted video sources. In this paper, we present a framework to ensure the data integrity of the stored videos, allowing authorities to validate whether video footage has not been tampered with. Our proposal uses a lightweight blockchain technology to store the video metadata as blockchain transactions to support the validation of video integrity. Our evaluations show that the overhead introduced by employing the blockchain to create the transactions introduces a minor latency of a few milliseconds.
Real-Time and Robust Hydraulic System Fault Detection via Edge Computing

We consider fault detection in a hydraulic system that maintains multivariate time-series sensor data. Such a real-world industrial environment could suffer from noisy data resulting from inaccuracies in hardware sensing or external interference. Thus, we propose a real-time and robust fault detection method for hydraulic systems that leverages cooperation between cloud and edge servers. The cloud server employs a new approach that includes a genetic algorithm (GA)-based feature selection that identifies feature-to-label correlations and feature-to-feature redundancies. A GA can efficiently process large search spaces, such as solving a combinatorial optimization problem to identify the optimal feature subset. By using fewer important features that require transmission and processing, this approach reduces detection time and improves model performance. We propose a long short-term memory autoencoder for a robust fault detection model that leverages temporal information on time-series sensor data and effectively handles noisy data. This detection model is then deployed at edge servers that provide computing resources near the data source to reduce latency. Our experimental results suggest that this method outperforms prior approaches by demonstrating lower detection times, higher accuracy, and increased robustness to noisy data. While we have a 63% reduction of features, our model obtains a high accuracy of approximately 98% and is robust to noisy data with a signal-to-noise ratio near 0 dB. Our method also performs at an average detection time of only 9.42 ms with a reduced average packet size of 179.98 KB from the maximum of 343.78 KB.
Analysis for the Evaluation and Security Management of a Database in a Public Organization to Mitigate Cyber Attacks

In data handling, security and privacy are essential for database administrators and users that own their information.Information about security models or architectures was analyzed for databases that minimize Cyber Attacks.The problem is that security models deployed in databases in public organizations suffer from computer attacks due to vulnerabilities in their security management systems.Data providers often publish the information for research purposes, publishing compromises on the privacy of the data of users registered in the entities.There are many security techniques for databases based on data encryption processes in transactions, however, the techniques found compromise information.The development of the document used the deductive method and exploratory research technique that allows us to study the information in the articles presented.In this document we have proposed a Security Model Assessment Prototype for Databases, a Prototype of Security Management Architecture in Blockchain for a Database, a Database Security Algorithm, a Logical Structure of the Management System in Blockchain, and a Prototype to mitigate cyberattacks.The use of a hybrid blockchain model provides the system with the optimal security that public organizations in relation to test results performed with a 99.50% security effectiveness.The simulations presented in this document were conducted based on in-depth studies and the data were randomly placed with a range according to the study results based on real-world situations.INDEX TERMS Blockchain, cyber attacks, cyber security, data base, public organization.up to 99.50% in security, this simulation was performed by means of random data based on real situations.
Voice-Transfer Attacking on Industrial Voice Control Systems in 5G-Aided IIoT Domain

At present, specific voice control has gradually become an important means for 5G-Internet-of-Things-aided industrial control systems, such as controlling the operation and adjustment of industrial Internet of Things equipment through telephone voice of the controller. However, the security of specific voice control system needs to be improved, because the voice cloning technology based on transfer learning can easily simulate the voice of the controller, which may lead to industrial accidents and other potential security risks. Therefore, this article mainly aims to study and understand the principle of voice cloning attack technology, putting forward a voice clone attack method, in order to prepare for the construction of a specific voice recognition system in the future. At present, the key technology of voice cloning attack is how to solve the problem that the target speaker's personalized speech with high quality cannot be synthesized under small samples. In fact, voice cloning is a very challenging problem because speech is more difficult to be represented in the hidden space of the model. We propose a transductive voice transfer learning method to learn the predictive function from the source domain and fine-tune in the target domain adaptively. The target learning task and the source learning task are both synthesizing speech signals from the given audio, while the datasets of both domains are different. By adding different penalty values to each instances and minimizing the expected risk, an optimal precise model can be learned. In addition, an evaluation method to verify the audio similarity of the target speaker was given to show the similarity between the synthesized audio and the original audio. Many details of the experimental results show that our method can effectively synthesize the speech of the target speaker with small samples.
The Holy Grail of Quantum Artificial Intelligence

We discuss the synergetic connection between quantum computing and artificial intelligence. After surveying current approaches to quantum artificial intelligence and relating them to a formal model for machine learning processes, we deduce four major challenges for the future of quantum artificial intelligence: (i) Replace iterative training with faster quantum algorithms, (ii) distill the experience of larger amounts of data into the training process, (iii) allow quantum and classical components to be easily combined and exchanged, and (iv) build tools to thoroughly analyze whether observed benefits really stem from quantum properties of the algorithm.
Adapting Text Embeddings for Causal Inference

Does adding a theorem to a paper affect its chance of acceptance? Does labeling a post with the author's gender affect the post popularity? This paper develops a method to estimate such causal effects from observational text data, adjusting for confounding features of the text such as the subject or writing quality. We assume that the text suffices for causal adjustment but that, in practice, it is prohibitively high-dimensional. To address this challenge, we develop causally sufficient embeddings, low-dimensional document representations that preserve sufficient information for causal identification and allow for efficient estimation of causal effects. Causally sufficient embeddings combine two ideas. The first is supervised dimensionality reduction: causal adjustment requires only the aspects of text that are predictive of both the treatment and outcome. The second is efficient language modeling: representations of text are designed to dispose of linguistically irrelevant information, and this information is also causally irrelevant. Our method adapts language models (specifically, word embeddings and topic models) to learn document embeddings that are able to predict both treatment and outcome. We study causally sufficient embeddings with semi-synthetic datasets and find that they improve causal estimation over related embedding methods. We illustrate the methods by answering the two motivating questions---the effect of a theorem on paper acceptance and the effect of a gender label on post popularity. Code and data available at this https URL}{this http URL
Software Architecture Degradation in Open Source Software: A Systematic Literature Review

Software architecture (SA) has a prominent role in all stages of system development.Given the persistent evolution of software systems over time, SA tends to be eroded or degraded.Such phenomenon is called architectural degradation.In light of this phenomenon, the current study focuses on problems of architectural erosion in the open-source software (OSS).There has been a significant research activity on the OSS over the last decade.Nonetheless, the architectural degradation problems in the OSS are still scattered and disorganized.In addition, there has been no systematic attempt made on existing studies to provide evidence, insight and better understanding for researchers and practitioners.The main objective of the present study is to provide a profound understanding and to review the existing studies on the architectural erosion of the OSS.In this study, we conduct a systematic literature review (SLR) to gather, organize, classify, and analyze the architectural degradation of previous papers published until the year 2020.The data for this study were collected from eight major online databases (ACM, Springer, ScienceDirect, Taylor, IEEE Explorer, Scopus, Web of Science, and Wiley).A total of 74 primary studies were identified as the final samples of this research.The results indicated that rapid software evolution, frequent changes, and the lack of developers' awareness are the most common causes occurred in architecture degradation.Meanwhile, the prominent key indicators of architectural erosion symptoms are code smells and architectural smells.Additionally, the results indicated the most commonly used of the proposed solution for addressing architectural erosion is the metrics-based strategy.Acknowledging the limitations of the current study, more studies are needed that focus on determining other causes that are still ambiguous and improving the other solutions to provide better results in the precision and effectiveness of addressing architectural erosion.
Efficient and Robust Instrument Segmentation in 3D Ultrasound Using Patch-of-Interest-FuseNet with Hybrid Loss

Instrument segmentation plays a vital role in 3D ultrasound (US) guided cardiac intervention. Efficient and accurate segmentation during the operation is highly desired since it can facilitate the operation, reduce the operational complexity, and therefore improve the outcome. Nevertheless, current image-based instrument segmentation methods are not efficient nor accurate enough for clinical usage. Lately, fully convolutional neural networks (FCNs), including 2D and 3D FCNs, have been used in different volumetric segmentation tasks. However, 2D FCN cannot exploit the 3D contextual information in the volumetric data, while 3D FCN requires high computation cost and a large amount of training data. Moreover, with limited computation resources, 3D FCN is commonly applied with a patch-based strategy, which is therefore not efficient for clinical applications. To address these, we propose a POI-FuseNet, which consists of a patch-of-interest (POI) selector and a FuseNet. The POI selector can efficiently select the interested regions containing the instrument, while FuseNet can make use of 2D and 3D FCN features to hierarchically exploit contextual information. Furthermore, we propose a hybrid loss function, which consists of a contextual loss and a class-balanced focal loss, to improve the segmentation performance of the network. With the collected challenging ex-vivo dataset on RF-ablation catheter, our method achieved a Dice score of 70.5%, superior to the state-of-the-art methods. In addition, based on the pre-trained model from ex-vivo dataset, our method can be adapted to the in-vivo dataset on guidewire and achieves a Dice score of 66.5% for a different cardiac operation. More crucially, with POI-based strategy, segmentation efficiency is reduced to around 1.3 seconds per volume, which shows the proposed method is promising for clinical use.
Deep Learning Based Security Model for Cloud based Task Scheduling

Scheduling plays a dynamic role in cloud computing in generating as well as in efficient distribution of the resources of each task.The principle goal of scheduling is to limit resource starvation and to guarantee fairness among the parties using the resources.The demand for resources fluctuates dynamically hence the prearranging of resources is a challenging task.Many task-scheduling approaches have been used in the cloud-computing environment.Security in cloud computing environment is one of the core issue in distributed computing.We have designed a deep learning-based security model for scheduling tasks in cloud computing and it has been implemented using CloudSim 3.0 simulator written in Java and verification of the results from different perspectives, such as response time with and without security factors, makespan, cost, CPU utilization, I/O utilization, Memory utilization, and execution time is compared with Round Robin (RR) and Waited Round Robin (WRR) algorithms
DeText

Ranking is the most important component in a search system. Most search systems deal with large amounts of natural language data, hence an effective ranking system requires a deep understanding of text semantics. Recently, deep learning based natural language processing (deep NLP) models have generated promising results on ranking systems. BERT is one of the most successful models that learn contextual embedding, which has been applied to capture complex query-document relations for search ranking. However, this is generally done by exhaustively interacting each query word with each document word, which is inefficient for online serving in search product systems. In this paper, we investigate how to build an efficient BERT-based ranking model for industry use cases. The solution is further extended to a general ranking framework, DeText, that is open sourced and can be applied to various ranking productions. Offline and online experiments of DeText on three real-world search systems present significant improvement over state-of-the-art approaches.
The impact of online identity theft on customers’ willingness to engage in e-banking transaction in Ghana: A technology threat avoidance theory

(2020). The impact of online identity theft on customers' willingness to engage in e-banking transaction in Ghana: A technology threat avoidance theory. Cogent Business & Management: Vol. 7, No. 1, 1832825.
Unsupervised Deep Multi-Similarity Hashing With Semantic Structure for Image Retrieval

With the advance of Convolutional Neural Network, deep hashing methods have shown the great promising performance in large-scale image retrieval. Without depending on extensive human-annotated data, unsupervised hashing is more applicable to image retrieval tasks compared to supervised methods. However, due to the lack of fine-grained supervised signals and multi-similarity constraints, most state-of-the-art unsupervised deep hashing algorithms cannot ensure the correct fine-grained similarity ranking for image pairs. In this paper, we propose a novel unsupervised deep multi-similarity hashing framework to learn compact binary codes by jointly exploiting global-aware and spatial-aware representations, called Unsupervised Deep Multi-Similarity Hashing with Semantic Structure (UDMSH). Specifically, to obtain distinguishing characteristics, we develop a sub-network by jointly learning global semantic structures from Convolutional Neural Network (CNN) and inherent spatial structures from Fully Convolutional Network (FCN). By computing the cosine distance for deep features from image pairs, we construct a similarity matrix with semantic structure, then utilize this matrix to guide hash code learning process. Based on it, we carefully design a multi-level pairwise loss to preserve the correct fine-grained similarity ranking. Furthermore, we introduce Hamming-isometric mapping into unsupervised hashing framework to decrease the quantization errors. Extensive experiments on three widely used benchmarks prove that our proposed UDMSH outperforms several state-of-the-art unsupervised hashing with respect to different evaluation metrics.