Reviewerly/exHarmony huggingface dataset:
Automated Diagnosis of Melanoma Histopathological Images Based on Deep Learning Using Trust Counting Method

Improving the speed and accuracy of pathological diagnosis is a key challenge for pathological diagnosis and treatment instructions. And the automated diagnosis of melanoma histopathology slides is one of the potential methods in this task. We proposed a deep learning-based automated diagnosis system focusing on automatic diagnosis, which can diagnose melanoma in histopathological images end-to-end. In the system, an image processing algorithm was used for image slicing and image preprocessing, a ResNet50 model was used for patch-level prediction, and a novel statistics method called the trust counting method was proposed for slide-level diagnosis. The system's derivation and validation were implemented in a multi-centre database, including 701 WSIs from multiple body parts. In the melanoma pathological slices task, the system achieved an area under the receiver operating characteristic curve of 0.986, a sensitivity of 0.938, and a specificity of 0.947. And compared with the classical statistical methods, the system has achieved higher performance. The results of our study suggest that the proposed deep learning diagnosis system has feasibility for melanoma diagnosis.
“Can You Help Me Measure My Blood Sugar?” Co-design of a Voice Interface to Assist Patients and Caregivers at Home

Assistive technologies can have a direct and positive impact on the life of users, because they allow patients to be hospitalized in the comfort of their own home while still receiving high quality care from the clinical team and their caregivers. This article outlines the development of a digital assistant equipped with voice and multimedia capabilities for patients hospitalized at home. Following a first phase of requirements' gathering and exploration of user needs, we developed a voice app (e.g. an Alexa Skill) that provides answers in a multi-medial format to patients and caregivers via a smart speaker equipped with a screen. Preliminary results of the experimentation show that the digital assistant was perceived as useful by the patients and their caregivers, even though they also found some limitations that could lead to improvements in the future.
Managing Big Data Stream Pipelines Using Graphical Service Mesh Tools

Current big data frameworks like Apache Flink and Spark enable efficient processing of large-scale streaming data in a distributed setup. For the management of such data pipelines and the computing resources, we propose a combination of a graphical tool for pipeline management, Apache StreamPipes, and container management tools like Kubernetes. For evaluation, we implemented a use case with data preprocessing, vehicle power consumption, and driving behavior services in StreamPipes. We discuss the capabilities of StreamPipes in managing and executing complex stream processing pipelines and also evaluate the possible integration of container and service mesh tools (i.e., Istio) with StreamPipes. Furthermore, we implemented and evaluated a service management layer in our system design to provide extended features. In particular, we evaluated the delay when such a complex pipeline is restarted, e.g. for updates or reconfiguration.
DRMACert: Certification for Disaster Response Mobile Applications

As the scale of damage of natural disasters is increasing, it is required to have effective and secure disaster response mobile applications that collect disaster-related data, disseminate vital information about shelters, emergency personnel, and surrounding area, and generate escape routes. As we have seen in the recent disaster situations, the existing mobile applications do not fully address the needs to secure people's lives and properties. In this paper, we identify the problems on the existing application and derive the requirements for disaster response applications. To satisfy the requirements, we propose the DRMACert, a standard certificate that proves the effectiveness, security, and compliance of a disaster response application. In addition, we present its certification process that employs both an expert-based evaluation and a system-based evaluation for rapid and unbiased examination.
Sampling of Power System Graph Signals

While sampling in classical signal processing is well-developed and studied, sampling in the Graph Signal Processing framework and its application domains are fairly new. In this paper, sampling of power grids' graph signals are discussed and analyzed in the context of two applications including the recovery of missing measurements during cyber stresses and the optimal PMU placement problem. The effects of various topological and power-dynamical factors in the system are evaluated for the selection of the sampling-set for power grid graph signals and graph signal reconstruction performance. Moreover, a novel sampling-set selection criterion based on the error introduced in the process of band-limiting the graph signal has been proposed.
Distance Transform Pooling Neural Network for LiDAR Depth Completion

Recovering dense depth maps from sparse depth sensors, such as LiDAR, is a recently proposed task with many computer vision and robotics applications. Previous works have identified input sparsity as the key challenge of this task. To solve the sparsity challenge, we propose a recurrent distance transform pooling (DTP) module that aggregates multi-level nearby information prior to the backbone neural network. The intuition of this module is originated from the observation that most pixels within the receptive field of the network are zero. This indicates a deep and heavy network structure has to be used to enlarge the receptive field aiming at capturing enough useful information as most processed signals are uninformative zeros. Our recurrent DTP module can fill in empty pixels with the nearest value in a local patch and recurrently transform distance to reach farther nearest points. The output of the proposed DTP module is a collection of multi-level semi-dense depth maps from original sparse to almost full. Processing this collection of semi-dense depth maps alleviates the network from the input sparsity, which helps a lightweight simplified ResNet-18 with 1M parameters achieve state-of-the-art performance on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) depth completion benchmark with LiDAR only. Besides the sparsity, the input LiDAR map also contains some incorrect values due to the sensor error. Thus, we further enhance the DTP with an error correction (EC) module to avoid the spreading of the incorrect input values. At last, we discuss the benefit of only using LiDAR for nighttime driving and the potential extension of the proposed method for sensor fusion and the indoor scenario. The code has been released online at https://github.com/placeforyiming/DistanceTransform-DepthCompletion.
Distributed Ledger Technology Based Immutable Authentication Credential System (D-IACS)

Based on the first three Industrial Revolutions (IRs), the Fourth Industrial Revolution (4IR) is changing the society like never before, as a result of storms of technologies. Distributed Ledger Technology (DLT) supported blockchain technology is fourth industrial revolution (4IR) as it transforming the current internet. After the invention of internet, DLT can be considered next technological breakthrough. It is anticipated to transform not only the technical structure of our information technology and communication, but also has potential to re-model the very-fabric of society. As far as blockchain adoption is concern, rapid transformation of business, governments and existing traditional system into purely blockchain based system have many challenges and barriers, especially in developing countries. It will take years for blockchain to dribble into our social and economic infrastructure. We proposed a DLT based model and framework that supports and facilitates conventional transformation of existing systems towards 4IR based DLT systems that can accommodate smooth communication between the legacy systems with the latest systems. Hence, this adds value to the efforts and the man hours invested for making the systems in history. This article investigates the different blockchain platforms in terms of contract language, governance, data access, privacy, transparency, mining and suggests the most suitable DLT platform for Pakistan&#x2019;s education sector.
Cloud Computing Image Recognition System Assists the Construction of the Internet of Things Model of Administrative Management Event Parameters

In order to successfully apply the Internet of Things and cloud computing to the administrative management of spatial structures and realize the systematization, digitization, and intelligence of administrative management, this article draws on research experience in related fields and considers the data characteristics and computing tasks of administrative management. The whole cycle of transmission, storage, postprocessing, and visualization is the main line of research, and a cloud computing-based spatial structure administrative management IoT system is constructed. First, by summarizing the application status of the Internet of Things, the general Internet of Things system is summarized into three levels, and combined with the specific work in the spatial structure administrative management, the overall framework of the spatial structure administrative management of the Internet of Things system is proposed, and the functional sublayers are carried out. Secondly, in response to the above problems, through the traditional image recognition system research and practical application investigation, in order to meet the user's requirements for the computing efficiency and recognition accuracy of the image recognition system, an image recognition system in the cloud computing environment is proposed. It proposes a fuzzy evaluation algorithm of health grade hierarchy analysis optimized for the index system and scoring system and a calculation method that uses time series to identify regular outliers. The optical image pixel-level fusion method and the infrared and visible image fusion method based on complementary information are proposed, and the image fusion software is developed. Finally, in order to enable the application layer to use cluster resources to efficiently and intelligently process massive monitoring data containing redundancy, heterogeneity, anomalies, and many other defects, according to the calculation process of each specific task of data preprocessing and postprocessing in the application layer, demonstrations are made one by one. After analysis, it is concluded that vertical storage of data blocks according to different sensor channels is the optimal strategy.
VowPass: Novel Method to Generate Secure and Memorable Passwords

Passwords are the most common authentication mechanism that defenses against unauthorized access to computers and personal information. However, due to increase online accounts' number and password complexity roles, users struggle to create a different password for each account and remember each one that created differently. Consequently, for these challenges, there has been increasing in insecure users' behaviors such as using predictable data, personal information, dictionary words, or a unified password. These habits expose users and organization to intensive risk. Password memorability increasing is an important factor to reduce users' unsecure behaviors. Protect passwords from begin cracked in automation “dictionary or brute force” increases security level. So, having a method to create a unique formula that generates different passwords complying with the best practices and policies that related to passwords with the ability to remember and regenerate. That helps users and organizations to be protected from massive risk and danger. The paper proposed two novel algorithms to facilitate secure password generation, improve memorability, and reduce the chance of a successful dictionary or brute force attack.
Click-through Rate Prediction Model Based on Dynamic Graph Attention Mechanism Network

Most of the current models are based on the user’s specific historical behavior for interest mining, when the user behavior is sparse, it is difficult to capture the user’s intentions, resulting in insufficient predictive power and other problems. Therefore, a prediction model based on Dynamic Graph Attention Network (DGAT) is proposed. This model uses a recurrent neural network to model dynamic user behavior, and uses a graph neural network to model the user’s social impact, which enriches user behavior and solves the problem of behavior sparseness. In addition, the local activation unit and adaptive attention mechanism are introduced into the model to enhance the generalization ability of the model. Experiments have proved that the DGAT model outperforms the existing models in various indicators.
A Lightweight Visual Question Answering Model based on Semantic Similarity

The key of visual question answering is to learn the semantic alignment of image objects and question words. The typical methods use the attention mechanism to achieve this goal. However, calculating the attention weight of image objects and question keywords requires an attention function, a function usually required a large number of parameters. Focusing on this issue, this paper proposes a lightweight visual question answering model based on semantic similarity. Firstly, the image features and question features are mapped to the common visual-semantic space, and the multi-modal semantic similarity matrix is constructed by using cosine similarity. Then, the multi-level potential semantic space is further explored by using multi-channel convolution neural network to map the semantic similarity matrix into two different attention distributions. Finally, the joint representation of image and text is learned through the multimodal fusion, which will be fed into the classifier and to predict the correct answer. The co-attention achieved by the proposed method with very few parameters. The experiment results show that the proposed model can effectively learn multimodal semantic alignment with a small number of parameters and achieve competitive or better performance than the state-of-the-art methods on VQA v2.0 dataset.
Parameter Analysis of Electronic Gadgets During Homa

All of us are aware that the modern mechanical and electrical systems in joint collaboration of AI, CPS and IoT have affected our life much in drastic manner, at one end, they have provided ease and comfort, at other end, the electrical and magnetic radiations emitted by them have threatened the mental and physical fitness too. Surprisingly, the Asian Agnihotra process is much effective in reducing the electrical, magnetic radiations, emitting from electronic devices. The present manuscript deals with the computational analysis of electrical, magnetic radiations and power density measurement of environmental effect and gadgets after the Homa terapy. In gap of certain time, readings were recorded and it was observed that drastic reduction occurred at the place where Yajna was performed. This gap was curtailing gradually as time increases. The phenomenon can certainly be used to address the threats of human health due to electronic gadgets in 21st century.Also it supports the concepts of Smart cities where one can easily identify the numerous effects due to Yajna process.
Additional Tools for Locating and Quantifying a Range's Stray Signals

Earlier works have shown the benefits of imaging stray signals in a range with planar-scanner data. This paper discusses some additional tools that can be employed for stray-signal identification. Related range diagnostics are presented that employ Fourier spectral and holographic processing of 1D linear scans through the quiet zone. For the special case of a compact range, the interpretation of arrival angles from the paraboloidal reflector surface is explored. Measured data from multiple facilities are presented that were used to locate, quantify, and remedy the unwanted signals.
Dimensionality Reduction and Visualization of <i>Bharatanatyam Mudras</i>

Cultural dances are practiced all over the world. The study of various gestures of the performer using computer vision techniques can help in better understanding of these dance forms and for annotation purposes. Bharatanatyam is a classical dance that originated in South India. Bharatanatyam performer uses hand gestures (mudras), facial expressions and body movements to communicate to the audience the intended meaning. According to Natyashastra, a classical text on Indian dance, there are 28 Asamyukta Hastas (single-hand gestures) and 23 Samyukta Hastas (Double-hand gestures) in Bharatanatyam. Open datasets on Bharatanatyam dance gestures are not presently available. An exhaustive open dataset comprising of various mudras in Bharatanatyam was created. The dataset consists of 15[Formula: see text]396 distinct single-hand mudra images and 13[Formula: see text]035 distinct double-hand mudra images. In this paper, we explore the dataset using various multidimensional visualization techniques. PCA, Kernel PCA, Local Linear Embedding, Multidimensional Scaling, Isomap, t-SNE and PCA–t-SNE combination are being investigated. The best visualization for exploration of the dataset is obtained using PCA–t-SNE combination.
Cloud Hopping: One Library’s Experience Migrating from One LSP to Another

In 2018 Atkins Library at UNC Charlotte became one of the first libraries to complete a migration to Ex Libris' Alma from OCLC's WorldShare Management Services. Focusing on bibliographic and holdings data, this article describes the challenges that Atkins staff confronted in moving to the new system. They include efforts to remediate data before the migration, problematic initial data loads, communication issues with Ex Libris' migration staff, and unsuccessfully migrated serials holdings data. The article will also focus on data clean-up efforts post-migration and offer a summary of the benefits and drawbacks of the migration.
Mapping geometric and electromagnetic feature spaces with machine learning for additively manufactured RF devices

Multi-material additive manufacturing enables transformative capabilities in customized, low-cost, and multi-functional electromagnetic devices. However, process-specific fabrication anomalies can result in non-intuitive effects on performance; we propose a framework for identifying defect mechanisms and their performance impact by mapping geometric variances to electromagnetic performance metrics. This method can accelerate additive fabrication feedback while avoiding the high computational cost of in-line electromagnetic simulation. We first used dimension reduction to explore the population of geometric manufacturing anomalies and electromagnetic performance. Convolutional neural networks are then trained to predict the electromagnetic performance of the printed geometries. In generating the networks, we explored two inputs: one image-derived geometric description and one using the same description with additional simulated electromagnetic information. Network latent space analysis shows the networks learned both geometric and electromagnetic values even without electromagnetic input. This result demonstrates it is possible to create accelerated additive feedback systems predicting electromagnetic performance without in-line simulation.
The Synchronization of Reading and Writing in Implementation of TDOA Estimator on a Single Board Computer

This paper discusses the topic of synchronizing the reading in writing in an application to real-time TDOA estimation. The paper gives a brief lay down on the classic TDOA estimation algorithm and specifically addresses the peculiarities of its implementation in a real-time environment. There are two main factors that should be considered in the stage of design: the processing time is dependent on the settings and some random factors affect processing time in an unpredictable way. The buffer can be introduced to deal with these factors. However, this solution requires a clear and efficient algorithm of synchronization of reading and writing data. In this paper, we propose a simple and small set of variables that is sufficient to synchronize processes to implement real-time TDOA estimation. The solution supports the various overlap coefficients and time windows that exceed the actual size of the input buffer. The proposed solution is installed on the Raspberry Pi, tested, and proved to be functional. Some practical details of the implementation are also discussed.
Vision: Security-Usability Threat Modeling for Industrial Control Systems

Industrial Control System (ICS) that run large-scale systems such as water, power and manufacturing are increasingly in focus given high profile attacks against such infrastructures. These systems are connected to IT systems and the Internet, the intersections of their users – typically control systems engineers and operators – with security requirements and systems add to the complexity of the threats faced by these environments. The challenges of usable security in IT systems have been studied extensively, including work on security-usability threat modeling (i.e. lack of usability exacerbating security issues). However, no work has examined similar challenges within ICS settings where, in addition to the regular requirements of information confidentiality, information integrity and information availability, requirements such as processsafety, processintegrity and processreliability are paramount for the users. Using the case of a Programmable Logic Controller (PLC), we detail the workflow that the user undertake for a security task. We analyze this workflow using STRIDE, an established threat modeling approach. We then map the threats against an existing security-usability threat model for IT systems whilst also taking into account the specific process-related requirements critical to ICS users. We then derive an initial security-usability threat model for ICS as a first step towards further work in this regard.
Adaptive Rule from the Philosophy of Science Viewpoint

Adaptive rule is a proposed development model of the association rule that answers the weaknesses of previous research and takes an inventory of current needs. In the adaptive rule the minimum support value is determined automatically by the system. In addition, it can generate rules that come from several different databases and rank the rules based on the specified criteria. In order not to become trapped into pseudo-science, this adaptive rule needs to be studied from the philosophy of science. According to the philosophy of science, science must fulfill three aspects, namely ontology, epistemology, and axiology. This paper contains a study conducted on the Adaptive rule starting from determining the side of ontology, epistemology, and axiology in accordance with the theory of the Philosophy of Science. In addition, the position of the adaptive rule on the scientific revolutions proposed by Thomas Kuhn was also analyzed to find out the position and opportunities in the future. If it is related to Thomas Kuhn's scientific revolutions theory, the Adaptive rule is currently in a crisis phase. To ensure that the adaptive rule is not pseudo-science, it is also reviewed the deductive testing phase proposed by Karl Popper. From the discussion and analysis carried out, it can be concluded that adaptive rules are not included in pseudo-science.