Reviewerly/exHarmony huggingface dataset:
Next Generation Digital Pathology: Emerging Trends and Measurement Challenges for Molecular Pathology

Digital pathology is revolutionising the analysis of histological features and is becoming more and more widespread in both the clinic and research. Molecular pathology extends the tissue morphology information provided by conventional histopathology by providing spatially resolved molecular information to complement the structural information provided by histopathology. The multidimensional nature of the molecular data poses significant challenge for data processing, mining, and analysis. One of the key challenges faced by new and existing pathology practitioners is how to choose the most suitable molecular pathology technique for a given diagnosis. By providing a comparison of different methods, this narrative review aims to introduce the field of molecular pathology, providing a high-level overview of many different methods. Since each pixel of an image contains a wealth of molecular information, data processing in molecular pathology is more complex. The key data processing steps and variables, and their effect on the data, are also discussed.
An empirical study of the Python/C API on evolution and bug patterns

Abstract Python is a popular programming language, and a large part of its appeal comes from diverse libraries and extension modules. In the bloom of data science and machine learning, Python frontend with C/C++ native implementation achieves both productivity and performance and has almost become the standard structure for many mainstream software systems. However, feature discrepancies between two languages such as exception handling, memory management, and type system can pose many safety hazards in the interface layer using the Python/C API. In this paper, we carry out an empirical study of the Python/C API on evolution and bug patterns. The evolution analysis includes Python/C API design in CPython compilers and its usage in mainstream software. By designing and applying a static analysis toolset, we reveal the evolution and usage statistics of the Python/C API and provide a summary and classification of 9 common bug patterns. In Pillow, a widely used Python imaging library, we find 48 bugs, 19 of which are undiscovered before. Our toolset can be easily extended to access different types of syntactic bug‐finding checkers, and our systematical taxonomy to classify bugs can guide the construction of more highly automated and high‐precision bug‐finding tools.
Analysis of Cross Site Request Forgery (CSRF) Attacks on West Lampung Regency Websites Using OWASP ZAP Tools

Technological developments in the field of increasingly advanced computers and networks have caused many organizations to use web applications to provide business services. With the increasing popularity of the internet, the number of cyber-attacks has also increased. To overcome these negative impacts, the role of network security is very necessary. The Cross Site Request Forgery (CSRF) method is a penetration technique aimed at exploiting website security vulnerabilities and there is one tool commonly used to find security vulnerabilities on websites, namely OWASP ZAP. The research has succeeded in proving security vulnerabilities on the website of the West Lampung district by conducting attack simulations. From the results of the experiment, it was found that there were 12 alerts with low risk on the website of West Lampung Regency. In 12 alerts there are 53 URL pages that are vulnerable to attack.
The business of cybervetting

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Unauthorized and privacy‐intrusive human activity watching through Wi‐Fi signals: An emerging cybersecurity threat

Summary Nowadays, wireless radio signals are ubiquitous and are around us; some signals pass through us, and some reflect off us. Substantial advancements in recent years demonstrate that such signals are utilized for diverse emerging applications, including people activity, motion watches, healthcare, and so forth. A few questions would be that may raise severe concerns in future cybersecurity and private domains. For example, what if Wi‐Fi signals are utilized to watch a person doings and actions, which are mostly without the person's authorization and authentication. How far such signal utilization can attack privacy intrusively, silently, more particularly, what/where we do, say, command, see, write, draw, go, perform, everything can be known. In this article, we investigate watching human activities by leveraging Wi‐Fi signals and discuss a few application prototypes. We attempt to learn whether or not attackers have the ability to passively watch our Internet activity as well as physical activities and motions through Wi‐Fi. With all‐new advances, one must be aware that cyberattackers may apply unauthorized use of these advances to their benefit. We discuss some of the countermeasures and approaches to mitigate these risks with Wi‐Fi signal leveraging.
A fast classification method of mass data in Internet of things based on fuzzy clustering maximum tree algorithm

In order to improve the classification accuracy and shorten the classification time of mass data, a fast classification method of mass data in the Internet of things based on fuzzy clustering maximum tree algorithm is proposed. Reduce the dimension to process the mass data of the Internet of things, establish the time series of the mass data of the Internet of things, and complete the preprocessing of the mass data of the Internet of things. Extract the feature vector of the Internet of things mass data, and use the fuzzy clustering maximum tree algorithm to perform fuzzy clustering analysis on the Internet of things mass data, so as to realize the classification of the Internet of things mass data. The results show that the recall rate of the proposed method is as high as 97.5%, the root mean square error is only 0.030, and the classification time is only 12.3 ms.
Generating accurate 3D gaze vectors using synchronized eye tracking and motion capture

Assessing gaze behavior during real-world tasks is difficult; dynamic bodies moving through dynamic worlds make gaze analysis difficult. Current approaches involve laborious coding of pupil positions. In settings where motion capture and mobile eye tracking are used concurrently in naturalistic tasks, it is critical that data collection be simple, efficient, and systematic. One solution is to combine eye tracking with motion capture to generate 3D gaze vectors. When combined with tracked or known object locations, 3D gaze vector generation can be automated. Here we use combined eye and motion capture and explore how linear regression models generate accurate 3D gaze vectors. We compare spatial accuracy of models derived from four short calibration routines across three pupil data inputs: the efficacy of calibration routines was assessed, a validation task requiring short fixations on task-relevant locations, and a naturalistic object interaction task to bridge the gap between laboratory and "in the wild" studies. Further, we generated and compared models using spherical and Cartesian coordinate systems and monocular (left or right) or binocular data. All calibration routines performed similarly, with the best performance (i.e., sub-centimeter errors) coming from the naturalistic task trials when the participant is looking at an object in front of them. We found that spherical coordinate systems generate the most accurate gaze vectors with no differences in accuracy when using monocular or binocular data. Overall, we recommend 1-min calibration routines using binocular pupil data combined with a spherical world coordinate system to produce the highest-quality gaze vectors.
Weak tagging and imbalanced networks for online review sentiment classification

Sentiment classification aims to complete the automatic judgment task of text sentiment tendency. In the sentiment classification task of online reviews, traditional deep learning models require a large number of manually annotated samples of sentiment tendency for supervised training. Faced with massive online review data, the feasibility of manual tagging is worrisome. In addition, the traditional deep learning model ignores the imbalanced distribution of the number of classification samples, which will lead to a decline in classification performance in the practical application of the model. Considering that the online review data contains weak tagging information such as scores and labels, and the distribution is imbalanced, a weak tagging and imbalanced networks for online review sentiment classification is constructed. The experimental results show that the model significantly outperforms the traditional deep learning model in the sentiment classification task of hotel review data.
Simultaneous clustering and variable selection: A novel algorithm and model selection procedure

The growing availability of high-dimensional data sets offers behavioral scientists an unprecedented opportunity to integrate the information hidden in the novel types of data (e.g., genetic data, social media data, and GPS tracks, etc.,) and thereby obtain a more detailed and comprehensive view towards their research questions. In the context of clustering, analyzing the large volume of variables could potentially result in an accurate estimation or a novel discovery of underlying subgroups. However, a unique challenge is that the high-dimensional data sets likely involve a significant amount of irrelevant variables. These irrelevant variables do not contribute to the separation of clusters and they may mask cluster partitions. The current paper addresses this challenge by introducing a new clustering algorithm, called Cardinality K-means or CKM, and by proposing a novel model selection strategy. CKM is able to perform simultaneous clustering and variable selection with high stability. In two simulation studies and an empirical demonstration with genetic data, CKM consistently outperformed competing methods in terms of recovering cluster partitions and identifying signaling variables. Meanwhile, our novel model selection strategy determines the number of clusters based on a subset of variables that are most likely to be signaling variables. Through a simulation study, this strategy was found to result in a more accurate estimation of the number of clusters compared to the conventional strategy that utilizes the full set of variables. Our proposed CKM algorithm, together with the novel model selection strategy, has been implemented in a freely accessible R package.
Privacy-Preserving In-Bed Pose and Posture Tracking on Edge

In-bed behavior monitoring is commonly needed for bed-bound patient and has long been confined to wearable devices or expensive pressure mapping systems. Meanwhile, vision-based human pose and posture tracking while experiencing a lot of attention/success in the computer vision field has been hindered in terms of usability for in-bed cases, due to huge privacy concerns surrounding this topic. Moreover, the inference models for mainstream pose and posture estimation often require excessive computing resources, impeding their implementation on edge devices. In this paper, we introduce a privacy-preserving in-bed pose and posture tracking system running entirely on an edge device with added functionality to detect stable motion as well as setting user-specific alerts for given poses. We evaluated the estimation accuracy of our system on a series of retrospective infrared (LWIR) images as well as samples from a real-world test environment. Our test results reached over 93.6% estimation accuracy for in-bed poses and achieved over 95.9% accuracy in estimating three in-bed posture categories.
Beyond deep learning

Microbial communities are found throughout the biosphere, from human guts to glaciers, from soil to activated sludge. Understanding the statistical properties of such diverse communities can pave the way to elucidate the common mechanisms ...Multiple ecological forces act together to shape the composition of microbial communities. Phyloecology approaches—which combine phylogenetic relationships between species with community ecology—have the potential to disentangle such forces but are often ...
Data and model hybrid-driven virtual reality robot operating system

To realize efficient remote human-computer interaction of robots, a robot remote operating system based on virtual reality and digital twin is proposed. The system builds a digital twin model based on the Unity 3D engine to establish a connection with the robot entity, assisting the online remote programming and real-time manipulation of the robot unit. The system uses HTC VIVE to build a virtual reality framework. To actualize the mutual drive between the real space and the virtual space, a mathematical model of the robot is constructed through the forward and inverse kinematics of the robot. Through the combination of eye-tracking-based eye movement interaction and the unique controller interaction of virtual reality system, a multi-sensory multi-input collaborative interaction method is accomplished. The method realizes the robot joints driving of users using multiple interaction methods simultaneously, simplifies the robot programming and control procedure, and optimizes the operation experience. Tests demonstrate that the system is capable of effectively providing monitoring, teleoperation and programming services for remote interaction of robots.
Medical chatbot using AI and NLP

The use of chatbots has grown rapidly across industries, including marketing, assistive systems, education, healthcare, cultural heritage, and entertainment. This paper discusses the incentives for using chatbots and explains how useful chatbots are in various contexts. As intelligent software and hardware, also known as intelligent agents, are developed and analyzed, Artificial Intelligence (AI) is becoming more and more integrated into daily lives. From manual labor to complex procedures, intelligent agents are capable of performing a wide range of tasks. One of the simplest and most common forms of intelligent human-computer interaction is the chatbot, which is a classic example of an artificial intelligence Human-Computer Interaction (HCI) system. A chatbot is described as "a computer program designed to simulate interaction with human users, particularly over the Internet." In addition to chatbots, it also called smart bots, interactive agents, digital assistants, and intelligent conversational objects. In the midst of the COVID-19 pandemic, going to the doctor is no longer an indulgence. A chatbot is a Natural Language Processing (NLP) based chatbot to help with basic medical questions. Only the best knowledge of a chatbot can be used to answer medical questions.
Design Thinking Method to Develop a Digital Evidence Handling Management Application

Handling digital evidence in forensics is a very crucial task. Incorrect handling can cause the evidence to become invalid as proof of a crime in court. The procedure of handling digital evidence, starting from its collection, usage, and storage, affects its acceptability in the judicial process. Therefore, a digital evidence management system becomes imperative for police researchers and investigators. This study aims at designing such a system using the design thinking method, which goes through five stages: empathy, definition, idea, prototype, and test. The result of the study is a web-based system prototype. The prototype user testing attains a system usability scale (SUS) value of 60. The SUS value means that the prototype is in the category of marginal low and indicates that the prototype does not meet the feasibility and needs improvement.
Automatic Detection of Knives in Complex Scenes

Smart Cities use a variety of Information and Communication Technologies (ICT) and databases to improve the efficiency and efficacy of city services. Security is one of the main topics of interest in this context. The increase in crime rates demands the development of new solutions for detecting possible violent situations. Video surveillance (CCTV) cameras can provide a large amount of valuable information contained in images which can be difficult to be analyzed by humans in an efficient form. Identifying and classifying weapons in such images is a challenging problem that can be driven by the application of Deep Learning techniques. Object detection algorithms, especially advanced Machine Learning ones, have demonstrated impressive results in a wide range of applications. However, they can fail in certain application scenarios. This work describes a novel proposal for knife detection in complex images. This is a challenging problem due to the multiple variabilities of these objects in scenes (i.e., changing shapes, sizes and illumination conditions, among others), which can negatively impact the performance of mentioned algorithms. Our approach analyzed the combination two super-resolution techniques (as a preprocessing stage) with one object detection network to effectively solve the considered problem. The results of our experiments show that the proposed methodology can produce better results when detecting small objects having reflecting surfaces (i.e., knives) in scenes. Moreover, the approach could be adapted for surveillance applications that need real-time detection of knives in places monitored by cameras.
Human Body Pose Estimation in Multi-view Environments

This chapter tackles the challenging problem of human pose estimation in multi-view environments to handle scenes with self-occlusions. The proposed approach starts by first estimating the camera pose—extrinsic parameters—in multi-view scenarios; due to few real image datasets, different virtual scenes are generated by using a special simulator, for training and testing the proposed convolutional neural network based approaches. Then, these extrinsic parameters are used to establish the relation between different cameras into the multi-view scheme, which captures the pose of the person from different points of view at the same time. The proposed multi-view scheme allows to robustly estimate human body joints’ position even in situations where they are occluded. This would help to avoid possible false alarms in behavioral analysis systems of smart cities, as well as applications for physical therapy, safe moving assistance for the elderly among other. The chapter concludes by presenting experimental results in real scenes by using state-of-the-art and the proposed multi-view approaches.
Video Analytics in Urban Environments: Challenges and Approaches

This chapter reviews state-of-the-art approaches generally present in the pipeline of video analytics on urban scenarios. A typical pipeline is used to cluster approaches in the literature, including image preprocessing, object detection, object classification, and object tracking modules. Then, a review of recent approaches for each module is given. Additionally, applications and datasets generally used for training and evaluating the performance of these approaches are included. This chapter does not pretend to be an exhaustive review of state-of-the-art video analytics in urban environments but rather an illustration of some of the different recent contributions. The chapter concludes by presenting current trends in video analytics in the urban scenario field.
Waste Classification with Small Datasets and Limited Resources

Automatic waste recycling has become a very important societal challenge nowadays, raising people’s awareness for a cleaner environment and a more sustainable lifestyle. With the transition to Smart Cities, and thanks to advanced ICT solutions, this problem has received a new impulse. The waste recycling focus has shifted from general waste treating facilities to an individual responsibility, where each person should become aware of selective waste separation. The surge of the mobile devices, accompanied by a significant increase in computation power, has potentiated and facilitated this individual role. An automated image-based waste classification mechanism can help with a more efficient recycling and a reduction of contamination from residuals. Despite the good results achieved with the deep learning methodologies for this task, the Achille’s heel is that they require large neural networks which need significant computational resources for training and therefore are not suitable for mobile devices. To circumvent this apparently intractable problem, we will rely on knowledge distillation in order to transfer the network’s knowledge from a larger network (called ‘teacher’) to a smaller, more compact one, (referred as ‘student’) and thus making it possible the task of image classification on a device with limited resources. For evaluation, we considered as ‘teachers’ large architectures such as InceptionResNet or DenseNet and as ‘students’, several configurations of the MobileNets. We used the publicly available TrashNet dataset to demonstrate that the distillation process does not significantly affect system’s performance (e.g. classification accuracy) of the student network.
How Social versus Informational Motivations Affect the Language of User-Generated Content

This research report proposes a novel conceptual distinction between two motivations to post user-generated content – social motivation and informational motivation – and tests five linguistic features characterizing user-generated content that was posted driven predominantly by one of these two motivations. We employed an open-source text analysis tool to analyze real-world and experimentally obtained datasets and supported our theory by experimentally manipulating motivation and measuring linguistic outcomes. Theoretical and practical implications are discussed.
Pengembangan Media Animasi Pada Pembelajaran Elektronika Siswa SMK

The purpose of this development research is to describe the process of developing electronic learning animation media for SMK students, describe the validation of animation media development from material experts and electronic learning media experts of SMK students, describe the feasibility of electronic learning animation media for SMK students. The data used is descriptive qualitative which is the main data through instruments from material expert respondents and media experts. The research used the Borg and Gall models which were adjusted based on 6 stages due to the situation and conditions of the Covid-19 pandemic, namely needs analysis, product specification planning, product design, product validation, product revision, and dissemination. The use of Canva Pro was used in the animation media development process with the response of material experts obtained an overall average of 79.17%, the category of "very satisfactory". Meanwhile, from media / design experts obtained an overall average of 94.18%, the category is "very satisfactory". Thus, animation learning media as a research product is declared suitable for use in electronic learning for SMK students.