Reviewerly/exHarmony huggingface dataset:
An adaptive machine learning pipeline for predicting the recurrence of gastric cancer

The advancement of medical science and technology has provided more methods for the diagnosis and treatment of malignant tumors, and the survival period of cancer patients has been significantly extended. However, many patients with malignant tumors still have recurrence and metastasis after effective treatment. Exploring the mechanism of tumor recurrence and metastasis to predict the recurrence and metastasis of cancer is a major clinical issue. At the same time, the rapid development of the Human Genome Project and gene microarray technology has enabled the activity of many genes in the patient's body to be intuitively measured through the chip. The rapid development of machine learning has contributed to the data mining and medical science of this DNA microarray technology. Therefore, this project aims at the above-mentioned problems and predicts the location and time of recurrence by analyzing a large amount of clinical data and 2enetic data. Firstly, we perform simple data cleaning and normalization processing on clinical data and genetic data; second, perform differential gene screening; next, select principal component analysis, sparse principal component analysis, nuclear principal component analysis, and multi-dimensional scaling algorithms to reduce the dimensional of the data. Finally, the genetic data uses random forest, support vector machine, linear support vector machine, guided aggregation algorithm, gradient boosting algorithm, and ensemble learning for machine learning, and then finds the best parameters and methods through grid search, and selects the appropriate model Evaluation method. The clinical data is manually selected and classified using machine learning. Finally, the results of clinical data and genetic data are combined to predict the site of recurrence. Using the above method to predict the recurrence and the location of the recurrence, a good effect was achieved. Taking whether to recurrence as an example, the accuracy rate of the verification set reached 0.825, the recall rate reached 0.801, the Fl score reached 0.800. Through the retrospective study and prediction of gastric cancer recurrence, the model proposed in this paper has potential clinical value.
Towards an effective user interface for data exploration, data quality assessment and data integration

Domain professionals (e.g. medical professionals) find it difficult to directly interact with Linked Data datasets in order to undertake common tasks that they typically need to do in order to explore their research hypotheses. We propose the development of a tool that integrates the common tasks involved through one user interface to support data exploration, data quality assessment and data integration. To initiate the necessary user-centered design process, we have designed an electronic storyboard to start the user requirements and user research, and evaluated this storyboard through an experiment in which four participants, identified as medical professionals in the healthcare domain, use the electronic storyboard to complete tasks regarding data exploration, data quality and data integration at the schema level. Preliminary findings indicate that the designed user interface will not be directly useful for the medical professionals but rather will be more useful for team members within the team of those medical professionals.
Anonymizing communication flow identifiers in the Internet of Things

We tackle the problem of privacy breaching in IPv6 over Low-Power Wireless Personal Area Networks (6LoWPANs) where an attacker may be able to identify the communicating entities. We propose a solution based on: (i) anonymizing both IP and MAC addresses, as well as port number at the source host, using a random pseudonyming scheme, and (ii) anonymizing the IP address and port number of the destination host, using a Tor-like network. We analyzed the effect of the Tor entry node location on the performance of our solution in three different scenarios: the Tor entry node is located (i) inside the 6LoWPAN, (ii) at the IPv6 Low-power Border Router (6LBR), or (iii) completely outside the 6LoWPAN. Using Cooja simulator, we showed that our solution outperforms state-of-the-art solutions by making it more difficult to identify communication flows by improving the anonymity and unlinkability of the communicating entities without significantly affecting energy consumption, communication delay, and network bandwidth.
Using Boltzmann Entropy to Measure Scrambling Degree of Grayscale Images

Image data security is a key issue in storage, transmission. Some scrambling techniques have been developed to encrypt digital images. However, how to quantitatively measure the scrambling degree of images scrambled by various techniques is an issue. To address such an issue, this study proposes an evaluation scheme of scrambling degree from the perspective of thermodynamics. The whole image is considered an isolated thermodynamics system where pixels are employed to simulate gas molecules. Specifically, Boltzmann entropy (thermodynamics entropy) is employed to measure the scrambling degree of scrambled images. The proposed scheme involves two levels (i.e., the plain image, bit planes). Four images with different complexities are used in simulation experiments. The experimental results show that this proposed scheme is simple and effective in measuring the scrambling degree of grayscale images. This study indicates that Boltzmann entropy can be used to optimize the encryption of images.
Shape Corner Detection through Enhanced Curvature Properties

Image corner detection is a principal task for successful pattern recognition and identification. Finding corners of a shape usually involve two steps, firstly, shape segmentation and, secondly, locating corners in its boundary. This paper presents a robust method for detecting shape corner points using enhanced curvature properties. Numerous applications like image retrieval, object recognition, and motion tracking uses these corner points, which contain essential information about their location or position in a particular shape. Six existing shape detectors and descriptors, i.e., the Harris, SUSAN, Harris-Laplace, CSS, SIFT, and global and local curvature properties (GLCP), have been compared with the proposed shape detection method yielding its performance. The binary image dataset has been utilised as a benchmark to test the experiment. The experiment exhibits a significant number of true corners of the shape and outperforms the existing methods in terms of accuracy and reliability.
Rotated TOR-5P Laplacian Iteration Path Navigation for Obstacle Avoidance in Stationary Indoor Simulation

Lately, autonomous navigation has undergone constant research to develop superior path searching capabilities for self-directed navigation. This study attempt to enhance the path navigation competence of a mobile robot by using the computational technique, namely the Half-Sweep Two-Parameter Over Relaxation 5-Point Laplacian (HSTOR-5P) method, to solve pathfinding problems iteratively. The harmonic functions, which are the solution of Laplace’s equation, are the sources for generating the potential function of the mobile robot’s configuration space. As a result, the experiment shows that a smooth and optimal path in a given space is capable to create from any departure to the target point. Furthermore, it is proved that the HSTOR-5P approach was more effective than its predecessors in solving mobile robot pathfinding problems concerning iterations count and time taken.
Detection of Objects from Noisy Images

Image noise is a prevalent issue often created by inadequate lighting, cameras of low quality, compression of images and other factors. While low image quality is anticipated to degrade visual identification outcomes, most present object recognition techniques and benchmarks, such as Pascal Visual Object Classes Challenge and Microsoft Common Objects in Context Challenge, concentrate on images of comparatively high quality. Meanwhile, the identification of objects in noisy images in surveillance and other fields is a prevalent issue. In this paper we discuss the detection of objects in noisy images and suggest a new low-cost technique to detect image objects from noisy images. Receiving the benefits of the Single Shot MultiBox Detector (SSD), we have presented an extensive experimental assessment with conventional detectors retrained on noisy images. Results are provided for the prevalent benchmark for object detection Pascal Visual Object Classes. Comparing with other image detection approaches, our technique has provided satisfactory performance at the time of detecting image objects from noisy images. The method can be very effective to the autonomous industries to resolve the object detection related difficulties created by insufficient lighting and lower regulation pictures.
A Short Bout of Exercise With and Without an Immersive Virtual Reality Game Can Reduce Stress and Anxiety in Adolescents: A Pilot Randomized Controlled Trial

Anxiety and stress are prominent issues for the adolescent population. Physical activity is known to reduce symptoms of anxiety and stress; however, many adolescents lack the time or motivation to exercise regularly, particularly during stressful exam weeks. Virtual Reality (VR) has the potential to make exercise more enjoyable and more engaging than exercise alone. We aimed to investigate the immediate effect of a 10-min dodgeball exercise session, with and without a VR headset, on self-reported stress, anxiety and cognitive performance in adolescents during times known to induce stress in high school, such as exam weeks. Participants were randomly assigned to a VR group ( n = 16) where participants were immersed in a virtual dodgeball environment (exergame), or a dodgeball group ( n = 14) which played a simple game of one-on-one dodgeball. Executive function was measured using the Trail Making Test (TMT) Parts A and B. Anxiety was self-reported on the Pediatric Anxiety Short Form 8a (PASF). Stress was self-reported on the Psychological Stress Experiences-Short Form 8a (PSES). Both groups significantly improved their TMT A and B performance and reduced stress and anxiety scores with effect size ranging from 0.59 to 1.2 (main effect of time p &amp;lt; 0.001 for all outcomes). There were no significant differences between groups and no time by group interaction for any outcome. A short bout of exercise, with or without VR, during stressful high school exam weeks was shown to be effective for immediate reduction of stress and anxiety and enhancement of cognitive function in a small sample of high school students. High schools looking to apply interventions to help their students manage anxiety and stress should consider encouraging them to take a “time-out” to exercise and play. The cost-effectiveness of exergames inside the school settings and implications for academic success should be investigated in future research.
Drafting a Comprehensive Schema for the Development of Gamified Learning Application for National University

In adhering to the call for providing accessible education for Filipinos, National University is opening its doors to various innovative concepts to enhance the teaching methods and delivery of its courses. Various means of e-learning capabilities and practices are explored, distance learning and gamification included. Applying gamification as other form of teaching methods is recognized and viable within the university's context. The study is an attempt to explicitly design a schema on applying gamification on the course of computer hardware and software servicing. A mobile game concept is outlined along with its possible implementation.
Generate Individually Optimized Blendshapes

Blendshape based animation is a technique commonly used to animate the face. To generate realistic animation for different faces, creating appropriate blendshapes for each different face is essential. There have been many attempts to find the production-level blend shapes. However, many existing methods mostly require a professional artist's intuition with manual intervention. In this paper, we present a novel approach to automatically generate individually optimized blendshapes from real-time captured facial expressions. The proposed method generates the blendshape from the captured face with two methods: linear regression and an autoencoder. Among results from two methods, we select the trained result that is more similar to the original face. The adopted blendshape could be used to animate the original face more naturally. In addition, the generated blendshape is utilized to retarget the original face animation to another face while preserving the original face's animation characteristic. Comparison of results by animating the face on the screen show linear regression is suitable for retargeting the facial expressions without using the complicated neural networks.
Indoor Privacy-preserving Action Recognition via Partially Coupled Convolutional Neural Network

With indoor intelligent surveillance gradually integrated into daily life, privacy-preserving of user is facing a major challenge. In order to solve the contradiction between user privacy-preserving and action recognition, we propose a partially coupled convolutional neural network (CNN) model that only uses anonymous videos of low resolution (LR). Firstly, in the pre-process, the optical flow characteristics of low resolution (16×12 pixels) and high resolution (HR) (64×48 pixels) videos are extracted respectively, and continuous multi-frame optical flow superposition is performed to represent the video action. Secondly, super-resolution technology is used to improve the characteristics of LR optical flow. Finally, a partially coupled CNN model is trained. HR data is added to the training, improve the recognition performance of LR data in test. Experiments on two public datasets show that the method we proposed is better than other state-of the-art methods, which has the dual advantages of privacy-preserving and high recognition accuracy.
Learning human insight by cooperative AI: Shannon-Neumann measure

Abstract A conceptually sound solution to a complex real-world challenge, is built on a solid foundation of key insights, gained by posing ‘good’ questions, at the ‘right’ times/places. If the foundation is weak, due to insufficient human insight, the resulting, conceptually flawed solution, can be very costly or impossible to correct downstream. The response to the global 2020 pandemic, by countries using just-in-time supply/production chains and fragmented health-care systems, are striking examples. Here, Artificial intelligence (AI) tools to help human insight, are of significant value. We present a computational measure of insight gains, which a cooperative AI agent can compute, by having a specific internal framework, and by observing how a human behaves. This measure enables a cooperative AI to maximally boost human insight, during an iterated questioning process—a solid foundation for solving complex open-ended challenges. It is an AI-Human insight bridge, built on Shannon entropy and von Neumann utility. Our next paper will addresses how this measure and its associated strategy, reduce a hard cooperative inverse reinforcement learning game, to simple Q-Learning, proven to converge to a near-optimal policy.
Mitigating domain mismatch in face recognition using style matching

Despite outstanding performance on public benchmarks, face recognition still suffers due to domain mismatch between training (source) and testing (target) data. Furthermore, these domains are not shared classes, which complicates domain adaptation. Since this is also a fine-grained classification problem which does not strictly follow the low-density separation principle, conventional domain adaptation approaches do not resolve these problems. In this paper, we formulate domain mismatch in face recognition as a style mismatch problem for which we propose two methods. First, we design a domain discriminator with human-level judgment to mine target-like images in the training data to mitigate the domain gap. Second, we extract style representations in low-level feature maps of the backbone model, and match the style distributions of the two domains to find a common style representation. Evaluations on verification and open-set and closed-set identification protocols show that both methods yield good improvements, and that performance is more robust if they are combined. Our approach is competitive with related work, and its effectiveness is verified in a practical application.
Multi-atlas based segmentation of human cerebellum

Atlas-based segmentation is a high-level technique which provides highly accurate results particularly in the anatomical segmentation of medical images. The main idea of this technique consists in using a dataset of atlases which is perceived as a priori information necessary for providing the automatic segmentation of images. In the current state of the art of atlas-based techniques, the segmentation task is carried out based on a fixed database of atlases. In this article, we propose a multi-atlas segmentation method for brain MRI images based on a dynamic atlas database which is progressively updated each time a new case is added. The experiments show that the suggested technique seems to perform well compared to conventional multi-atlas based segmentation methods.
Public service innovation through the application of mobile-based smart city concepts in Sukun sub-district, Malang City

Abstract Public service provides services to the public carried out by the government whose services can be in the form of goods, services, or administration. The sub-district is a public service provider from the application stage to the publication phase of the document. Where people need not go to several agencies to take care of licensing documents, but enough to come to the district in their respective regions. Districts as the spearhead of government administrators must carry out their functions as the embodiment of excellent public service. However, in reality, public services at the sub-district level are of poor quality. Less responsive, less informative, and convoluted bureaucracy is often a major problem in service. Even though in the current technological era, the sub-districts should adopt technology for their activities. Innovation in using smart city concept technology in sub-districts is the key to solving existing problems because integrating entities in the smart city concept can facilitate the exchange of information, speed of data exchange, and rapid decision-making by service officers the public. So that the public services organized by the sub-district become more quality.
The Cryptographic Complexity of Anonymous Coins: A Systematic Exploration

The modern financial world has seen a significant rise in the use of cryptocurrencies in recent years, partly due to the convincing lure of anonymity promised by these schemes. Bitcoin, despite being considered as the most widespread among all, is claimed to have significant lapses in relation to its anonymity. Unfortunately, studies have shown that many cryptocurrency transactions can be traced back to their corresponding participants through the analysis of publicly available data, to which the cryptographic community has responded by proposing new constructions with improved anonymity claims. Nevertheless, the absence of a common metric for evaluating the level of anonymity achieved by these schemes has led to numerous disparate ad hoc anonymity definitions, making comparisons difficult. The multitude of these notions also hints at the surprising complexity of the overall anonymity landscape. In this study, we introduce such a common framework to evaluate the nature and extent of anonymity in (crypto) currencies and distributed transaction systems, thereby enabling one to make meaningful comparisons irrespective of their implementation. Accordingly, our work lays the foundation for formalizing security models and terminology across a wide range of anonymity notions referenced in the literature, while showing how “anonymity” itself is a surprisingly nuanced concept, as opposed to existing claims that are drawn upon at a higher level, thus missing out on the elemental factors underpinning anonymity.
Augmented reality for assist apron operation safety supervisor in aircraft inspection processes

With the development of aviation, the issue of aviation safety is become more and more important. Especially for airplane maintenance and inspection task. As processes in the aircraft inspection after the plane take off are error prone and time consuming, efforts are made to implement these technologies to support apron operation safety supervisors during the inspection process. In this paper, we will show a concept to facilitate efficiency of assisting airport supervisor in aircraft inspection process. We propose a graphical user interface that contain the menu-guided instructions and digital inspection document, which will increase the efficiency of manual process. Furthermore, in order to help supervisor have an better understanding of inspecting processes, by attaching the 3D model of aircraft physical components, they can see corresponding hint information. Seeing instructional graphics overlaid directly on the actual task environment can significantly improve a user's understanding compared to viewing instructions displayed on a nearby monitor or in a paper manual. In order to get the better view display effect, we used the one of the most advanced Augmented Reality (AR) glasses, the Microsoft Hololens2. In the inspector's views, the part of airplane's 3D model will immediately overlaid on the physical component. This Augment Reality Vision can be used for inspection purpose. And any inspection relate information can be directly attached to the aircraft component. For example, following the instruction, 3D model animation and inspection guide document information will appear on the aircraft component's surface, which will improve the efficiency of inspection process. In addition, remote collaboration is discussed applying augmented reality devices in an inspection process, which will ensure inspector and expert has same vision in different site, that can improve the efficiency of dealing complicate problem.
WITHDRAWN: Chaotic based encryption algorithms for speech signal and cryptographic requirements: A brief survey

Chaotic cryptography has been a recent development by researchers due to its interesting properties such as non-linear behavior, sensitivity to initial conditions, ergodicity, mixing, confusion and diffusion etc. This paper is a brief review of various standard encryption algorithms, cryptographic requirements for design of chaotic based cryptosystem and chaos-based speech encryption algorithms. This study also gives various statistical tests needs to be considered for conformity about suitable randomness of the binary sequences generated using either hardware or software means forcryptographic applications as key sequence.