Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling and systems approaches for elucidating information processing in multi-site phosphorylation systems

Multisite phosphorylation is a basic ingredient of cellular signalling and information processing and involves the modification of substrates by kinases and phosphatases at multiple locations. Multisite phosphorylation (and modification in general) has emerged as a key underlying mechanism by which cells exhibit control different aspects of their functioning and decision making. In the past decade, various studies have focussed on the behaviour of multisite phosphorylation, theoretically, analysing both distributive and processive mechanisms of multisite phosphorylation. There have also been a number of experimental studies in multisite phosphorylation in a range of cellular pathways and contexts. While these studies have provided important insights, there is a significant gap in understanding the role of multisite phosphorylation and its role in signalling pathways. In this thesis, a structured modelling and systems engineering approach is employed to elucidate different aspects of multisite phosphorylation and its role in cellular pathways. This is developed through a series of investigations. The first investigation demonstrates that distributive multisite phosphorylation systems can exhibit a biphasic dose response thus demonstrating an in-built competing effect in the modification mechanism, a feature not shared by processive mechanisms. A series of analyses is able to pin down when this biphasic response will and will not occur. In the second investigation, the main focus is on the effect of the modification mechanism on information processing characteristics. A framework is developed to analyse the interplay between distributive and processive mechanisms leading to mixed mechanisms. Analysis of mixed mechanisms shows that bistablility and biphasic dose response curves can be eliminated as a consequence of the interplay of distributive and processive mechanisms. On the other hand, the interplay between these two mechanisms can result in sustained
Feasibility of end-to-end trainable two-stage U-Net for detection of axillary lymph nodes in contrast-enhanced CT based on sparse annotations

Manual detection of lymph nodes by a radiologist is time-consuming, error-prone and suffers from interobserver variability. We propose a mostly generic computer-aided detection system, which can be trained in an end-to-end fashion, to automatically detect axillary lymph nodes using state of the art fully convolutional neural networks. We aim at a system that can be easily transferred to other body regions such as the mediastinal region. Our pipeline is a two-stage approach, where first a volume of interest (VOI) (axillary region) is localized and then axillary lymph node detection is performed within the VOI. The training was done on 58 CT volumes from 36 patients comprising 300 axillary lymph nodes. On our test dataset, consisting of 75 axillary lymph nodes in the size range 5–10 mm and 17 larger than 10 mm from 30 different patients, we achieved a sensitivity of 83% with 6.7 FPs per volume on average.
WPA: Write Pattern Aware Hybrid Disk Buffer Management for Improving Lifespan of NAND Flash Memory

Recently, consumer electronics devices have started adopting NAND flash memories as their storage units. NAND flash memories are suitable as portable consumer device storages, as they have several advantages such as non-volatility, high density and low power consumption. However, flash memories are difficult to be used directly as storages in consumer devices because of a limited lifespan, which is one of the major drawbacks of NAND flash memory. Therefore, researchers have investigated ways for improving the lifespan of flash memories. In this article, we propose a hybrid disk buffer management policy, which is called the write pattern aware hybrid disk buffer management policy (WPA). The key advantage of WPA is the reduction of write access to the flash-based storage, and this is possible because WPA analyzes the write access pattern that occurred through our pattern aware migration priority by LRU (PaMP-LRU) algorithm and a page eviction list (PEL). We compared our proposed policy with existing hybrid disk buffer policies to evaluate the performance. Experimental results show that under conditions of write-intensive workloads, the performance of WPA is found to be higher than that of other disk buffer policies. In particular, the improvement of WPA hit ratio is 87.6% on average, and WPA reduces block erase counts in flash storage 38.2% on average, and up to 66.8%, resulting in improving the lifespan of NAND flash memory, compared to other disk buffer management policies.
Neural RRT*: Learning-Based Optimal Path Planning

Rapidly random-exploring tree (RRT) and its variants are very popular due to their ability to quickly and efficiently explore the state space. However, they suffer sensitivity to the initial solution and slow convergence to the optimal solution, which means that they consume a lot of memory and time to find the optimal path. It is critical to quickly find a short path in many applications such as the autonomous vehicle with limited power/fuel. To overcome these limitations, we propose a novel optimal path planning algorithm based on the convolutional neural network (CNN), namely the neural RRT* (NRRT*). The NRRT* utilizes a nonuniform sampling distribution generated from a CNN model. The model is trained using quantities of successful path planning cases. In this article, we use the A* algorithm to generate the training data set consisting of the map information and the optimal path. For a given task, the proposed CNN model can predict the probability distribution of the optimal path on the map, which is used to guide the sampling process. The time cost and memory usage of the planned path are selected as the metric to demonstrate the effectiveness and efficiency of the NRRT*. The simulation results reveal that the NRRT* can achieve convincing performance compared with the state-of-the-art path planning algorithms. Note to Practitioners—The motivation of this article stems from the need to develop a fast and efficient path planning algorithm for practical applications such as autonomous driving, warehouse robot, and countless others. Sampling-based algorithms are widely used in these areas due to their good scalability and high efficiency. However, the quality of the initial path is not guaranteed and it takes much time to converge to the optimal path. To quickly obtain a high-quality initial path and accelerate the convergence speed, we propose the NRRT*. It utilizes a nonuniform sampling distribution and achieves better performance. The NRRT* can be also applied to other sampling-based algorithms for improved results in different applications.
Pengaruh Permainan Ular Tangga dan Buku Cerita Bergambar sebagai Media Promosi Kesehatan Terhadap Praktek Cuci Tangan Pakai Sabun

ABSTRACT The Effect Of Game Snakes And Ladders Illustrated Book Stories As Media Of Health Promotion Toward The Practice Of Handwashing: Children are the group most vulnerable to the transmission of the disease. Morbidity of diarrhea in children aged 5-14 years in Pekalongan in 2013 as many as 6,014 or 21.6% of total patients with diarrhea. Transmission of the disease in children can be prevented by hand washing with soap (CTPS). Handwashing child can through health education. Media used in a variety of health education as a game of snakes and ladders and picture books. This study aims to determine the effectiveness of the game of snakes and ladders and picture books as a medium for health promotion against the practice of handwashing with soap in primary school students in the District Karanganyar Regency Pekalongan. Using a quasi-experimental research design. Samples were elementary school students grades 4 and 5 SDN Kayugeritan many as 46 people and SDN Kulu many as 46 people with purposive sampling technique. The instrument used was a game of snakes and ladders, picture books and hand-washing practices check list. Data analysis using the Mann Whitney.The results showed that there was a difference in effectiveness between the game of snakes and ladders and picture books as a medium for health promotion against the practice of handwashing with soap in primary school students in the District Karanganyar Pekalongan with value 0,000 <0,05. Snakes and ladders game media is more effective than a picture story book. Based on the research results it is recommended that the health institutions to improve health education about the practice of washing hands with soap and using health education media easy and interesting for children of elementary school students like snakes and ladders game media. Keywords: effectiveness, health education, snakes and ladders games, picture books
Scratch My Back and I’ll Scratch Yours: The Impact of the Interaction Between User Effort and Recommendation Agent Effort on Perceived Recommendation Agent Quality

Product recommendation agents (RAs) assist online firms to adapt their suggested offers to users’ preferences, there by lowering consumers’ decision effort and improving their decision accuracy. The abundance of product choices available on the internet has made the need for RAs essential mainly due to the increased decision effort by consumers associated with making a selection. The concept of effort has been central in decision-making, yet evidence on the role of effort has been contradictory on whether it should be regarded merely as a cost for the user or as a benefit as well since higher effort improves the odds of a better decision. Building on Social Exchange theory, we suggest that interactions between users and RAs are driven by the concepts of perceived interdependence and reciprocity. We suggest that perceived user effort decreases, whereas RA effort increases, perceived RA quality. We further posit that users evaluate RAs based on their own individual effort expended in relation to their perceptions of the effort that the RA has put into the process of generating recommendations. The evidence from two experimental studies and an integrative data analysis suggests that the negative effect of user effort on RA quality is reduced when users perceive greater RA effort. Such an effect is negatively moderated by user familiarity. Our findings enrich the understanding of the conflicting role of user effort in decision-making and offer important insights into how online firms can improve the utilization of their RAs.
A Deep-Learning-Based Self-Calibration Time-Reversal Fingerprinting Localization Approach on Wi-Fi Platform

With the increasing demands for indoor location-based services, fingerprinting-based localization attracts considerable attention, due to that it could achieve high localization accuracy using simple equipment. However, the main problem of fingerprinting localization is that with the change of the indoor environment, the fingerprint database would be outdated, which inevitably leads to localization performance degradation. To tackle this issue, based on deep learning, this article proposes a self-calibration time-reversal (TR) fingerprinting localization approach to mitigate the effects of environmental changes without updating the fingerprint database. In the offline stage, the amplitude autoencoder (A-AE) and the phase autoencoder (P-AE) are, respectively, trained without labels to record features of the current environment. In the online stage, the trained A-AE and P-AE are used to adaptively calibrate the real-time measurements which may have been distorted due to the environmental changes. Based on the calibrated measurements, a modified TR resonating strength (TRRS) is presented for localization. The experimental results confirm the effectiveness of the proposal.
A Low-Complexity Equalizer for Video Broadcasting in Cyber-Physical Social Systems Through Handheld Mobile Devices

In Digital Video Broadcasting-Handheld (DVB-H) devices for cyber-physical social systems, the Discrete Fractional Fourier Transform-Orthogonal Chirp Division Multiplexing (DFrFT-OCDM) has been suggested to enhance the performance over Orthogonal Frequency Division Multiplexing (OFDM) systems under time and frequency-selective fading channels. In this case, the need for equalizers like the Minimum Mean Square Error (MMSE) and Zero-Forcing (ZF) arises, though it is excessively complex due to the need for a matrix inversion, especially for DVB-H extensive symbol lengths. In this work, a low complexity equalizer, Least-Squares Minimal Residual (LSMR) algorithm, is used to solve the matrix inversion iteratively. The paper proposes the LSMR algorithm for linear and nonlinear equalizers with the simulation results, which indicate that the proposed equalizer has significant performance and reduced complexity over the classical MMSE equalizer and other low complexity equalizers, in time and frequency-selective fading channels.
HTML: Hierarchical Transformer-based Multi-task Learning for Volatility Prediction

The volatility forecasting task refers to predicting the amount of variability in the price of a financial asset over a certain period. It is an important mechanism for evaluating the risk associated with an asset and, as such, is of significant theoretical and practical importance in financial analysis. While classical approaches have framed this task as a time-series prediction one – using historical pricing as a guide to future risk forecasting – recent advances in natural language processing have seen researchers turn to complementary sources of data, such as analyst reports, social media, and even the audio data from earnings calls. This paper proposes a novel hierarchical, transformer, multi-task architecture designed to harness the text and audio data from quarterly earnings conference calls to predict future price volatility in the short and long term. This includes a comprehensive comparison to a variety of baselines, which demonstrates very significant improvements in prediction accuracy, in the range 17% - 49% compared to the current state-of-the-art. In addition, we describe the results of an ablation study to evaluate the relative contributions of each component of our approach and the relative contributions of text and audio data with respect to prediction accuracy.
Interactive Video Retrieval in the Age of Deep Learning – Detailed Evaluation of VBS 2019

Despite the fact that automatic content analysis has made remarkable progress over the last decade – mainly due to significant advances in machine learning – interactive video retrieval is still a very challenging problem, with an increasing relevance in practical applications. The Video Browser Showdown (VBS) is an annual evaluation competition that pushes the limits of interactive video retrieval with state-of-the-art tools, tasks, data, and evaluation metrics. In this paper, we analyse the results and outcome of the 8th iteration of the VBS in detail. We first give an overview of the novel and considerably larger V3C1 dataset and the tasks that were performed during VBS 2019. We then go on to describe the search systems of the six international teams in terms of features and performance. And finally, we perform an in-depth analysis of the per-team success ratio and relate this to the search strategies that were applied, the most popular features, and problems that were experienced. A large part of this analysis was conducted based on logs that were collected during the competition itself. This analysis gives further insights into the typical search behavior and differences between expert and novice users. Our evaluation shows that textual search and content browsing are the most important aspects in terms of logged user interactions. Furthermore, we observe a trend towards deep learning based features, especially in the form of labels generated by artificial neural networks. But nevertheless, for some tasks, very specific content-based search features are still being used. We expect these findings to contribute to future improvements of interactive video search systems.
Distributed Secondary Control for Islanded Microgrids Cluster Based on Hybrid-Triggered Mechanisms

Considering the communication resources limitation, the hybrid-triggered mechanism based distributed control of islanded microgrids cluster is proposed, which can restore the frequency to the rated value and realize the active power sharing when the disturbance occurs. The hybrid-triggered mechanism consists of the self- and event-triggered mechanisms, which are configured at each leader and follower distributed generation to determine the inter-microgrids and intra-microgrid information transmission, respectively. The communication burdens can be sharply reduced since the information is transmitted aperiodically only when the proposed triggering conditions are satisfied under the hybrid-triggered mechanism. Moreover, Zeno behavior is analyzed to be avoided to make the hybrid-triggered mechanism reasonable and practicable for practical islanded microgrids cluster. The simulation verifies the effectiveness of theoretical results.
What is the Human Mobility in a New City: Transfer Mobility Knowledge Across Cities

With the advances of web-of-things, human mobility, e.g., GPS trajectories of vehicles, sharing bikes, and mobile devices, reflects people’s travel patterns and preferences, which are especially crucial for urban applications such as urban planning and business location selection. However, collecting a large set of human mobility data is not easy because of the privacy and commercial concerns, as well as the high cost to deploy sensors and a long time to collect the data, especially in newly developed cities. Realizing this, in this paper, based on the intuition that the human mobility is driven by the mobility intentions reflected by the origin and destination (or OD) features, as well as the preference to select the path between them, we investigate the problem to generate mobility data for a new target city, by transferring knowledge from mobility data and multi-source data of the source cities. Our framework contains three main stages: 1) mobility intention transfer, which learns a latent unified mobility intention distribution across the source cities, and transfers the model of the distribution to the target city; 2) OD generation, which generates the OD pairs in the target city based on the transferred mobility intention model, and 3) path generation, which generates the paths for each OD pair, based on a utility model learned from the real trajectory data in the source cities. Also, a demo of our trajectory generator is publicly available online for two city regions. Extensive experiment results over four regions in China validate the effectiveness of the proposed solution. Besides, an on-field case study is presented in a newly developed region, i.e., Xiongan, China. With the generated trajectories in the new city, many trajectory mining techniques can be applied.
Adapting gamified learning systems using educational data mining techniques

Artificial intelligence (AI) provides opportunities to improve the effectiveness of e‐learning by increasing students' engagement. Adaptive e‐learning uses AI to support individual learners by responding to their different learning needs which can be determined by analyzing their navigation history of e‐learning systems using data mining methods. Educational data mining (EDM) discovers new patterns of learning and teaching to facilitate the process of decision‐making to serve education improvement. Gamification is another way of increasing students' engagement by using game elements in a nongame context. In this paper, the gamification technique and EDM methods were used in combination with adaptive learning to increase the students' engagement and learning performance. An adaptive gamified learning system (AGLS) was developed which combines gamification, classification, and adaptation techniques to increase the effectiveness of e‐learning. This paper studies the impact of gamification and adaptive gamification on the effectiveness of e‐learning through increasing students' engagement and learning performance. AGLS was applied to the data structure course. Results showed that adaptive gamification has a positive effect on students' engagement and learning performance compared to just gamification.
Bitcoin's dynamic peer‐to‐peer topology

Topology discovery is a prerequisite when investigating the network properties; with the enormous number of Bitcoin users and performance issues, it becomes critical to analyse the network in a fashion that makes it possible to detect all Bitcoin's nodes and understand their behaviour. In massive, dynamic, and distributed peer‐to‐peer (P2P) networks like Bitcoin, where thousands of updates occur per second, it is hard to obtain an accurate topology representing the structure of the network as a graph with nodes and links by using the traditional local measurement approaches based on batches, offline data, or on the discovery of the topology around a small set of nodes and then combine them to discover an approximate network topology. All of which present some limitation when applying them on blockchain‐based networks. In this paper, we propose a topology discovery system that performs a real‐time data collection and analysis for Bitcoin P2P links, which assembles incoming nodes information for deeper graph analysis processing. The topology discovery system allows us to gain knowledge on the Bitcoin network size, the network stability in terms of reachable, churn, and well‐connected nodes, as well as some data regarding the effects of some countries' Internet infrastructure on Bitcoin traffic.
Acceleration-Level Configuration Adjustment Scheme for Robot Manipulators

In this article, configuration adjustment (CA) for robot manipulators at the joint-acceleration level is presented. Specifically, a new acceleration-level performance index for achieving CA is designed by employing the neurodynamic method. Thus, based on this performance index, and by incorporating joint physical constraints (i.e., joint-configuration, -velocity, and -acceleration limits), a novel acceleration-level CA (ALCA) scheme for robot manipulators is proposed and investigated. The proposed ALCA scheme is transformed into a quadratic program and calculated using a neural network solver. Comparative simulation results obtained with a four-link robot manipulator are presented to substantiate the effectiveness and superiority of the proposed ALCA scheme compared with those of the velocity-level CA scheme. Moreover, simulations and experiments are conducted on a practical Epson robot manipulator to demonstrate the physical realization of the proposed ALCA scheme.
Patterns in Mainstream Programming Games

Studies have found serious games to be good tools for programming education. As an outcome from such research, several game solutions for learning computer programming have appeared. Most of these games are only used in the research field where only a few are published and made available for the public. There are however numerous examples of programming games in commercial stores that have reached a large audience.This article presents a systematic review of publicly available and popular programming games. It analyses which fundamental software development concepts, as defined by the ACM/IEEE Computer Science Curricula, are represented in these games and identifies game design patterns used to represent these concepts.This study shows that fundamental programming concepts and programming methods have a good representation in mainstream games. There is however a lack of games addressing data structures, algorithms and design. There is a strong domination of puzzle games. Only two of the 20 studied games belong to a different genre. The eleven game design patterns identified in this study have potential to contribute to future efforts in creating engaging serious games for programming education.