Miracle-dz/UniFAR-FTUs huggingface dataset:
Label Card Segmentation and Calibration for Computerized wound Measurement System

Monitoring the changes in wound size over time is one of the most important parameters for assessment of the applied treatment plan in wound care. The current manual methods for wound measurement have several drawbacks including high error rates, time consuming and also causing patient discomfort because of pain or infection. There are some computerized tools available in clinical settings known as digital planimetry which require the clinician to identify wound boundaries and calibrate them manually. However these methods are a barrier to achieving clinical quality benchmarks as the accuracy decreases drastically with improper camera lens orientation and some other factors. This paper proposes an automated calibration method for wound measurement based on the segmentation of reference label card and geometrical image operations. Due to the uncontrolled setting of the camera for our images, segmentation of the label card can be very challenging, thus preventing accurate calibration and wound size measurement. We proposed a label card segmentation method based on probability map approach and superpixel region growing. Theoretically, the white label card regions would have a distinguished probability map in the white channel compared to other regions, generating a reliable initial segmentation. Morphological operations and edge detection are then performed to obtain a more accurate final segmentation. Based on the segmented label card, the size of the pixel can be calibrated, and the size of the wound can be automatically calculated. Experimental results confirm the feasibility of the proposed method by demonstrating segmentation accuracy in terms of the Jaccard index of 94.77%, Dice coefficient of 97.20%, and contour matching score of 94.21%. The wound measurement accuracy also shows very promising results.
Hybrid-Based Malware Analysis for Effective and Efficiency Android Malware Detection

In the last decade, Android is the most widely used operating system. Despite this rapidly increasing popularity, Android is also a target for the spread of malware. Android admits the installation of applications from other unauthorized markets. This fact allows malware developers to place malicious apps and engage Android devices. So far, malware analysis and detection systems have been developed to use both static analysis and dynamic analysis. However, existing research is still lagging in the performance of detecting malware efficiently and accurately. For accurate malware detection, it often utilizes many resources from resource-limited mobile devices. Therefore, this research proposes a solution by developing and testing an efficient and accurate machine learning and deep learning model for this problem. We used the malware genome dataset and the Drebin project for static analysis and used the CICMalDroid dataset for dynamic analysis. From these two datasets, we extract 261 combined features of the hybrid analysis. To test the model that was built, we took 311 application samples consisting of 165 benign apps from the play store and 146 malicious apps from VirusShare. The test results show that the hybrid analysis model can increase detection by about 5%. Further testing also revealed that the extreme gradient boosting (XGB) assemble model is the best accuracy and efficiency model.
PerDNN: Offloading Deep Neural Network Computations to Pervasive Edge Servers

Emerging mobile applications, such as cognitive assistance based on deep neural network (DNN), require low latency as well as high computation power. To meet these requirements, edge computing (also called fog computing) has been proposed, which offloads computations to edge servers located near mobile clients. This paradigm shift from cloud to edge requires new computing infrastructure where edge servers are pervasively distributed over a region.This paper presents PerDNN, a system that executes DNNs of mobile clients collaboratively with pervasive edge servers. PerDNN dynamically partitions DNN computation between a client and an edge server to minimize execution latency. It predicts the next edge server the client will visit, calculates a speculative partitioning plan, and transfers the server-side DNN layers to the predicted server in advance, which reduces the initialization overhead needed to start offloading, thus avoiding cold starts. We do not incur excessive network traffic between edge servers, though, by migrating only a tiny fraction of the server-side DNN layers with negligible performance loss. We also use GPU statistics of edge servers for DNN partitioning to deal with the resource contention caused by multi-client offloading. In the simulation with human trace datasets and execution profile of real hardware, PerDNN reduced the occurrence of cold starts by up to 90%, achieving 58% higher throughput when clients change their offloading servers, compared to a baseline without proactive DNN transmission.
Analysis of Deep Features for Image Aesthetic Assessment

The performance of image aesthetic assessment has been significantly improved by deep learning techniques in comparison to traditional hand-crafted feature-based methods. However, there has not been an attempt to analyze the learned features in deep learning approaches. This paper presents in-depth analysis of the deep models and the learned features by the models for image aesthetic assessment in various viewpoints. We consider binary classification of the mean (average aesthetic level) and standard deviation (subjectivity of aesthetic perception) of aesthetic ratings. In particular, our analysis is based on transfer learning among image classification and aesthetic classifications for comparative analysis. Our results highlight the similarity and transferability of learned features among the classification tasks, and comparison of the trained models and convolutional filters.
One Step Ahead: A Framework for Detecting Unexpected Incidents and Predicting the Stock Markets

Unexpected incidents can be destructive or even disastrous, affecting the financial markets. Incidents such as the 9/11 attacks (2001), the Fukushima nuclear disaster (2011), and the COVID-19 outbreaks (2019, 2020) severely shocked both local and global markets. For investors, it is crucial to quantify the key facts that affect the incidents’ impacts, and to estimate the reactions of the markets accurately and efficiently for global event-driven investment strategies. Though Web data and other alternative data allow such a possibility, it is still very challenging to mine noisy and often biased heterogeneous data sources, and construct a unified framework for modeling global markets across across time and regions. As a first attempt, we build a framework that extracts incident facts globally based on a deep neural network, feeds them into models built on a global event database complemented with novel socioeconomic datasets (e.g. nightlight data from satellites), and predicts stock market directions in a simulated real-world setting with interpretable results that outperform various baselines. Specifically, we study terrorist attacks in three countries for over 20 years on average, as a first effort to systematically quantify the impact on stock markets at a large scale using novel indicators.
The MuTube Dataset for Music Listening History Retrieval and Recommendation System

Recommendation systems are widely used in music streaming services. This paper presents a system to collect user’s music preference and music textual features in YouTube as well as to provide music recommendations based on collaborative filtering. As cold start and data sparsity are two severe issues in collaborative filtering, additional features for the item are necessary. We propose a method to aggregate both implicit feedback and textual features collected from YouTube to improve the recommendation performance. Experiment results indicate that the recommendation playlists generated by this system both match individual’s and group preference.
Deep Learning based Time Series Forecasting

For decision-makers in the forecasting sector, decision processes like planning of facilities, an optimal day-to-day operation within the domain etc., are complex with several different levels to be considered. These decisions address widely different time horizons and aspects of the system, making it difficult to model. The advent of deep learning in forecasting solved the need for expensive hand-crafted features and deep domain knowledge. The work aims at giving a structure to the existing literature for time-series forecasting in deep learning. Based on the underlying structures of the technique, such as RNN, CNN, and Transformer, we have categorized various deep learning-based time series forecasting techniques and provided a consolidated report. Additionally, we have performed experiments to compare these techniques on 4 different publicly available datasets. Finally, based on these experiments, we provide an intuitive reasoning behind these performances. We believe that this work shall help the researchers in choosing relevant techniques for future research.
Elasticity Control for Latency-Intolerant Mobile Edge Applications

Elasticity is a fundamental property required for Mobile Edge Clouds (MECs) to become mature computing platforms hosting software applications. However, MECs must cope with several challenges that do not arise in the context of conventional cloud platforms. These include the potentially highly distributed geographical deployment, heterogeneity, and limited resource capacity of Edge Data Centers (EDCs), and end-user mobility.In this paper, we present an elasticity controller to help MECs overcome these challenges by automatic proactive resource scaling. The controller utilizes information on the physical locations of EDCs and the correlation of workload changes in physically neighboring EDCs to predict request arrival rates at EDCs. These predictions are used as inputs for a queueing theory-driven performance model that estimates the number of resources that should be provisioned to EDCs in order to meet predefined Service Level Objectives (SLOs) while maximizing resource utilization. The controller also incorporates a grouplevel load balancer that is responsible for redirecting requests among EDCs during runtime so as to minimize the request rejection rate. We evaluate our approach by performing simulations with an emulated MEC deployed over a metropolitan area and a simulated application workload using a real-world user mobility trace. The results show that our proposed pro-active controller exhibits better scaling behavior than a state-of-the-art re-active controller and increases the efficiency of resource provisioning, thereby helping MECs to sustain resource utilization and rejection rates that satisfy predefined SLOs while maintaining system stability.
A* algorithm for GIS-based pipeline route selection in Veracruz, Mexico

This article presents the application of the A* algorithm for GIS-based pipeline route selection. The problem proposes finding the minimum cost route for an oil pipeline in a geographic space. A comparison of six heuristic functions within the A* algorithm, considering different topographical characteristics for route selection, is reported. Realistic problem instances and solved in the experimental evaluation, considering existing information of Veracruz, Mexico. Experimental results indicate that the Diagonal heuristic computed the best results for instances with high impact costs (29.5% in average) and the Manhattan and Chebyshev heuristics computed the best results for less restrictive instances (2.2% in average) and improved over a real-world pipeline design in the studied area. The proposed approach contributes with the design of alternative routes for linear facilities planning in the decision-making process.
On Combining Publish/Subscribe with Append-only Logs for IoT Data

Publish/subscribe systems and in particular the MQTT protocol are often used to cope with the messaging needs of modern Internet of Things (IoT) systems, i.e. between the IoT devices and the related services. Their benefits lie in their decoupling properties, easing the seemless movement of the systems components across the increasingly distributed hardware substrate of cloud, fog, and edge. However, publish/subscribe systems alone are rarely suitable for all usecases commonly associated with the IoT. One important example is the lack of persistent storage of sensor data in popular pub/sub system, such as MQTT, which often leads to the anti-pattern of constantly subscribing and locally storing the data at the subscriber side, resulting in multiple independent copies accross the network. In this paper, we present our analysis of integrating storage technologies into pub/sub: We show how append-only logs, considered to be the prevailing storage paradigm in some IoT focused systems, can be added to common pub/sub systems. Three options are outlined in detail regarding features. We further show on an abstract level how the glue code combining the solutions might look like.
Tsallis Entropy Based Labelling

In the field of supervised classification, the quality of training data is an essential aspect of accurate learning, along with the selection of a learning algorithm or parameters optimisation. To improve the quality of training data, it is necessary to reflect an annotator’s idea on to which class any given instance belongs in the form of a label as flexibly and accurately as possible. However, in conventional problem settings used in machine learning, the number of labels per instance is uniformly fixed at a certain value, and it is implicitly assumed that annotators provide labels under such a constraint. Thus, in this study, we propose an annotation framework; Tsallis entropy based labelling, which models a method that dynamically selects the number of labels for every single given instance depending on the uncertainty regarding the class to which each instance belongs. Using the proposed framework, an annotator’s instinctive uncertainty about classification task is expressed based on the Tsallis entropy and Tsallis self-information. In addition, the proposed framework has a well-organised mathematical structure that includes some typical annotation models. We conduct an experiment to evaluate the proposed framework and demonstrate that it outperforms another annotation model in terms of the labels accuracy; In the comparison model, the number of labels per instance is deliberately set at a fixed value for all instances. Moreover, we exemplify that the conventional single labelling scheme is not always the best option, which reveals the fact that increasing the number of labels per instance does not necessarily hinder the labels accuracy.
A Hybrid Half-bridge LLC Resonant Converter and Phase Shifted Full-bridge Converter for High Step-up Application

High step-up DC/DC converters are popular in renewable energy generation systems, like photovoltaic (PV) and fuel cell. To achieve high step-up conversion ratio, in this paper, an input-parallel and output-series hybrid topology of half-bridge LLC resonant converter and phase shifted full-bridge converter (PSFB) is proposed. The following features have been achieved: 1) high voltage conversion ratio: due to the output series connection, the system voltage conversion ratio equals to the sum of LLC resonant converter and PSFB converter. In addition, the voltage doubler rectifier is adopted for the LLC resonant converter to further increase the voltage gain; 2) LLC resonant converter is operating at the series resonant frequency operating point, its voltage conversion ratio is constant and independent of the load, and the high efficiency conversion is achieved; 3) function decoupling idea is adopted for the proposed topology, most of the power is handled by the high efficiency conversion unit-LLC resonant converter, while the PSFB unit is used to regulate the output voltage; 4) since both two converter units are isolated DC/DC converter, fully isolation between the input voltage and output voltage is achieved. Operational principles, design considerations, and experimental validation are presented.
Evaluation of thread-local garbage collection

This paper discusses thread-local garbage collection (GC), a technique of automatic memory management aimed at improving GC throughput and reducing GC pauses in managed runtimes. It exploits the observation that most objects do not escape the scope of the thread that allocated them; therefore, memory occupied by those objects can be reclaimed locally within the thread. Performance measurements demonstrate that the proposed scheme has an advantage over a highly optimized stop-the-world collector used in an experimental JVM on some throughput-oriented benchmarks.
A User Pairing Technique for Mixed Traffic Types in Downlink NOMA

This paper proposes a resource allocation technique for downlink non-orthogonal multiple access network (NOMA) under mixed real-time (RT) and best effort (BE) user traffic. To achieve quality of service (QoS) requirement of real-time users and to accomplish fairness among BE users, we propose a user pairing technique along with a water-filling solution with lower bound constraint for BE users. The proposed algorithm fulfills the QoS requirements of real-time users while satisfying fairness among BE users. Simulation results show that our proposed algorithm performs better than baseline techniques in terms of mean data rate, QoS achievement percentage of RT users, and fairness for BE users.
ST-VLAD: Video Face Recognition Based on Aggregated Local Spatial-Temporal Descriptors

How to integrate the temporal and spatial continuity information, when designing the video texture description operator, is crucial to realize video face recognition and facilitate video analysis and understanding, however, it has still yet to be properly addressed. In this paper, a novel video face recognition algorithm is proposed based on an aggregated local spatial-temporal descriptor (ST-VLAD), followed by a novel Fisher Criterion-based weight-learning method, which portrays the local information of the video more accurately, therefore largely improving the representation ability of description vectors. The proposed descriptor was tested on two representative databases, Honda/UCSD and YouTube Face database, achieving accuracies of 89.7% and 87.3%, respectively. The proposed method greatly outperformed the other existing state-of-art methods, suggesting a potential broad utility in the field of video face recognition.
A Multiclass Deep Learning Approach for LULC Classification of Multispectral Satellite Images

In general, a visual interpretation technique is adopted for mapping of Land Use / Land Cover (LULC) using temporal satellite data. Although highly accurate, the process is tedious, time consuming and requires a significant amount of domain knowledge. This limitation introduces a scope for partial automation to reduce manual effort involved in interpretation, while maintaining baseline accuracy. The research explores a novel multi-class training approach using a Deep Learning (DL) model to generate major LULC classes. Five spectral bands, namely Blue, Green, Red, Near-Infrared (NIR) and Short wave Infrared (SWIR) from the Sentinel-2A satellite, covering Mandya, Karnataka, India was used to train the model. An existing LULC map of the region was used as an input for automatically generating labeled training samples and a modified SegNet was implemented for classification. Four major LULC classes of interest - water bodies, forest lands, croplands, built-up were classified with an average F1 score of 0.84. The trained model applied to other regions has shown encouraging results which makes this an effective method to explore the generation of LULC maps.
Intelligent Traction Control Method Based on Model Predictive Fuzzy PID Control and Online Optimization for Permanent Magnetic Maglev Trains

Considering that the speed control system of the suspended permanent magnetic maglev train is more complicated and the parameters are more unstable than those of other trains, the traditional speed-tracking algorithm has large tracking errors, frequent controller output changes, high energy consumption, and decreasing the passengers’ riding comfort. To improve the shortcomings of the traditional automatic train operation (ATO) control algorithm, this paper proposes a predictive fuzzy proportional-integral-derivative control algorithm with weights (WM-F-PID). The main contribution of this work is to propose a cascaded predictive fuzzy PID (F-PID) control algorithm architecture with weights and use an improved steepest descent method to calculate online the weight of the F-PID controller input occupied by the predictive controller output. Compared with the proportional-integral-derivative (PID), F-PID, model predictive control (MPC), and simple cascade predictive fuzzy PID (M-F-PID) control algorithms, this control algorithm effectively improves train tracking accuracy and comfort and reduces train energy consumption and stopping errors.
A Malware Detection Approach Using Malware Images and Autoencoders

Most machine learning-based malware detection systems use various supervised learning methods to classify different instances of software as benign or malicious. This approach provides no information regarding the behavioral characteristics of malware. It also requires a large amount of training data and is prone to labeling difficulties and can reduce accuracy due to redundant training data. Therefore, we propose a malware detection method based on deep learning, which uses malware images and a set of autoencoders to detect malware. The method is to design an autoencoder to learn the functional characteristics of malware, and then to observe the reconstruction error of autoencoder to realize the classification and detection of malware and benign software. The proposed approach achieves 93% accuracy and comparatively better F1-score values while detecting malware and needs little training data when compared with traditional malware detection systems.
Autonomous Object Detection in Satellite Images Using Wfrcnn

Object detection in remote sensing images has been a topic of interest that has gradually gained attention over the years due to the wide variety of related applications. Even though there is an extensive number of methods developed for object detection, there are still several challenges that remain unsolved, such as visual appearance variations, occlusions, and background clutter. Satellite images reveal a texture problem; it is difficult to differentiate between the background and the object of interest. In order to overcome this problem and exploit more of the spectral features of images, Discrete Wavelet Transform (DWT) is embedded into one of the most superior methods for object detection, which is Faster Region-based Convolutional Network (FRCNN). The accuracy of FRCNN is boosted by introducing the wavelet decomposition. The performance of the proposed strategy is tested, evaluated, and compared to the original FRCNN using two different datasets.
Properties and Shapes of Matcha with Various Milling Method

（1） 石臼抹茶，ボールミル抹茶，粗粉抹茶の中位径は，15～20μm，最大径は50μm以上であり，微粉抹茶，ジェットミル抹茶の中位径は，5μm以下，最大径は10μm以下であった．（2） ジェットミル抹茶の円形度は，石臼抹茶，ボールミル抹茶と比較して高く，円形度の違いが伸展性試験や流動性に影響を及ぼしていると推測された．（3） 伸展性試験は，粒度の微細なジェットミル抹茶の伸展性が粗い粒度の粗粉抹茶と比較して2倍以上良かった．また，触感においては，伸展性試験の結果以上に，微細さや粗さを感じた．（4） ジェットミル抹茶の流動性は，流動開始振動加速度が一定しており，流動速度や流量も一定であった．これに対して，微粉抹茶は流動開始振動加速度が一定せず，流動速度や流量も一定でなかった．これは，円形度が高いことに起因すると考えられる．（5） 微粉抹茶の起泡性は，粗粉抹茶と比べて非常に優れていた．泡沫の起泡性向上や安定化には，粒子が微細であるとこが重要であることが分かった．