Miracle-dz/UniFAR-FTUs huggingface dataset:
AI-based prediction for early detection of Tuberculosis in India based on environmental factors

Machine Learning and Deep Learning can play an essential role in determining the spread of diseases. The proposed system aims at predicting the spread of Tuberculosis by understanding the impact of various climatic and pollution parameters on the disease. The proposed solution takes into consideration the information related to Tuberculosis in different districts of India; and the climatic and pollution parameters for those regions. This information is then used to understand the sustainability conditions of Tuberculosis and correlation of different environmental factors with a number of cases of Tuberculosis. This can then help in the prediction of the spread of disease. The system will also provide visualizations depicting the spread pattern of Tuberculosis, of the different regions affected in the past and the regions which may get affected in the near future.
Poster: A Light Weight Service Discovery Mechanism in Robot Systems

Benefiting from the major breakthrough of AI technology and increasing application of AIoT (AI + IoT), the intelligent robot system achieves high developing speed in recent years. As a core component of the intelligent system, service discovery plays a crucial role in system reliability. Because of the high CPU usages in conventional service discovery, a light weight service discovery mechanism is required in such a resourcelimited robot system. To improve the efficiency of CPU usages, we propose a weak centralized mechanism and a socket based notification mechanism, to reduce the amount of event in service discovery. The evaluation results show that our proposed light weight service discovery mechanism can reduce 95% CPU usages in average, compared with the conventional service discovery used in ROS2, which is an industry standard in robot systems.
A novel approach for predictive maintenance combining GAF encoding strategies and deep networks

One of the most important challenge in the Industry 4.0 era is to provide advanced maintenance mechanisms able to avoid or minimize failures during production processes. In this paper, we propose a novel approach to the predictive maintenance problem that combines encoding strategies to convert temporal data into images and classical deep learning strategies. In particular, the basic idea is to exploit GAF (Gramian Angular Field) encoding to obtain images from time series related to production systems, which can be used in pre-trained convolutionary neural networks for better prediction performance and thus to create a more efficient and simple predictive maintenance scheme. Some experiments on several standard datasets show the advantages of the proposed techniques w.r. t. to other approaches in the literature.
Multi-UCAV Air Combat in Short-Range Maneuver Strategy Generation using Reinforcement Learning and Curriculum Learning

We present an approach for learning a reactive maneuver strategy for a UCAV formation involved in a short-range multi-UCAV air combat engagement. Specifically, we define an efficient state representation, which breaks down the complexity caused by the large state space in a multi-UCAV air combat engagement. Then a parameter sharing dueling deep Q-network (PS-DDQN) algorithm is proposed to train the UCAV formation. The learning reactive maneuver strategy is shared among our UCAVs to encourage cooperative behaviors. In addition, curriculum learning and self-play extend the maneuver strategy to more difficult scenarios. Thus, speeding up the training process and improving the learning effect. Finally, the effectiveness of the algorithm and the intelligence degree of maneuver strategy is verified by the simulation test of convergence and maneuver strategy quality.
Precision Tuning of an Accelerometer-Based Pedometer Algorithm for IoT Devices

Internet of Things (IoT) is considered as one of the most evolving technologies. It impacts almost every aspect of life in the modern world. One thing that prevents this technology from reaching its full potential is the too high memory and energy consumption of IoT devices. An aspect of this problem is the numerical computations performed by IoT devices which are generally carried out in higher precision than needed, by lack of techniques to tune this precision in function of the actual needs of the application. To surpass this limitation, we present our tool POP based on a static program analysis, done by abstract interpretation. POP provides a mixed precision tuning that finds the instructions and variables that may use lower precision with respect to the user accuracy requirements on the results. We demonstrate the efficiency of POP to tune an accelerometer-based pedometer algorithm for embedded applications.
A LoRaWAN-MAC Protocol Based on WSN Residual Energy to Adjust Duty Cycle*

With the expansion of the scale of wireless sensor networks, people have higher requirements for the real-time nature of receiving information and the power consumption of network equipment. LoRa technology for low-power wide-area networks has been produced, but the energy consumption of network terminal nodes is still facing huge problems. Challenge. In this thesis, the REDS strategy based on LoRa technology is proposed. First, the residual energy is used to detect the preamble in the beacon, so that CAD can judge the effective preamble from the noise to avoid false wake up. Then use the remaining energy of the nodes in the remote Sink area of the network to activate the node and perform CAD channel detection. The node selects the communication access method according to the channel quality. When the network traffic load is low, the node communicates according to the CSMA-CA competition method. When the traffic load in the network is high, the nodes communicate and access according to the dynamic duty cycle method. It is proved that the network delay can be reduced by 29% and the energy efficiency can be improved by 19%. Thereby avoiding data collisions, improving channel utilization, and weighing the trade-off between network delay and network energy consumption.
Discovering Gene-Disease Associations with Biomedical Word Embeddings

Finding the right target for a disease is critical in the drug development process. This paper presents a machine learning approach for predicting gene-disease associations that (i) employs biomedical word embeddings as features for a classifier trained on Open Targets Platform (OTP) data that (ii) generalises beyond a specific disease or gene class. We train, evaluate and compare different word embedding models and classifiers for the task at hand. We validate the approach by training on a past OTP release and show that it can assist in identifying probable positive associations among current low evidence associations, confirmed by a recent OTP release. Furthermore, we train word embedding models on different time slices of biomedical articles from ScienceDirect and demonstrate that the trained classifier predicts associations that have not explicitly been mentioned in the training corpus, 5 years into the future.
Semi-Supervised Learning Framework for Aluminum Alloy Metallographic Image Segmentation

The purpose of aluminum alloy metallographic image segmentation is to automatically recognize and segment the microstructures of aluminum alloy, which is an important topic in the fields of materials science research and product assessment. In order to achieve satisfactory segmentation results, we always need to label each pixel in metallographic image. This labeling work is very costly in terms of time and human effort. In this paper, we propose a semi-supervised learning framework which is able to use a small number of labeled images to achieve outstanding performance. Based on this framework, we first implement a semi-supervised segmentation method for aluminum alloy metallographic images. The U-Net is used as the basic segmentation model in this method. Second, we propose a self-paced semi-supervised segmentation method to reduce the interfere caused by the mislabeled images. To further improve the segmentation performance, we embed the improved pseudo-label selection strategies and loss functions in our framework, and implement four improved semi-supervised segmentation methods. In addition, we construct a new aluminum alloy metallographic image dataset to evaluate the proposed semi-supervised learning framework. The experiments indicate that our method is able to achieve very competitive results with a small number of labeled images compared to the state-of-the-art supervised method using a large number of pixel-level labels.
Semantic Segmentation of Lotus Leaves in UAV Aerial Images via U-Net and DeepLab-based Networks

In the applications of remote sensing images, separating or segmenting the desired objects from the vast background is usually challenging. For example, calculating the number of single crops on agricultural land and the area of crops occupied per unit field, and quantizing the amount of new leaf buds in a forest area. Even though the targets of interest are visually recognizable, sometimes a large amount of them make an automatic segmentation tool preferable than the manual operation.In this work, the real-world problem we attempt to solve is to segment the lotus leaves from the background including water, field, and other plants. The segmentation result can be used to either monitor the growth rate of lotus or evaluate the severity of pests and diseases according to different color contrast. The look-down color image data were collected through a quadrotor UAV at different altitudes up to 20 meters. The image taken at the lowest altitude was chosen to delineate the groundtruth manually. We adopted two deep learning methods, U-net and DeepLab_v3+, to perform automatic leaves segmentation. The results show that both methods have similar performance but the latter one requires more computation. Then the conditional random fields (CRF) are applied as a post-processing to clean up the fragmented segmentation result.
Predicting Smartphone Users’ Future Locations through Deep Reinforcement Learning

This paper demonstrates how people’s future movement patterns can be predicted by learning from their location history from past several weeks. We introduce a novel scheme of applying deep reinforcement learning model on a relatively recent, real-world location history dataset collected from ordinary users’ smartphone daily usage. Through a collaborative project between our affiliated university and commercial companies which distribute smartphone applications, 7,236 users of those applications agreed to continuously share their location history and participated over a period of 3 months in year 2017. Our experiments on this dataset reveals that deep reinforcement learning can be successfully applied to predict locations where users are likely to visit in the near future.
A Conflict-Aware Capacity Control Mechanism for Last-Level Cache

As the number of cores on a processor chip increases, the capacity and the number of levels of the cache hierarchy increases, which causes higher energy consumption of the computing system. However, the usage of a cache hierarchy may vary significantly among applications. Thus, determining an appropriate cache hierarchy for each application is crucial to improve performance and energy efficiency. In this paper, we propose a mechanism to improve energy efficiency by adapting a cache hierarchy to individual applications. First, our mechanism bypasses and disables some of the cache levels if their contributions to performance are small. Then, based on the cache utility, the mechanism optimizes the capacity and associativity of the last-level cache. The experimental results with the PARSEC benchmarks show that the proposed mechanism can improve energy efficiency by 26% and 10%, compared with the baseline and cache-level bypassing mechanisms, respectively.
Hybrid Prediction Model For Type-2 Diabetes With Class Imbalance

Diabetes mellitus is a fast-growing disease affecting millions of people around the globe. According to the statistics obtained from the International Diabetes Federation, the growth of diabetic cases around the world is estimated to be around 10.2% (578 million) by 2030 and 10.9% (700 million) by 2045. Machine learning techniques can be used for early diagnosis of diabetes so that individuals can adapt to better lifestyle habits and diet plan to prevent further complications of symptoms. In this paper we have implemented an efficient model to classify type-2 diabetes using a hybrid approach. The dataset which is relatively imbalanced with respect to the number of diabetic or non-diabetic individuals was obtained from the Biostatistics program of Vanderbilt University, USA which contains parameters like Glucose, Age, Cholesterol, Weight, Waist/Hip ratio of 390 African-American individuals. The features from the dataset were obtained by filter and wrapper methods. The dataset was balanced by applying oversampling and undersampling methods. A voting ensemble model consisting of 5 algorithms was then used as a classifier with model validation done using stratified K-fold technique. The model’s performance was assessed using recall, precision, F1 score and accuracy. The results obtained by comparison of the models with and without sampling have shown significant improvements in recall. The main motive is to improve recall and have reasonable precision at the same time.
A Study of Self Distillation for Mango Image Classification

We study a knowledge transfer approach called self distillation on a mango image dataset. Taking the deepest part of a convolutional neural network as the teacher, the self distillation approach transfers the relatively richer knowledge of the deepest part to shallow parts of this network, which are viewed as the students. We verify that this approach is effective in the target mango image dataset. Furthermore, we propose two more losses to improve performance considering data characteristics. In the discussion, we not only verify effectiveness of self distillation, but also point out weakness of the current approach, which unveils potential improvement for self distillation in the future.
Predicting missing and noisy links via neighbourhood preserving graph embeddings in a clinical knowlegebase

Clinical knowledge graphs (KG) are often incomplete in their early stages of development, prone to human error, and have limited quality control. Due to continuous human updates to these clinical large-scale graphs, data errors are very high and costly. These errors originate in the form of biases, experiences, and assumptions leading to noise. Hence, enterprise-grade clinical knowledge graphs deployed in practical settings must be evaluated to remove errors and assist human maintainers to enrich the KG using predictive techniques. In this study, we propose a novel application of a neighborhood- based relationship entity embedding method to simultaneously predict both noisy and missing links between the disease and symptom entities in a clinical knowledge graph. The relationship or link embedding method is a derivation of a knowledge graph embedding framework. Our evaluations and experiments demonstrate that the proposed methodology achieves substantial improvements in comparison to state-of-the-art baseline methods in clinical KG link prediction. In practical settings, the clinical missing link and noise correction method has reduced the manual data review process for clinicians and also predicted potential relationships that existed between a disease and a symptom in the clinical KG.
AppPACK: A Packaging Model for Single-Purpose Lightweight Virtualization Environment

In recent years, container technology has caught the attention of the communities by its performance and compactness. Although the design of modern container tools (e.g., Docker and podman) serves as a single-purpose application provider, existing deployed containers still contain extra tools that are unnecessary for a single-purpose process. The existence of unnecessary files and tools is directly proportional to higher security risk. Besides, extraneous files often make the container heavier and slow down its performance. This paper introduces a novel Lightweight Virtualization packaging model for creating profiles for a single-purpose application from an existing multi-purpose container environment, called AppPACK. Specifically, the model can generate a package containing minified versions of images, kernel, and virtual machine profiles from a target application. The experiment results show that AppPACK can provide an image of 1.1 to 37 times smaller in size compared to the original version. The experiment on execution shows that using AppPACK profiles could fasten the booting process from 1.1 to 6 times compared to the non-AppPACK version. The comparison between AppPACK and previous approaches shows that proposed model can provide more compatible and smaller versions in most cases.
GCN with Clustering Coefficients and Attention Module

Graph convolutional networks (GCN) exploit graph connectivity through their adjacency matrix. However, the assignment of equal importance to every one-hop neighbor and incognizance of intra-neighbor connectivity restricts its performance. Graph attention networks (GAT) address the problem of treating all neighbors equally by employing a self-attention mechanism, which encodes the node of interest using the weighted features of its one-hop neighbors. This imparts appropriate similarity based weight between connected nodes. The learning of weights of nodes takes into consideration the intra-neighbor connectivity between nodes common to a neighborhood. However, this ignores the nature of neighborhood of the node of interest. In this work, we propose to encode the nature of the neighborhood of a node onto itself by appropriately weighing the node’s self-loop. The local clustering coefficient of a graph explores to what extent which nodes in a graph tend to bunch together. A vertex with a low clustering coefficient and a large degree shows connectivity between different classes. Similarly, a vertex with a high clustering coefficient and low degree represents tightly coupled similar vertices belonging to the same class. Thus, the node self-loop is initially weighed with the local clustering coefficient. Experiment results on the benchmark graph dataset, including Cora, Citeseer, and Pubmed, shows that even with sparse two layers, the local clustering coefficient based self-loop outperforms the baseline GCN by ≈ 3%. The Ablation study and learning curve prove that the proposed model remains stable and learns the optimal feature representation in comparatively less number of epochs.
High-Performance Pentacene Organic Thin-Film Transistor Based on Room-Temperature- Processed Hf0.13La0.87O as Gate Dielectric

High-performance pentace organic thin-film transistors (OTFTs) with room-temperature-processed Hf<sub>0.13</sub> La<sub>0.87</sub>O as gate dielectric and Pd as gate electrode have been fabricated on both rigid Si and flexible polymide (PI) substrates. The OTFT on PI (Si) can achieve a high carrier mobility of 10.3 cm<sup>2</sup>V<sup>−1</sup>s<sup>−1</sup> (12.5 cm<sup>2</sup>V<sup>−1</sup>s<sup>−1</sup>), and has a negligible hysteresis of −0.17 V (−0.08 V), small sub-threshold swing of 0.12 V/dec (0.11 V/dec) and low threshold voltage of −0.65 V (−0.86 V). After the devices have been exposed to the air for 30 days without encapsulation, their carrier mobilities degrade by less than 10%, indicating that the devices have good stability in the air.
Three-step Algorithms for Detection of High Degree Nodes in Online Social Networks

This paper considers the problem of influential users detection in online social networks. Identifying of such key entities is of interest in many areas: marketing, politics, information security, business. The degree of the node of the corresponding graph is used as a popularity indicator in this work. Network query limitation is the main challenge in discovering their structure. Therefore, our task is to detect a percentage of the highest degree network nodes under a budget restriction. We propose a three-step crawling algorithm in two versions to solve the problem. We experimentally show its efficiency at various budget limits and superiority over known crawling strategies. For example, to detect top-1% of hubs with 90% precision, one needs to crawl 5% of graph nodes in average with our 3-StepBatch algorithm. We also show that our algorithm performs well for different target set sizes, from 0.01% to 10% of the graph.
Joint Traffic Offloading and Aging Control in 5G IoT Networks

The widespread adoption of 5G cellular technology will evolve as one of the major drivers for the growth of IoT-based applications. In this paper, we consider a Service Provider (SP) that launches a smart city service based on IoT data readings: in order to serve IoT data collected across different locations, the SP dynamically negotiates and rescales bandwidth and service functions. 5G network slicing functions are key to lease appropriate amount of resources over heterogeneous access technologies and different site types. Also, different infrastructure providers will charge slicing service depending on specific access technology supported across sites and IoT data collection patterns. We introduce a pricing mechanism based on Age of Information (AoI) to reduce the cost of SPs. It provides incentives for devices to smooth traffic by shifting part of the traffic load from highly congested and more expensive locations to lesser charged ones, while meeting QoS requirements of the IoT service. The proposed optimal pricing scheme comprises a two-stage decision process, where the SP determines the pricing of each location and devices schedule uploads of collected data based on the optimal uploading policy. Simulations show that the SP attains consistent cost reductions tuning the trade-off between slicing costs and the AoI of uploaded IoT data.