Miracle-dz/UniFAR-FTUs huggingface dataset:
CURETO: Skin Diseases Detection Using Image Processing And CNN

Busy lifestyles these days have led people to forget to drink water regularly which results in inadequate hydration and oily skin, oily skin has become one of the main factors for Acne vulgaris. Acne vulgaris, particularly on the face, greatly affects a person’s social, mental wellbeing and personal satisfaction for teens. Besides the fact that acne is well known as an inflammatory disorder, it was reported to have caused serious long-term consequences such as depression, scarring, mental illness, including pain and suicide. In this research work, a smartphone-based expert system namely “Cureto” is implemented using a hybrid approach i.e. using deep convolutional neural network (CNN) and natural language processing (NLP). The proposed work is designed, implemented and tested to classify Acne density, skin sensitivity and to identify the specific acne subtypes namely whiteheads, blackheads, papules, pustules, nodules and cysts. The proposed work not only classifies Acne Vulgaris but also recommends appropriate treatments based on their classification, severity and other demographic factors such as age, gender, etc. The results obtained show that for Acne type classification the accuracy ranges from 90%-95% and for Skin Sensitivity and Acne density the accuracy ranges from 93%-96%.
A Method for Extracting Keywords from English Literature Based on Location Feature Weighting

Natural language processing (NLP) is a frontier technology in the field of artificial intelligence. Keywords extraction is a key link in NLP and plays an important role in NLP. TFIDF algorithm is considered as the most important invention in information mining. This paper uses the position characteristics of words in the title and full text in the text, and makes weighted improvement on the basis of TFIDF algorithm to improve the accuracy of keyword extraction. In this paper, 400 articles of ACM were used as the training data set, 40 articles as the test set, and accuracy rate, recall rate and F1 value were used as the evaluation criteria. Experimental data show that this method improves the accuracy of keyword extraction and improves the performance of the original algorithm.
Wideband optical amplifier based on Tm:Er:Yb:Ho co-doped germanate glass

In this paper, an innovative Tm:Er:Yb:Ho co-doped germanate fiber glass amplifier, pumped at λp = 980 nm, is designed and optimized, to obtain a wideband/three band amplification in the wavelength range λ = 1.4 – 2.1 μm. A preliminary investigation has shown the dependence of the optical gain on the dopant concentrations, the fiber length and the pump power. To obtain a suitable three-band gain, a Particle Swarm Optimization (PSO) algorithm is applied in order to perform a global optimization.
Three-Port Zero-Power RFID Flood Sensor for IoT Applications

In this paper, a novel battery-less RFID based wireless sensor node is proposed. The proposed wireless sensor is completely passive and works by combining UHF RFID and a capacitance-based flood sensor. The capacitive sensor passively alters the phase of the backscattered RFID signal based on the level of the water. This phase change is easily detected at the receiver by a non-coherent IQ demodulator. The passive nature of this sensor avoids deteriorating the read range of a regular UHF RFID tag, making it ideal for home and industrial based applications.
Numerical Analysis of Reactive Power Distribution between Two Coupled Coils by Poynting Vector

This paper analyses the reactive power distribution of an inductive power transfer (IPT) system with two coupled coils based on the Poynting vector. The imaginary part of the complex Poynting vector is analyzed by the cross-product of the magnetic and electric field at an arbitrary point to represent the reactive power density. The system is modelled with a time-varying current flowing through the primary coil, and the reactive power distribution is analyzed under both no-load and loaded conditions. Based on general theoretical analysis, three-dimensional numerical analysis by Computer Simulation Technology (CST) software was conducted to show the reactive power density distribution. It has found that the imaginary components of the Poynting vectors concentrate around the primary coil when the secondary is open-circuited without active power flow. When the secondary is connected with a uniformly distributed resistive load, the results show that the imaginary parts of the Poynting vectors concentrate along with the primary and the secondary coil respectively. This work is useful for understanding how the reactive power distributes and circulates between the two coils of an IPT system.
Ringing Test for Third-Order Ladder Low-Pass Filters

A proposed ringing test for 3rd-order ladder low-pass filters is presented. As a high-order network operates in under-damping, ringing occurs and makes the system unstable. The alternating current conservation technique is proposed to measure the self-loop function in a transfer function. The values of theoretical calculation are verified by the SPICE simulation and practical measurement results.
Intelligent Fruit Maturity Assessment Platform Using Convolutional Neural Network: IFMAP

As the fight against climate change and the environmental crisis continues, the need for solutions to reduce our carbon footprint increases dramatically. In this paper, we propose a platform to help prevent food waste among Canadian households using an innovative artificial intelligence (AI) system that implements Convolutional Neural Networks (CNN) to analyse pictures of fruits taken with cell phones to determine their current maturity state. Following these results, we have created a native mobile application for the platform which is detailed in the article. We also explain the overall workflow of the platform as well as the different neural networks used and tested for this task. Afterwards, we discuss the use of a wide range of sensors to give more insights about the fruit to our neural networks. We then show a use case of this application on a banana, which was analysed accurately by the neural networks. Finally, we display different results we obtained for the CNNs such as the top performance of different architectures, which can classify the data up to the accuracy of 97.11%. We also demonstrate that a CNN (LeNet) training time is three times faster on a GPU compared to a CPU.
Automatic Extraction of Cause-Effect-Relations from Requirements Artifacts

Background: The detection and extraction of causality from natural language sentences have shown great potential in various fields of application. The field of requirements engineering is eligible for multiple reasons: (1) requirements artifacts are primarily written in natural language, (2) causal sentences convey essential context about the subject of requirements, and (3) extracted and formalized causality relations are usable for a (semi-)automatic translation into further artifacts, such as test cases. Objective: We aim at understanding the value of interactive causality extraction based on syntactic criteria for the context of requirements engineering. Method: We developed a prototype of a system for automatic causality extraction and evaluate it by applying it to a set of publicly available requirements artifacts, determining whether the automatic extraction reduces the manual effort of requirements formalization. Result: During the evaluation we analyzed 4457 natural language sentences from 18 requirements documents, 558 of which were causal (12.52%). The best evaluation of a requirements document provided an automatic extraction of 48.57% cause-effect graphs on average, which demonstrates the feasibility of the approach. Limitation: The feasibility of the approach has been proven in theory but lacks exploration of being scaled up for practical use. Evaluating the applicability of the automatic causality extraction for a requirements engineer is left for future research. Conclusion: A syntactic approach for causality extraction is viable for the context of requirements engineering and can aid a pipeline towards an automatic generation of further artifacts from requirements artifacts.
Simulation Study using Average Difference Algorithm to Analyze the Outlierness Degree of Spatial Observations

Attribute values are the main elements in calculating degree of outlierness of spatial objects. The problem arises when the spatial outliers with extreme values are the nearest neighbors of a central object. In this study, several scenarios are simulated to verify the effect of spatial outliers’ extreme values to the degree of outlierness of its nearest neighbors, based on Average Difference Algorithm. The results confirmed the effect can lead to falsely detected spatial outliers. The algorithm detect the true spatial outliers correctly if their values are three sigma away from the mean attribute values of its nearest neighbors.
Recent Development of Security Issues of Black Hole and Gray Hole Attacks in V2X Network

V2X (Vehicle-to-X) network is an important support to automated vehicle. However, for automated vehicle safety, if the security of V2X network cannot be guaranteed, automated vehicles may not obtain required correct information, which is a fatal problem. In V2X network, black hole and gray hole attacks are critical threats of network availability and may prevent legitimate users from receiving packets. In this review, the definition, classification and impact of the attacks on V2X network are presented, and the detection and prevention technologies are classified and analyzed, respectively. Finally, three open issues and future trends are proposed, which are considering specific characters of V2X network, paying attention to artificial intelligent technologies in detection and utilizing edge computing architecture as the local trusted environment in detection and prevention.
Improving Indoor Positioning Systems Accuracy in Closed Buildings with Kalman Filter and Feedback Filter

In recent years, location-based technology has been widely used, one of which is the Indoor Positioning System (IPS). This technology is specifically designed for indoor use. IPS technology can be used for various purposes in various fields, for example to find the location of doctors in hospitals, to help find routes at department stores and exhibitions, or even to find objects in a room. In a closed room, sometimes the signal strength becomes unstable due to several effects called multipath and shadowing. This signal instability can cause a decrease in the position accuracy level. Filtering can be used as a solution. This research compares the performance of several filtering methods in reducing RSSI signal fluctuations so as to improve positioning accuracy. Two different Kalman Filters and the Feedback Filter are used as a comparison. The accuracy performance of each filtering method are compare. In addition the effect of multipath and shadowing are considered. The filtering method delay are also analysed. The results show that a modified Kalman Filter method has the highest accuracy in improving the IPS performance with an MSE value of 1.09.
Connectivity resilience assessment of urban road networks under earthquake based on Bayesian network

In view of the fact that existing metrics such as reliability and vulnerability cannot effectively and comprehensively describe road network performances under sudden major disruptive events, a connectivity resilience metric together with two resilience-based component importance measures are proposed. Bayesian network (BN) is served as a modelling tool which is used to assess the road network resilience and component importance under different earthquake magnitudes, and a case study is conducted on the Nguyen and Dupuis network. The results show that the higher the earthquake level, the lower the system resilience. The road network resilience can be deduced from links resilience and critical links can be found by BN. Two resilience-based link importance measures can effectively rank the importance of links, and the importance ranks of most links change over time. All these conclusions verify the feasibility of the proposed model.
Hierarchical Joint Control for Urban Mixed-Autonomy Traffic Optimization

With the fast development of autonomous vehicle technologies, the vehicle fleet will be made up of a mixture of human-driven vehicles and autonomous vehicles (AVs) in the coming 20–30 years. To efficiently utilize abundant data to deal with the mixed-autonomy traffic control problem, this paper formulates and approaches the problem using a deep reinforcement learning framework (DRL). DRL is a promising data-driven approach for traffic signal control and AVs control in a large-scale grid. However, traffic control based DRL is quite challenging since the complexity of control and the large search space of the policy. To deal with these issues, we propose a hierarchical joint control framework based on prior knowledge. Specifically, traffic signals at intersections and AVs are controlled by their local controllers, set according to well-adjusted policies; while the coordination of the traffic signals at intersections and the coordination among AVs are determined by two master controllers, respectively. Thus, the control of the whole grid is handled by two master controllers. In this way, the dimension of the action space is greatly decreased and the control operates much smoother. We verify our method by implementing a series of experiments in SUMO. The numerical experiments demonstrate the potential of the mixed-autonomy traffic control, compared with traditional traffic signal systems without AVs. We also demonstrate that our method is easy to train and operates robustly.
Self-Interference Cancellation Based Hybrid Beamforming Design for Full-Duplex mmWave Communication with Partially-Connected Structures

The ever increasing demand for throughputs in next-generation wireless communication systems has been put forward for satisfying the exponentially incremental data transmissions. Millimeter wave (mmWave), massive multipleinput multiple-output (MIMO) and full-duplex (FD) are three disruptive technologies to meet the throughputs requirement, which can supply abundant spectral resources, provide multiplexing gain in space, as well as allow simultaneous transmissions, respectively. In this paper, we consider combining these three enablers to form a FD mmWave massive MIMO communication system. Due to the high costs and power consumption of the massive amount of radio-frequency chains components, especially when working on mmWave frequencies, as well as the strong self-interference (SI) that significantly degrades the performance, we focus on the SI cancellation based hybrid beamforming design for the FD mmWave system with partially-connected structures. Resorting to null space projection, we propose our hybrid beamforming design to cancel the SI as well as consider the spectral efficiency (SE) performance. Numerical results verify that our proposed hybrid beamforming design can indeed improve the SE performance when the signal-to-noise ratio (SNR) and the number of RF chains are large enough.
The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing

Enabling autonomous driving (AD) can be considered one of the biggest challenges in today? s technology. AD is a complex task accomplished by several functionalities, with environment perception being one of its core functions. Environment perception is usually performed by combining the semantic information captured by several sensors, i.e., lidar or camera. The semantic information from the respective sensor can be extracted by using convolutional neural networks (CNNs) for dense prediction. In the past, CNNs constantly showed stateof-the-art performance on several vision-related tasks, such as semantic segmentation of traffic scenes using nothing but the red-green-blue (RGB) images provided by a camera. Although CNNs obtain state-of-the-art performance on clean images, almost imperceptible changes to the input, referred to as adversarial perturbations, may lead to fatal deception. The goal of this article is to illuminate the vulnerability aspects of CNNs used for semantic segmentation with respect to adversarial attacks, and share insights into some of the existing known adversarial defense strategies. We aim to clarify the advantages and disadvantages associated with applying CNNs for environment perception in AD to serve as a motivation for future research in this field.
Prober: Practically Defending Overflows with Page Protection

Heap-based overflows are still not completely solved even after decades of research. This paper proposes Prober, a novel system aiming to detect and prevent heap overflows in the production environment. Prober leverages a key observation based on the analysis of dozens of real bugs: all heap overflows are related to arrays. Based on this observation, Prober only focuses on array-related heap objects, instead of all heap objects. Prober utilizes static analysis to label all susceptible call-stacks during the compilation, and then employs the page protection to detect any invalid accesses during the runtime. In addition to this, Prober integrates multiple existing methods together to ensure the efficiency of its detection. Overall, Prober introduces almost negligible performance overhead, with 1.5% on average. Prober not only stops possible attacks on time, but also reports the faulty instructions that could guide bug fixes. Prober is ready for deployment due to its effectiveness and low overhead.
NotMle: Community Detection in an Inference Way

Learning the node-to-community partition of network (aka. community detection) lies at the core of network analysis, which have proven to be successful for various tasks. How to improve community detection results via using various features is a challenging task. Existing community detection models integrate links and attributes information of each node in a comprehensive manner, towards a single aspect to represent the node's general role. Yet, a real-world entity could be multifaceted, where it connects to different domains due to different motives that are not necessarily correlated. In this paper, we propose a practical framework for community detection based on attribute and structure information for large networks, namely, Non-exhaustive overlapping community detection in attributed networks via two-stage Maximum likelihood estimation, (for short NotMle). The attribute information can be effectively captured and transformed by attribute embedding to encode the combination with structure information. Then, the link strength among communities is designed to adjust the impact of these structural information on community generation based on the contribution of the structure to the clusters, and the node assignment allow for the nature of the real network (overlapping and outliers). Experimental results on several benchmarks demonstrate encouraging results while learning an interpretable latent structure.
Properties Of Ultra-Thin Radio-frequency Sputtered Aluminum Doped Zinc Oxide Thin Films For Solar Cell Applications

We optimized radio-frequency (RF) sputtering parameters for depositing low-thickness (ca 100 nm) aluminum-doped zinc oxide (AZO) for solar cell applications aimed at reducing optical losses commonly associated with transparent conducting oxides (TCO), which causes a significant reduction in the efficiency. X-ray diffraction (XRD) analysis and high-resolution scanning electron microscopy (HR-SEM) aided in studying the variations in the structure and morphology with deposition parameters and temperature. The optical characterizations using UV-Vis spectral measurements showed above 90% transmittance, with resistivity characterizations using four-point probe and Hall measurements. We defined a quality factor (QF) to characterize the performance of TCO layers, considering equally the resistivity and transmittance as they play vital roles in their TCO applications. The reported parameters produced films with lower resistivities and high QF for TCO applications with no use for post-deposition temperature treatments.
A Small Sample Face Recognition Method Based on Deep Learning

Face recognition is a hot topic in the field of artificial intelligence in recent years. In many practical applications, face data acquisition is difficult, the accuracy of face recognition based on small samples can’t reach a satisfactory accuracy. To solve this problem, a lightweight convolutional neural network (LWCNN) base on deep learning is built for small sample data. In LWCNN, two convolutional blocks are used to extract the features of the input face image. Then the k-fold cross validation is adopted to verify the robustness of the network. The simulation results show that compared with other three methods, our proposed LWCNN achieves better recognition accuracy in small sample space and avoids the over-fitting problem to a certain extent.
LTE Base Station Congestion Mitigation using Game Theory and Radio Resource Allocation Techniques

This paper proposes the implementation of an LTE base station congestion mitigation algorithm that is developed based on the principles of non-cooperative game theory. This algorithm provides a unique game model for radio resource allocation as well as congestion mitigation in an LTE network. The game theoretic controller’s congestion management capability is compared with other LTE based congestion mitigation models such as Discard Timer and Random Early Detection in an end to end wireless network. The transport layer protocol for user packets considered is Transmission Control Protocol. This study focuses on users with low mobility or connected in a fixed wireless network. The results from this study suggest that congestion management using non-cooperative game theory can be successfully implemented and it provides a more robust alternative to traditional congestion mitigation models such as Discard Timer or Random Early Detection for LTE networks.
Identification of Key Nodes Based on Integrating of Global and Local Information

Identification of key nodes is a significant issue in the research of complex networks such as communication networks, power networks, etc. Identifying key nodes of complex networks and protecting them in a targeted manner can improve the security of the network. The traditional key nodes identification algorithms only concern a one-sided characteristic of nodes, usually, a specific algorithm is only suitable for a specific type of network. In this paper, we propose an algorithm SDB based on the measure of the degree and betweenness, which represents both the local features and the global features. This algorithm integrates the degree and betweenness centrality through the connection strength between nodes, so as to calculate the importance values of nodes and rank them. We evaluate the network performance after node removal in terms of network susceptibility value and network efficiency decline rate etc. The experimental results show that our proposed SDB algorithm helps find key nodes effectively in various types of complex networks, such as communication, transportation, biological and artificially generated networks, etc. The network can be divided into multiple clusters faster after removing these key nodes, which seriously damages the network connection.