Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation and Classification of the Brain Tumor MRI using Machine Learning Technique

The proposed work implements a Machine-Learning-Technique (MLT) to evaluate and classify the tumor regions into low/high grade based on the analysis carriedout with the brain MRI slices. The MLT implements a sequence of procedures, such as pre-processing, post-processing and classification procedures. The pre-processing enhances the tumor section based on Social Group Optimization (SGO) algorithm assisted Fuzzy-Tsallis thresholding. The robustness of the proposed thresholding is also confirmed by considering the noise corrupted MRI slices. The post-processing implements the Level-Set Segmentation (LSS) to mine the tumor region. The performance of the LSS is validated with segmentation procedures, like Active-Contour (ACS) and Chan-Vese (CVS) technique. The fundamental data of the tumor section is then extracted using the Gray Level Co-occurrence Matrix (GLCM) and most dominating features are then chosen with a statistical test. Finally, a two-class classifier is implemented using the Support Vector Machine with Radial Basis Function (SVM-RBF) kernel and its performance is then validated with other classifiers, like the Random-Forest and k-Nearest Neighbor. The outcome of the proposed work confirms that, implemented tool with the SVM-RBF helps to achieve an accuracy of >94% on the benchmark BRATS2015 database.
Improved Positioning of Delivery Quad copter Using GPS - Accelerometer Multimode Controller and Unscented Kalman Filter

In last area of technology, the quadrotor helicopter became essential equipment of everyday man use. Delivery by quadcopter has increased significantly in the last few years in different private and military fields because of its indispensable features such as autonomous flight, vertical take-off and landing. Most quadcopters used for delivery service utilize GPS signal for its autonomous flight to control itself and follow the desired path to the destination. Despite the prevalence of autonomous flight, it produces many challenges. GPS signal denial is one of the most critical challenges that may occur. GPS signal denial problem leads to affect the quadcopter control and potentially get lost. Many studies have tried to provide a solution toward this issue, yet, most solutions focused on specific applications and terms rather than achieving a general solution. In this paper, a multimode control algorithm is proposed as a solution for GPS signal denied through the autonomous flight. The algorithm consists of two control modes: GPS and accelerometer modes with Unscented Kalman Filter (UKF). The results show that the proposed algorithm is reliable and suitable for various applications of the quadcopter in different environments. The proposed algorithm can keep the quadcopter under control during GPS signal denial period and continue moving toward the desired path.
A diversity-sensitive long-term care for gay and lesbian elders in need of care – Results of the research project GLESA / Eine diversitätssensible Pflege für schwule und lesbische Pflegebedürftige – Ergebnisse des Forschungsprojekts GLESA

Abstract This paper presents results of the research project „Gleichgeschlechtliche Lebensweisen und Selbstbestimmung im Alter“ (GLESA) by the Alice Salomon University of Applied Sciences, Berlin, and the Berlin School of Economics and Law concerning the needs of gay and lesbian elders regarding long-term care. The main focus is on the expectations and perceptions of long-term care (facilities) of the interviewees living in a gay housing project in Berlin, Germany. The study is based on 26 interviews: with eleven gay, one lesbian and three heterosexual tenants – two of them lived in a shared community with long-term care services. Another eleven interviews were conducted with experts (five cis-female, four cis-male and two transgender) working in social and health services (social workers, carers and psychologists). The data was gathered via problem-centered interviews (Witzel 2000) and analysed with Mayrings‘ (2007) qualitative content analysis. Long-term care aspects were one out of five dimensions of the analysis. The study illustrates the discomfort of LGB elders regarding regular care services. The interviewees prefer LGB(T*I)-friendly facilities, in part because they demonstrate overt signs of diversity and promise a high competence of LGB(T*I) personnel in terms of self-determination, awareness, visibility and knowledge about LGBT*I communities. Finally, the authors advocate enhancing the concept of culture-sensitive long-term care according to diversity-sensitive aspects. A better understanding of diversity will help to better consider individuality and biographies in long-term care (facilities) and to support the social inclusion of LGB(T*I) elders in need of care.
Perceptual Engineering and Related Research Based on the Design of Door and Window Hardware Handles

The main purpose of this research is to introduce the sensibility engineering to explore the relationship between the modeling style of the door and window hardware handle and the sensibility vocabulary, and through the results of this research, the design of the door and window hardware handle is more scientific. The analysis establishes a basis for its styling design and consumer perception criteria and manufacturer design. The purpose of this study is to solve the post-evaluation problem of product design. The method proposes a design evaluation method based on Analytic Hierarchy Process (AHP). Firstly, the product is decomposed into several indicators to be evaluated and scored, and the data is calculated and the weight values of each index are calculated. The overall score of the program is further ordered. The result is the accuracy of the sorting result verified by the market sales survey data of 3 door and window hardware handles, which provides a reference for the effective evaluation of product design. The conclusion is that the introduction of AHP into design evaluation can effectively reduce the disadvantages of many factors in the design evaluation, such as the factors to be evaluated, the reliance on the evaluator’s experience and knowledge, and the difficulty of judging the relationship between each factor and the final solution. Influences.
ASE-BAN, a Wireless Body Area Network Testbed

— Miniature Body Area Networks used in health care support greater mobility to patients and reduces actual hospitalization. This paper presents the preliminary implementation of a wireless body area network gateway. It is designed to implement the gateway functionality between sensors/actuators attached to the body and a host server application. The gateway uses the BlackFin BF533 processor from Analog Devices, and uses Bluetooth for wireless communication. Two types of sensors are attached to the network: an electro-cardio-gram sensor and an oximeter sensor. The testbed has been successfully tested for electro-cardio-gram data collection, and using wireless communication in a battery powered configuration.
B-TSCA: Blockchain Assisted Trustworthiness Scalable Computation for V2I Authentication in VANETs

The rapid development of 5G networks has made smart driving possible. The vehicular ad-hoc networks (VANETs) are the main environment for smart driving, providing road information, instant communication between vehicle and vehicle (V2V) or vehicle and infrastructure (V2I). The information interaction security of VANETs is critical to the proper functioning of the traffic. Much research in recent years has focused on secure communication in VANETs, especially the secure V2V or V2I communications. However, current security schemes often require complex identity re-authentication when vehicles enter a new infrastructure coverage, which greatly reduces the efficiency of the entire network. In addition, the emergence of blockchain has created opportunities to overcome the challenges in VANETs mentioned above. In this article, blockchain is utilized to enhance the scalability of the trustworthiness scalable computation. The proposed blockchain assisted trustworthiness scalable computation based V2I authentication (B-TSCA) scheme achieves rapid re-authentication of vehicles through secure ownership transfer between infrastructures. Note that, trustworthiness scalable computation assisted by blockchian technology ensures the decentralization and non tamperability of the scalable computation result. The security analysis indicates that B-TSCA scheme is a CDH-secure scheme. The time cost of the novel handover authentication phase is half of that of the initial one as is presented in the simulation.
Small Object Detection with Multiple Receptive Fields

Small object detection has been a problem in deep learning convolutional neural network models. A multi-rate dilated convolution module is proposed to form a feature map to locate small objects. Benefiting from the fact that the dilated convolution does not add extra complexity while maintaining the characteristics of the high-resolution feature map, this paper replaces the traditional convolution network by setting the dilated convolution with the ratio of 1, 2, and 5, and fuses the different convolution rate convolutions. The feature map branch, extracting high-level features, using the FPN algorithm as the basic network framework, combining a variety of receptive field feature maps, enhancing the detection ability of the algorithm for small objects, and the convergence accuracy is greatly improved. Experiments show that the proposed method achieves 81.9% MAP in the PASCAL VOC2007 dataset, which exceeds the traditional object detection algorithm.
Strong Assertions of IoT Network Access Requirements

The Manufacturer Usage Description (MUD) specification describes the access and network functionality required a device to properly function. The MUD description has to reflect the software running on the device and its configuration. Because of this, the most appropriate entity for describing device network access requirements is the same as the entity developing the software and its configuration. A network presented with a MUD file by a device allows detection of misbehavior by the device software and configuration of access control. This document defines a way to link a SUIT manifest to a MUD file offering a stronger binding between the two.
Fog‐SDN: A light mitigation scheme for DDoS attack in fog computing framework

Cloud computing is one of the most tempting technologies in today's computing scenario as it provides a cost‐efficient solutions by reducing the large upfront cost for buying hardware infrastructures and computing power. Fog computing is an added support to cloud environment by leveraging with doing some of the less compute intensive task to be done at the edge devices, which reduces the response time for end user computing. But the vulnerabilities to these systems are still a big concern. Among several security needs, availability is one that makes the demanded services available to the targeted customers all the time. Availability is often challenged by external attacks like Denial of service (DoS) and distributed denial of service (DDoS). This paper demonstrates a novel source‐based DDoS mitigating schemes that could be employed in both fog and cloud computing scenarios to eliminate these attacks. It deploys the DDoS defender module which works on a machine learning–based light detection method, present at the SDN controller. This scheme uses the network traffic data to analyze, predict, and filter incoming data, so that it can send the filtered legitimate packets to the server and blocking the rest.
WINDOWS PRIVILEGE ESCALATION THROUGH NETWORK BACKDOOR AND INFORMATION MINING USING USB HACKTOOL

A privilege escalation in the Windows system can be defined as a method of gaining access to the kernel system and allowing the user to have an administrative access to the local admin account system on the computer. This paper describes the proof of concept attack scheme using Universal Serial Bus (USB) Hacktool. The attack scheme, the same interaction on the physical access to the computer system could be accomplished by the attacker using a little effort on social engineering and specialized USB Hacktool to take over the computer system in full where it will collect valuable information and escalate the administrative privilege to gain unauthorized admin access which further attack can be done like setting up an open port for backdoor access. The evaluation of this paper gives a significant value as for educational purpose for proof of concept security project. The implementation on this project could help the responsible team to take necessary action toward physical security access to their computer or workstation.
A SIMSCAPE based design of a dual maximum power point tracker of a stand-alone photovoltaic system

This paper presents the simulation of a dual maximum power point tracker (dual-MPPT) and attempt to get the global maximum power point GMPP under partial shading conditions for a solar photovoltaic module using MATLAB SIMSCAPE. Traditional single MPP trackers are less efficient than dual MPP trackers and have greater sensitivity to partial shading. By using dual MPP trackers, one can get several features such as the possibility of connecting two arrays with different string sizes or different solar azimuths or tilts within high efficiency. This paper focuses on making the photovoltaic system work at maximum possible power under partial shading condition by using dual MPP trackers to achieve the convergence toward the global maximum power point GMPP.
Control plane hardware design for optical packet switched data centre networks

Optical packet switching for intra-data centre networks is key to addressing traffic requirements. Photonic integration and wavelength division multiplexing (WDM) can overcome bandwidth limits in switching systems. A promising technology to build a nanosecond-reconfigurable photonic-integrated switch, compatible with WDM, is the semiconductor optical amplifier (SOA). SOAs are typically used as gating elements in a broadcast-and-select (B\S it should run on packet timescales, which becomes increasingly challenging as line rates get higher. The scheduler, used for the allocation of switch paths, limits control clock speed. To this end, the research contribution was the design of highly parallel hardware schedulers for crossbar and Clos network switches. On a field-programmable gate array (FPGA), the minimum scheduler clock period achieved was 5.0~ns and 5.4~ns, for a 32-port crossbar and Clos switch, respectively. By using parallel path allocation modules, one per Clos node, a minimum clock period of 7.0~ns was achieved, for a 256-port switch. For scheduler application-specific integrated circuit (ASIC) synthesis, this reduces to 2.0~ns; a record result enabling scalable packet switching. Furthermore, the control plane was demonstrated experimentally. Moreover, a cycle-accurate network emulator was developed to evaluate switch performance. Results showed a switch saturation throughput at a traffic load 60\% of capacity, with sub-microsecond packet latency, for a 256-port Clos switch, outperforming state-of-the-art optical packet switches.
A Hybrid Approach for 3D Lung Segmentation in CT Images Using Active Contour and Morphological Operation

Lung segmentation is the initial step for detection and diagnosis for lung-related abnormalities and disease. In CAD system for lung cancer, this step traces the boundary for the pulmonary region from thorax in CT images. It decreases the overhead for a further step in CAD system by reducing the space for finding the ROIs. The major issue and challenging task for the segmentation is the inclusion of juxtapleural nodules in the segmented lungs. The chapter attempts 3D lung segmentation of CT images using active contour and morphological operations. The major steps in the proposed approach contain: preprocessing through various techniques, Otsu's thresholding for the binarizing the image; morphological operations are applied for elimination of undesired region and, finally, active contour for the segmentation of the lungs in 3D. For experiment, 10 subjects are taken from the public dataset of LIDC-IDRI. The proposed method achieved accuracies 0.979 Jaccard's similarity index value, 0.989 Dice similarity coefficient, and 0.073 volume overlap error when compared to ground truth.
Development of a hyperbolic equation solver and the improvement of the OpenFOAMR two-phase incompressible flow solver

The first part of this thesis proposed new, fully conservative and less oscillatory hyperbolic partial differential equation solvers. Based on the multi-moment method and the Constrained Interpolation Profile Conservative Semi-Lagrangian (CIP-CSL) family of schemes, a new scheme called CIP-CSL3U is introduced to combine with an existing scheme, CIP-CSL3D. Two ENO-like indicators are proposed, which are used to select during runtime a stencil that can efficiently minimise numerical oscillation as well as numerical diffusion. The proposed schemes (CIP-CSL3DU and CIP-CSL3ENO) are validated using various benchmark problems. Discontinuities, as well as smooth solutions, are captured simultaneously with almost no numerical oscillation for nonsmooth solutions. Benchmark tests also show that the results are fourth-order accurate for smooth solutions, and can be applied to compressible and incompressible fluid flow problems. The second part of this work concerns the improvement of the two-phase incompressible flow solver in OpenFOAM. A geometric Level Set method is implemented to couple with a Volume-of-Fluid solver in OpenFOAM. An interface reconstruction algorithm based on cell tetrahedralisation is implemented to work on 2D and 3D unstructured meshes, on serial as well as parallel. The Coupled Level Set Volume-of-Fluid (CLSVOF) solver is validated against scalar transport problems on various mesh types in 2D and 3D. Results indicate a significant improvement over the standard OpenFOAM solver interFoam and some advantAbstract iii age over the newer OpenFOAM solver, interFlow. Mass conservation properties of the VOF method are also retained. The CLSVOF solver is then used to simulate fluid flows with surface tension effects, showing better agreement with experiments and reference solutions compared to standard OpenFOAM solvers. Simulations indicated that CLSVOF could handle complex fluid flows with surface tension dominance as well as with high density ratios. The calculation of curvature using the Level Set field contributed to the improvement in simulations. A simulation of a liquid jet in a gaseous crossflow also showed reasonable agreement with empirical models with some breakup details captured. Key words: level-set, volume-of-fluid, ENO, CIP-CSL, OpenFOAM
Wireless Communications and Networking for Unmanned Aerial Vehicles

A thorough treatment of UAV wireless communications and networking research challenges and opportunities. Detailed, step-by-step development of carefully selected research problems that pertain to UAV network performance analysis and optimization, physical layer design, trajectory path planning, resource management, multiple access, cooperative communications, standardization, control, and security is provided. Featuring discussion of practical applications including drone delivery systems, public safety, IoT, virtual reality, and smart cities, this is an essential tool for researchers, students, and engineers interested in broadening their knowledge of the deployment and operation of communication systems that integrate or rely on unmanned aerial vehicles.
Integration designs toward new‐generation wearable energy supply‐sensor systems for real‐time health monitoring: A minireview

Wearable sensing systems, as a spearhead of artificial intelligence, are playing increasingly important roles in many fields especially health monitoring. In order to achieve a better wearable experience, rationally integrating the two key components of sensing systems, that is, power supplies and sensors, has become a desperate requirement. However, limited by device designs and fabrication technologies, the current integrated sensing systems still face many great challenges, such as safety, miniaturization, mechanical stability, energy-efficiency, sustainability, and comfortability. In this review, the key challenges and opportunities in the current development of integrated wearable sensing systems are summarized. By summarizing the typical configurations of diverse wearable power supplies, and recent advances concerning the integrated sensing systems driven by such power supplies, the representative integrated designs, and micro/nanofabrication technologies are highlighted. Lastly, some new directions and potential solutions aiming at the device-level
Attention Privileged Reinforcement Learning For Domain Transfer

Applying reinforcement learning (RL) to physical systems presents notable challenges, given requirements regarding sample efficiency, safety, and physical constraints compared to simulated environments. To enable transfer of policies trained in simulation, randomising simulation parameters leads to more robust policies, but also significantly extends training time. In this paper, we exploit access to privileged information (such as environment states) often available in simulation, in order to improve and accelerate learning over randomised environments. We introduce Attention Privileged Reinforcement Learning (APRiL), which equips the agent with an attention mechanism and makes use of state information in simulation, learning to align attention between state- and image-based policies while additionally sharing generated data. During deployment we can apply the image-based policy to remove the requirement of access to additional information. We experimentally demonstrate accelerated and more robust learning on a number of diverse domains, leading to improved final performance for environments both within and outside the training distribution.
Natural Key Discovery in Wikipedia Tables

Wikipedia is the largest encyclopedia to date. Scattered among its articles, there is an enormous number of tables that contain structured, relational information. In contrast to database tables, these webtables lack metadata, making it difficult to automatically interpret the knowledge they harbor. The natural key is a particularly important piece of metadata, which acts as a primary key and consists of attributes inherent to an entity. Determining natural keys is crucial for many tasks, such as information integration, table augmentation, or tracking changes to entities over time. To address this challenge, we formally define the notion of natural keys and propose a supervised learning approach to automatically detect natural keys in Wikipedia tables using carefully engineered features. Our solution includes novel features that extract information from time (a table’s version history) and space (other similar tables). On a curated dataset of 1,000 Wikipedia table histories, our model achieves 80% F-measure, which is at least 20% more than all related approaches. We use our model to discover natural keys in the entire corpus of Wikipedia tables and provide the dataset to the community to facilitate future research.