Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Applied for Histological Diagnosis of Breast Cancer

Deep learning, as one of the currently most popular computer science research trends, improves neural networks, which has more and deeper layers allowing higher abstraction levels and more accurate data analysis. Although deep convolutional neural networks, as a deep learning algorithm, has recently achieved promising results in data analysis, the requirement for a large amount of data prevents its use in medical data analysis since it is challenging to obtain data from the medical field. Breast cancer is a common cancer in women. To diagnose this kind of cancer, breast cell shapes in histopathology images should be examined by senior pathologists. The number of pathologists per population in the world is not enough, especially in Africa, and human mistake may occur in diagnosis procedure. After the evaluation of deep learning methods and algorithms in breast histological data processing, we tried to improve the current systems’ accuracy. As a result, this study proposes two effective deep transfer learning-based models, which rely on pre-trained DCNN using a large collection of ImageNet dataset images that improve current state-of-the-art systems in both binary and multiclass classification. We transfer pre-trained weights of the ResNet50 and DesneNet121 on the Imagenet as initial weights and fine-tune these models with a deep classifier with data augmentation to detect various malignant and benign samples tissues in the two categories of binary classification and multiclass classification. The proposed models have been examined with optimized hyperparameters in magnification-dependent and magnification-independent classification modes. In the multiclass classification, the proposed system achieved up to 98% accuracy. As for binary classification, the proposed system provides up to 100% accuracy. The results outperform previous studies accuracies in all defined performance metrics in breast cancer CAD systems from histological images.
Robotic Swarm Control for Precise and On-Demand Embolization

Existing approaches for robotic control of magnetic swarms are not capable of generating magnetic aggregates precisely in an arbitrarily specified target region in a fluidic flow environment. Such a swarm control capability is demanded by medical applications such as clinical embolization (i.e., localized clogging of blood vessels). This paper presents a new magnetic swarm control strategy to generate aggregates only in a specified target region under fluidic flow. Within the target region, the magnetic field generates sufficiently large magnetic forces among magnetic particles to maintain the aggregates’ integrity at the junctions of blood vessels. In contrast, unintended aggregates outside the target region are disassembled by fluidic shear. The aggregation control approach achieved a mean absolute error of 0.15 mm in positioning a target region and a mean absolute error of 0.30 mm in controlling the target region’s radius. With thrombin coating, 1 µm magnetic particles were controlled to perform embolization both in vitro (using microfluidic channel networks) and ex vivo (using porcine tissue). Experiments proved the effectiveness of the swarm control technique for on-demand, targeted embolization.
The impact of Industry 4.0 on work: A synthesis of the literature and reflection about the future

Industry 4.0 is a result of technological evolution and is intended to promote technological transformations in industry at different levels. The impact in human employment has been perceived as a major threat and is a matter of concern. Some authors argue that automation will bring unimaginable changes as soon as computers get more intelligence and as machines become able to perform complex tasks more efficiently than humans. However, technological progress is also pointed out as a stimulus for human-beings to develop the competencies that differentiate them from the machines. In this context, this study aims to explore the impacts of adopting Industry 4.0 technologies on work. The results of a comprehensive literature review provide an integrated perspective to identify and understand such impacts, analysing them in four categories: evolution of employment and creation of new jobs, human-machine interaction, new competencies creation / development, and, organizational and professional changes
Clustering on Ranked Data for Campaign Selection

Recently the ranked data are commonly seen in the era of Internet and e-commerce where the consumers give their opinion in the form of ranks of a set of items online. The consumers are asked to put the ranks on items according to their order of preference. The applications of clustering ranked data are target marketing, campaign selection, top $k$ -items, etc. The objective of this paper is to implement campaign selection process using clustering feedback data which are in rank ordered. To implement the proposed method we divide our experiments into two parts first, group the ranked data (consumer feedback), by applying different distance calculations, e.g., Kendall’s tau, Spearman’s rho square, Spearman’s footrule, Cayley’s distance. Second, use the knowledge derived from the groups in campaign selection process. We have compared our proposed clustering algorithm with existing algorithms on different real datasets, and results showed the effectiveness of our proposed algorithm.
A Survey of IoT Routing Protocols based on Security and Trust Management

Internet of Things can be viewed as wide spread network of physical objects in which devices have sensing and interacting interfaces in-built like as Global Positioning System (GPS),Sensors, LAN interfaces and Radio Frequency Identification Devices (RFID) etc. These objects or things managed, monitored and controlled remotely as these nodes are connected to the internet. It is guesstimate that 50 to 60 billion wireless objects will connect to IoT network by 2020. The Internet of things is the next step advancement in mobile and wireless technology that is beneficial to different aspects such as business, medical, transportation etc. Obviously, Security is the major factor during use of IoT applications. Despite, Heterogeneity nature of IoT devices makes privacy and security of devices and user’s information is a critical challenge that need to be addressed. As large amount of data generated by communications between users and objects make harder to secure routing and management systems. This paper is providing a comprehensive study of architecture of IoT, existing secure and trusted routing protocols. Furthermore, this survey is analyzing weaknesses and threats in existing routing approaches as well as critical challenges in secure and trustworthy routing in IoT for future research.
Wireless Power Transfer to Multiple Loads Over a Long Distance With Load-Independent Constant-Current or Constant-Voltage Output

The regulated output characteristic is crucial for a wireless power transfer (WPT) system. Most studies focus on the single-load system or the system with multiple evenly distributed loads; however, the loads might distribute in space at different distances from the power supply in practice. In this article, a WPT hybrid repeater system is proposed to power multiple loads over a long distance. The output mode of each load in the system can be switched separately to realize the load-independent constant-current (CC) or constant-voltage (CV) output. Based on four compensation network combinations of a two-load WPT repeater system with CC/CV output, the structure of the $N$ -load WPT hybrid repeater system with load-independent CC/CV output is derived. With the ZPA input characteristic, fixed operating frequency, and high system efficiency, the proposed system can be easily used in various medium- or low-power applications. The effects of system parameters on output characteristics, power transfer capability, and system efficiency are investigated. A three-load experimental prototype was built to verify the theoretical analysis. Measurement results were in good agreement with the analysis, and the maximum measured efficiency reached 83%.
Uncertainty-Based Adaptive Sensor Fusion for Visual-Inertial Odometry under Various Motion Characteristics

We propose an uncertainty-based sensor fusion framework for visual-inertial odometry, which is the task of estimating relative motion using images and measurements from inertial measurement units. Visual-inertial odometry enables robust and scale-aware estimation of motion by incorporating sensor states, such as metric scale, velocity, and the direction of gravity, into the estimation. However, the observability of the states depends on sensor motion. For example, if the sensor moves in a constant velocity, scale and velocity cannot be observed from inertial measurements. Under these degenerate motions, existing methods may produce inaccurate results because they incorporate erroneous states estimated from non-informative inertial measurements. Our proposed framework is able to avoid this situation by adaptively switching estimation modes, which represents the states that should be incorporated, based on their uncertainties. These uncertainties can be obtained at a small computational cost by reusing the Jacobian matrices computed in bundle adjustment. Our approach consistently outperformed conventional sensor fusion in datasets with different motion characteristics, namely, the KITTI odometry dataset recorded by a ground vehicle and the EuRoC MAV dataset captured from a micro aerial vehicle.
Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest, were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications have significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.
An Energy-Efficient Service Scheduling Algorithm in Federated Edge Cloud

Federated edge cloud (FEC) is an edge cloud environment where multiple edge servers in a single administrative domain collaborate together to provide real-time services. This environment reduces the possibility of violating the quality of service (QoS) requirements of target services by locating delay-sensitive services at nearby edge servers instead of deploying them on the cloud. However, as the number of edge servers increases, the amount of energy consumed by servers and network switches also increases. This creates another challenge for how to schedule delay-sensitive services over FEC, while minimizing the total energy consumption and reducing the QoS violation of a service at the same time. This paper proposes an energy-efficient service scheduling algorithm in FEC. The proposed algorithm is based on an observation that as the number of edge servers along the service path is reduced, the total energy consumption can be minimized. Traditional approaches place services using their maximum traffic requirements to ensure QoS without considering the actual traffic change. In contrast, the proposed algorithm schedules them with actual traffic requirements to increase the number of services co-located in a single server. This maximizes the consolidation of services in a single server and thus minimizes the energy consumption. Moreover, when edge servers are overloaded, the proposed algorithm reconfigures the service path such that service migration overhead and energy consumption are minimized while guaranteeing the QoS requirements of services. The simulation results show that the proposed algorithm improves energy efficiency by up to 21% and lowers the service violation rate by up to 80% against existing approaches.
Too Close to Comfort? A New Approach of Designing a Soft Cable-Driven Exoskeleton for Lifting Tasks under Ergonomic Aspects

To this point, available exoskeletons for industrial applications still lack a broad acceptance by the users on the shop floor, allegedly due to discomfort and restraining of movements. Exoskeletons are in close physical interaction with the user. For an everyday use at work, the kinematic chain of the human and the exoskeleton have to meet the needs of every possible user in terms of high usability and a positive user experience. Focusing on aspects like users' wearing comfort, reduction of interaction forces and an easy set-up, a new concept for an exoskeleton that supports the elbow movement during lifting tasks was developed. To avoid misalignments between the exoskeleton and the human, and to allow a full range of movement, a soft cable-driven structure was chosen. In an iterative design process, a basic structure made of a rather stiff fabric with elastic inlays was developed. The cut is meant to suit a wide range of anthropometric measures while ensuring a tight fit for a good transfer of forces. Using soft materials and cables poses a challenge for calculating, simulating and measuring the force distribution not only in the exoskeleton, but also in the human tissue and bones. Therefore, a suitable model of the kinematic human-machine-chain and a method for testing the new concept were developed. Since an ergonomic design and the users' needs were of high priority in the design process, the robustness and the maximum load capacity of the system are initially left out of this concept. In this paper, the design of the soft fabric-based structure will be presented as well as the kinematic design of the cable train and the implementation thereof.
A New High-Frequency Iron Loss Model Including Additional Iron Losses due to Punching and Burrs’ Connection

A variable magnetic permeability is introduced to the skin effect coefficient to reflect the influence of flux density variation on eddy current losses. The evaluation results show that the new iron loss model can cover a wide range of frequencies without using piecewise functions. Two types of punched edges are defined and the additional iron losses are tested, separated, and modeled, so that the punching and burrs’ connection effects on iron losses can be considered more accurately. The iron loss model proposed in this article is very useful for a more accurate design of high-speed motors including the punching effects.
Correspondence Identification in Collaborative Robot Perception through Maximin Hypergraph Matching

Correspondence identification is an essential problem for collaborative multi-robot perception, with the objective of deciding the correspondence of objects that are observed in the field of view of each robot. In this paper, we introduce a novel maximin hypergraph matching approach that formulates correspondence identification as a hypergraph matching problem. The proposed approach incorporates both spatial relationships and appearance features of objects to improve representation capabilities. It also integrates the maximin theorem to optimize the worst-case scenario in order to address distractions caused by non-covisible objects. In addition, we design an optimization algorithm to address the formulated non-convex non-continuous optimization problem. We evaluate our approach and compare it with seven previous techniques in two application scenarios, including multi-robot coordination on real robots and connected autonomous driving in simulations. Experimental results have validated the effectiveness of our approach in identifying object correspondence from partially overlapped views in collaborative perception, and have shown that the proposed maximin hypergraph matching approach outperforms previous techniques and obtains state-of-the-art performance.
A 3DCNN-LSTM Hybrid Framework for sEMG-Based Noises Recognition in Exercise

Recently, surface electromyography (sEMG) has been used to detect running-related works. sEMG provides a non-invasive and real-time method that allows quantification of muscle energy. However, noises in sEMG signals are a serious issue to be considered as these will interrupt the analysis of muscular activity. Hence, this work aims at distinguishing between sEMG valid signals and noises during running exercise by taking advantage of the combination of 3D-CNN and LSTM, which we called 3D-LCNN. Furthermore, according to the possible cases that happen in the sEMG data-collection procedure, we proposed two data-augmentation approaches to expend our sEMG dataset, which are the simulation of the surface electrodes displacement on the skin and the muscle fatigue. Experiment results show that the classification accuracy of the proposed 3D-LCNN can achieve 90.52%. Additionally, this work provides excellent service-oriented architecture (SOA). The recognition process can be done after the subject placed the sEMG sensors and performed a trial. Therefore, the process can help clinicians or therapists to distinguish between sEMG valid signals and noises more efficiently.
Deep Recurrent Neural Network for Bandwidth Prediction in Software Defined Data Center Networks

With the advent of 5G SDN (Software-defined networking), there has been a huge growth in demand for uninterrupted flow of data across SDN controlled devices. This is due to high bandwidth consuming applications like online games, live telecast/broadcast, video conferencing, etc., which requires continuous available bandwidth. Ensuring bandwidth availability and seamless service delivery of these applications pose great challenges. Network monitoring and load prediction would play an important role in allocating available bandwidth efficiently. However, existing art lacks prediction capability and has a significant overhead in terms of time when used in SDNs. In this paper, we propose a deep learning model using Long Short Term Memory (LSTM) to inculcate prediction capability with minimal overhead. With this built-in intelligence in routing, network and computation resources can be allocated efficiently. Our solution can predict average Tx and Rx load across a link in network based on telemetry data from data center switches like cisco nexus 9000, juniper, arista, etc. The telemetry scheme we used is push based streaming telemetry with almost negligible overhead. The proposed model predicts utilized bandwidth with about 90% accuracy. This prediction capability in networks can be exploited by service provider who in turn provides applications with higher Quality of Experience (QoE) to the end-users.
Industrial Implementation and Performance Evaluation of LSD-SLAM and Map Filtering Algorithms for Obstacles Avoidance in a Cooperative Fleet of Unmanned Aerial Vehicles

In this paper we present an industrial implementation and performance evaluation of the problem of obstacles detection by drones using autonomous navigation systems. The software module that has been developed as well as the tests conducted are part of a large industrial R&D Vitrociset project called SWARM: an AI-Enabled Command and Control (C&C) system, able to execute and review ISR missions for mini/micro cooperative fleets of heterogeneous UAVs. The presented software module, that is currently under test, has been developed to recognize obstacles and drive correctly the drones, using images acquired by low cost RGB video cameras, whose features of lightness and reduced size allow them to be installed on mini/micro UAVs. Moreover, this setup does not require special calibration and preconfiguration processes like the ones necessary for example using stereo video camera systems. The real-time recognition of obstacles in the surrounding environment has been obtained and evaluated through the implementation, performance evaluation and tests of the LSD-SLAM and map filtering algorithms; the core of the study has been realized starting from the integration of these algorithms with a simulated drone in a synthetic environment. The areas of interest have been identified through the filtering of a computer generated map: the module was then integrated into the SWARM project platform, allowing the control of a single drone's movement and making it ready for use in a cooperative fleet environment.
Algorithmics and Modeling Aspects of Network Slicing in 5G and Beyonds Network: Survey

One of the key goals of future 5G networks is to incorporate many different services into a single physical network, where each service has its logical network isolated from other networks. Besides, Network Slicing (NS) is considered as the key technology for meeting the service requirements of diverse application domains. Recently, NS faces several algorithmic challenges for 5G networks. This paper provides a review related to NS architecture with a focus on relevant Management and Orchestration (MANO) architecture across multiple domains. In addition, this survey paper delivers a deep analysis and a taxonomy of NS algorithmic aspects. Finally, this paper highlights some of the open issues and future directions.
Optical Character Recognition for Autonomous Stores

Autonomous stores are a new trend in the retail world. Vision-based product identification is one of the frequently used sensing modalities. There are different approaches and design configurations for the vision sensing but they all are based on extracting features for the product appearance, such as color, shape, dimension. None of the proposed solutions rely on reading the package label of the product being identified.In this paper, we present an Optical Character Recognition (OCR) framework, inspired by natural language processing, for package label detection and transcription from product images. To the best of the authors knowledge, this is the first autonomous store system that considers an OCR sensing modality. This is a step forward to a closer approximation to the human cognitive way of purchasing products.
Towards an Intelligent Collaborative Robotic System for Mixed Case Palletizing

In this paper, a novel human-robot collaborative framework for mixed case palletizing is presented. The framework addresses several challenges associated with the detection and localisation of boxes and pallets through visual perception algorithms, high-level optimisation of the collaborative effort through effective role-allocation principles, and maximisation of packing density. A graphical user interface (GUI) is additionally developed to ensure an intuitive allocation of roles and the optimal placement of the boxes on target pallets. The framework is evaluated in two conditions where humans operate with and without the support of a Mobile COllaborative robotic Assistant (MOCA). The results show that the optimised placement can improve up to the 20% with respect to a manual execution of the same task, and reveal the high potential of MOCA in increasing the performance of collaborative palletizing tasks.