Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolving mobile networks towards 5G. A framework to infer the state of connectivity infrastructure in dense urban areas

Despite the growing trend towards the use of big data methodologies, the application of such techniques to inform telecommunications policy is still relatively limited. Although the deployment of mobile broadband networks is primarily driven by market forces, policy-makers and governments play an important role, particularly with regards to spectrum allocation and regulation. However, decision-makers usually lack data-driven insights to guide these processes. In this paper we propose a methodology to (i) infer the state of connectivity infrastructure and (ii) understand how Mobile Network Operators (MNOs) have combined spectrum resources and network densification to deploy 4G services. The methodology draws on crowdsourced data from a mobile app, including received signal power (RSRP) and received signal quality (RSRQ). Using UK data from 2017, we apply this method to Greater London to illustrate the capabilities of the framework. The results suggest that mobile broadband networks are only capacity-constrained in dense urban areas with MNOs adopting different strategies to deal with network congestion. MNO1 and MNO3 rely on larger spectrum portfolios, while MNO2 and MNO4 depend more on network densification. Interestingly, MNOs sharing the same capacity-expansion strategy do not necessarily share sites nor have spectrum on similar frequencies. We also find that in suburban areas mobile networks are still primarily coverage-constrained, where data suggests MNOs have favoured lower frequency spectrum, while avoiding the deployment of higher frequencies for cost reasons. The implications of these findings are discussed with regard to the roll-out of 5G networks.
SISTEM NAVIGASI MOBILE ROBOT DALAM RUANGAN BERBASIS AUTONOMOUS NAVIGATION

Autonomous navigation is absolutely necessary in mobile-robotic, which consists of four main components, namely: perception, localization, path-planning, and motion-control. Mobile robots create maps of space so that they can carry out commands to move from one place to another using the autonomous-navigation method. Map making using the Simultaneous-Localization-and-Mapping (SLAM) algorithm that processes data from the RGB-D camera sensor and bumper converted to laser-scan and point-cloud is used to obtain perception. While the wheel-encoder and gyroscope are used to obtain odometry data which is used to construct travel maps with the SLAM algorithm, gmapping and performing autonomous navigation. The system consists of three sub-systems, namely: sensors as inputs, single-board computers for processes, and actuators as movers. Autonomous-navigation is regulated through the navigation-stack using the Adaptive-Monte-Carlo-Localization (AMCL) algorithm for localization and global-planning, while the Dynamic-Window-Approach (DWA) algorithm with Robot-Operating-System-(ROS) for local -planning. The results of the test show the system can provide depth-data that is converted to laser-scan, bumper data, and odometry data to single-board-computer-based ROS so that mobile-controlled teleoperating robots from workstations can build 2-dimensional grid maps with total accuracy error rate of 0.987%. By using maps, data from sensors, and odometry the mobile-robot can perform autonomous-navigation consistently and be able to do path-replanning, avoid static obstacles and continue to do localization to reach the destination point.
DeSVig: Decentralized Swift Vigilance Against Adversarial Attacks in Industrial Artificial Intelligence Systems

Individually reinforcing the robustness of a single deep learning model only gives limited security guarantees especially when facing adversarial examples. In this article, we propose DeSVig, a decentralized swift vigilance framework to identify adversarial attacks in an industrial artificial intelligence systems (IAISs), which enables IAISs to correct the mistake in a few seconds. The DeSVig is highly decentralized, which improves the effectiveness of recognizing abnormal inputs. We try to overcome the challenges on ultralow latency caused by dynamics in industries using peculiarly designated mobile edge computing and generative adversarial networks. The most important advantage of our work is that it can significantly reduce the failure risks of being deceived by adversarial examples, which is critical for safety-prioritized and delay-sensitive environments. In our experiments, adversarial examples of industrial electronic components are generated by several classical attacking models. Experimental results demonstrate that the DeSVig is more robust, efficient, and scalable than some state-of-art defenses.
Survival and Risk Analysis in MOOCS

The most significant disadvantage in the use of MOOCs is their high drop out rates, which reaches between 88.3% and 90%. This research examined the desertion from 15 MOOCs, the following factors and their relation to desertion were assessed, characteristics of the students (gender, language, educational level, age, interest in the final certificate) and aspects of the courses (data lost at the time of registration, duration of the MOOC, course discipline, number of total questions and by evaluation). The results, based on survival analysis, indicate the probability of desertion is the highest on the first and last weeks of the MOOC. Our findings show that the probability of dropout is affected by the number of questions in each evaluation, the educational level of the participants, their age and their interest in the certificate. Missing data in the registration form was found to be associated with desertion being a significant pre-dictor of risk of quitting. Finally, the lack of interest in the certificate was found to be a predictor of the highest risk of dropping out. Possible strategies to reduce the desertion behavior at MOOCs are discussed.
Survey: secure opportunistic routing protocols in wireless networks

Opportunistic routing (OR) protocols in wireless network schemes are a rich research field. These protocols select the best path in order to send information and take the broadcasting medium into consideration. OR tends to reach better reliability and performance than traditional routing (TR). However, they are both equally vulnerable to the same attacks because wireless networks might be deployed in hostile or unwatched environments. The approaches in terms of routing are mainly focusing on energy preservation, robustness, etc. However few work has been done to secure routing protocols especially in OR. In this paper, we are going to give an analysis of OR protocols their classifications as well as an overview on the security approaches available for opportunistic routing protocols.
Partially connected hybrid precoding design for millimeter wave MIMO systems

To solve the problem of spectral efficiency and energy efficiency of hybrid precoding in millimeter-wave communication system. This paper considers alternately using the minimization framework to increase beamforming gain. However, such framework because the radio frequency domain phase modulation network are using phase shifters, which result in non-convex constraints, and known solutions suffer from high computational complexity. Before using the alternate minimization architecture, it is proved that the phase angle of the ordered right singular vector of the channel matrix can be used to initialize the analog precoder, and hence, the complicated optimization procedures used to search the near-optimum analog precoding matrix can be avoided. In addition, the antenna array response vectors at the transmitter are not required. The simulation results show that the performance of the proposed algorithm is better than the traditional partial-connected algorithm and the complexity is lower, especially in the case of high signal-to-noise ratio.
Properties of Communication Graph of Academic Web

Web graph is the most popular model of real web fragments used in Web science. The study of communities in the web graph contributes to a better understanding of the organization of the web fragment and the processes taking place in it. Communities of the web-graph fragments of the real Web are often poorly differentiated and more difficult meaningful interpretation. It is proposed to select in the web graph a communication graph containing only those vertices (and arcs between them) that have counter arcs, and already in it to investigate the problem of splitting into communities. By analogy with social studies, the ties implemented through the edges in the communication graph are proposed to be called “strong” and all others “weak”. Thematic communities with meaningful interpretation are built on strong ties. At the same time, weak ties facilitate communication between sites that do not have common features in the sphere of activity, geography, subordination, etc., and basically keep the web fragments connected even in the absence of strong ties. Experiments carried out for a fragment of the academic Web of Russia show the possibility of meaningful interpretation of the results and the prospects of this approach.
Power Saving Proxies for Web Servers

Electricity is a major cost in running a data centre, and servers are responsible for a significant percentage of the power consumption. Given the widespread use of HTTP, both as a service and a component of other services, it is worthwhile reducing the power consumption of web servers. In this paper we consider how reverse proxies, commonly used to improve the performance of web servers, might be used to improve energy efficiency. We suggest that when demand on a server is low, it may be possible to switch off servers. In their absence, an embedded system with a small energy footprint could act as a reverse proxy serving commonly-requested content. When new content is required, the reverse proxy can power on the servers to meet this new load. Our results indicate that even with a modest server, we can get a 25% power saving while maintaining acceptable performance.
A Survey on Security Aspects for 3GPP 5G Networks

With the continuous development of mobile communication technologies, Third Generation Partnership Project (3GPP) has proposed related standards with the fifth generation mobile communication technology (5G), which marks the official start of the evolution from the current Long Term Evolution (LTE) system to the next generation mobile communication system (5GS). This paper makes a large number of contributions to the security aspects of 3GPP 5G networks. Firstly, we present an overview of the network architecture and security functionality of the 3GPP 5G networks. Subsequently, we focus on the new features and techniques including the support of massive Internet of Things (IoT) devices, Device to Device (D2D) communication, Vehicle to Everything (V2X) communication, and network slice, which incur the huge challenges for the security aspects in 3GPP 5G networks. Finally, we discuss in detail the security features, security requirements or security vulnerabilities, existing security solutions and some open research issues about the new features and techniques in 3GPP 5G network.
Solar fed BLDC motor drive for mixer grinder using a buck-boost converter

In existing mixer grinders, the universal motor is used due to its high starting torque than the induction motor. To achieve higher speed for smaller devices, the implementation of the universal motor becomes cheaper. The absence of brushes and the reduction of noise in the BLDC extends its lifetime, making it ideal for a mixer grinder. The solar-powered BLDC motor drive for a mixer grinder is presented in this paper. A DC-DC buck boost (BB) converter is utilised to operate the PV (photovoltaic) array at its highest power. The proposed hysteresis current (HC) control BLDC system is developed in the MATLAB. A comparison performance is demonstrated between the commercially available mixer grinder and the simulated proposed system.
Performance Analysis of Quality Measurement for Biomedical Images

With the rise used of biomedical images for diagnosis of diseases of a patient at various hospitals, the quality measurement of images is necessary for better diagnosis. The need for biomedical images quality measurement due to the transmission of images from one place to another place within hospitals or remote health centers. The lots of distortions may affect the quality of biomedical images during transmission. So, in this paper, the various image quality assessment parameters discussed for biomedical images. The paper gives information on various full reference-based objective assessment (OA) image quality measurement (FR-IQM) standards. The paper also gives performance analysis of FR-IQM standards for biomedical images.
Performance Evaluation of Vehicle Routing Heuristics

This thesis has researched the automation of performance evaluation of vehicle routing heuristics. The trade-off between solution quality, which is composed of multiple variables, and runtime make performance evaluation challenging. Therefore, it is often done by human experts. The research question of this thesis is: “How can we determine a performance measure that correctly represents the trade off between quality and runtime in vehicle routing heuristics? ”. A literature review revealed that much research was done on performance evaluation, but not on heuristics specifically. The performance profile, a cumulative distribution function, is said to reflect all major performance characteristics of a solver. This, combined with a clustering algorithm, is used in this thesis in a classifier to detect performance anomalies. The performance profile needs a performance measure, for which three options were introduced: the area under the chart, the quality at the same time and the maximum difference. Through experimentation, 18 measure configurations were tested and rated on their accuracy and apparent issues. Three of the measure configurations have promising results, with an accuracy of roughly 80%.
Peculiarities of making bolt and rivet connections with interference when assembling the aircraft MS-21

In this paper the peculiarities of installation of fasteners with tension during assembly of MS-21 aircraft are considered. In particular, we considered the aspects of the necessity of using bolt and rivet connections with tension during the assembly of the aircraft fuselage. We have developed a finite element model of the installation process with the rivet bolt in the hole of the package using the system of finite element analysis Simufact. We used the Elastoplastic Concept of Material Behaviour with Nonlinear Hardening Section. And obtained the characteristics of the stress-strain state of the bolt-ribbon joint elements at various moments of rod retraction, including at the predicted moment of shank tear off. It has been revealed that it is expedient to bring the retraction force up to the value close to destructive one. Determined the residual deformations after removal of axial load from the rod. It is proved that it is necessary to actualize the existing normative and technical documentation with specification of the value of the tool impact force on the rivet bolt rod.
The Importance of Careful Filtration Technique in Obtaining High Recovery Yields of Indium-111 OncoScint CR/OV

Objective: lndium-111 OncoScint CR/OV (satumomab pendetide) is the first FDA-approved radiolabeled monoclonal antibody for human diagnostic use. In our experience with preparing OncoScint CR/OV according to the manufacturer's package insert procedure, we have noted that the recovery of the labeled antibody after the required filtration through a 0.22-J.Lm filter was variable; sometimes filtration yields as low as 60% of the initial activity injected into the reaction vial were observed. In this study we investigated whether or not the filtration yield of the radiolabeled immunoconjugate in the injection syringe could be improved. Methods: We used either a scrupulous technique in withdrawing the material from the reaction vial into the dose syringe through the 0.22-J.Lm filter or the dilution of the radiolabeled antibody solution with saline before careful withdrawal in our attempts to improve the yield. Results: Our results indicate that it is possible to obtain a consistent recovery of greater than 80% of the labeled antibody without using any diluent if care is taken to completely remove the liquid from the reaction vial. Conclusions: In our studies performed according to the manufacturer's directions, the yield was acceptable. An improvement in recovery less than 5% was achieved with the addition of saline which was statistically significant. Therefore, we do not recommend using saline to dilute 111 ln OncoScint CR/OV before filtration.
More than tweets

The application of machine translation (MT) in crisis settings is of increasing interest to humanitarian practitioners. We collaborated with industry and non-profit partners: (1) to develop and test the utility of an MT system trained specifically on crisis-related content in an under-resourced language combination (French-to-Swahili); and (2) to evaluate the extent to which speakers of both French and Swahili without post-editing experience could be mobilized to post-edit the output of this system effectively. Our small study carried out in Kenya found that our system performed well, provided useful output, and was positively evaluated by inexperienced post-editors. We use the study to discuss the feasibility of MT use in crisis settings for low-resource language combinations and make recommendations on data selection and domain consideration for future crisis-related MT development.
Queue-Aware Beam Assignment and Rate Control for Time-Varying mm-Wave-Based Full-Duplex Small Cell Networks

This letter focuses on the resource management problem for uplink (UL) of millimeter wave based full-duplex small cell networks with consideration of user equipments’ (UEs’) queue backlog and time-varying channels. The original problem aims to maximize UL UEs’ long-term quality of experience (QoE) under queue stability constraint. To solve the intractability caused by the long-term functions, the considered problem is converted into a series of beam assignment (BA) and rate control (RC) problems in each time slot by leveraging Lyapunov method. Furthermore, novel closed-form solutions (CFSs) for BA and RC policies are derived by considering beam usage constraints; whereas an iterative algorithm is proposed based on the CFSs. Simulation results verify the superiority of the proposed algorithm based on its performance on system delay and QoE.
Computerized Text Analysis: Assessment and Research Potentials for Promoting Learning

: Rapid advancements in computing have enabled automatic analyses of written texts created in educational settings. The purpose of this symposium is to survey several applications of computerized text analyses used in the research and development of productive learning environments. Four featured research projects have developed or been working on (1) equitable automated scoring models for scientific argumentation for English Language Learners, (2) a real-time, adjustable formative assessment system to promote student revision of uncertainty-infused scientific arguments, (3) a web-based annotation tool to support student revision of scientific essays, and (4) a new research methodology that analyzes teacher-produced text in online professional development courses. These projects will provide unique insights towards assessment and research opportunities associated with a variety of computerized text analysis approaches.
VISUAL AND ANATOMICAL OUTCOMES OF ANTI–VASCULAR ENDOTHELIAL GROWTH FACTOR THERAPY IN EXUDATIVE AGE-RELATED MACULAR DEGENERATION AND VITREOMACULAR INTERFACE DISEASE: Vitreomacular Adhesion and Epiretinal Membrane

Purpose: To describe clinical and imaging features of eyes with and without vitreomacular interface disease (VMID) treated with intravitreal anti–vascular endothelial growth factor injections for exudative age-related macular degeneration, followed over an average of 2.5 years. Methods: Retrospective interventional case series involving 32 eyes with VMID and 146 eyes without traction. Best-corrected visual acuity (BCVA), manually measured central foveal thickness from optical coherence tomography imaging, and the number and timing of intravitreal anti–vascular endothelial growth factor injections were reviewed. Results: Eyes with VMID and exudative age-related macular degeneration received more intravitreal injections (mean 14.7) for 4 years than eyes without traction (mean 9.5) (P = 0.0224). Eyes with VMID had similar BCVA to eyes without traction at baseline (P = 0.8013) and Year 1 through Year 4 (P = 0.5417, 0.6275, 0.4574, 0.0570, respectively). Best BCVA showed significant improvement over baseline BCVA in eyes with (logarithm of the minimum angle of resolution 0.67–0.47, P = 0.0182) and without (logarithm of the minimum angle of resolution 0.61–0.44, P = 0.0001) VMID. Central foveal thickness was similar to eyes without traction at baseline and Year 1 through Year 3 and declined over time in eyes with VMID. Conclusion: In eyes with VMID, anti–vascular endothelial growth factor therapy resulted in improved BCVA and decreased central foveal thickness despite continued vitreomacular traction. Eyes with VMID required more intravitreal anti–vascular endothelial growth factor agents than eyes without VMID.
Generating images of partial face using landmark based k-nearest neighbor

One of the most common approaches to address the partial face recognition challenge is to crop the full face image into segments. The problem is how the full face image must be cropped in a uniform way to generate informative segments. The un-blindly strategy was applied in this paper to generate informative segments, it depends on localizing the facial landmarks and selecting the more informative facial points as a key points, as more as the k-nearest neighbor concept was explored to select the k nearest landmark points to the key points. Two landmark localization techniques were experimented, the suitable technique resulted in segments which are overlapped due to the supervised clustering technique that explored in this paper to cover important biometric face regions, not repeated and covered most probabilities in which it is possible to distinguish the query face from the available part of it.
Review on Text Sequence Processing with use of different Deep Neural Network Model

This paper underlines the importance of Deep Neural Network model in different learning tasks for Natural language processing. Deep learning techniques carry a lot of support to handle a large amount of trained or labeled data sets. Deep Neural Network model involves multiple processing layers to learn Sequential representations of Text data to achieve excellent performance in many domains. In this paper, we review significant deep learning models with end to end approach for Natural language processing that makes a more accurate assumption on the word sequence as well as represents a different experimental analysis for NLP tasks in terms of text and words with the help of CNN, RNN, LSTM, and GRU bidirectional Encode-Decoder. Numerous NLP tasks are involved in semantic execution to understand and generate a complete sentence where different Deep learning models represent awareness about numerous NLP tasks including sequential information processing. That’s a reason why Deep Neural Network Model gradually became more famous for different NLP applications. Even we have endorsed reinforcement learning as an extension to the Deep neural model which is frequently used in Natural Language Processing. This paper differentiates the Deep Neural Model in various language processing tasks and directs the selection of Deep Neural Model in different NLP tasks.