Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine learning approach for student engagement automatic recognition from facial expressions

Digital revolution can significantly improve the quality of education. There have been already discussions for a long time about the advantages, disadvantages and opportunities for transforming traditional classroom activities. Modern students use smartphones and tablets 'from birth', and for the majority of academic subject areas students can often obtain more complete, accurate and up-to-date information from the Internet than from lectures. Is it interesting for students to learn? Are they in time with the professor? Is the presentation clear? How deep are students engaged in learning in the classroom? These issues come to the forefront in the era of digital education. However, it was almost unrealistic to control the level of student engagement until recently: for example, only in the Moscow campuses of the Financial University the classes are held daily from 8.30 to 22.00 in more than 500 classrooms. Existing information systems for student engagement automatic recognition are focused on analyzing individual engagement of students and schoolchildren. We propose a system that constantly analyzes the flow of data from video cameras installed in classrooms, uses machine learning models to identify students' faces, recognize their emotions and determine the level of engagement, and then aggregates engagement data on student groups, faculties, courses, etc. on interactive dashboards. The training dataset consisted of 2,000 faces was used for machine learning model identification with boosted decision trees algorithm (ADABoost). The quality metrics (Accuracy, Precision, Recall, AUC) on a test dataset of 500 students faces were all above 0,81. The system is developed as an elastically scalable cloud service that automatically collects video streams from cameras installed in classrooms and forms the resulting metrics of the students and groups' engagement in the Microsoft Azure cloud.
DC Microgrid Protection: A Comprehensive Review

DC microgrids have attracted significant attention over the last decade in both academia and industry. DC microgrids have demonstrated superiority over AC microgrids with respect to reliability, efficiency, control simplicity, integration of renewable energy sources, and connection of dc loads. Despite these numerous advantages, designing and implementing an appropriate protection system for dc microgrids remains a significant challenge. The challenge stems from the rapid rise of dc fault current which must be extinguished in the absence of naturally occurring zero crossings, potentially leading to sustained arcs. In this paper, the challenges of DC microgrid protection are investigated from various aspects including, dc fault current characteristics, ground systems, fault detection methods, protective devices, and fault location methods. In each part, a comprehensive review has been carried out. Finally, future trends in the protection of DC microgrids are briefly discussed.
Multi-core optical fibers in FTTH/FTTA/FTTS solutions supporting IoT

The paper presents a comparative classification of reference models used in IoT technology. The layers that directly determine the effectiveness of providing information at a distance are indicated. Examples of the construction of cores used in telecommunications silica-fibers are presented. Exemplary simulation models of SM-MCF and FM-MCF modal structures has been presented. Attention was paid to the need to construct MCFs, which can be used in local access networks as well as in distribution and indoor networks. Applications of MCFs in the networks operating in FTTH, FTTA and FTTS configurations are presented. During the presentation of the network solutions, proposal for the construction of a hybrid fiber-optic cable consisting of MCFs, SCFs, and TMCs are described. The proposed cable construction has been also used in the presented FTTH/FTTA network solutions.
Analysis of the impact of social networking sites using web content mining and induction method

With the prevailing presence of social media, we cannot deny the fact that it influences the life of people. Usually teenagers tend to use this technology either to socialize or entertain themselves. In this paper, we have presented a framework to perform a qualitative analysis of social media networking sites using their social media reviews. The researchers gathered 503 links from Facebook, Twitter, Instagram and other related articles using web content mining. And, induction method was used to restrict and classify the harvested data. Researchers makes use of Word2Vec model that would find the similarity of one word to another. The experimental results show 81.39% precision, 67% accuracy, 70.93% recall and 75.58% F-Measure.
DCDS: Data centric dispatcher selection protocol for cellular enabled VANET communication

The Vehicular Ad‐hoc Network (VANET) is one of the emerging research areas in recent days, in which accomplishing the robustness and security are the demanding tasks. To obtain these objectives, various offloading‐based techniques are developed in the traditional works. Still, it remains with the limitations of high mobility, no assurance for data acceptance, and limited applicability for multiple data exchange units. Thus, this paper aims to develop a Data Centric Dispatcher Selection (DCDS) algorithm based on data offloading for a secure information exchange in VANET. It aims to enable the cellular‐based VANET connection by using the application layer protocol without taking the Pending Interest (PI)‐based information. Also, the Content Centric Network (CCN) concept is utilized to exchange the data between for upcoming internet. In this environment, the position of own vehicle and neighboring vehicle are known to each vehicle by using the Global Positioning System (GPS) and security messages. Moreover, the communication does not depend on the device's name, but it depends on the content name in CCN. Because of the availability of multiple interfaces in CCN, there is no content loss in the network. During simulation, various performance measures are used to evaluate the performance of the proposed technique, which includes throughput, packet loss rate, average end‐to‐end delay, total Satisfied Interest Packet (SIP), Forwarded Interest Packet (FIP), and average Interest Satisfactory Delay (ISD). Also, the superiority of this paper is proved by comparing it with some existing techniques such as Internet Protocol Television Quality of Service (IPTV‐QoS), Michael Oche (MO), and RobUst Forwarder Selection (RUFs).
Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query-based summarisation

This paper describes Macquarie University’s contribution to the BioASQ Challenge (BioASQ 6b, Phase B). We focused on the extraction of the ideal answers, and the task was approached as an instance of query-based multi-document summarisation. In particular, this paper focuses on the experiments related to the deep learning and reinforcement learning approaches used in the submitted runs. The best run used a deep learning model under a regression-based framework. The deep learning architecture used features derived from the output of LSTM chains on word embeddings, plus features based on similarity with the query, and sentence position. The reinforcement learning approach was a proof-of-concept prototype that trained a global policy using REINFORCE. The global policy was implemented as a neural network that used tf.idf features encoding the candidate sentence, question, and context.
A Framework in Big Data Analytics using MapReduce for Education System

-Big data came into existence as the data generation increased exponentially and the traditional data processing tools became incapable to manage those complex datasets. Big data is diversely used in fields like politics, business, weather forecasting, science and research, e-Commerce, healthcare, and so on. Advent of big data analytics has not excluded its impact on the education system. The recent advancements have given raised to the rapid growth in the amount of data stored in educational databases. In this paper, we propose a framework on big data analytics using MapReduce for education system. The analysis of data generated in education sector can enhance the learning process of the student. It helps in discovering student’s academic progress, behavior and predicting future performances. The placement analysis can assist the career development of the student. These analysis will help to the overall development of the student and also to achieve enormous productivity of the organization.
Techniques for Automated Classification and Segregation of Mobile Mapping 3D Point Clouds

We present an approach for the automated classification and segregation of initially unordered and unstructured large 3D point clouds from mobile mapping scans. It derives disjoint point sub-clouds belonging to general surface categories such as ground, building, and vegetation. It provides a semantics-based classification by identifying typical assets in road-like environments such as vehicles and post-like structures, e. g. , road signs or lamps, which are relevant for many applications using mobile scans. We present an innovative processing pipeline that allows for a semantic class detection for all points of a 3D point cloud in an automated process based solely on topology information. Our approach uses adaptive segmentation techniques as well as characteristic per-point attributes of the surface and the local point neighborhood. The techniques can be efficiently implemented and can handle large city-wide scans with billions of points, while still being easily adaptable to specific application domains and needs. The techniques can be used as base functional components in applications and systems for, e. g. , asset detection, road inspection, cadastre validation, and support the automation of corresponding tasks. We have evaluated our techniques in a prototypical implementation on three datasets with different characteristics and show their practicability for these representative use cases.
Your comments matter: incorporating viewers’ comments for ranking online video content using bibliometrics

ABSTRACT The quality of user-generated content over World Wide Web media is a matter of serious concern for both creators and users. To measure the quality of content, webometric techniques are commonly used. In recent times, bibliometric techniques have been introduced to good effect for evaluation of the quality of user-generated content, which were originally used for scholarly data. However, the application of bibliometric techniques to evaluate the quality of YouTube content is limited to h-index and g-index considering only views. This paper advocates for and demonstrates the adaptation of existing Bibliometric indices including h-index, g-index and M-index exploiting both views and comments and proposes three indices hvc, gvc and mvc for YouTube video channel ranking. The empirical results prove that the proposed indices using views along with the comments outperform the existing approaches on a real-world dataset of YouTube.
Secure Remote Sensing Image Registration Based on Compressed Sensing in Cloud Setting

High-resolution remote sensing image registration has recently been widely applied in many fields such as agriculture, forestry, and urban planning. A high-resolution remote sensing image can depict the texture features of ground details more clearly, but it also brings new challenges to registration such as texture similarity, image storage space, and image information leakage. In this paper, to solve the above problems, we propose a finite-state chaotic compressed sensing (CS) cloud remote sensing image registration method. First, chaotic CS with a finite state not only saves storage space required for an image but also improves the security of the image in the transmission process. Next, we process the image in the cloud platform, improve the speed of image processing, and facilitate real-time data processing. Finally, we propose an improved scale invariant feature transform (SIFT) image registration method based on local and global information (LG-SIFT) that reduces the impact of texture similarity on high-resolution remote sensing images. The experimental results show that the runtime of the original processing method is twice as long as that of the proposed scheme and that the accuracy of registration improves considerably.
Learning to Tradeoff Between Energy Efficiency and Delay in Energy Harvesting-Powered D2D Communication: A Distributed Experience-Sharing Algorithm

Efficient energy management, especially in terms of energy efficiency (EE) optimization, is the cornerstone to realize the energy harvesting (EH)-powered device-to-device (D2D) communication underlaying cellular network. However, too much focus on energy management is likely to negatively impact other quality-of-service, such as transmission delay. Consequently, this paper aims to investigate an EE and delay tradeoff (EDT) optimization scheme in EH-powered D2D communication underlaying cellular network (EH-DCCN). A more practical scenario, one cellular user and multiple EH-powered D2D pairs matching model and limited energy and data storage space, is considered. In view of the characteristic of the available transmission energy, a weighted EE and delay utility problem is modeled to investigate the EDT optimization scheme. Due to the complexity of the modeled problem, this paper proposes a modified distributed ${Q}$ -learning (QL) algorithm, namely experience-sharing distributed cooperation learning (EDCL) algorithm, to tackle the EDT optimization issue and enhance the convergence speed. Numerical results discuss convergence speed of EDCL performance, proper experience-sharing interval setting, as well as EDT performance. With the proper experience-sharing interval, EDCL can obtain the near-optimal performance as the classical centralized QL mechanism with more rapid convergence rate by sacrificing the appropriate tolerable additional signaling overhead.
Processing the Results of Electroencephalography for Patients Suffering from Depression after Neuro-electrostimulation Course: Case Study

The article presented the results of electroencephalography (EEG) signal processing in a case study of neuro-electrostimulation application for patients suffering from depression. Neuro-electrostimulation was performed by the SYMPATHOCOR-01 device in two modes - multichannel and single-channel stimulation. The analysis of changes in the EEG activity maps during neuro-electrostimulation course was carried out. The common conclusion for all patients is an increase in the homogeneity for the distribution of spectral power density for EEG signals. A quantitative method for estimating the level of the brain zones activation was proposed. For patients from the multichannel stimulation group, an increase in the activation level was observed. It was noted that for patients from the single-channel stimulation group there were zones in which a significant decrease in the level of activation was observed.
Path Tracking of Wheeled Mobile Robots Based on Dynamic Prediction Model

In the field of path tracking control for wheeled mobile robots, researchers generally believe that the motion of robots meets non-holonomic constraints. However, the robot may sideslip by centrifugal force when it is steering. This kind of slip is usually uncontrollable and dangerous. In order to prevent sideslip and improve the performance of path tracking control, we propose a controller based on tire mechanics. Moreover, the new controller is based on the model predictive control, and this control method has proven to be suitable for path tracking of wheeled mobile robots. The proposed controller was verified by simulation and compared with a model predictive controller based on kinematics (non-holonomic constraints). As for the simulation results, the maximum values of displacement error, heading error, lateral velocity, and slip angle of the dynamic-based model predictive controller are 0.2086 m, 0.1609 rad, 0.1546 m/s, and 0.0576 rad, respectively. Compared with the kinematics-based model predictive controller, the maximum values of the above-mentioned parameters of the dynamic-based controller reduce by 86.55%, 72.25%, 96.30%, and 90.02%, respectively. The above-mentioned results show that the proposed controller can effectively improve the accuracy of path tracking control and avoid sideslip.
Document Classification System According to the Degree of Semantic Link Expressed Fuzzy Function

At present, information retrieval systems are simply expressed with a combination of keyword search according to the direct keyword matching method to get the information that users need. Because of this, documents retrieval systems serve too many documents due to term ambiguity. This makes the user need extra time and effort to get closer the document. To overcome these problems, this paper proposes the information retrieval system based on the content that connects documents according to the degree of semantic link that expresses a fuzzy value by fuzzy function. This paper also proposes an algorithm that produces a hierarchical structure using the degree of concept and content among documents. As a result, we are able to select and to provide user-interested documents.
Privacy-Oriented Blockchain-Based Distributed Key Management Architecture for Hierarchical Access Control in the IoT Scenario

The rapid development of the Internet of Things (IoT) and the explosive growth of valuable data produced by user equipment have led to strong demand for access control, especially hierarchical access control, which is performed from a group communication perspective. However, the key management strategies for such a future Internet are based mostly on a trusted third party that requires full trust of the key generation center (KGC) or central authority (CA). Recent studies indicate that centralized cloud centers will be unlikely to deliver satisfactory services to customers because we place too much trust in third parties; therefore, these centers do not apply to user privacy-oriented scenarios. This paper addresses these issues by proposing a novel blockchain-based distributed key management architecture (BDKMA) with fog computing to reduce latency and multiblockchains operated in the cloud to achieve cross-domain access. The proposed scheme utilizes blockchain technology to satisfy the decentralization, fine-grained auditability, high scalability, and extensibility requirements, as well as the privacy-preserving principles for hierarchical access control in IoT. We designed system operations methods and introduced different authorization assignment modes and group access patterns to reinforce the extensibility. We evaluated the performance of our proposed architecture and compared it with existing models using various performance measures. The simulation results show that the multiblockchain structure substantially improves system performance, and the scalability is excellent as the network size increases. Furthermore, dynamic transaction collection time adjustment enables the performance and system capacity to be optimized for various environments.
An Efficient B-tree Index Scheme for Flash Memory

B-tree index structure is widely used for many database management systems and file systems which are popularly implemented on flash-based storage device nowadays. Flash memory has been popularly used because of its prominent advantages such as fast access speed, nonvolatile, high reliability, low power consumption. Due to hardware restriction such as erase-before-write, however, the performance of ﬂash memory signiﬁcantly degrades when directly implementing B-tree on flash memory. This study introduces an efficient B-tree index scheme that improves the performance of B-tree index on flash memory called EBIF. EBIF can reduce the number of flash operations and minimize the number of pages used to store the B-tree by applying overflow mechanism and eliminating redundant index units in the index buffer. The experimental results show that EBIF yields a better performance than those of the other existing variants of B-tree index.
Arterial Traffic Estimation Using Field Detector and Signal Phasing Data

Author(s): Gan, Qijian, PhD; Skabardonis, Alexander, PhD | Abstract: In this project, a novel approach has been developed to estimate the traffic states on arterial road links controlled by signalized intersections using both loop detector data and signal phasing information. We derived a trapezoidal fundamental diagram that includes two occupancy thresholds to categorize the traffic states into three different regimes: uncongested, congested, and downstream queue spillback. The parameters used to compute these two thresholds are closely related to road geometry, detector layout, signal settings, and vehicle dynamics, which can be obtained from the field data. A case study was performed using field data from three intersections along Huntington Drive in the City of Arcadia in the I-210 Connected Corridors pilot. Flow-occupancy relations were obtained for both advance and stopline detectors at the intersection approaches, and it was found that advance detector information is more reliable because it is less impacted by the traffic signal. The proposed trapezoidal fundamental diagram was validated using a dataset with six months of detector data.
Principal filters of some ordered Γ-semigroups

For an intra-regular or a left regular and left duo ordered Γ-semigroup M , we describe the principal filter of M which plays an essential role in the structure of this type of poΓ-semigroups. We also prove that an ordered Γ-semigroup M is intra-regular if and only if the ideals of M are semiprime and it is left (right) regular and left (right) duo if and only if the left (right) ideals of M are semiprime.
Structural alignment in incidental word learning . To appear in

Young children can sometimes acquire new vocabulary words with only limited, indirect exposure (Carey & Bartlett, 1978). We propose that structural alignment processes lead to fluent detection of commonalities and differences that facilitate incidental word learning. To test this, we adapted the Carey and Bartlett paradigm, varying the alignability of the objects that 4year-olds saw while hearing the novel word chromium. In Experiment 1, children in the High-Alignment condition were significantly better than those in the Low-Alignment condition at identifying chromium objects in a subsequent task. In Experiment 2, we ruled out an alternative account by equalizing the overall amount of information presented to the two groups. We also found that the advantage of high alignment persisted after two-to-four days. These results suggest that structural alignment is a mechanism by which children can learn word meanings even in incidental word learning situations.
Investigation of Equivalent Unsprung Mass and Nonlinear Features of Electromagnetic Actuated Active Suspension

Electromagnetic actuated active suspension benefits active control and energy harvesting from vibration at the same time. However, the rotary type electromagnetic actuated active suspension introduces a significant extra mass on the unsprung mass due to the inertia of the rotating components of the actuator. The magnitude of the introduced unsprung mass is studied based on a gearbox type actuator and a ball screw type actuator. The geometry of the suspension and the actuator also influence the equivalent unsprung mass significantly. The suspension performance simulation or control logic derived should take this equivalent unsprung mass into account. Besides, an extra force should be compensated due to the nonlinear features of the suspension structure and it is studied. The active force of the actuator should compensate this extra force. The discovery of this paper provides a fundamental for evaluating the rotary type electromagnetic actuated active suspension performance and control strategy derived as well as controlling the electromagnetic actuated active suspension more precisely.