Miracle-dz/UniFAR-FTUs huggingface dataset:
A Gamification Framework for Long-Term Engagement in Education Based on Self Determination Theory and the Transtheoretical Model of Change

Long-term engagement of students in educational processes is crucial for learning success. However, a high degree of motivation is needed to stay focued on tedious tasks like homework, learning vocabulary, repeating grammar or doing exercises on a regular basis. Many students lose interest quite soon. Gamification has proven to help improve intrinsic motivation, but not all gamification experiments ended successfully. This paper introduces a gamification framework for long-term engagement based on two theories: Self Determination Theory (SDT) to improve intrinsic motivation and the Transtheoretical Model of change to consider criteria that support long-term behavior change. The framework defines a basic gamification structure that can easily be adapted to completely different gamification experiences.
A Thermal Energy Usage Prediction Method for Electric Thermal Storage Heaters Based on Deep Learning

The electric thermal storage heater is a new type of electronic equipment which can provide full day heating by making use of low-priced electricity provided by power plants during nighttime. However, the traditional electric thermal storage heater cannot make use of the relationship between the thermal storage time and the thermal demand of each user, which may cause energy waste when the thermal energy stored exceeds the user's demand or thermal energy shortage caused by insufficient reserve. Aiming to solve the problems, this paper presents a method for thermal energy usage prediction based on recurrent neural networks. Considering the continuous variability of environmental data, we use Bi-directional LSTM to achieve more accurate prediction results by combining the correlation between past information and future information. In addition, aiming at the problem of weight distribution, the attention mechanism is used the prediction algorithm and achieve the goal of rationally assigning weight parameters which can effectively reduce the error rate after the model is updated. Experiments show that the proposed algorithm with self-learning ability can reasonably predict the user's thermal storage time. It can effectively avoid the energy loss caused by storing too much thermal energy while satisfying the daily thermal energy needs of users.
A Target Detection System for Mobile Robot Based On Single Shot Multibox Detector Neural Network

Recent years, neural networks are used widely in the field of image processing. Neural networks have made great achievements in image classification, target detection, and semantic segmentation since 2012. In this paper, a target detection system for mobile robot is proposed based on the Single Shot Multibox Detector (SSD) neural network. The SSD neural network is trained to learn the features of input images using labeled dataset. The network model will converge using gradient descent algorithm during training. Then, the network becomes robust enough and is able to locate targets in images. In our research, we find that the performance of network is so perfect that it can hardly be done using traditional image processing algorithms. Especially, SSD network is most suitable for our system. The SSD network shows great adaptability, with 76.2% mAP (mean average precision) in the test dataset during our experiments. Detection algorithm based on SSD network has a great performance during experiments.
Investigating the Data Science Skill Gap: An Empirical Analysis

With big data analytics constantly growing in importance for contemporary organizations so does the need for skilled professionals. Perhaps the most critical item noted in the age of data is the lack of people with the required skill-set to turn raw data into actionable insight. Building on this pressuring issue, the objective of this paper is to survey the status quo of technical and business-related data analytics skills in a range of different industries and identify the most important skills that will be needed in the next few years. To do so, this study builds on a sample of 202 survey responses from key executives from Norwegian firms. Our analysis reveals the level of skill-fulfilment in for technically and business-oriented employees in a number of key industries. In addition, we use survey data from an additional sample of 27 executives and interviews with 6 managers and provide a ranking of the perceived importance of data analytics-related skills according to respondents in three categories, technical skills, business and project management skills, and soft skills. Our study concludes with findings regarding the skill-gap that exists in the domain of data science as well as suggestions on how to fulfil these needs, indicating specific subject-areas that are of heightened importance.
Compact Antenna Using IDC Based CRLH-TL Unit Cell for L-Band Applications

In this paper, a modification to the conventional interdigital capacitor (IDC) based composite right/left-handed transmission line (CRLH-TL) unit cell is presented. The proposed modification can be utilized for further antenna size reduction. The modified IDC CRLH unit cells have a uniform finger width along its length and change the position of shoring vias. This introduced modification aids in the further reduction of the antenna size as compared to that of the antenna with unmodified IDC unit cell with the same dimensions. The simulation and measurement results of the antenna with the modified unit cell is presented. The variations in the reflection coefficient characteristics are presented and compared with that of the unmodified antenna. For same unit cell dimensions, the unmodified unit cell has a resonance (-1st) of 1.50 GHz, whereas the with the modified unit cell it can be reduced up to 1.56 GHz.
Cloud Fingerprinting: Using Clock Skews To Determine Co Location Of Virtual Machines

Abstract : Cloud computing has quickly revolutionized computing practices of organizations, to include the Department of Defense. However, security concerns over co-location attacks have arisen from the consolidation inherent in virtualization and from physical hardware hosting virtual machines for multiple businesses and organizations. Current cloud security methods, such as Amazons Virtual Private Cloud, have evolved defenses against most of the well-known fingerprinting and mapping methods in order to prevent malicious users from determining virtual machine co-location on the same hardware. Our solution to co-locating virtual machines unhindered was to derive their clock skews, orthe temporal deviation of the system clock over time. Capturing normal TCP traffic to analyze timestamps from a virtual machine in the cloud, our results were inconclusive in demonstrating that co-located virtual machines will have similar clock skews due to large, inconsistent packet delays. Our research demonstrates a potential vulnerability in cloud defenses so that cloud users and providers can take appropriate steps to prevent malicious co-location attacks.
Design Optimization of the Rotor of a V-type Interior Permanent Magnet Synchronous Motor using Response Surface Methodology

This paper deals with an optimum rotor configuration of a V-type interior permanent magnet synchronous motor (IPMSM) by using an estimation technique called response surface methodology (RSM). The experiment required for RSM analysis of the magnetic devices was done using a numerical model based on the finite element method. Applying this methodology in electric motors design involves identifying an empirical model, that corelates the performance parameters of the electric motor with chosen designed variables. This design approach was applied using the analysis of the air-gap magnetic flux density spatial distribution to enhance performance of the IPMSM.
A Lightweight LFSR-Based Strong Physical Unclonable Function Design on FPGA

Physical unclonable function (PUF), a reliable physical security primitive, can be implemented in FPGAs and ASICs. Strong PUF is an important PUF classification that provides a large “Challenge-Response” pairs (CRP) space for device authentication. However, most of the traditional strong PUF designs represented by the arbiter PUF are difficult to implement on FPGA. We propose a new lightweight strong PUF design that can dynamically reconfigure while maintaining high entropy and large CRP space. We implement the PUF on a 28-nm FPGA. The experimental results show that the uniformity of the PUF is 49.8%, the uniqueness is 49.9%, which is close to the ideal value, and the hardware overhead is very small. This design is easy to implement and suitable for device authentication on FPGA.
A Study of Early Warning System in Volume Burst Risk Assessment of Stock with Big Data Platform

The high remuneration of stock market has attracted most investors. However, there are many factors affecting the trading price fluctuations in the stock market. In order to reduce the investment risk investor may use many measuring tools. The prediction of stock trading price is one of the most important topics in financial market. Financial time series forecasting is a popular application of machine learning methods. Previous research reports that advanced forecasting methods can accurately predict the trading price changes in financial markets. This study intends to use the real-time stream data processing architecture in the big data Spark framework to analyze the real-time stock trading volume according to the bursting comprehensive index of trading volume, and send risk notifications according to different trading volume levels. Investors can choose the best timing to invest. The results of this study show that investors who want to operate in high-risk environment can use stock trading volume risk rating criteria to increase profit.
Low Latency Hardware-Accelerated Dynamic Memory Manager for Hard Real-Time and Mixed-Criticality Systems

This paper presents a novel hardware architecture of dynamic memory manager providing memory allocation and deallocation operations. Due to very low and constant latency of these operations with respect to the actual number and location of free blocks of memory, the proposed solution is suitable for hard real-time and mixed-criticality systems. The proposed hardware-accelerated memory manager implements Worst-Fit algorithm for selection of a suitable free block of memory that can be used by the external environment, e.g. CPU or any custom hardware. The proposed solution uses hardware-accelerated max queue, which is a data structure that continuously provides the largest free memory block in two clock cycles regardless of the actual number or constellation of available free blocks. The proposed memory manager was verified using simplified version of UVM and applying billions of randomly generated instructions as test inputs. A synthesis into Intel FPGA Cyclone V was performed, and the synthesis results are presented as well. The memory manager was also synthesized into 28 nm technology with 1 GHz clock frequency and the power supply voltage of 0.9 V. The ASIC synthesis results show that the proposed memory manager consumes additional chip area from 35% to 70% of the managed memory.
Research on Multi-Algorithm-Based Hierarchical Recognition of Power Customer Appeals

The capture and analysis of power customer appeals is an essential prerequisite for improving service level. Although data mining technology has been widely used in the related analysis recently, limitations still remain such as one-sidedness, subjectivity and poor timeliness. In this paper, a power customer appeals recognition model based on hierarchical classification utilizing Adaboost, SVM, Random Forest comprehensively was proposed. The pro-posed model now can identify 25 types of power customer appeals from 95598 voice-transcription texts. The model adopts hierarchical classification strategy and takes advantage of different algorithms for different categories. Experimental results showed that the accuracy of the proposed model reaches 70.82%, which is 20% higher than the non-hierarchical classification method. Based on the proposed model, the data from the entire network can be analyzed in real-time, and more information can be detected, such as customer appeal hotspot, distribution characteristics, etc. The information can be used to achieve the early warning of risks and to optimize the resources allocation. This will provide great significance to improve service quality and customer satisfaction, as well as reducing operating costs.
Power Consumption Predicting and Anomaly Detection Based on Long Short-Term Memory Neural Network

Identification of abnormal power consumption data is a critical and difficult task to guarantee efficiency and reliability of the smart grid. This paper proposes a power consumption predicting and anomaly detection approach based on LSTM (Long Short-Term Memory) neural network. Compared to the ARIMA (Autoregressive Integrated Moving Average) algorithm, LSTM model demonstrates a decrease of 22% in forecasting error. Our simulations are performed based on actual power consumption data of ten thousands of users from Chongqing, China in the period of one year. Different from previous studies, we predict electricity consumption and detect anomaly in long-term prospective. We focus on seasonal and monthly trends, instead of power consumption pattern from typical commercial and residential buildings at metering granularity at once every 15 or 30 minutes. Electricity theft identification is significantly improved compared to previous unsupervised algorithms.
Patch antenna with improved performance using DGS for 28GHz applications

A compact size patch antenna resonating at 28GHz is presented in this paper. The antenna is simple in structure and fed using microstrip line which enhances its use with different circuitry. The antenna is designed keeping in mind the specification proposed by European Standard Telecommunications series (ETSI). Proposed antenna shows good results in term of return loss and gain, the antenna also satisfies the Federal communication commission (FCC) proposed bandwidth for 28GHZ i.e. 27.5-28.25GHz. ROGERS RT/Duroid 6002 is used as a substrate of the antenna, while the antenna is simulated using commercially available software Higher Frequency Structural Simulator (HFSS).
On the Exact Rate-Memory Trade-off for Multi-access Coded Caching with Uncoded Placement

We study a cache-aided content delivery network consisting of a central server which hosts a catalog of $N$ files, and $K$ caches each with limited memory $M$ which store content related to the files. There are $K$ users, each of which requests a file from the catalog, and has access to the data stored in $L\geq 1$ neighboring caches (with a cyclic wrap-around). The server transmits a common message to all the users, so that each of them can recover their requested file. This setup was recently studied in [1], where a coloring-based placement and coded-delivery policy was proposed and the required server transmission size was shown to be order-optimal with respect to information-theoretic bounds. We propose an alternate index coding-based placement and delivery scheme for this setup, which performs better than the previously proposed strategy. Furthermore, for multiple special cases including the ($N, K\leq 4, L$) and $(N, K, L=K-1)$-setups, we show that the scheme is exactly optimal under the restriction of uncoded placement. This extends other recent work [2], [3] which studies exact optimality for the single cache-access case ($L=1$) to the multi cache-access case ($L > 1$).
On the Energy Cost of Robustness and Resiliency for Virtual Network Function Placement

The placement of Virtual Network Functions (VNF) in distributed data centers is an important problem to solve for the next generation cloud based telecom architectures. This is because where to place the VNFs and how to route the traffic in the physical network impacts the energy consumption of the cloud infrastructure, the resiliency of the service chains and the SLA with the tenants. For network operators, it is important to minimize the operational costs of their infrastructure, provide robustness of the placement and routing in order to cope with potential hardware failures and imprecise resource demand specifications. In this paper, we develop a new optimization model for the green multi-period VNF placement and traffic routing problem, where different service chain configurations exist over time. The model is formulated as a Mixed Integer Linear Program (MILP), considers latency due to network propagation and VNF processing and provides different protection methods for the NFV traffic routing to cope with link failures. By applying Soyster’s robustness principle, the model yields a network configuration that can cope with load that deviates from the expected demand. Because the MILP is complex to solve, we develop a fast variable fixing heuristic. In our numerical evaluation, we use the virtualized Evolved Packet Core and study the energy cost of different robustness levels and protection schemes for VNF service flow routing.
Simulative Comparison of Polymer and Ceramic Encapsulation on SiC-MOSFET Power Modules under Thermomechanical Load

The need for small, high efficient (98%) and high temperature capable $(200^{\circ}{\rm{C}})$ power electronics lead to the development of SiC-power modules, which can satisfy these advanced requirements. Power electronics have often to resist harsh environments and provide high reliability. A typical way to fulfill these demands is the application of silicone gels and mold compounds, which can be used up to $200^{\circ}{\rm{C}}$. In order to protect power electronics for application at higher temperature up to $250^{\circ}{\rm{C}}$ and overcome the low thermal conductivity of polymer encapsulates new ceramic compounds were developed. Hence they are new to the market, there are less experiences using this compounds to increase reliability of SiC power modules. The aim of this study is to compare the effect of organic mold compounds and ceramic compound encapsulates onto a single chip SiC power module with finite element modeling (FEM) of power cycling loads. With the used assembly and interconnection technologies (AIT) such as active metal brazed (AMB) ceramics, silver sintering and Al|Cu-ribbon bond hybrids, the expected failure is a ribbon bond-lift off. The focus is therefore on the elastic and plastic deformation in the ribbon bond foots. As the stiffness of the encapsulate increases, the strain in the ribbon bonds can be reduced significantly. This allows a significant increase in reliability and lifetime, if the encapsulation does not introduce new failure mechanism. Through a comparison of FEM-results to experimental tested samples, the simulations are validated and a lifetime prediction is made.
Dual-Mode Motor Control Scheme for Electric Motorcycle

The purpose of this paper is to design a dual-mode motor control scheme for electric motorcycle that can automatically switch the motor control scheme to sensorless field-oriented control when the Hall element is detected a fault by a real-time self-test program. In addition, the main content of this essay is described that how to use the sliding mode state observer to estimate the rotor position of the motor, how to start the sensorless control mode at zero motor speed and how to switch the control mode in a reasonable condition by self-checking the Hall element. At the same time, the hardware scheme and software algorithm are designed. The experimental results show that the designed electric motorcycle motor control system is reasonable and feasible, and has high practicality.
Software Defined Network Simulation Using OpenNet for Vehicular Network

Interconnected vehicles, which communicate using the same concept of mobile networks, where a wireless connection is established spontaneously to perform information exchange between vehicles, is known as Vehicular Network (VANETs). High-speed moving user devices often experience information loss due to lack of connectivity, which stems from traditional network systems where the routing and switching system are installed on the same equipment. Software-defined network (SDN) embraces the dynamic nature of the vehicular network by separating the control plane from the data plane, but has limited the operational capabilities, as it is often a single network controller based systems. Multiple controllers provide fault tolerance. However, it is important to validate the augmented solution of multiple controllers using a network simulator, which would be cost-effective and feasible. In this paper capability comparison between network simulators NS3, MiniNet and OpenNet have been performed. OpenNet with POX controller has been used to simulate SDN and results show significant improvement over MiniNet and NS-3. Further, performance analysis is done on simulated data to describe the behavior of networking using TCP transmission and ping operation.
Review on Sentiment Lexicons

Sentiment lexicon is referred as Lexical Resource for sentiment analysis. It contains lexical units with their sentiment orientations. This paper introduces different sentiment lexicons with their explanations and state of art in the field of sentiment analysis and opinion mining. In Natural Language processing different kinds of lexicons plays important role in extracting useful information from text data. Lexicons are used in the pre-processing task which is a very important in text mining.
Teleporting an unknown state using quantum Fisher information parameters

The main aim of this work is to teleporte a two qubit state via quantum teleportation protocol, where a single atomic-field entangled state is considred as a quantum channel. By controlling the relevant parameters encoded in the teleported state we investigate the quantum Fisher information evolution. Our results show that, it is possible to estimate in terms of quantum Fisher information the weight and the phase parameters encoded in the initial teleported state. It shown that, the maximum amount Fisher information appears periodically. Interestingly, heigh estimation is investigated for the maximum amount Fisher information bounds and vice versa.
Meaningful Learning Through Virtual Tutors: A Case Study

Civil engineering students take the Surveying and Geoinformation course to learn how to illustrate objects on the earth surface. Students create maps using coordinates, lengths, areas, volumes, angles and slopes. The course is the foundation of other courses such as Road Design and Hydrology. Therefore, students in Surveying and Geoinformation require meaningful learning for applying their knowledge during their careers. However, the course has a fail rate higher than 30%. Course professors analyzed the situation and found that a high percentage of students lack basic concepts in geometry and trigonometry. Since professors could not use the class time for basic subjects, they followed the idea of automated reviews and developed an intervention for students to use virtual tutors to refresh (or acquire) the knowledge required to be successful at the course. Results show that students using virtual tutors were able to use their basic knowledge on more complex problems, improving significantly compared to previous courses that did not use the tools.