Miracle-dz/UniFAR-FTUs huggingface dataset:
Combined Analysis of Compilation History and Transition in Similarity between Source Codes for Detecting Stumbling in Construction of Program Logic

In programming exercises, a few professors and assistants generally instruct many students. To conduct precise guidance, it is important for professors but difficult to detect the learning situation of each student, such as whether they are having difficulties in learning. However, there have been no effective methods for extracting occurrences of stumbling that do not result in compilation errors since most existing methods have targeted only those that do. In this research, we aimed at developing methods for detecting when inexperienced students make mistakes in expressing the intended processing as a program (i.e. stumbling in the construction of program logic). In this paper, we initially describe a method we developed for extracting candidates of such stumbling on the basis of an analysis of compilation histories. We also describe methods for estimating such stumbling from among extracted candidates by analyzing transitions in the similarity between source codes. Finally, we describe an experiment done using multiple methods of similarity calculation and discuss the characteristics of our methods on the basis of results.
Subspace-Driven Output-Only Based Change-Point Detection in Power Systems

The use of Phasor Measurement Units (PMU) has enabled monitoring the power system dynamics online. There are many PMUs installed which will give a comprehensive view in real or near real time of the entire system. In general, there are many operating conditions of power systems which requires different tools to analyze the synchrophasor data provided by PMUs. Changes in power system monitoring must be detected as quickly as possible so that the system operators can choose a different tool for data analysis. Therefore, in this paper, changes in the working conditions of the power system are detected using a subspace state-space based change-point detection method. The detection algorithm proposed in this paper is sequential detection which aims at both detection accuracy and detection speed. We have tested our proposed algorithm to detect many various kinds of changes in power system operations and verified that the algorithm is able to detect changes successfully, including very small ones in the system such as the low-amplitude forced oscillations buried in the system's ambient conditions in a real- time manner. The algorithm is tested in a set of real-world data to show its practical usage in large systems with imperfect data.
Detection of Network Congestion and Denial of Service (DoS) Attacks in Cognitive Radio Networks

A Cognitive Radio Network (CRN) is based on a technology that enables secondary users (SUs) to access available licensed spectrum not occupied by primary users (PUs). However, due to its open and wireless nature, a CRN is vulnerable to fraudulent attacks, which might attempt to eavesdrop or modify the contents of packets being transmitted. Moreover, denying the opportunity to SUs to use a free band leads to underutilization of the spectrum space. In this context, it is most important as well as challenging to differentiate between networks under denial of service (DoS) attack from the ones experiencing congestion. This paper adopts the SUs performance measures of packet loss probability, mean queue length and mean throughput of the transmission node in order to devise a packet delivery ratio (PDR) for SUs aiming to determine whether or not the network is experiencing a DoS attack. PDR in this case is the ratio of the number of packets successfully forwarded from the encryption node to the SU transmitter. To this end, a generalized stochastic Petri net (GSPN) is proposed in order to investigate if the network is under a DoS attack and suggest a preventive strategy for an efficient network protection. Based on the application of the Mobius Petri Net Package, typical numerical simulation experiments are carried out and related operational interpretations are made.
Replacement of Synchronous Generator by Virtual Synchronous Generator in the Conventional Power System

Virtual synchronous generator (VSG) has been proposed to mimic the synchronous generator in terms of voltage establishment and provision of virtual inertia from electric energy storage. It thus facilitates increased power electronics connected renewable penetration while maintaining the system stability. Besides virtual inertia, a well-functioning VSG also includes frequency droop control and voltage regulation, which corresponds to the turbine governor and automatic voltage regulation in the synchronous generator (SG). This leads to the question of whether such a VSG can fully replace the SG in the conventional power system and achieve 100% power electronics generation penetration. This paper tries to answer this question by first comparing SG and VSG and then replacing all of the SG by VSG in the IEEE 39-bus system under the assumption of the infinite electric energy storage capacity. The results verify that the VSG has the same function as SG and the system remains stable under 100% VSG penetration.
Dynamic Cascading Failure Analysis in Congested Urban Road Networks With Self-Organization

An efficient freight transportation system is a core part of the modern urban logistics. The transportation system depends on an efficient urban road network. In urban road networks, the failure of some components leads to others failing in succession, triggering cascading failures. This may cause the collapse of the freight transportation system. A cascading failure model is proposed for analyzing the propagation of failures on urban road networks, which can provide guidance for the planning of freight transportation routes in case of emergency. When analyzing the cascading failure process, failed components are commonly deleted permanently. Travelers will avoid failed components, resulting in no increase in traffic flow on the failed components. At the same time, vehicles on these failed components will evacuate slowly to alleviate congestion. Considering this point, this paper proposes a dynamic cascading failure model. This model is established based on a hybrid routing strategy in which traffic flow is distributed according to global and local information. Specifically, the new input traffic demand is assigned along efficient paths in an urban road network (using global information). Efficient paths are determined according to the weights of all edges in urban road networks. Meanwhile, failed edges transfer some traffic flow to their normally working neighbors by self-organization (using local information). The proportion of transferred traffic flow is determined by the residual capacity of the failed edges’ normally working neighbors. The feasibility of the proposed model is verified in a partial road network in Changchun, China.
Cloud Infrastructure Estimation and Auto-Scaling Using Recurrent Cartesian Genetic Programming-Based ANN

Use of cloud resources has increased with the increasing trend of organizations and governments towards cloud adaptation. This increase in cloud resource usage, leads to enormous amounts of energy consumption by cloud data center servers. Energy can be conserved in a cloud server by demand-based scaling of resources. But reactive scaling may lead to excessive scaling. That, in turn, results in enormous energy consumption by useless scale up and scale down. The scaling granularity can also result in excessive scaling of the resource. Without a proper mechanism for estimating cloud resource usage may lead to significant scaling overheads. To overcome, such inefficiencies, we present Cartesian genetic programming based neural network for resource estimation and a rule-based scaling system for IaaS cloud server. Our system consists of a resource monitor, a resource estimator and a scaling mechanism. The resource monitor takes resource utilizations and feeds to the estimator for efficient estimation of resources. The scaling system uses the resource estimator’s output for scaling the resource with the granularity of a CPU core. The proposed method has been trained and tested with real traces of Bitbrains data center, producing promising results in real-time. It has shown better prediction accuracy and energy efficiency than predictive scaling systems from literature.
A Review Paper: Security Requirement Patterns for a Secure Software Development

Security requirements are the major reasons for secure software developments. Many methods, model and approaches have been designed by many researchers to ensure a correct built of security requirement. The security patterns approach aims to benefit security requirements by allowing requirement engineers who are not security experts to better identifying and understanding security concerns and leads to a correct implementation. In this study, we evaluate various security patterns that existed in Software Requirements Engineering. Based on a literature search conducted traditionally, we report our findings on classifying and structuring this security requirements patterns. Derived from this study, a future direction for our research is clarified.
Classification Based Inter-Frame Prediction in Video Compression

Video compression is the technique of reducing the original size or storage of the video content. Traditional video coding uses the block-based translation motion model for this purpose. This approach often fails for preserving the good quality of compression, if the motion discontinuities exist in the video frames. A Block that contains the boundary of moving object need more bit for better prediction of the current frame. In the presence of complex motion model the traditional method catastrophically fails to predict the objects having the multiple motion. In this paper, we present a technique for the classification of blocks either it contains the single motion or multiple motion. Experimental result shows that the average accuracy for classification of HD sequences is 85.76% and the achieved bit rebate is up to 1.083% over standalone HEVC.
Multi-Objective optimization Based Resource Utilization with Loss Minimization in Microgrid

Energy management is a critical issue in microgrid which is associated with efficient utilization of available resources at minimum operating cost. The intermittent power generation by renewable energy sources demands optimal allocation of resources to avoid interruption due to reduced power availability. This paper presents a framework formulated as a multi-objective optimization that ensures maximum utilization of available infrastructural facilities along with reduced system loss. Maximization of line flow to its load carrying capability allows efficient utilization of available resources and controlling power flow through shortest possible path ensures reduced system loss. Applicability of the proposed method is shown on two different test systems - modified 5-bus system and modified 9-bus system.
M-estimation in Low-Rank Matrix Factorization: A General Framework

Many problems in science and engineering can be reduced to the recovery of an unknown large matrix from a small number of random linear measurements. Matrix factorization arguably is the most popular approach for low-rank matrix recovery. Many methods have been proposed using different loss functions, for example the most widely used L_2 loss, more robust choices such as L_1 and Huber loss, quantile and expectile loss for skewed data. All of them can be unified into the framework of M-estimation. In this paper, we present a general framework of low-rank matrix factorization based on M-estimation in statistics. The framework mainly involves two steps: firstly we apply Nesterov's smoothing technique to obtain an optimal smooth approximation for non-smooth loss function, such as L_1 and quantile loss; secondly we exploit an alternative updating scheme along with Nesterov's momentum method at each step to minimize the smoothed loss function. Strong theoretical convergence guarantee has been developed for the general framework, and extensive numerical experiments have been conducted to illustrate the performance of proposed algorithm.
Short-term wind speed prediction model of VMD-FOAGRNN based on Lorenz disturbance

Accurate wind speed prediction is the key to wind energy development and utilization. However, due to the intermittent, random and chaotic nature of wind speed, wind speed prediction has the problem of low accuracy. So a short-term wind speed prediction model of VMD-FOAGRNN based on Lorenz perturbation is proposed in this paper to predict wind speed. Firstly, the actual wind speed sequence is decomposed into multiple components by variational mode decomposition (VMD). After that, the generalized regression neural network (GRNN) model is used for training and prediction for each component, and the GRNN model is optimized by the fruit fly optimization algorithm (FOA) to improve the wind speed prediction accuracy. Next, the predicted values of the respective components are reconstructed to obtain the preliminary prediction values of wind speed. Finally, in consideration of the influence of atmospheric disturbance on wind speed, the Lorenz equation is introduced to correct the preliminary prediction values of wind speed to obtain the final wind speed prediction values. The high prediction accuracy and versatility of the prediction model of this paper are verified by experimenting with the wind speed data of two wind farms.
8-Watt Linear Three-Stage GaN Doherty Power Amplifier for 28 GHz 5G Applications

This paper presents a three-stage Ka-band Doherty power amplifier fabricated using 0.15 μm GaN-on-SiC HEMT technology. Peak-power greater than 8-watts, 30 dB of small-signal gain, and over 20% of PAE at 6 dB output back-off has been achieved for 27 to 29.5 GHz operation. In addition, the raw linearity of the Doherty amplifier has been measured using a 400 MHz, 64-QAM CP-OFDM signal with a 9.6 dB peak-to-average-power-ratio. Uncorrected ACPR as good as -40 dBc and EVM as low as 2% demonstrate the potential of this amplifier for 5G applications.
Robust Continuous Control for a Class of Mechanical Systems Based on Nonsingular Terminal Sliding Mode

In this paper is proposed a control structure for a class of mechanical systems; this structure consists of a continuous controller based on nonsingular terminal sliding mode control plus uncertainty and disturbance estimator. Closed-loop stability is proved by designing an adequate sliding surface and showing the existence of sliding modes by the fulfillment of the reaching law. A controllers comparison using the nonsingular terminal sliding mode, first-order sliding mode, PID, and the proposed control structure is carried out through numerical simulations of a pendulum system, where the $l_{2}$ index is used to measure the performance of the controllers. Moreover, real-time experiments are performed in a mechanical system with a pneumatic actuator. The theoretical, numerical, and experimental results validate the feasibility, performance, and robustness of the proposed control structure.
Study on A Modified Adaptive Compressed Sensing Algorithm in Wireless Sensor Network of Microgrid

In this paper, an improved adaptive compressed sensing algorithm is proposed to meet the requirement towards data transmission that the wireless sensor network applied in the microgrid should be precise and low-energy. The algorithm proposed in this paper takes reconstruction property and energy balance of node into account when choosing observation vectors, in order to avoid damage on the whole network structure caused by over-rapid consumed energy of certain nodes, residual energy of nodes is also considered in this improved algorithm to make the algorithm able to decrease the transmission quantity of overall data and balance the load of various nodes in the wireless sensor network at the same time. It can be seen in the simulation result that compared with the conventional algorithm, the algorithm proposed in the paper is obviously improved in aspects of network lifetime and precision of data reconstruction in wireless sensor network of microgrid.
Improved Constant Switching Frequency Torque Regulator based DTC of IM Fed by 3L-NPC Inverter for Wide Speed Region

Classical direct torque control (DTC) is popular for its simplicity and fast control algorithm in motor drives. However, it has two main weaknesses; unfixed inverter switching frequency and large torque ripples due to torque hysteresis controller (THC). In DTC drives, torque ripple can be minimized by replacing the two-level inverter with a three-level neutral-point clamped (3L-NPC) inverter. Nevertheless, switching frequency is still variable and low which produces higher torque ripple and asymmetrical switching signals for the inverter. To alleviate these problems, a conventional constant switching frequency torque regulator-based DTC (CSFTR-DTC) for 3L-NPC inverter was presented in the literature. However, the conventional CSFTR-DTC deteriorates from flux-droop at sector transitions at low speed operation. This results in undesirable speed oscillation and high total harmonic distortion (THD) of the stator current. Moreover, the torque ripple is still relatively high. In this paper, an improved interleaving CSFTR-DTC is proposed to modify the duty cycle of torque-error status in three-level inverter to improve flux-regulation and reduce torque ripples. Simulation results are presented to validate the effectiveness of the proposed method over the conventional method.
Analysis of the mating process of plug-in cable connectors for the cable harness assembly task

In this paper, we analyze the mating process of plug-in cable connectors for wiring harness assembly tasks by showing the challenging difficulties around this task including the possible solutions: a new gripper design concept and analyzing the permissible error range (tolerance) between the connectors. For wiring harness assembly tasks, knowing the accurate pose of the grasped cable connector in the gripper is very critical for the successful mating process, and conventional mating processes rely on force control or mechanical passive compliance. Nowadays, we notice that collaboration robotic manipulators or small size industrial robotic manipulators attain high accuracy and repeatability levels (sub-millimeter) thus demonstrate very precise position control capacities. Using those capacities of the robotic manipulator, we analyze the permissible error range of connector for the mating process. For that reason, we propose a new design of a gripper for an accurate alignment and holding of the position between the cable and the gripper. Therefore, if we know the exact position of the connector in reference to the gripper, and the tolerance between the connectors is larger than error position of the robot arm, the mating process can be achieved by just using position control of conventional industrial robots.
Bimodal Emotion Recognition using Deep Belief Network

The emotions are very important in human daily life. In order to make the machine can recognize the human emotional state, and it can intelligently respond to need for human, which are very important in human-computer interaction. The majority of existing work concentrate on the classification of six basic emotions only. In this research work propose the emotion recognition system through the multimodal approach, which integrated information from both facial and speech expressions. The database has eight basic emotions (neutral, calm, happy, sad, angry, fearful, disgust, and surprised). Emotions are classified using deep belief network method. The experiment results show that the performance of bimodal emotion recognition system, it has better improvement. The overall accuracy rate is 97.92%
Push-Based Network-efficient Hadoop YARN Scheduling Mechanism for In-Memory Computing

In the big data era, data-intensive cluster computing systems like Hadoop, have gained much popularity, and YARN, the second generation of Hadoop becomes the general resource manager in the Hadoop ecosystem. In the distributed computing scenarios, data locality (scheduling tasks on where the data resides) is essential to the performance since higher data locality brings lower network transmission cost and higher throughput. However, we find that the native YARN scheduling mechanism has little data locality and the delay scheduling strategy leads to the long-tail effect while achieving data locality for in-memory computing scenarios. Therefore, in this paper we propose the push-based YARN scheduling mechanism for the in-memory computing environment. First, we classify the ResourceRequests into various categories. Then, we prune the non-local ResourceRequests to achieve fast datalocality in-memory computation. Finally, we push the left longtail ResourceRequests to the data-locality nodes to avoid the long-tail effect. The experimental results demonstrate that the proposed scheduling mechanism achieves nearly 100% datalocality percentage comparing to the native YARN scheduling mechanism that only achieves 10%˜20% data-locality percentage. Under the identical data-locality percentage, the proposed pushbased scheduling mechanism promotes nearly 20% throughput and reduces nearly 10% application running time comparing to the existing delay scheduling mechanism used in YARN.
Fuzzy Coordination Control Method of AC/DC Power Interface

Aiming at solving the coordination control problem for AC/DC power interface in hybrid AC/DC microgrid, this paper proposes a fuzzy coordination control (FCC) method. This method artfully take advantage of the characteristics of the adaptive inverse control (AIC), and effectively combines advantages of droop control, AIC and secondary control (SC) via fuzzy theory. In the case of large power fluctuations, the method could reduce the overshoot of transient response due to the AIC cannot quickly adjust the droop coefficient. In the case of small power fluctuations or the hybrid AC/DC microgrid has reached steady state, the method could reduce the oscillation caused by the over-adjustment of AIC to the droop coefficient, and could effectively maintain the advantage of AIC in improving the inherent contradiction between power variation and frequency (voltage) deviation. In short, the FCC could obtain a faster and smoother dynamic response and a better steady-state response. Finally, the correctness and feasibility of FCC are validated via simulation case studies using MATLAB.
Real-Time Resource Slicing for 5G RAN via Deep Reinforcement Learning

With the rapid growth of Internet of Things (IoT), network slicing is regarded as an important technology to support the multi-users' needs for 5G mobile network. Network slicing allows network operators to provide services to different users, which can improve the rational utilization of network and hardware resources. In order to ensure the quality of service and build low-cost network infrastructure services, it is a challenging problem to find an appropriate resource allocation mechanism. In this paper, we discuss resource allocation in 5G radio access network (RAN). Considering the real-time resource request of the slice user, we propose a semi-Markov decision system model, which enables the virtual network provider to effectively satisfy the different user demands in real time. Then, we propose a resource slicing algorithm based on deep reinforcement learning (RS-DRL), which aims to improve the long-term benefits of virtual network providers and the utilization of slicing resources. We evaluate the performance of the RS-DRL through evaluations and comparisons. The results show that the proposed RS-DRL algorithm can effectively improve the performance and achieve the long-term benefits quickly.