Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Programming Autograder Formative Data to Understand Student Growth

This Innovative Practice Full Paper presents the experience of using formative data produced by an autograder tool to understand how students complete assignments in a second year computing course. Autograding has increasingly been used in computing courses to evaluate student work. Using autograders supports the scalability of classes and provides other advantages such as accurate assessment, reproducibility over semesters, and rapid feedback. As a consequence of using an autograder, students produce a trace of their problem-solving process while completing assignments. In contrast to the summative assessment performed on a final submission, this trace captures the formative steps that were involved in the development of the final submission. Analyzing this trace can provide insights into student problem-solving methodology as well as the structure of the problems being assigned, and the impact of classroom interventions during an assignment. To produce this view into student learning, we have developed an autograder with the initial goal of improving student instruction. It uses a combination of static and dynamic analysis techniques to support accurate assessment and the ability to check for more complex requirements than can be captured by approaches that compare program output with a ground truth. The tool has been used in a sophomore level course on data structures & algorithms. This course uses the Java programming language and introduces topics such as lists, searching, sorting, binary search trees, priority queues, hash tables, and graphs. Our current analysis work focuses on analyzing traces produced by students to identify difficult parts of an assignment (as represented by grading regressions), and the path through possible solution states that students take as they complete an assignment. In this paper, we discuss the design of this tool, what insights the data captures provide into student learning, and give ideas for future directions supported by gathering formative assessment data.
Hand Crafted Features for Efficient Lung Cancer Diagnosis Using Stacked Autoencoder

The most critical steps to improve the clinical management of the lung cancer are the early detection and the accurate diagnosis. In this study, an automated system for lung cancer diagnosis from one computed tomography (CT) scan is developed to distinguish between malignant and benign nodules. This system utilizes two different kinds of features to describe the lung nodules. These features indicate the nodule’s preceding growth rate, which is considered the major point in pulmonary nodule diagnosis. Analytical Local Binary Pattern is implemented to characterize the pulmonary nodule texture. Special functions called spherical harmonics are used to characterize the nodule surface. Finally, a stacked autoencoder is utilized to reduce the dimensionality of the modeled features values and to eliminate the noise in the data followed by a probability-based linear classifier to diagnose the nodule. The proposed system is tested using Lung Image Database Consortium (LIDC) database. The effectiveness of the presented framework is confirmed where the system accuracy, sensitivity, specificity, and area under the ROC curve of 93.79%, 94.36%, 92.80%, and 0.9764 respectively are achieved.
DirectionNet: Road Main Direction Estimation for Autonomous Vehicles from LiDAR Point Clouds

In recent years, “end-to-end” autonomous driving methods based on deep learning have received increasing attention. Most of these methods take raw sensor data as input and directly generate vehicle control commands. The autonomous driving system is so complex that overfitting is inevitable, which will seriously affect the robustness and safety of the entire system. Although deep learning has shown outstanding performance over traditional machine learning in many tasks including environment perception, using end-to-end technologies that lack explanatory in the autonomous driving system will reduce system’s robustness and safety. Different from existing perception tasks, inspired by human’s driving style, we extract the key points of global route planning’s projection on roads, which is called the “road main direction”. This “road main direction” is then used for the subsequent driving tasks, guiding the vehicle along the road. Instead of using image as input, which is very susceptible to weather and illumination, our model directly processes the LiDAR point clouds. The results show that our method can extract the main direction of the road in different scenarios to provide direction guidance for the unmanned vehicle without a prior high-definition map.
ImageSeeker: a content-based image retrieval system

In many areas of commerce, government, academia, and medicine, large collections of digital images are being used. Usually, the only way of searching these collections is by their name, or by browsing which is unpractical for large number of images. ImageSeeker aims at providing an improved technique to enhance image searching. It focuses on extracting visual contents from images and annotating them; it is based on the concept of CBIR (Content Based Image Retrieval) to retrieve the contents of the image based on what it has learnt during past trainings. When a user requests an image for a certain object, all images containing the same object will show up. ImageSeeker maintains high accuracy in finding matching results to the user's query. The system was tested on images containing natural scenes, specifically, see, sand, grass, clouds and sky.
A Battery Impedance Online Identification Method Based on Double Side Band Small Signal Injection of Inverter

In this paper, a novel fast battery impedance online identification method based on double side band small signal injection through controlling of the battery-connected inverter is proposed. The principle of double side band small signal injection and impedance identification has been discussed. This method takes short time and shows high accuracy while hardly affects the normal operation of the system. It is economical since there is no need to add extra signal injection equipment but using the existing battery-connected inverter in application. In view of the widely-used battery-inverter topology in electrified transportation field, this method has potential to popularize in future. Comparing with the constant-amplitude AC signal injection, the double side band small signal injection method can be with more frequency points in identification with the same period.
A New Regulator Design for Manipulator Robots Implemented in a 6 DOF CoBot

Through the design technique known as energy molding, a control structure for robot manipulators is proposed that is based on a combination of the hyperbolic tangent function and the hyperbolic cosine function. The proposed control scheme is used to carry out by means of simulation the position control or regulation of the cooperative robot manipulator of 6 dof UR5, the results obtained are compared with the PD and hyperbolic tangent control structures through the L2 norm.
Toddler-inspired embodied vision for learning object representations

Recent time-contrastive learning approaches manage to learn invariant object representations without supervision. This is achieved by mapping successive views of an object onto close-by internal representations. When considering this learning approach as a model of the development of human object recognition, it is important to consider what visual input a toddler would typically observe while interacting with objects. First, human vision is highly foveated, with high resolution only available in the central region of the field of view. Second, objects may be seen against a blurry background due to toddlers’ limited depth of field. Third, during object manipulation a toddler mostly observes close objects filling a large part of the field of view due to their rather short arms. Here, we study how these effects impact the quality of visual representations learnt through time-contrastive learning. To this end, we let a visually embodied agent “play” with objects in different locations of a near photo-realistic flat. During each play session the agent views an object in multiple orientations before turning its body to view another object. The resulting sequence of views feeds a time-contrastive learning algorithm. Our results show that visual statistics mimicking those of a toddler improve object recognition accuracy in both familiar and novel environments. We argue that this effect is caused by the reduction of features extracted in the background, a neural network bias for large features in the image and a greater similarity between novel and familiar background regions. The results of our model suggest that several influences on toddler’s visual input statistics support their unsupervised learning of object representations.
Customer-Side Load Forecasting Method Based on Two-Level Clustering and MGRU-AT

At present, the potential analysis of adjustable load resources is mostly based on refined model to describe its demand and regulation ability, and less consider the load data drive and the correlation within the data. Accurate and effective classification of different types of adjustable load resources by historical load data, and then considering the characteristics of adjustable load, accurate prediction can effectively improve the accuracy of load potential calculation. An adjustable load power prediction method ( CK-MGRU-AT ) based on improved Canopy and K-Medoids two-level clustering, multi-channel gate recurrent unit ( MGRU ) and attention mechanism ( AT ) are proposed in this paper. Firstly, the improved Canopy algorithm is used to obtain the cluster center samples of various types of adjustable resources. Then the load data with similar characteristics are aggregated into a cluster by K-Medoids algorithm, which makes GRU more conducive to extracting the time characteristics of load data. Finally, the MGRU-AT model is used to extract the time characteristics of each cluster, and it is finally mapped to the power prediction outputs of different types of adjustable loads through the full connection layer. In order to verify the advantages of the proposed model ( CK-MGRU-AT ) in the stability of prediction accuracy, this paper uses the historical load data of industrial and commercial users in a typical region for experimental analysis. The results show that the proposed model reduces the average absolute error at least 0.0003, the mean square error 0.0149, and increases the R2 index at least 0.0027.
Adaptive Backstepping Control for Upper Limb Rehabilitation Robot using PSO Tuning

This paper presents adaptive backstepping control for two degrees of freedom upper limb exoskeleton rehabilitation robot for passive rehabilitation therapy. The optimal backstepping controller parameters and adaptation law parameter are determined by minimizing a cost function using the particle swarm optimization algorithm. The optimization process is performed offline to determine the optimal parameters once and then implement them in the controller. The cost function is designed to improve the tracking performance and dynamic characteristics.
Temporal Discriminative Micro-Expression Recognition via Graph Contrastive Learning

Micro-Expressions (MEs) are involuntary and im-perceptible facial movements that reflect the underlying emotions and inner activities. Recently, ME recognition technology has been widely used in several fields such as medical treatment. Due to the subtle variations among the video sequence and the limited training data, the ME recognition task still remains a challenging problem. Existing methods tend to address the ME recognition problem from two aspects: (1) Data augmentation and (2) Expression signal amplification. Few works realize the importance of temporal variation hidden in the ME sequence. Based on the above observation, we propose a Graph Contrastive Learning (GCL) framework to effectively perceive subtle temporal variation for robust ME recognition. Specifically, the strong spatial feature representation is captured through the transformer-based ME feature encoder. Then, the proposed GCL builds the graph structure for the ME sequence and introduces the graph convolution to model the temporal relationship. To capture and highlight the temporal variation hidden in the ME sequence, a contrastive learning framework is designed to discriminately learn the differences between the normal and the abnormal ME samples. Both quantitative and qualitative experimental results show the effectiveness and superiority of our method compared with the prior state-of-the-arts.
Cyberbullying Detection in Code-Mixed Languages: Dataset and Techniques

The advent of the Internet is a boon to society. However, many of its banes cannot be undermined, and cyberbullying is one of them. In this work, we have created a benchmark corpus for cyberbullying detection in code-mixed languages. In India, most communications on different social media platforms are based on Hindi and English languages, and language switching is a common practice in digital communication. To investigate how code-mixed data can be handled effectively, BERT language model and VecMap based bilingual embedding along with a two-channel convolutional neural network model, namely BERT+VecMap-CNN, have been used. The input to one channel is the BERT language model and that to the other is the bilingual word embedding based on VecMap. As a baseline, we used standard machine learning models, as well as deep neural network models like CNN and LSTM. Our proposed model outperforms the baselines with overall accuracy and F1-measure values of 81.12%, and 81.03%, respectively. Furthermore, a different benchmark code-mixed dataset has been considered to show the robustness of our proposed model.1
Optimal Strategy of Energy Aggregators in the Energy and Regulation markets: Chance-Constrained Approach

Multiple energy systems have become increasingly interdependent in recent years. In the smart grids, energy aggregators (EAs) as intermediaries between consumers and energy markets are responsible to supply the required energy of consumers. However, fluctuations in consumption could endanger the expected cost of EAs. Therefore, EAs shall use flexible resources to diminish the unexpected variation of demand. Among various types of energy resources, battery energy storages (BESs) and combined heat and power (CHP) units are flexible alternatives to provide the required electricity and heat demand of consumers. In this work, a linear model is presented for the participation of EAs in the energy and regulation markets based on the electrical and thermal requirements of consumers. To model the impacts of stochastic consumption, the chance-constrained programming approach is used. Moreover, the McCormick relaxation is addressed to linearize the cost function of the CHP unit. The performance is evaluated via a case study and various confidence levels. Simulation results indicate that increasing the confidence level from 0.75 to 0.85 results in a 5.4% increase in EA’s costs.
Kinematics and Dexterity Analysis of a Compound Continuum Manipulator for Minimally Invasive Surgical

The excellent performance of the continuum manipulators in a small space provides many advantages for developing minimally invasive surgical instruments. However, few researchers have paid attention to how to measure the dexterity of this type of manipulator, although dexterity is one of the key factors in determining whether it can be used in more fields. In this paper, a novel compound continuum manipulator (CCM) is proposed to design a minimally surgical instrument which has high-dexterity. An improved Levenberg Marquardt (ILM) method is designed to establish the inverse kinematics model of the CCM. The calculation time of the ILM method is more than 2000 times faster compared with the LM method. Then, the dexterity indexes of the manipulators based on inverse kinematics are proposed. The dexterity indexes of the manipulators show that the dexterity of the CCM is 1.231 times that of the CTM. This paper is critical for designing minimally invasive surgical instruments with high dexterity.
Preventing Multistage Discriminative Event-based Model From Catastrophic Forgetting: A HAT Trick

In order to diagnose Alzheimer's disease at the early stage, we put forward a multistage discriminative eventbased model (MDEBM) in our previous research. Although this model succeeded in classifying the patients into three groups with cognitively nomal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD), we discover that its accuracy becomes more problematic with the increasing real-world cases. Thereby, in this paper, we proposed an advanced version of the MDEBM, which overcomes the weakness by introducing an incremental learning method called Hard Attention to the Task (HAT). As a result, our model can continuously absorb the new data and extend its existing knowledge with the aim of addressing further tasks. The effectiveness of our model is verified by experiments on the ADNI dataset.
Application of unsupervised learning techniques to DNA microarray databases: a comparative case study

We present the application of the unsupervised machine learning clustering techniques K-Means and Fuzzy C-Means to DNA microarray gene expression data from breast cancer tissues. The mathematical basis and the development of the algorithm of the techniques are shown, and the Expectation Maximization technique is presented as an alternative for the assignment of missing data. Results show that the best method for the clustering of the genetic expression data under study is the Fuzzy C-Means algorithm.
Current-limiting control of interlinking converter to maintain power balance in hybrid microgrids

In this paper, a novel nonlinear current-limiting controller that maintains the desired power balance in a hybrid microgrid, is proposed for interlinking converters (ICs). The RMS value of the IC current is analytically proven to remain below a desired maximum value at all times using nonlinear ultimate boundedness theory, without limiting separately the d− and q−axis current components and without using adaptive saturation units, as done in conventional approaches. Furthermore, the stability of the closed loop system is investigated through root locus analysis for different values of the controller gain. Finally, the performance of the proposed control scheme is validated through detailed simulation results of an interlinking converter connected to both an AC and a DC subgrid.
Student Misconceptions about Loops in Introductory Programming Courses and the Influence of Representations

Research Work in Progress Paper: Loops are a fundamental concept of programming. For novice programmers loops generally and nested loops specifically pose big difficulties. This paper identifies common student misconceptions about loops in a first-year C++ introductory computer science course and the relation of these misconceptions to representations. These findings could help design effective instructional methods.
RADAR: Reactive and Deliberative Adaptive Reasoning - Learning When to Think Fast and When to Think Slow

When designing and deploying Reinforcement Learning (RL) algorithms, one typically selects a single value for the discount rate, which results in an agent that will always be equally reactive or deliberative. However, similarly to humans, RL agents can benefit from adapting their planning horizon to the current context. To enable this, we propose a novel algorithm: RADAR: Reactive and Deliberate Adaptive Reasoning. RADAR enables an agent to learn to adaptively choose a level of deliberation and reactivity according to the state it is in, given that there are cases where one mode of operation is better than the other. Through experiments in a grid world, we verify that the RADAR agent has the capability to adapt its reasoning modality to the current context. In addition, we observe that the RADAR agent exhibits different preferences regarding its thinking modes when a penalty for mental effort is included in its mathematical formulation.
Performance Analysis of Slotless Dual-Stator and Single-Rotor Axial-Flux Permanent Magnet Machine

Axial-flux permanent magnet (AFPM) machines are well suited for wide range of applications. Three-phase dual-stator and single-rotor AFPM motor with slotted and slotless stator topologies with two different slot/pole configurations are studied in this paper. 18slot/12pole, and 27slot/24pole configuration with 10 kW rated power and 12,000 rpm rated speed are compared for different performance aspects. The enclosure of the motor is held constant with several other parameters to obtain a fair comparison. Cost analysis is performed to address the cost-effectiveness of the AFPM machines. Finally, mechanical structural analysis is performed to ensure the safe and reliable operation of all four rotors at the maximum speed of 17,000 rpm.
Estimation of multiple scattering by iterative inversion, Part I: Theoretical considerations

A review has been given of the surface-related multiple problem by making use of the so-called feedback model. From the resulting equations it has been concluded that the proposed solution does not require any properties of the subsurface. However, source-detector and reflectivity properties of the surface need be specified. Those properties have been quantified in a surface operator and this operator is estimated as part of the multiple removal problem. The surface-related multiple removal algorithm has been formulated in terms of a Neumann series and in terms of an iterative equation. The Neumann formulation requires a nonlinear optimization process for the surface operator; while the iterative formulation needs a number of linear optimizations. The iterative formulation also has the advantage that it can be integrated easily with another multiple removal method. An algorithm for the removal of internal multiples has been proposed as well. This algorithm is an extension of the surface-related method. Removal of internal multiples requires knowledge of the macro velocity model between the surface and the upper boundary of the multiple generating layer. In Part II (also published in this issue) the success of the proposed algorithms has been demonstrated on numerical experiments and field data examples.
Long-term Visual Place Recognition

In this work, we study the long-term performance of visual place recognition in urban outdoor environment. A long-term benchmark is constructed from the Oxford RobotCar dataset. It contains sequences of the same route traversed over a period of approx. 500 days. We carefully selected three gallery sequences, one training sequence and 15 query sequences that cover different seasons, times of day and weather. The RobotCar sequences from the first half year have several problems, for example, only partial routes and inaccurate location data. We circumvent these problems by reversing the time. In the benchmark dataset the gallery and training images are the latest and the query sequences go gradually back in time. Our experiments provide the following findings. 1) the selected gallery sequence has strong impact on performance, and 2) additional training sequences help to mitigate differences between the gallery sequences. In addition, results indicate that 3) there is a long-term trend of performance degradation over time. The degradation can be quantified as about 6 percentage points per 100 days and, therefore, the initial performance of 40% eventually drops below 20% at the end.