Miracle-dz/UniFAR-FTUs huggingface dataset:
Prerequisite-Driven Deep Knowledge Tracing

Knowledge tracing serves as the key technique in the computer supported education environment (e.g., intelligent tutoring systems) to model student's knowledge states. While the Bayesian knowledge tracing and deep knowledge tracing models have been developed, the sparseness of student's exercise data still limits knowledge tracing's performance and applications. In order to address this issue, we advocate for and propose to incorporate the knowledge structure information, especially the prerequisite relations between pedagogical concepts, into the knowledge tracing model. Specifically, by considering how students master pedagogical concepts and their prerequisites, we model prerequisite concept pairs as ordering pairs. With a proper mathematical formulation, this property can be utilized as constraints in designing knowledge tracing model. As a result, the obtained model can have a better performance on student concept mastery prediction. In order to evaluate this model, we test it on five different real world datasets, and the experimental results show that the proposed model achieves a significant performance improvement by comparing with three knowledge tracing models.
Nonlinear Voltage Regulation Strategy for a Fuel Cell/Supercapacitor Power Source System

The main purpose of this work is to model and control a fuel cell/supercapacitor power source system to feed a DC-bus with a constant output voltage despite load changes. The fuel cell is modeled as a nonlinear current-dependent voltage source meanwhile, the supercapacitor is represented as an ideal capacitor. The power converters are modeled using a average model techniques. Furthermore, the proposed nonlinear state feedback control method is based on backstepping techniques and Lyapunov stability analysis to ensure that error signals converge to zero. A load estimator is designed using Immersion-Invariance (I&I) theory to track load changes through available measurements. Finally, simulation results show that the proposed control scheme stabilizes the system when step changes are applied to the load.
Analysis and Simulation of Transformer Isolated High Current 48 V DC Power Supply with DC-UPS Capability Based on SCALDO Technique for Google's New Open Rack Power Architecture

Energy efficiency of power converters used in computer server farms is a major issue today since these facilities consume huge amounts of energy on a continuous basis. In 2016, Google introduced a 48 V rack power architecture at the Open Computing Project (OCP) summit to replace 12 V, and announced their plans to shift towards 48 V DC rails with DC-UPS. This was with the main target of reducing ohmic losses in the DC-distribution by 16 times and transformer losses by 30%. Compared to well-developed switching converter techniques, a new linear high efficiency RFI/EMI free DC-DC converter technique, namely SCALDO, has the potential to be used for these 48 V DC server power supplies. This paper presents analytical and simulation details of an electrically isolated, 48 V DC, 20 A extended-SCALDO based design. Experimental validation of this technique has been established by developing a proof of concept prototype, a scaled down 30-to-6-V DC converter. The paper highlights the advantages of combining the fly-back and forward modes similar to a switching converter, for charging a supercapacitor bank which acts as energy reservoir, where the final 48 V DC rail is regulated by a simple SCALDO converter extended to supply 20 A, and achieving DC UPS capability by over-sizing the supercapacitor.
Entire Regularization Path for Sparse Nonnegative Interaction Model

Building sparse combinatorial model with non-negative constraint is essential in solving real-world problems such as in biology, in which the target response is often formulated by additive linear combination of features variables. This paper presents a solution to this problem by combining itemset mining with non-negative least squares. However, once incorporation of modern regularization is considered, then a naive solution requires to solve expensive enumeration problem many times for every regularization parameter. In this paper, we devise a regularization path tracking algorithm such that combinatorial feature is searched and included one by one to the solution set. Our contribution is a proposal of novel bounds specifically designed for the feature search problem. In synthetic dataset, the proposed method is demonstrated to run orders of magnitudes faster than a naive counterpart which does not employ tree pruning. We also empirically show that non-negativity constraints can reduce the number of active features much less than that of LASSO, leading to significant speed-ups in pattern search. In experiments using HIV-1 drug resistance dataset, the proposed method could successfully model the rapidly increasing drug resistance triggered by accumulation of mutations in HIV-1 genetic sequences. We also demonstrate the effectiveness of non-negativity constraints in suppressing false positive features, resulting in a model with smaller number of features and thereby improved interpretability.
An All-Digital Time-Domain Smart Temperature Sensor With a Cost-Efficient Curvature Correction

This paper presents an all-digital time-domain smart temperature sensor (TDSTS) with a cost-efficient curvature correction, which can effectively improve the accuracy and cost. Inverter-based TDSTSs are low cost and low complexity but they exhibit a significant curvature, which severely impairs their accuracy. Linearity enhancement through linear interpolation reduces the curvature and results in a twofold improvement in the accuracy. In this paper, the proposed curvature correction not only simplified the scheme to reduce the hardware cost but also successfully eliminated the curvature to achieve the most suitable accuracy. In addition, the correction procedure was also simplified. The TDSTS was implemented in eight Xilinx field-programmable gate arrays for performance verification and occupied 74 slices per sensor. The logic utilization for the proposed curvature-corrected circuit was 17 slices, and its cost was only 23% of the hardware cost. The experimental results indicated that the maximum inaccuracy decreased from 5 °C to 1.8 °C over the temperature range of −20 °C–100 °C, resulting in an approximate threefold improvement in the accuracy. Compared with a previous study, the improvements of 30% and 43% in the accuracy and cost, respectively, successfully validate the proposed technique.
Dead-Time Analysis of a Universal SiC-GaN-Based DC-DC Converter for Plug-In Electric Vehicles

A new universal full-directional SiC-GaN-based two-phase DC-DC converter has been recently introduced. It is supposed to replace with DC-DC battery charger and DC-DC traction drive converters in Plug-In Electric Vehicles. The SiC-GaN-based converter is designed to have the advantage of low switching power loss of GaN in hard-switching application and also avoid a high number of paralleled phases for high power multi-phase DC-DC converter applications. Especially in this paper, a dead-time analysis is done for the state-of-the-art high power GaN and SiC components of the proposed converter. To this aim, a new Spice-based simulation method is presented to obtain precise dead-time power loss calculations. Furthermore, the effect of negative OFF gate-source voltage is investigated.
A Stereo Camera Based Static and Moving Obstacles Detection on Autonomous Visual Navigation of Indoor Transportation Vehicle

This paper proposes an indoor moving obstacle avoidance system with stereo camera for an autonomous transportation vehicle. Using three-dimension (3D) data, the obstacle avoidance system first separates the floor and non-floor data. Then, using density-based spatial clustering of applications with noise, the system classifies obstacles as static or moving based on optical flows from the data's center of gravity. Next, using Enhanced Vector Field Histogram method on depth data, the system locates a moving space for the vehicle and estimates the velocity and angular velocity of any moving obstacles using Kalman filter. In addition, the system selects a space for passing with Enhanced Vector Field Histogram method only for static obstacles. The system predicts whether avoidance is possible based on estimated velocity of obstacle. If obstacles cannot be avoided, another path is selected. The proposed system is validated in experiments using a moving robot as the test vehicle in traffic situations where collision with other moving objects (i.e., obstacles) is predicted. The results show that the robot effectively avoids moving obstacles by turning or changing velocity.
Heterogeneous Data Integration by Learning to Rerank Schema Matches

Schema matching is a task at the heart of integrating heterogeneous structured and semi-structured data with applications in data warehousing, process matching, data analysis recommendations, Web table matching, etc. Schema matching is known to be an uncertain process and a common method of overcoming this uncertainty is by introducing a human expert with a ranked list of possible schema matches from which the expert may choose, known as top-K matching. In this work we propose a learning algorithm that utilizes an innovative set of features to rerank a list of schema matches and improves upon the ranking of the best match. The proposed algorithm assists the matching process by introducing a quality set of alternative matches to a human expert. It also serves as a step towards eliminating the involvement of human experts as decision makers in a matching process altogether. A large scale empirical evaluation with real-world benchmark shows the effectiveness of the proposed algorithmic solution.
SSDMV: Semi-Supervised Deep Social Spammer Detection by Multi-view Data Fusion

The explosive use of social media makes it a popular platform for malicious users, known as social spammers, to overwhelm legitimate users with unwanted content. Most existing social spammer detection approaches are supervised and need a large number of manually labeled data for training, which is infeasible in practice. To address this issue, some semi-supervised models are proposed by incorporating side information such as user profiles and posted tweets. However, these shallow models are not effective to deeply learn the desirable user representations for spammer detection, and the multi-view data are usually loosely coupled without considering their correlations. In this paper, we propose a Semi-Supervised Deep social spammer detection model by Multi-View data fusion (SSDMV). The insight is that we aim to extensively learn the task-relevant discriminative representations for users to address the challenge of annotation scarcity. Under a unified semi-supervised learning framework, we first design a deep multi-view feature learning module which fuses information from different views, and then propose a label inference module to predict labels for users. The mutual refinement between the two modules ensures SSDMV to be able to both generate high quality features and make accurate predictions.Empirically, we evaluate SSDMV over two real social network datasets on three tasks, and the results demonstrate that SSDMV significantly outperforms the state-of-the-art methods.
Classify high dimensional datasets using discriminant positive negative association rules

The purpose of this paper is to investigate the ability of binary classification using the possitive negative association rules mining (PNARs) for large dataset. The PNARs (as understood until now) called narrow PNARs (NPNARs for short) as well as their generalized forms called expanded PNARs (EPNARs for short) will be investigated. The paper proposes a classification algorithm based on mining the discriminant NPNARs and EPNARs. The algorithm integrates Dimensionality Reduction, Rule Generation, Removal of redundant rules and Classification. The number of classified instances in testing datasets and the classification performance using the valid disriminant NPNARs and EPNARs found out by the proposed algorithm are compared with each other and with 12 other classification algorithms.Experimental results show that the mining of EPNARs rather than that of NPNARs has prospects to be a classification technique applied in the real world. The paper also outlines some issues that need to be researched in the near future so that using the mining of the discriminant EPNARs becomes an efficient technique for classifying high dimensional datasets.
A Model-Based Approach for Design Time Elasticity Rules Generation

Elasticity is a key feature of cloud systems. It allows service providers to adapt resource provisioning to workload changes without impacting the users. For this purpose, the system is periodically reconfigured according to workload variations using elasticity rules or policies. The challenge is to come up with those elasticity rules that efficiently provision resources to handle the workload variations and meet the quality of service expressed in the Service Level Agreements (SLAs). The definition of such elasticity rules is a challenging task. In this paper, we propose the generation of the elasticity rules automatically at system configuration design time, i.e. offline. We propose a model based approach that generates automatically these elasticity rules using the information acquired from the dimensioning and configuration of the system.
Cascaded Adaptive Super Twisting controller for DC/DC converters in electrical vehicle applications

During the past decade, power electronics research has focused on the development of DC/DC power converters for electric vehicle (EVs) applications, and it is estimated that there is still a huge potential for this field during the coming years. In this paper, an enhanced super twisting sliding mode controller is presented, discussed and tested experimentally for DC/DC converters in electric vehicle applications. An interesting point comes from the proposed strategy which reduces by half the number of gains to adjust, ensures a chatter-free response, gives finite-time convergence and improved accuracy. The proposed controller is compared with different controllers such as PI and conventional super twisting sliding mode. The comparison is performed at variable supply voltages and output currents to show the effect of these variables on the converter performance. The experimental results show that the dynamic response of the DC/DC converter is significantly improved compared to classical controls.
Deep Semantic Correlation Learning Based Hashing for Multimedia Cross-Modal Retrieval

For many large-scale multimedia datasets and web contents, the nearest neighbor search methods based on the hashing strategy for cross-modal retrieval have attracted considerable attention due to its fast query speed and low storage cost. Most existing hashing methods try to map different modalities to Hamming embedding in a supervised way where the semantic information comes from a large manual label matrix and each sample in different modalities is usually encoded by a sparse label vector. However, previous studies didn't address the semantic correlation learning challenges and couldn't make the best use of the prior semantic information. Therefore, they cannot preserve the accurate semantic similarities and often degrade the performance of hashing function learning. To fill this gap, we firstly proposed a novel Deep Semantic Correlation learning based Hashing framework (DSCH) that generates unified hash codes in an end-to-end deep learning architecture for cross-modal retrieval task. The major contribution in this work is to effectively automatically construct the semantic correlation between data representation and demonstrate how to utilize correlation information to generate hash codes for new samples. In particular, DSCH integrates latent semantic embedding with a unified hash embedding to strengthen the similarity information among multiple modalities. Furthermore, additional graph regularization is employed in our framework, to capture the correspondences from the inter-modal and intra-modal. Our model simultaneously learns the semantic correlation and the unified hash codes, which enhances the effectiveness of cross-modal retrieval task. Experimental results show the superior accuracy of our proposed approach to several state-of-the-art cross-modality methods on two large datasets.
Developing of Aging Wine Model by Dielectric Barrier Discharge (DBD) at Atmospheric Pressure

Toxics in alcohol have been strongly negative affected to human life in large area, especially the alcohol with the huge poison inside like methanol, aldehyde, furfural, acidity$\dots$ It killed a thousand people, in developing countries particular and in the world general. The disadvantages of the currently state are the using treatment systems with low treatment efficiency, costly and unstable due to using microbiological, biochemistries, combining physical chemistry method. In this study, the treatment alcohol from laboratory using dielectric barrier discharge (DBD) at low temperature and at atmospheric pressure was designed and developed to solve the above disadvantages. An 800ml/h treatment system was completed and assembled. In order to prove the stability of the processing system, before and after treating, alcohol samples were collected and measured with methanol, aldehyde, furfural, acidity by the Quality Assurance and Testing Center. The treating duration and applied voltage were studied. The results show that with high discharge density, the toxics in alcohol rapidly increased due to oxidation reaction. However, at high applied voltage and long treating duration the treatment efficiency increased. The results also showed that treatment efficiency reached to 60-80% and match The Vietnamese Standard 7043: 2013 prepared by the TCVN/TC/F9 TCVN National Standardization Technical Committee, the Directorate for Standards and Quality, Ministry of Science and Technology
A Finite-time Sliding Mode Observer for a Class of Perturbed Nonholonomic Systems

In this paper, an equivalent output injection based sliding mode observer is proposed for estimating the states of a class of perturbed n-dimensional nonholonomic systems. In the proposed design, we introduce a new transformation based on state scaling which renders the perturbed system in a triangular input form, amenable for the design of the observer. Thereafter, the equivalent control method is adopted in a sequential manner to estimate each of the system states one after another, in finite time such that ultimately all the states are estimated. Thus the entire state estimates are available for feedback after a finite time, in the presence of structured perturbations affecting the system. Simulations are performed on a unicycle mobile robot, to demonstrate the effectiveness of the proposed observer design.
Towards a Software Centric Approach for Ontology Development: Novel Methodology and its Application

The last few years have witnessed a paradigm shift in World Wide Web, from a global information space of connected documents to Semantic Web. It has been observed that the ontologies are playing a pivotal role in the growth of Semantic Web as it offers an effective knowledge representation with appropriate formalisms. Semantic Web technologies based on ontology have emerged as an appropriate engineering solution to the problems of developing systems that assure the integration of data from different sources with high end interoperability to provide seamless services to web users. Development of domain ontology exhibits both structural and logical complexity comparable to the development of software systems. However, the first one is more complex compared to any form of software development due to various factors. Though they are two complementary engineering branches, the maturity and popularity level of later is too high compared to the former. Unlike Software Engineering, absence of effective methodologies eligible to claim the tag 'standardized' aimed at supporting the development of ontology restricts the availability of large scale domain ontologies. Methodologies leveraged from Software Engineering to ontology development by extending the matured process models can bridge the gap between the above mentioned engineering branches to a significant extent. This paper proposes a software centric novel methodology for ontology development with a defined Ontology Development Life Cycle (ODLC) in terms of stages, workflows, activities and techniques. The applicability of the proposed methodology has been shown by developing prototype ontology.
Statistical Machine Translation for Bilingually Low-Resource Scenarios: A Round-Tripping Approach

In this paper we apply the round-tripping algorithm to Statistical Machine Translation (SMT) for making effective use of monolingual data to tackle the training data scarcity. In this approach, the outbound-trip (forward) and inbound-trip (backward) translation tasks make a closed loop, and produce informative feedback to train the translation models. Based on this produced feedback we iteratively update the forward and backward translation models. The experimental results show that translation quality is improved for $\mathbf{Persian\leftrightarrow Spanish}$ translation task.
Reversible Watermarking for the Security of Medical Image Databases

Medical images of different modalities are produced on a daily basis in the medical centers around the globe. The same is transmitted over the Internet to other medical practitioner to get the second opinion from doctors located in remote areas. Numbers of issues are raised with this transmission of medical information, for example, authenticity and confidentiality of medical information. To overcome these important issues, a medical image watermarking system has been proposed which first segments the input medical image into a region of interest (ROI) and region of non interest (RONI) areas. Later it implants two different watermarks in ROI and RONI. The first watermark is fragile watermark which is a binary pattern and is inserted in ROI and the other is robust watermark which is the composite watermark and is inserted in the RONI. The simple Least Significant Bit (LSB) substitution technique is employed for casting the watermarks. CT scan medical images are used for experiments. The simulation results reveal that proposed system provides better security of medical images as well addresses both the issues of authenticity and confidentiality.