Miracle-dz/UniFAR-FTUs huggingface dataset:
Image-Enhanced Multi-level Sentence Representation Net for Natural Language Inference

Natural Language Inference (NLI) task requires an agent to determine the semantic relation between a premise sentence (p) and a hypothesis sentence (h), which demands sufficient understanding about sentences from lexical knowledge to global semantic. Due to the issues such as polysemy, ambiguity, as well as fuzziness of sentences, fully understanding sentences is still challenging. To this end, we propose an Image-Enhanced Multi-Level Sentence Representation Net (IEMLRN), a novel architecture that is able to utilize the image to enhance the sentence semantic understanding at different scales. To be specific, we introduce the corresponding image of sentences as reference information, which can be helpful for sentence semantic understanding and inference relation evaluation. Since image information might be related to the sentence semantics at different scales, we design a multi-level architecture to understand sentences from different granularity and generate the sentence representation more precisely. Experimental results on the large-scale NLI corpus and real-world NLI alike corpus demonstrate that IEMLRN can simultaneously improve the performance. It is noteworthy that IEMLRN significantly outperforms the state-of-the-art sentence-encoding based models on the challenging hard subset and challenging lexical subset of SNLI corpus.
Architectures and Design Techniques of Digital Time Interpolators

This paper provides an in-depth review of the principle, architecture, and design techniques of digital time interpolators. An assessment of the advantages and drawbacks of analog time interpolators are presented first. It is followed with a comprehensive examination of reported digital time interpolators including gated inverter time interpolators, harmonic-rejection time interpolators, voltage-division time interpolators, and pipelined time interpolators. The design considerations of digital time interpolators including input signaling, output signaling, input-output isolation, and shoot-through are studied in detailed. The performance of recently reported digital time interpolators is compared. Finally, a 5-bit digital-to-time converter with 2-bit time interpolation is designed in a TSMC 65 nm 1.0 V CMOS technology and analyzed using Spectre from Cadence Design Systems with BSIM4.4 device models. Simulation results are presented.
A United Approach to Learning Sparse Attributed Network Embedding

Recently, the Network Representation Learning (NRL) techniques, which target at learning the low-dimension vector representation of graph structures, have attracted wide attention due to the effectiveness on various social-oriented application. Though large efforts have been made on the joint analysis combining node attributes with the network structure, they may usually fail to summarize the weighted correlations within nodes and attributes, especially when the nodes suffer extremely sparse attributes. To that end, in this paper, we propose a novel Sparse Attributed Network Embedding (SANE) framework to learn the network structure and sparse attribute information simultaneously in a united approach. Specifically, we first embed the nodes and attributes into a low-dimensional vector space. Then we introduce the pairwise method to capture the interaction between nodes and sparse attributes, and aggregate the attribute information of neighbors to alleviate sparsity for obtaining a better vector representation of node embeddings, which will be used in following network representation learning task. Along this line, we maintain the network structure by maximizing the probability of predicting the center node according to surrounding context nodes. Different from previous work, we introduce an attention mechanism to adaptively weigh the strength of interactions between each context node and the center node, according to the node attribute similarity. Furthermore, we combine the attention network with CBOW model to learn the similarity of the network structure and node attributes simultaneously. Extensive experiments on public datasets have validated the effectiveness of our SANE model with significant margin compared with the state-of-the-art baselines, which demonstrates the potential of adaptively attribute analysis in network embedding.
A Cell-based MITC3+ Flat Shell Element for Frequency Analyses of Plates and Shells Using Substructuring Methods

A cell-based three-node triangular flat shell element with a bubble node is introduced to analyze frequencies of plates and shells. By smoothing the in-plane strains on sub-triangular domains created by connecting the bubble node to element nodes and applying the divergence theorem to derivatives in the gradient matrices, the integration of corresponding stiffness matrices are transformed from surface to line integrals. To remove the shear locking, the MITC3+ technique for three-node triangular degenerated shell elements with a bubble node is employed. The Craig-Bampton (CB) and enhanced Craig-Bampton (ECB) methods, which are well-known reduced-order modeling techniques based on substructuring strategy, are used for efficiently solving frequency analyses. Numerical results demonstrate the reliability and superiority of the present element, called CS-MITC3+, when combined with the substructuring methods to compute frequencies of plate and shell structures.
Optimal trade-off between hard and soft-switching to achieve energy saving in industrial electric vehicles

Ahstract-DCIDC battery chargers used in industrial electric vehicle applications are designed to be size, weight and power efficient. Wide bandgap (WBG) devices which enable high frequency operations help reduce size of magnetics as well as the converter volume achieving better efficiencies compared to Si based solutions. It has not been often discussed in literature but in case of battery chargers the overall saved energy becomes a better performance indicator than the efficiency at rated power alone. This is due to their peculiar charge/discharge characteristics. In this paper, the design of a GaN (Gallium nitride) converter for an industrial battery charger application is studied. An optimal design procedure aiming at optimizing the converter energy utilization efficiency (EUE) over the complete state of charge (SoC) as well as minimizing BOM (Bill of Material) costs due to magnetics is presented in order to highlight the energy perspective. The results show considerable energy savings compared to conventional design methods.
Multiport Interface Converter with an Energy Storage for Nanogrids

Nanogrid is a small-scale microgrid that consists of loads, sources and storages interfaced with power electronic converters. Conventionally, the droop control provides power sharing. To establish autonomous operation with stable voltage and reliable power quality, an energy management system (EMS) in the form of a bidirectional multiport converter with energy storage at the PCC is used. Unlike typical approach, this paper proposes an EMS concept for nanogrids based on a single grid-forming voltage source. This allows using a simple voltage control algorithm instead of the droop control; in addition, it is compatible with all conventional power electronic interfaces. The EMS concept, its operation modes and control strategies are described. Some case studies are presented and analyzed by computer simulations.
Hybrid Segmentation Method for Grayscale Images

In this paper, we propose a hybrid segmentation method based on the fuzzy clustering approach and mathematical morphology. First, the grayscale image is segmented using a Fast and Robust Fuzzy C-means algorithm that takes into account the spatial information. Then, the clustering result is refined using some morphological operations. The fastness and the robustness of the proposed method are demonstrated on a synthetic and some real images, where valuable results were obtained.
Independent Feature and Label Components for Multi-label Classification

Investigating correlation between example features and example labels is essential to solve classification problems. However, identification and calculation of the correlation between features and labels can be rather difficult for high-dimensional multi-label data. Both feature embedding and label embedding have been developed to tackle this challenge, and a shared subspace for both labels and features are usually learned by existing embedding methods to simultaneously reduce dimensionality of features and labels. In contrast, this paper suggests to learn separated subspaces for features and labels by maximizing the independence between components in each subspace and maximizing the correlation between these two subspaces. The learned independent label components indicates fundamental combinations of labels in multi-label datasets, which thus helps to reveals the correlation between labels. On the other hand, the learned independent feature components lead to a compact representation of example features. The connections between the proposed algorithm and existing embedding methods have been discussed. Experimental results on real-world multi-label datasets demonstrate the necessity of exploring independence components from multi-label data and the effectiveness of the proposed algorithm.
Context-based Sarcasm Detection in Hindi Tweets

Sentiment analysis is the way of finding ones' opinion towards any specific target. Sarcasm is a special type of sentiment which infers the opposite meaning of what people convey in the text. It is often expressed using positive or intensified positive words. Nowadays, posting sarcastic messages on social media like Twitter, Facebook, WhatsApp, etc., became a new trend to avoid direct negativity. In the presence of sarcasm, sentiment analysis on these social media texts became the most challenging task. Therefore, an automated system is required for sarcasm detector in textual data. Many researchers have proposed several sarcasm detection techniques to identify sarcastic text. These techniques are designed to detect sarcasm on the text scripted in English since it is the most popular language in social networking groups. However, parallel research for sarcasm detection on different Asian languages like Hindi, Telugu, Tamil, Urdu, and Bengali are not yet explored. One of the reasons for the less exploration of these languages for sarcastic sentiment analysis is the lack of annotated corpus even though they are popular in a large networked society. In this article, we proposed a context-based pattern i.e. “sarcasm as a contradiction between a tweet and the context of its related news” for sarcasm detection in Hindi tweets. The proposed approach utilized Hindi news as the context of a tweet with in the same timestamp and attained an accuracy of 87 %.
Robustness Analysis of Voltage Control Strategies of Smart Transformer

The increasing penetration of Distributed Generators (DG) in the modern electric distribution network poses high priority on the problem of the stability. In this article the Harmonic Stability of a Smart Transformer-fed microgrid is investigated under different control strategies. The considered microgrid is composed by a Smart Transformer and three Distributed Generators, considering the bandwidth of the DGs unknown. The robustness is evaluated analysing the eigenvalues as a consequence of a variation of the DGs bandwidth. The system is modelled as a Multi Input Multi Output System (MIMO); the eigenvalue based analysis is carried out to assess the stability and compare the robustness of the traditional double-loop PI and a state-feedback (SF) integral controller. The results show that the SF controller ensures a higher robustness than the traditional PI controller with respect to increasing bandwidths of the DGs.
Vision-navigated Bilateral Control for Master-slave Teleoperation System

In this paper, we consider a vision-navigated bilateral control for master-slave teleoperation system. Firstly, a brief summary of the passivity-based visual feedback system is provided. Next, a vision-navigated teleoperation system is constructed by combining the visual feedback system and the master-slave teleoperation system. After that, we propose the vision-navigated bilateral control law and discuss the stability analysis based on the Lyapunov method. Finally, the simulation results show the validity of the proposed vision-navigated bilateral control.
Energy Regeneration-Based Hybrid Control for Transfemoral Prosthetic Legs Using Four-Bar Mechanism

This paper presents an energy regeneration-based hybrid control method for transfemoral prosthetic legs using a four-bar linkage mechanism. To do that, we consider a model of transfemoral prosthetic leg with three-degrees of freedom that employs a knee mechanism using a four-bar linkage mechanism. We also focus on suggesting a practical strategy for effective implementation rather than a complex algorithm. In this point of view, we devise a hybrid controller for the prosthetic leg. Actually, the motions of the hip mechanism of the leg are controlled by a PID control method and the knee joint is controlled by an impedance control method. We also consider an electrical energy regeneration module for effective energy utilization. Through an exemplary walk simulation, we show the availability of the hybrid controller for such a transfemoral prosthetic leg and also address the advantages of using such a four-bar mechanism and an energy regeneration module for effective driving the knee joint. It is finally concluded that the proposed hybrid controller can be applied for effective control of transfemoral prosthetic legs using such a four-bar mechanism.
Study on the Applicability of Influence Coefficient Method Combined with Vector Analysis in Dynamic Balancing Rigid Rotor Using Flexible Supports

Dynamic balance using flexible supports is one of the commonly applied solutions to determine the position and unbalanced mass in rotors. However, dynamic balance using flexible supports are being determined experimentally, resulting in low accuracy and long calibration time. This paper presents some research results when applying the influence coefficient method to combine vector analysis (using balanced method on individual planes and trial mass). To verify experimentally the theory, HMC1501 sensors are attached at the supports to measure vibration induced displacement, an encoder 2.048 PPR is used to determine the position (phase angle $\varphi$ and a DSPIC30F4011 microcontroller is equipped to receive signal were used. The measured results were obtained by the Fast Fourier Transform (FFT) combined with numerical analysis, including phase angle j and unbalance mass U.
Two Stage Zero-resource Approaches for QbE-STD

In this paper, we explore the information in the acoustic representation for Query-by-Example Spoken Term Detection (QbE-STD) task. Several approaches have been employed to detect the spoken instance of the query in audio databases. Zero-resource approach attempts to detect the acoustically similar information without the use of phone recognizer. In this paper, we present two-stage frame-level matching for QbE-STD. At first stage, we used Gaussian posteriorgram and subsequence dynamic time warping (subDTW) to detect the segments within audio databases. In the second stage, we exploited several acoustic features along with Dynamic Time Warping (DTW) detection cues such as cosine similarity of term frequency vectors and the valley depth of detection obtained in subDTW. The score-level fusion of search system gave the performance comparable to phonetic posteriorgram on SWS 2013 database. We obtained 0.045 (i.e., 4.5 %) improvement in Maximum Term Weighted Value (MTWV) with the score-level fusion of all the evidence in MTWV as compared to subDTW on Mel Frequency Cepstral Coefficients (MFCC) Gaussian posteriorgram.
Mode selection for CoMP transmission with nonideal synchronization

This paper proposes a mode selection scheme to improve the spectral efficiency for coordinated multi-point (CoMP) transmission with phase synchronization errors (PSE). Upper bounds of average achievable rate for different CoMP transmission modes, such as coordinated beamforming (CB) and joint processing (JP), are derived by random matrix theory and asymptotic mathematical approximation. According to these upper bounds, the proposed scheme switches CoMP transmission mode between CB and JP adaptively to enhance the average achievable rate. Simulation results show that these upper bounds agree well with the average achievable rates for both JP and CB, and the proposed scheme outperforms traditional single mode CoMP transmission when PSE exist.
Image Processing To Transform Agricultural Scenario

Every field across the globe has turned to be a beneficiary of technological advancements. It is high time for agricultural fields to reap the fruits. Specially designed low altitude Hexacopter aids the processing. Many applications are yet to be unearthed, but this paper deals with three crucial objectives namely Terrain analysis, Animal Intrusion Avoidance and Mango cutting. To analyze the terrain a new framework descending from region growing technique is employed along with thresholding. For the purpose of mango detection, HSV color detection algorithm, Edge detection and Stereo vision are combined, embedded with a robotic arm cutter to achieve the purpose. Animal intrusion avoidance can be attained through a hybrid version of region based approach and motion detection algorithm that can perfectly solve the riddle
Position and Torque Sensorless Motion Transmission Using Parameter Identification Based on Least Mean Squares Method

Motion transmission is a technology which is expected to be used for teleoperation systems. Sensorless control can improve fault tolerance of teleoperation robots exposed to harsh environments. However, sensorless control methods are generally sensitive to parameter errors of an actuator. A previously proposed sensorless motion transmission method especially requires the accurate value of resistance. Therefore, this work aims to identify resistance of two direct current motors used as a master slave system. Taking account of the master slave system of which common mode and differential mode correspond respectively to torque transmission and velocity synchronization, a least mean squares method jointly using a voltage injection is newly applied to the differential mode. Simulation results validated the proposed identification method and indicated that the sensorless motion transmission succeeded using the identified resistance value. The proposed novel method will improve the usefulness of sensorless motion transmission by adaptability to parameter errors.
Quality-Focused Data Delivery in Wireless Sensor Network for Drinking Water Distribution System

Stable and reliable operation of the drinking water distribution system is very important for vital human needs. Moreover, its integration with the use of Wireless Sensor Networks gives a valid way for better monitoring of water quality. However, this doesn’t provide a higher resilience of the system in a case of many possible disruptions, caused by natural, inattentive or malicious factors. More or less, the vulnerability of this system performance highly depends not only on the power supply but on the data transmission quality factors, which affect a sensitive data in all stages of its delivery. Due to this, the authors proposed a novel idea by developing a digital model for monitoring of quality-focused parameters in a data delivery over Wireless Sensor Network based drinking water distribution system in Latvia. We proposed a concept of Quality of Resilience for overall system of Wireless Sensor Network for control of water distribution system. We also proposed and implemented a digital model, investigations, insights for self-organization in Wireless Sensor Networkbased water distribution system, which would help to increase the system’s resilience to various short-time disruptions. This model implements a quality control in data delivery and provides the opportunity to re-organize topology of the sensors, aiming to maintain a stable operation of a system and reduce a percentage of messages from data processing system (broker) for data retransmission in a failure of any gateway – controller. This integrated solution for self-organization of sensors and data delivery with a selected Quality of Service level would increase both - the overall system’s resilience in a case of shortterm disruptions and the stable system performance at that time.
Hierarchical Hybrid Feature Model for Top-N Context-Aware Recommendation

Precise prediction of users' behavior is critical for users' satisfaction and platforms' benefit. A user's behavior heavily depends on the user's general preference and contextual information (current location, weather etc.). In this paper, we propose a succinct hierarchical framework named Hierarchical Hybrid Feature Model (HHFM). It combines users' general taste and diverse contextual information into a hybrid feature representation to profile users' dynamic preference w.r. t context. Meanwhile, we propose an n-way concatenation pooling strategy to capture the non-linear and complex inherent structures of real-world data, which were ignored by most existing methods like Factorization Machines. Conceptually, our model subsumes several existing methods when choosing proper concatenation and pooling strategies. Extensive experiments show our model consistently outperforms state-of-the-art methods on three real-world data sets.
Parameter Tuning of UPFC Supplementary Damping Controller Considering Communication Latency

UPFC (unified power flow controller) can control bus voltage, line flow and enhance system dynamic stability using different controlling strategies. Its supplementary damping controller may exert great importance to system dynamic stability. Firstly, this paper adds UPFC into the system linearized model and applies DTA (damping torque analysis) to UPFC analysis and deduces the DTA sensitivity. On this basis, the DTA model considering time-delay is obtained according to the frequency domain model of time-delay, which can be applied to UPFC supplementary controller parameter tuning combined with the phase compensation method. Finally, validity and feasibility of the proposed model are demonstrated in two practical cases. Simulation results indicate the method could inhibit oscillations effectively and improve system stability.
Thermal Resistance Advanced Calculator (TRAC)

A novel simulation tool, named Thermal Resistance Advanced Calculator, is presented Such a tool allows the straightforward definition of a parametric detailed thermal model of family of electronic packages with Manhattan geometry, in which anew thermal properties and/or geometrical details can vary in a chosen set. It then applies a novel parametric MOR-based approach for the automatic and fast extraction of a parametric compact thermal model of this family of electronic packages. Lastly, it automatically determines the JEDEC thermal metrics from this parametric compact thermal model for any choice of parameters in a negligible amount of time. The method is validated through the analysis of a family of exposed pad Quad Flat Packages.