Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Event Detection and Description in Continuous Video Streams

Dense video captioning is a fine-grained video understanding task that involves two sub-problems: localizing distinct events in a long video stream, and generating captions for the localized events. We propose the Joint Event Detection and Description Network (JEDDi-Net), which solves the dense video captioning task in an end-to-end fashion. Our model continuously encodes the input video stream with three-dimensional convolutional layers, proposes variable-length temporal events based on pooled features, and generates their captions. Proposal features are extracted within each proposal segment through 3D Segment-of-Interest pooling from shared video feature encoding. In order to explicitly model temporal relationships between visual events and their captions in a single video, we also propose a two-level hierarchical captioning module that keeps track of context. On the large-scale ActivityNet Captions dataset, JEDDi-Net demonstrates improved results as measured by standard metrics. We also present the first dense captioning results on the TACoS-MultiLevel dataset.
Energy harvesting using magnetic shape memory alloy under biaxial mechanical loading

Magnetic shape memory alloys (MSMAs) are a kind of smart materials which are of great importance due to some unmatched behaviors when subjected to a mechanical loading or a magnetic field. In this work, energy harvesting from an MSMA specimen is studied. The MSMA specimen is embedded in a common magnetic circuit in this field of research and by applying mechanical loading, its effect on the specimen magnetic characteristics and induced voltage is investigated. Mechanical loading is applied in the form of strain-input and three kinds of 2D loadings are used including sinusoidal-sinusoidal, sinusoidal-triangular and triangular-triangular. It is demonstrated that the highest voltage is obtained in sinusoidal-sinusoidal loading and there is a negative correlation between the phase difference of the applied loadings in two directions and output voltage.
Regulation of chaperone/effector complex synthesis in a bacterial type III secretion system

Type III protein secretion systems (T3SSs), which have evolved to deliver bacterial proteins into nucleated cells, are found in many species of Gram‐negative bacteria that live in close association with eukaryotic hosts. Proteins destined to travel this secretion pathway are targeted to the secretion machine by customized chaperones, with which they form highly structured complexes. Here, we have identified a mechanism that co‐ordinates the expression of the Salmonella Typhimurium T3SS chaperone SicP and its cognate effector SptP. Translation of the effector is coupled to that of its chaperone, and in the absence of translational coupling, an inhibitory RNA structure prevents translation of sptP. The data presented here show how the genomic organization of functionally related proteins can have a significant impact on the co‐ordination of their expression.
The Research into Fault-Tolerant Design Usign Pass Transistor Logic

this research is focused on implementation of pass transistor logic (PTL) methods in the synthesis process of both the diagnostic equipment and the fault-tolerant circuits. The pass transistor logic is usually used for implementing XOR and MUX functions. For example, LUT in FPGA or XOR/XNOR gates in arithmetic logic units can be designed with PTL. We think that PTL method can be used to design novel logic gates with a self-diagnostic function and possibly self-recovery. The brief overview of pass transistor logics will be given in the first section of the paper. The reliability analysis and the examples of logic recovery using PTL are presented in this paper. The PTL designs are represented in digital devices but not for high reliable applications. That is why this subject should be further researched.
A deep learning based framework for converting sign language to emotional speech

This paper proposes a framework for converting sign language to emotional speech by deep learning. We firstly adopt a deep neural network (DNN) model to extract the features of sign language and facial expression. Then we train two support vector machines (SVM) to classify the sign language and facial expression for recognizing the text of sign language and emotional tags of facial expression. We also train a set of DNN-based emotional speech acoustic models by speaker adaptive training with an multi-speaker emotional speech corpus. Finally, we select the DNN-based emotional speech acoustic models with emotion tags to synthesize emotional speech from the text recognized from the sign language. Objective tests show that the recognition rate for static sign language is 90.7%. The recognition rate of facial expression achieves 94.6% on the extended Cohn-Kanade database (CK+) and 80.3% on the Japanese Female Facial Expression (JAFFE) database respectively. Subjective evaluation demonstrates that synthesized emotional speech can get 4.2 of the emotional mean opinion score. The pleasure-arousal-dominance (PAD) tree dimensional emotion model is employed to evaluate the PAD values for both facial expression and synthesized emotional speech. Results show that the PAD values of facial expression are close to the PAD values of synthesized emotional speech. This means that the synthesized emotional speech can express the emotions of facial expression.
Joint Learning for Non-standard Chinese Building Address Standardization*

Since there is no uniform specification for building address name in China, the same building address maybe has many different representations in Chinese natural language. The goal of the non-standard Chinese building address standardization task is to uniformly convert the non-standard building addresses from different social institutions to the standard building address defined by the public security organ, so that the spatial location information corresponding to the standard building address can be obtained. This plays an important role in the analysis and processing of big data in smart cities. Due to the large number of non-standard building addresses and the semantic ambiguity of addresses expressed in Chinese natural language, traditional methods based on string matching are difficult to meet the task requirements. To address these above problems, we propose an innovative joint learning approach based on hash map principle and word frequency theory for standardizing Chinese non-standard building addresses. Experimental results on the dataset constructed via crowdsourced technology show that approach has outstanding accuracy and adaptability to data from different sources.
Alternative Fabrication Strategies for the Production of Axial Flux Permanent Magnet Synchronous Motors for Enhanced Performance Characteristics

Axial flux permanent magnet (AFPM) motors, also known as disc type or pancake motors, offer many advantageous characteristics like high power density, compact design and diverse possibilities of motor design. Although distinctly better in few aspects compared to radial flux motors, there are definite reasons why axial flux motors have not been able to dominate the market, e.g, difficulties in part fabrication and motor assembly. In the wake of the development of new materials, it is possible to partly overcome these difficulties and simultaneously increase the power/torque density of the axial flux machine. This paper focuses on the identification of nonmagnetic components in an axial flux permanent magnet motor and the fabrication of these nonmagnetic parts using alternative materials and new fabrication technologies. For this purpose, the important motor parts like stator, rotor and housing are theoretically analyzed and the possibility of using nonmagnetic materials to manufacture them is verified. This will help in reducing the weight of the motor, offering enhancement of torque and power density. Less magnetic parts may also lead to better efficiency, as the losses due to induced currents are reduced.
Block Tensor Train Decomposition for Missing Value Imputation

We propose a new method for imputation of missing values in large scale matrix data based on a low-rank tensor approximation technique called the block tensor train (TT) decomposition. Given sparsely observed data points, the proposed method iteratively computes the soft-thresholded singular value decomposition (SVD) of the underlying data matrix with missing values. The SVD of matrices is performed based on a low-rank block TT decomposition for large scale data matrices with a low-rank tensor structure. Experimental results on simulated data demonstrate that the proposed method can estimate a large amount of missing values accurately compared to a matrix-based standard method.
Transformer-Based Neural Network for Answer Selection in Question Answering

Answer selection is a crucial subtask in the question answering (QA) system. Conventional avenues for this task mainly concentrate on developing linguistic tools that are limited in both performance and practicability. Answer selection approaches based on deep learning have been well investigated with the tremendous success of deep learning in natural language processing. However, the traditional neural networks employed in existing answer selection models, i.e., recursive neural network or convolutional neural network, typically suffer from obtaining the global text information due to their operating mechanisms. The recent Transformer neural network is considered to be good at extracting the global information by employing only self-attention mechanism. Thus, in this paper, we design a Transformer-based neural network for answer selection, where we deploy a bidirectional long short-term memory (BiLSTM) behind the Transformer to acquire both global information and sequential features in the question or answer sentence. Different from the original Transformer, our Transformer-based network focuses on sentence embedding rather than the seq2seq task. In addition, we employ a BiLSTM rather than utilizing the position encoding to incorporate sequential features as the universal Transformer does. Furthermore, we apply three aggregated strategies to generate sentence embeddings for question and answer, i.e., the weighted mean pooling, the max pooling, and the attentive pooling, leading to three corresponding Transformer-based models, i.e., QA-TF<inline-formula> <tex-math notation="LaTeX">$_{{W\! P}}$ </tex-math></inline-formula>, QA-TF<inline-formula> <tex-math notation="LaTeX">$_{{M\! P}}$ </tex-math></inline-formula>, and QA-TF<inline-formula> <tex-math notation="LaTeX">$_{{A\! P}}$ </tex-math></inline-formula>, respectively. Finally, we evaluate our proposals on a popular QA dataset WikiQA. The experimental results demonstrate that our proposed Transformer-based answer selection models can produce a better performance compared with several competitive baselines. In detail, our best model outperforms the state-of-the-art baseline by up to 2.37%, 2.83%, and 3.79% in terms of MAP, MRR, and accuracy, respectively.
Analysis of Digital Inverter Using Single and Multiple GaAs/AlGaAs Based Double Barrier Resonant Tunneling Diode

From the past to present progression of CMOS technology has carried out the advances of semiconductor electronics devices. But as the size of CMOS is getting harder to shrink and the limits of this technology is at the boundary level, the time has come to find a new robust technology to continue the journey towards high speed, low power consumption and small in size semiconductor electronics. Resonant Tunneling Diode (RTD) has the potential to takeover CMOS. RTD has a unique NDR (Negative Differential Resistance) region which shows reduction of size and complexity in analog and digital circuits. In this paper, single and multiple Gallium Arsenide/Aluminium Gallium Arsenide (GaAs/AlGaAs) based double barrier Resonant Tunneling Diode (RTD) used digital inverter circuits has been analyzed for both PDR1 (Positive Differential Resistance 1) and PDR2 (Positive Differential Resistance 2) regions.
Enhanced Spectral Efficiency of Spatial Modulation Based on Chirp Waveform

In this paper, a Modified Spatial Modulation Multiple-Input Multiple-Output-Chirp (MSM-MIMO-C) scheme is proposed to enhance the Spatial Modulation (SM) spectral efficiency. One of the drawbacks of SM is its limitation in increasing the data rates. Hence, in this proposal, we add new source information carrying which consist in using a battery of predefined chirps waveforms instead of a single waveform use. Obtained results are promising and outperform the ones of conventional SM in terms of Bit Error Rate (BER) and spectral efficiency.
A Survey on Replay Attack Detection for Automatic Speaker Verification (ASV) System

In this paper, we present a brief survey of various approaches used to detect replay attack for Automatic Speaker Verification (ASV) system. The replay spoofing attack is the most challenging task to detect as only few seconds of audio samples are required to replay genuine speaker's voice. Due to large availability and the widespread usage of the mobile/smart gadgets, recording devices, it is easy and simple to record and replay the genuine speaker's voice. The challenging task, in replay spoof attack is to detect the acoustical characteristics of the speech signal between the natural and replayed version. The speech signal recorded with the playback device contains the convolutional and additive distortions from the intermediate device. Background noise and channel degradations seriously constrain the performance of the system. The goal of this paper is to provide an overview of the replay attack focusing on 2nd ASVspoof 2017 challenge which is an emerging research problem in the field of anti-spoofing. This paper presents critical analysis of state-of-the-art techniques, various countermeasures, databases, and also aims to present current limitations along with road map ahead, i.e., future research directions in this technological challenging problem.
Graph Partitioning for Iterative Signal Reconstruction in Wireless Sensor Network

One of the important applications of graph signal processing (GSP) in wireless sensor network (WSN) is reconstruction of the bandlimited graph signals from local measurements. The reconstruction may be performed by iterative algorithm. Graph partitioning plays an important role in the signal reconstruction using iterative algorithm. In this paper, we propose a new partitioning approach to improve the convergence rate of the signal reconstruction. The proposed partitioning method relaxes calculating the maximum bandwidth of graph signal to be reconstructed.
A Blended Learning Approach to Enhance Student Learning for an Introductory Power Systems Course

This Innovation Practice Category Work-in-Progress Paper presents the implementation and results of a blended learning approach to design an introductory Power System Course to enhance student learning and pique their interest. The focus of this research is to develop a blended learning framework that converts the student from a passive listener to an active learner. Firstly, the lectures were moved online but instead of covering one topic and all the sub-topics in one video we covered each sub-topic in a separate video. This resulted in four to five videos a week between five to ten minutes. The shorter videos ensure that students learn the individual sub-topics before their attention gets divided. The classroom time was spent in relating the theory taught in the videos to practical real-life applications, live demonstrations of the concepts as well as solving mathematical tutorials. Secondly, a graded online quiz is conducted every two or three weeks that tests students on the video’s contents. Thirdly, quizzes with a competitive element built in are conducted during the classroom time to revise and reiterate the concepts covered that week. Quantitative and qualitative analysis show promising results on the implementation of blended learning in power systems.
A Digital Modeling Technique for Distortion Effect Based on a Machine Learning Approach

This paper describes an experimental result of modeling stomp boxes of the distortion effect based on a machine learning approach. Our proposed technique models the distortion stomp boxes as a neural network consisting of CNN and LSTM. In this approach, CNN is employed for modeling the linear component that appears in the pre and post filters of the stomp boxes. On the other hand, LSTM is employed for modeling the nonlinear component that appears in the distortion process of the stomp boxes. All the parameters are estimated through the training process using the input and output signals of the distortion stomp boxes. The experimental result indicates that the proposed technique may have a certain potential to replicate the distortion stomp boxes appropriately by using the well-trained neural network.
An Effective Tensor Completion Method Based on Multi-linear Tensor Ring Decomposition

By considering the balance unfolding scheme does help to catch the global information for tensor completion and the recently proposed tensor ring decomposition, in this paper a weighted multilinear tensor ring decomposition model is proposed for tensor completion and called MTRD. Utilizing the circular dimensional permutation invariance of tensor ring decomposition, a very balance matricization scheme $< k, d >$-unfolding is employed in MTRD. In order to evaluate MTRD, it is applied on both synthetic data and image tensor data, and the experiment results show that MTRD are able to achieve the desired relative square error by spending much less time than its compared methods, i.e. TMac-TT and TR-ALS. The results of image completion also show that MTRD outperforms its compared methods in relative square error. Specifically, TMac-TT and TR-ALS fails to get the same relative square error as MTRD and TR-ALS prevails TMac-TT but requiring a large amount of running time. To sum up, MTRD is more applicable than its compared methods.
An Integrated PPG and ECG Signal Processing Hardware Architecture Design of EEMD Processor

This study proposed an integrated Photoplethysmogram (PPG) and Electrocardiography (ECG) signal processing hardware architecture design based on Ensemble Empirical Mode Decomposition (EEMD) The proposed integrated dual signals EEMD processor is implemented in an on-board FPGA for on-line signal processing of the non-linear and non-stationary signal. The EEMD method is appropriate to analyze the non-linear PPG and ECG signals with assisting white noise and decompose the signal into 8 sets of Intrinsic Mode Functions (IMFs). The experimental results show that the hardware architecture proposed in this study can be applied to PPG and ECG signals, and can clearly analyze and separate high-frequency and low-frequency noise, and keep clear signals without any noise.
Content Based Image Retrieval Using Wavelet Moments and Local Binary Patterns in CIE-L*a*b* Color Space

This paper concerns content-based image retrieval (CBIR) systems, where still images can be retrieved by similarity searching on visual features like color, texture, shape and spatial information at pixels level. In this work CBIR is accomplished by a simple method which combines local binary patterns (LBP) and wavelet moments computed in CIE-L*a*b* color space. Accuracy of the combined approach is assessed using precision and recall measurements. Experimental results on Wang-1000 database show that combined features approach presents a satisfactory progress relative to single feature based on LBP or wavelet moments regarding to precision and recall and it outperforms some of the existing approaches.
18.8 A 192pW Hybrid Bandgap-Vth Reference with Process Dependence Compensated by a Dimension-Induced Side-Effect

A voltage reference circuit is an essential block of a system to generate various internal voltages. Since it consumes static power in standby modes, it plays an important role in energy management of battery-limited applications. The bandgap reference (BGR) has been a widely used approach since it provides a well-defined large value ($\sim 1.15\text{V}$) with strong immunity to process, supply and temperature changes. Recently proposed BGR approaches achieved a great reduction of power consumption by taking only complementary-to-absolute-temperature (CTAT) quantity from a PN junction while they obtained proportional-to-absolute-temperature (PTAT) quantity from alternative CMOS circuits such as a CTAT divider [1]or leakage-based two diodes [2, 3]. However, these BGR schemes are formed with multiple branches fed from a supply voltage above 1.4V and require power consumption of order larger than 10nW. To further reduce power consumption, threshold-based reference approaches with CMOS-only circuits have been proposed [4, 5]. However, generation of a practical voltage level by up-scaling of a threshold-based reference also causes an amplification of the uncertainty by the same factor. I addition, though [4]has successfully achieved sub-nW power consumption, the threshold voltage eventually suffers from a large sensitivity to process variation because the threshold voltage is affected by process and design parameters. To reduce the effect of process variation, [5]proposed a PMOS-only circuit. However, it requires a different body biasing for a threshold difference that is needed to generate a non-zero reference.
An Experience-Based Scheme for Energy-SLA Balance in Cloud Data Centers

The proliferation of cloud computing has resulted in the establishment of large-scale data centers containing thousands of computing nodes and consuming enormous amounts of electrical energy. However, the low-cost and high-efficiency slogans are getting louder and louder, and the IT industry is also striving for this pursuit. Therefore, it is vital to minimizing the energy consumption for cloud providers while ensuring the quality of service for cloud users. In this paper, we propose several heuristic strategies to optimize these two metrics based on a two-level management model under a heterogeneous cloud environment. First, to detect whether a physical node is continuously overloaded, we propose an empirical forecast algorithm, which predicts the future state of the host by statistically analyzing the historical utilization data of the host. Second, we propose a weighted priority virtual machine (VM) selection algorithm. For each VM on the overloaded host, we weight several utilization factors and calculate its migration priority. Then, we simulate the proposed approach and compare it with the existing overloaded hosts detection algorithms with different VM selection policies under different workloads.