Miracle-dz/UniFAR-FTUs huggingface dataset:
Image-to-Video Person Re-Identification With Temporally Memorized Similarity Learning

With the development of video surveillance in public safety field, there is an increasing research on person re-identification (re-id). In this paper, we address the image-to-video person re-id, in which the probe is an image and the gallery is consists of videos captured by nonoverlapping cameras. Compared with image, video sequence contains more temporal information that can be explored to improve the performance of re-identification system. However, it is challenging to model temporal information in the matching process of image-to-video person re-id. In this paper, we proposed a novel temporally memorized similarity learning neural network for this problem. In specific, the proposed network mainly consisted of two parts, including feature representation sub-network and similarity sub-network. In the first part, we adopted a convolutional neural network (CNN) to extract features from the input image. Given a video sequence of a person, features were first extracted from each its frame by using CNN and further forward to a long shot term memory (LSTM) network to encode the temporal information of video sequence. The outputs of LSTM were concatenated together as the feature vector of video sequences. Finally, the feature vectors of probe image and the video sequence were further forward to the similarity sub-network for distance metric learning. In the proposed framework, the feature representation and the similarity metric learning can be learned and optimized simultaneously. We evaluated the proposed framework on three public person re-id data sets, and the experimental results showed that the proposed approach is effective for the image-to-video person re-id.
Equivalent Resistance Control for Maximum Power Transfer Method of Piezoelectric Element in Vibration Power Generation

This paper proposes a novel equivalent resistance control for maximum power transfer method of piezoelectric element in vibration power generation. In the proposed method, the rectified voltage and current of a diode rectifier are in phase by the boost chopper. Thus, the input side of the boost chopper behaves as a resistor equivalently. For maximizing the generated power of the piezoelectric element, this resistance must be controlled to the internal impedance of the piezoelectric element by the law of the maximum power transfer. The basic principle of the proposed method is discussed, and then confirmed by computer simulation using Piece-wise Linear Electronical Circuit Simulation (PLECS). Simulation results demonstrate that the output power at buck chopper is 7.36 mW by the proposed method. This output power with the proposed method is 3.5 times greater than the output power without the proposed method.
Impedance Measurement of Megawatt-Level Renewable Energy Inverters using Grid-Forming and Grid-Parallel Converters

Harmonic resonance and power quality problems have been reported in grid-connected photovoltaic and wind power systems. The AC-side impedance of three-phase converter is an important characteristic, which can be effectively used as a design parameter to avoid instability and excessive harmonics. A number of methods to measure the three-phase AC impedance have been reported. However, solutions for high power applications such as wind and photovoltaic converters with a power rating of several megawatts, have not been discussed. This paper introduces a new method to measure the impedance from high power three-phase converter. The impedance is identified by perturbing the converter first by voltage-type injection utilizing high-power grid-forming inverter, and subsequently by current-type injection by utilizing low-power grid-parallel converter. The main benefit of the proposed setup is the possibility to measure the converter impedance online in its natural operating point both at high and low frequencies. The paper presents a proof-of-concept by validating the method using a switching model.
Security Event Recognition for Visual Surveillance

Abstract. With rapidly increasing deployment of surveillance cameras, the reliable methods for automatically analyzing the surveillance video and recognizing special events are demanded by different practical applications. This paper proposes a novel effective framework for security event analysis in surveillance videos. First, convolutional neural network (CNN) framework is used to detect objects of interest in the given videos. Second, the owners of the objects are recognized and monitored in real-time as well. If anyone moves any object, this person will be verified whether he/she is its owner. If not, this event will be further analyzed and distinguished between two different scenes: moving the object away or stealing it. To validate the proposed approach, a new video dataset consisting of various scenarios is constructed for more complex tasks. For comparison purpose, the experiments are also carried out on the benchmark databases related to the task on abandoned luggage detection. The experimental results show that the proposed approach outperforms the state-of-the-art methods and effective in recognizing complex security events.
Torsional Vibration Mitigation by Harmonic Inversion through SPWM Carrier Signal Control

The paper analyses torsional vibrations caused by electromagnetic torque harmonics in permanent magnet synchronous generators. A vibration mitigation method is proposed based on changing the phase angle of the carrier signal of sinusoidal PWM in order to invert the phase of the torque harmonic causing resonance. Since the frequency has not changed, the system is still in resonance. Thus, after a given time, the torque harmonic should be inverted again. The process is repeated as long as the system is at or near resonance. A criterion is presented to select the exact phase shift of the carrier needed in order to invert the torque harmonic. A method to calculate both the toggle time needed and the frequency range to apply such technique is presented. With the mitigation technique applied, two cases are considered on Matlab/Simulink, namely sustained operation at resonant frequency and accelerating the machine over some critical points.
Cognitive Transmit Subarray FDA Design for Integrated Radar-Communication Using Flexible Sidelobe Control

This paper presents cognitive overlapped-frequency diverse array (FDA) for joint radar-communications to achieve improved performance for moving radar target and communica- tion receiver(s). In this scheme, the FDA transmit array is divided into two subarrays and each subarray coherently transmits an orthogonal waveform with different frequency increments. In addition, we consider the time variant in FDA, which is usually ignored in the literature. Further, we propose Chebyshev window function to design two transmit weights matrices to achieve distinct sidelobe levels (SLLs) for communication operation but the same mainlobe for radar functionality. The communication information message is embedded into the orthogonal waveforms via the sidelobe control during each radar pulse. By using the cognitive closed-loop method, we can update the parameters in real time to improve moving target and communication receiver(s) tracking performance. Moreover, the information bits can be efficiently detected by the intended receiver due to equal magnitude in the SLLs. The effectiveness of the proposed scheme is verified by simulation results.
An SoC-based platform for integrated multi-axis motion control and motor drive

This paper presents a Field Programmable Gate Array (FPGA) -based platform for integrated multi-axis motion control and motor drive. As the development of FPGA, the integrated high-performance ARM processor on the FPGA has become the direction of FPGA development. This paper chooses the Xilinx Zynq-7020 programmable system-on-chip(SoC) which integrated dual-core ARM CPU and FPGA as a High-performance hardware platform. This platform is suits for the requirements of integrated multi-axis motion control and motor drive well, one ARM CUP completes the multi-axis position loop algorithm, speed loop algorithm and multi-axis trajectory generation, the other completes the human-computer interaction function. 4-axis current loop pipeline control and double sampling double updating current loop algorithm are accomplished by FPGA which can expand the current loop bandwidth and save FPGA logic resources. This platform’s architecture will improve the overall bandwidth of the system, achieve multi-axis synchronization accuracy at nanosecond level which makes position trajectory more accurate.
A New Modulation Method Applying Optimal Duty Cycle and Phase Shift for Bidirectional Isolated Three-Phase AC/DC Converter Based on Matrix Converter

This paper presents a new modulation method for high-frequency link bidirectional three-phase AC/DC converter based on matrix converter. Since the matrix converter does not have electrolytic capacitor, it achieves high power density and long lifetime system. The modulation method performs bidirectional active and reactive power control with sinusoidal line current by using optimal duty cycle and phase shift. The optimal duty cycle and phase shift are determined by numerical calculation based on the system mathematical model. Experimental results employing a 1kW laboratory prototype verify the capability to control active and reactive power with sinusoidal line current.
A Single-Phase Common Ground Boost Inverter for Photovoltaic Applications

The paper introduces a new single-phase boost inverter for photovoltaic applications. The introduced inverter merges a boost converter and a single-phase inverter in a single-phase single-stage inverter. The proposed inverter has a simpler topology and uses the minimum number of power electronic components. A high ac output voltage of the introduced topology is generated from a low dc voltage source by implementing one cycle control. Furthermore, the introduced topology shares a common ground between input voltage source and load. The operating principles, simulation results and circuit analysis of the single-phase boost inverter with common ground are illustrated. Also, a simple pulse width modulation technique is shown. The experimental results are also shown to validate the precise performance of the introduced inverter.
Exposure to Antibiotics Affects Saponin Immersion-Induced Immune Stimulation and Shift in Microbial Composition in Zebrafish Larvae

In the last decades, pollution of the environment by large scale use of antibiotics in agriculture and human medicine have led to increased antimicrobial resistance in both the environment and the host animal microbiome. Disturbances in the host microbiome can result in impaired immunity and reduced resilience of aquaculture species. Here, we investigated whether environmentally measured levels of the commonly used antibiotics ciprofloxacin and oxytetracycline influences the host microbiome and susceptibility toward saponin-induced immune stimulation in larval zebrafish. Firstly, neutrophil and macrophage reporter zebrafish larvae were exposed to different concentrations of soy saponin by immersion. A dose-dependent increase in neutrophil presence in the intestinal area was observed together with increased expression of immune genes il1b, tnfa, il22 and mmp9. To investigate the effect of antibiotics, larval zebrafish were immersed in ciprofloxacin or oxytetracycline in the presence or absence of a low dose of saponin. In vivo imaging revealed that antibiotic treatment did not reduce the number of neutrophils that were recruited to the intestinal area upon saponin exposure, although it did tend to lower pro-inflammatory cytokine levels. Microbial sequencing of whole larvae revealed that exposure to a low dose of saponin already shifted the microbial composition. The combination of oxytetracycline and saponin significantly increased α-diversity compared to the controls. In conclusion, the current study provides evidence that the combination of low levels of antibiotics with low levels of anti-nutritional factors (saponin) can induce inflammatory phenotypes and can modify the microbiota, which might lead to altered disease susceptibility.
Be careful of when: an empirical study on time-related misuse of issue tracking data

Issue tracking data have been used extensively to aid in predicting or recommending software development practices. Issue attributes typically change over time, but users may use data from a separate time of data collection rather than the time of their application scenarios. We, therefore, investigate data leakage, which results from ignoring the chronological order in which the data were produced. Information leaked from the "future" makes prediction models misleadingly optimistic. We examine existing literature to confirm the existence of data leakage and reproduce three typical studies (detecting duplicate issues, localizing issues, and predicting issue-fix time) adjusted for appropriate data to quantify the impact of the data leakage. We confirm that 11 out of 58 studies have leakage problem, while 44 are suspected. We observe biased results caused by data leakage while the extent is not striking. Attributes of summary, component, and assignee have the largest impact on the results. Our findings suggest that data users are often unaware of the context of the data being produced. We recommend researchers and practitioners who attempt to utilize issue tracking data to address software development problems to have a full understanding of their application scenarios, the origin and change of the data, and the influential issue attributes to manage data leakage risks.
HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding

Knowledge Graph (KG) embedding has emerged as an active area of research resulting in the development of several KG embedding methods. Relational facts in KG often show temporal dynamics, e.g., the fact (Cristiano_Ronaldo, playsFor, Manchester_United) is valid only from 2003 to 2009. Most of the existing KG embedding methods ignore this temporal dimension while learning embeddings of the KG elements. In this paper, we propose HyTE, a temporally aware KG embedding method which explicitly incorporates time in the entity-relation space by associating each timestamp with a corresponding hyperplane. HyTE not only performs KG inference using temporal guidance, but also predicts temporal scopes for relational facts with missing time annotations. Through extensive experimentation on temporal datasets extracted from real-world KGs, we demonstrate the effectiveness of our model over both traditional as well as temporal KG embedding methods.
Semantics as a Foreign Language

We propose a novel approach to semantic dependency parsing (SDP) by casting the task as an instance of multi-lingual machine translation, where each semantic representation is a different foreign dialect. To that end, we first generalize syntactic linearization techniques to account for the richer semantic dependency graph structure. Following, we design a neural sequence-to-sequence framework which can effectively recover our graph linearizations, performing almost on-par with previous SDP state-of-the-art while requiring less parallel training annotations. Beyond SDP, our linearization technique opens the door to integration of graph-based semantic representations as features in neural models for downstream applications.
Prediction Improves Simultaneous Neural Machine Translation

Simultaneous speech translation aims to maintain translation quality while minimizing the delay between reading input and incrementally producing the output. We propose a new general-purpose prediction action which predicts future words in the input to improve quality and minimize delay in simultaneous translation. We train this agent using reinforcement learning with a novel reward function. Our agent with prediction has better translation quality and less delay compared to an agent-based simultaneous translation system without prediction.
Novel Noncontact Eddy Current Measurement of Electrical Conductivity

A new noncontact method for measuring the electrical conductivity of metals based on eddy current testing is proposed for the physics that the logarithm of phase signals of impedance change is linear to that of electrical conductivity. A finite element model for simulations was built, and the mathematical relationship between the logarithm of phase signals and that of electrical conductivity was established with linear least-squares fitting. The effects of the excitation frequency, lift-off distance, and relative permeability were investigated and analyzed. The investigations of lift-off effect enable us to reduce it by substituting the impedance of the coil at certain lift-off distance for that of the coil in air as the reference. Finally, the experimental platform was developed, and the experimental results matched well with the simulated results. The relative errors fall within 3%, which demonstrates the feasibility of the presented method for non-ferrous metals.
Budget-Constrained Organization of Influential Social Events

Recently, the proliferation of event-based social services has made it possible for organizing personalized offline events through the users' information shared online. In this paper, we study the budget-constrained influential social event organization problem, where the goal is to select a group of influential users with required features to organize a social event under a budget B. We show that our problem is NPhard and can be formulated as a submodular maximization problem with mixed packing and covering constraints. We then propose several polynomial time algorithms for our problem with provable approximation ratios, which adopt a novel "surrogate optimization approach and the method of reverse-reachable set sampling. Compared with some related work that can only handle special cases of our problem but with exponential time complexity, our algorithms are much more efficient, and their superiorities on both the running time and the influence spread are demonstrated through extensive experiments using real social networks. "
Reconfigurable Converter with Multiple-Voltage Multiple-Power for E-Mobility Charging

This paper presents a reconfigurable power electronic converter that is able to produce multiple output voltages and multiple output powers to cater for the growing demand of electrified mobility (e-mobility) apparatus. The proposed converter aims to accommodate different charging needs for different e-mobility apparatus, unlike the existing charging infrastructure where it has been designed for a specific type and can only charge one equipment at a time. The proposed charger utilizes the well-known four-leg three-phase converter, three single-phase medium frequency transformers with multiple secondary windings and Active Front-End (AFE) bidirectional PWM rectifier modules. While the high frequency ac enables a compact system, with smaller passive components; the four-leg converter accommodates the potential unbalance caused by simultaneously charging different e-mobility apparatus. The proposed charging system also enables vehicle-to-grid (V2G) operation if required. The feasibility of the proposed reconfigurable power electronic converter is confirmed by intensive simulation studies for different charging scenarios.
Evaluation of Antenna Diversity Scheme for 868MHz Narrowband Communication Systems

This paper presents an empirical study of the spatial and polarization antenna diversity technique. It discusses the purpose and effectiveness of using antenna diversity technique in low power wireless devices intended to work in the 868 MHz band in an indoor environment. This document introduces a theoretical background for antenna diversity and discusses the channel characterization. Precise spatial measurement of the signal power has been carried out as a background for the antenna diversity modelling. Moreover a complete low cost solution was implemented, tested and the outcomes introduced. Provided measurements show quantifiable results that are debated in terms of suitability in a wireless communication system. In our experiment the achievable increase in link budget reaches 3 dB. This article provides the empirical dataset for radio systems developers presenting a method of improving radio link quality in systems with fixed protocols.
Salient-class location: help developers understand code change in code review

Code review involves a significant amount of human effort to understand the code change, because the information required to inspect code changes may distribute across multiple files that reviewers are not familiar with. Code changes are often organized as commits for review. In this paper, we found that most of the commits contain a salient class, which is saliently modified and causes the modification of the rest classes in a commit. Our user studies confirmed that identifying the salient class in a commit can facilitate reviewers in understanding code change. We model the salient class identification as a binary classification problem and extract a number of discriminative features from commit to characterize the salience of a class. The initial experiment result shows that the proposed approach can improve the efficiency of reviewers understanding code changes in code review.
On Computing Gradient of Products in Discretized Spaces and Its Effects in PDE Image Processing

Many digital signal and image processing methods involve computing the gradient of products of functions. However, the product rule for derivatives in continuous spaces, <inline-formula><tex-math notation="LaTeX">$\partial (fg)=g\partial f + f \partial g$</tex-math></inline-formula>, does not generally hold in discretized spaces. Hence, computing the gradient of products becomes ambiguous as the results depend on whether to treat the product <inline-formula><tex-math notation="LaTeX">$fg$</tex-math></inline-formula> as a single function or treat <inline-formula><tex-math notation="LaTeX">$f$</tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$g$</tex-math></inline-formula> as two separate functions and use the product rule. The two alternatives lead to different results, particularly for iterative solutions of differential equations since these small differences compound. Although this ambiguity is well-known, thus far a resolution has not been proposed in the literature. In this letter, we propose a mathematically rigorous procedure that selects the better alternative in the sense of yielding approximations that are closer to the continuous space results. As an example, we discuss the Perona-Malik anisotropic diffusion equation used in image processing.
Stabilization Method for Residential DC System Based on Passivity Criterion

In this paper, we assume a dc distribution board that can be attached and detached to dc sources and loads flexibly. In previous researches, stable criteria based on the impedance ratio conditions were proposed. These criteria can deal with one-way power transfer from a power source to a load. However, if a system has a secondary battery, it is difficult to apply the impedance-based conditions due to its bi-directional power flow. We study a stabilization method using a passivity-based stability criterion in a residential dc system. A virtual admittance was added with an input admittance of a converter in parallel using a feedforward control. Experimental results showed input bus voltage of a buck converter was stable when it satisfied the passivity condition.