Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Employee Participation in Enterprise Social Media on Their Job Performance

This study focuses on employee involvement in enterprise social media (ESM) and the impact of ESM on job performance. Few studies found empirical support for this perspective due to limited sample size either the difficulty of accessing the data on user behavior in ESM and their job performance. We addressed this research gap through a data-sharing agreement with a large, high-tech manufacturing enterprise with internal social media and conducted a cooperative study on the impact of employee behaviors involved in ESM on their job performance. We divided online activities on ESM into information-sharing behaviors, information-seeking behaviors and general usage behaviors and then employed hierarchical regression analysis to investigate how the various usage behaviors of employees on ESM affect their job performance significantly. We find that information-seeking behaviors on ESM have positive effects on job performance, but information-sharing behaviors on ESM have negative effects on job performance. Overall, the usage of social media within enterprise social media use could help improve employee job performance. Moreover, to test whether the timing of ESM usage (during working hours or off-work hours) may affect employee job performance, we add a variety of moderator variables into the model. The results show that information-sharing behaviors, information-seeking behaviors and general usage behaviors during working hours negatively moderate the relationship between ESM usage and job performance. The findings have valuable managerial implications for the use of ESM.
Miniaturized Conformal Arc Dielectric Resonator Antennas Using Dielectric and Metallic Loading

Compact arc-shaped conformal dielectric resonator antennas (DRAs) loaded by metal plates and high-dielectric-constant (HiK) patches are investigated. Their miniaturizing principles are analyzed from different aspects. Closed-form expressions are derived to characterize the function of the conformal ground in size reduction. Difference between the conventional flat ground and conformal one is discussed. As to lateral metal loading, its influence on radiation patterns are utilized to acquire additional function. Next, to replace the metallic structure, HiK patches are used as perfect electric conductor (PEC) walls. Their working mechanisms are analyzed through the electromagnetic theory in layered media, and waveguide-based simulation methods. As validation, a dielectric-loaded arc DRA is proposed. By working like a PEC, the thin dielectric patch reduces the DRA size to one half. Similar characteristics are observed between the dielectric- and metal-loaded antennas. Finally, a bare DRA and a miniaturized one are fabricated and measured. This research expands the miniaturizing theory of conformal arc DRAs.
Self-Monitoring, Self-Healing Biomorphic Sensor Technology

The deployment of autonomous sensors within electronic systems for both existing and emerging markets requires an increase in the reliability, security and dependability of the associated data generated. The availability of intelligent sensors that can self-adapt and ultimately self-heal would be a key step towards this objective. This paper presents ideas associated with the utilisation of sensor self-test principles and software algorithms able to generate sensor prognostics and drive adaptation, compensation and self-healing functions. Major initiatives supported both within Europe and further afield to migrate processing power to the “Edge”, deploy 5G technologies and integrate Artificial Intelligence across the system hierarchy provide technological platforms to deliver many of these concepts. An example associated with simple printed electrodes targeting corrosion detection and potentially the detection of hydrogen is presented in the context of a step towards full biomorphic capability.
Commutation Torque Ripple Reduction for Brushless DC Motor Based on an Auxiliary Step-Up Circuit

There exists torque ripple in the commutation process of brushless DC motor (BLDCM), which seriously restricts its application in the high-performance field. This paper proposes an auxiliary step-up circuit to suppress the commutation torque ripple of BLDCM, and the auxiliary step-up circuit consists of a transformer, a capacitor and a switch circuit. For effective suppression of the commutation torque ripple, the capacitor is charged in the non-commutation period. When the commutation occurs, the charged capacitor is added on the power source through a switch circuit to step up the dc-bus voltage in the commutation period, and the three-phase inverter is regulated to make the motor’s input voltage is equal to four times the back electromotive force (back EMF) amplitude. The auxiliary step-up circuit only transfers partial power required in the commutation process of the motor, and the capacity of the power components is reduced accordingly. The inductive energy stored in the transformer is returned to the power source in each switching cycle, which improves the energy utilization rate. In addition, this method can also shorten the commutation time. The validity and effectiveness of the proposed method is verified through experimental results.
RadixKV: A Memory Efficient and High Performance Key-Value Store

With the rapid development of the Internet of things (IoT), massive sensing data are continuously sent to data centers. Big sensing data call for efficient approaches on data storage and retrieval, because many IoT applications need real-time query performance. The Radix Tree is an efficient tree structure that has constant query time complexity which is much suitable for real-time query processing. However, it has high space cost and index building time, which will worsen the overall performance especially in update-intensive scenarios. In this paper, we propose a memory-efficient high-performance key-value system called RadixKV, which offers efficient improvements on the Radix Tree. We first analyze the online update performance of the Radix Tree and design an adaptive parallel index update strategy. Then, we propose Radix Array, which is a space optimized data structure for the Radix Tree. We conduct comparative experiments to evaluate the performance of RadixKV and compare RadixKV with previous works including Adaptive Radix Tree and Pre-order Radix Tree. The results show that RadixKV reduces 92.7% and 32.2% of space costs respectively, compared with the Adaptive Radix Tree and the Pre-order Radix Tree. In addition, RadixKV achieves the best write performance and has low index building time as well as query latency.
Kinematics Modeling of a Twisted and Coiled Polymer-Based Elastomer Soft Robot

Twisted and coiled polymer (TCP) is increasingly used for soft robots due to its good compaction. However, the lack of kinematics model of elastomer soft robots limits precise control. For this purpose, a kinematic model is proposed in this work for TCP-based elastomer soft robots. This paper has two main contributions. First, constant curvature assumption (CCA), the foundation in soft robots is conceptualized and the pioneering proof is constituted. Second, an innovative but foundational soft robot kinematic model is established based on CCA called constant curvature elastomer model (CCE). Experiments are conducted to verify the proposed kinematic model, showing the validity of the model. A soft endoscope for flaw detection in pipelines is shown as a practical case of TCP-based soft robots.
Automatic Virtual Network Embedding Based on Deep Reinforcement Learning

The performance of virtual network embedding determines the effectiveness and efficiency of a virtualized network, making it a critical part of the network virtualization technology. However, most existing algorithms fail to provide automatic embedding solutions in an acceptable running time. In this paper, we combine reinforcement learning with a novel neural network structure and propose a new virtual network embedding algorithm. The proposed algorithm can learn to embed virtual networks automatically. Extensive simulation results show that our algorithm achieves the best performance on most metrics compared with the existing typical and stateof-the-art solutions.
Time Series Join on Most Correlated Subsequences Using MapReduce

In recent years, a large amount of time series have been generated in various industries, and time series contains important information. Time series refers to a series of ordered real numbers that were collected with equal time intervals. Time series join on subsequences means to find the most similar or most correlated subsequence pairs from two time series. Joining two time series on subsequences using Pearson correlation coefficient can provide more useful information than other similarity measures. However, finding the most correlated subsequences is a time consuming task. In order to decrease the runtime of finding the most correlated subsequence pairs, we propose an approach adopting MapReduce programming model in this paper. Our method includes four steps. First, computing the shift cross product matrix of two time series using a parallel FFT algorithm. Second, splitting the two time series into multiple subsequences. Third, partitioning the shift cross product matrix into blocks according to the time series splitting method. Finally, the join operations on subsequences are performed parallel. In experiment, we conducted extensive tests on real datasets to verify the effectiveness of our approach.
Ultra-fine Particulate Detection using Mode-localized MEMS Resonators

This paper demonstrates the feasibility of employing mode localized mass sensing in coupled MEMS resonators for ultra-fine particulate detection applications. The potential benefits of this approach include higher parametric sensitivity and intrinsic common mode rejection as compared to the resonant frequency shift-based detection approach. Weakly coupled MEMS resonators demonstrated in this paper demonstrate sensitivity to fine particulates and a significant improvement in mass sensitivity for particulate quantification by employing an amplitude ratio shift output metric.
Achievable Rates of Full-Duplex Massive MIMO Systems with Mixed-ADC/DAC

In this paper, we consider a full-duplex (FD) large-scale multi-input multi-output (MIMO) system with a mixed analog-to-digital converters/digital-to-analog converters (ADCs/DACs) architecture. Specially, some of the antennas are used to connect with high-resolution ADCs/DACs, while the others are used to connect with low-resolution ADCs/DACs. Maximum ratio combining/maximum ratio transmission (MRC/MRT) are employed in the considered system. By adopting the additive quantization noise model (AQNM), under the perfect channel state information (CSI), we derive approximate expressions for the uplink and downlink rates, considering the loop interference (LI) and the influence of quantization error. The results indicate that in this system model, the rates of the uplink and downlink are proportional to the antennas at the base station (BS). In addition, the LI, which is an important parameter in the FD model, has an attenuation impact on the sum rate, and the greater the ratio of high-resolution ADCs/DACs, the more obvious the attenuation. Moreover, we study the impact of quantization bits on the downlink achievable rates. The results show that in different antenna allocation schemes, as the proportion of high-resolution ADC increases, the starting value of the downlink rate increases. We can also conclude that the achievable rate of each allocation scheme tends to be the same as the quantization bits increase in the case of the same total number of antennas.
Retinal Image Laser Marks Detection using a Convolutional Neural Network

This paper presents a retinal image classifier based on Convolutional Neural Networks which are commonly deep learning used tool. This classifier uses some texture based features to decide if an input image shows evidence of previous photocoagulation laser treatments. However, this classifier could help the expert human to examine the case which make the process easier and spend less time. The solution proposed as well as the datasets used in the training and performance evaluation are presented in detail. The results obtained are described and analyzed. The accuracy obtained was above 90%. Finally, some considerations on possible improvements and future developments are made to close the article.
PSOGT: PSO and Game Theoretic Based Task Allocation in Mobile Edge Computing

With the proliferation of mobile applications and the explosive growth of mobile devices, huge computing demands are generated, leading to more computational delays. The occur of mobile edge computing (MEC) pushes calculations to the edge of the network and then realize compute-intensive tasks on resource-constrained devices, in which task allocation is more difficult. In this paper, we design a particle swarm optimization (PSO) and game theoretic based task allocation for MEC. Firstly, in order to ensure nodes in the same group are closer, the maximizing minimum distance clustering algorithm is designed to generate the structure of the parallel group. Secondly, we propose a multi-task assignment model based on Nash equilibrium, and design the strategies of each task and the utility function. Then we use the PSO to find the Nash equilibrium point, minimizing the all tasks execution time and saving the energy cost and find the tasks that need to be offloaded to the group (the group is made up of base stations). Moreover, we use the priority setting algorithm to sort tasks and then upload tasks to the group in a certain order, thereby confirming the order of tasks uploaded on the device, which jointly considers the calculation time in base station and mobile device and transmission time. In addition, a task migration algorithm between the groups is proposed for congested groups, which is based on the group migration time and waiting time. Simulation results demonstrate the effectiveness of the PSOGT and it can effectively reduce delay for MEC.
An Efficient Inter-Node Communication System with Lightweight-Thread Scheduling

In the era of multi-/many-core processors, there are increasing needs for middleware of high-performance computing to exploit both inter-node and intra-node parallelism. To overlap communication and computation efficiently, many studies have focused on MPI+ULT, a combination of MPI for inter-node parallelism and user-level threads (ULTs) for intra-node parallelism. However, there are mainly two problems in the existing MPI+ULT implementations. First, the use of MPI_THREAD_MULTIPLE to invoke MPI functions from multiple threads causes a performance bottleneck. Second, some MPI+ULT systems focus on the use of non-blocking communication and programmers have to manage both the start and the end of communication explicitly. To solve these problems, we introduce a high-performance MPI+ULT implementation MPI+myth. MPI+myth focuses on implicit overlapping of communication and computation without any code modifications to the applications. Furthermore, it can avoid the overhead of multi-threaded MPI invocations using a communication dedicated thread and adopts a new scheduling technique which achieves efficient load balancing by avoiding a situation that a core is occupied by blocking ULTs. In the evaluation, we demonstrate significant performance improvement compared with the existing hybrid programming methods using several microbenchmarks and one mini application miniFE. In addition, we illustrate that MPI+myth has the potential to overlap communication and computation and our new ULT scheduling technique can achieve load balancing more efficiently than existing ULT scheduling techniques.
Energy Efficient Joint Power Optimization for Full-Duplex Relaying

Full-duplex (FD) relaying is a promising technology for the next generation communication systems. In this paper, we aim to jointly optimize the source and relay transmit powers to maximize the energy efficiency (EE) for both amplify-and-forward (AF) and decode-and-forward (DF) FD relay systems. The considered problem is challenging, which cannot be directly addressed by the classical Dinkelbach’s algorithm. We first develop an iterative algorithm based on concave-convex procedure (CCCP) to find a locally optimal solution. Furthermore, to reduce the computational complexity, we propose an efficient non-iterative method that only needs to solve a single convex problem and meanwhile achieves near-optimal performance. Simulation results show that the proposed solutions can achieve superior EE performance compared to conventional schemes.
A Quick Algorithm on Generating Concept Lattice for Attribute-Incremental Streaming Data

The explosive information growth facilitates the generation of increasingly massive streaming data. As a traditional soft computing approach for data analysis, Formal Concept Analysis (FCA) has the severe problem of low efficiency and long time for coping with massive streaming data. Therefore, devising an optimized efficiency algorithm of FCA for attribute-incremental streaming data is becoming a very urgent and challenging issue. In this paper, a quick algorithm on generating concept lattice for attribute-incremental streaming data is presented. Specifically, the correctness of the proposed algorithm is mathematically proved, and several real-world data sets are utilized in our experiments in order to compare with the existing two FCA algorithms. Experimental results demonstrate that the proposed algorithm significantly improves the efficiency for generating concept lattice of attribute-incremental streaming data. Particularly, the running time could be reduced by approximately 44% for Jazz dataset.
Efficient Caching for 360° Videos with Dynamic View Selection

Due to the emergence of 3D video applications like virtual reality, network storage management for 360° videos is expected to play an important role. In comparison with traditional videos, it is envisaged that a 360° video requires larger space to store multiple views. Nevertheless, efficient storage schemes for 360° videos in a proxy have not been explored in the literature. In this paper, therefore, we first observe that the storage space can be effectively reduced by leveraging real-time image-based generation schemes to synthesize the desired views in 360° videos. We then formulate a new optimization problem, named Dynamic View Synthesis and Selection Problem (DVSSP), to minimize the total network cost over time, which includes the cost of synthesis, local transmission, and fetching views from the remote server. Since the current storage influences the cost spent in the future, we then devise an online algorithm based on the dynamic programming technique to carefully examine the correlation between nearby views that are most likely to be requested in the following time slots. Simulation results manifest that the proposed algorithm can significantly improve the hit rate by more than 50% and reduce more than 45% total cost compared with various baseline algorithms.
Peruvian Ombudsman monthly social conflict reports analysis using knowledge management and artificial intelligence tools

Social conflict is a sensible issue in Peru, because its links to unsolved problems demanding public monitoring in order ensure the country’s stable performance. The effort of the Ombudsman’s Office in Peru collects mainly qualitative analysis, publishing information about social conflicts. Due to the complexity of this information, this study identifies a huge opportunity to use AI algorithms to perform natural language processing (NLP) and natural language understanding (NLU) on available data contributing with real applications to build a path for a more accurate and faster social conflict analysis in Peru, highlighting technological paths to develop a deeper AI NLP analysis
Progress and Undersampling In Digital Optical Frequency Control

We report on the recent progress in the development of digital implementation of Ultrastable Optical Frequency Controls, namely in the frame of experiments based on silicon cryogenic cavity, spherical Fabry-Pérot cavity, compact Ultra- Low Expansion (ULE) cavity and fiber link active compensation. With enhanced capabilities offered by the undersampling technique, we demonstrate similar performances but with a net gain in reconfigurability, flexibililty and dissemination over their analog counterpart.
Failure Order: A Missing Piece in Disk Failure Processing of Data Centers

To avoid data loss, data centers adopt disk failure prediction (DFP) technology to raise warnings ahead of actual disk failures, and process the warnings in the order they are raised, i.e., a first-in-first-out (FIFO) warning order. The FIFO-guided warning order can process warnings timely when disk failures are rare in data centers. With the growing scale of data centers, the increasing number of disk failures leads to a complex situation that multiple warnings are raised simultaneously, where the FIFO-guided warning order neither processes warnings timely, nor manages warnings properly due to lack of the priority of warnings. Thus, a real-time and finer-grained priority guidance for warning order management is an urgent need. To this end, we turn our attention to the failures since each warning corresponds to a fail event. The key insight is that the interdependence of failures, i.e., the order failure occurred, indicates the order of warning processing. With an accurate failure order, data centers can decrease the probability of data loss and the downtime of latency-sensitive applications by processing urgent warnings in advance. In this paper, we predict the failure order with a LambdaMART model, which is a state-of-the-art ranking algorithm in information retrieval. To avoid overly concerning on the correctness of high-rank warnings in information retrieval, we design a symmetric metric to evaluate the prediction evaluation of failure order. Experiment on a public dataset, provided by the Backblaze company, shows that our model outperforms the FIFO order and the order from previous DFP models.
Compressive Classification Based on One-Bit Measurements

Classification is an important task in the fields of signal processing and machine learning. Compressive classification (CC), which exploits the technique of compressive sensing (CS), appears recently that directly classifies signals in the compressed domain. The existing works have shown that compressive classification can achieve reliable classification based on few compressive measurements. To further reduce the complexity, this paper studies the one-bit CC based on one-bit CS, which only acquires the sign information of compressive measurements, and thus is appealing in many applications when the bandwidth and energy are constrained. When the sources follow Gaussian mixture model, we derive the upper bound of the probability of misclassification for one-bit CC. We also propose a practical one-bit CC scheme and show its promising performance by experiments with both synthetic data and real image data.
Eigenvector-Based Speech Mask Estimation for Multi-Channel Speech Enhancement

We present the Eigennet architecture for estimating a gain mask from noisy, multi-channel microphone observations. While existing mask estimators use magnitude features, our system also exploits the spatial information embedded in the phase of the data. The mask is used to obtain the Minimum Variance Distortionless Response (MVDR) and Generalized Eigenvalue (GEV) beamformers. We also derive the Phase Aware Normalization (PAN) postfilter, which corrects both magnitude and phase distortions caused by the GEV. Further, we demonstrate the properties of our eigenvector features, and compare their performance with three state-of-the-art reference systems. We report their performance in terms of SNR improvement and Word Error Rate (WER) using Google and Kaldi Speech-to-Text API. Experiments are performed on the WSJ0 and CHiME4 corpora, where competitive performance in both WER and SNR is achieved.