Miracle-dz/UniFAR-FTUs huggingface dataset:
Steady-State Analysis of the Effects of Residual Amplitude Modulation of InP-Based Integrated Phase Modulators in Pound–Drever–Hall Frequency Stabilization

Residual amplitude modulation of the phase modulator deployed in Pound–Drever–Hall frequency stabilization is an effect known to cause instabilities to the absolute wavelength of the stabilized laser. We present measurements and analysis of the residual amplitude modulation in an InP-based waveguide electro-optic phase modulator. The modulator is monolithically integrated in the output waveguide of a tuneable laser. The effects on the frequency stabilization of such a laser system to a reference etalon using a Pound–Drever–Hall frequency stabilization scheme are quantified. Frequency offset values in the stabilization point from the reference Fabry–Perot etalon resonance caused by the amplitude modulation are predicted and optimum operating points to minimize residual amplitude modulation are discussed. By operating an electro-refractive phase modulator at the proper bias point, we show that frequency offsets corresponding to less than 3 × 10−3 of the reference cavity full-width half-maximum can be achieved.
Color Decision System Based on Deep Learning and Fuzzy Inference System

Autonomous driving system has become a hot issue. Similar to autonomous driving system, Autonomous Guided Vehicle (AGV) works in industry field. It is controlled by its autonomous driving system. This system detects the driving environment by using sensors such as vision sensor, laser sensor, ultrasonic sensor and so on. Among these sensors, vision sensor can obtain various kinds of the information of the environment. In an image, which is captured by vision sensor, the environment is represented by its color, shape and so on. Vision-AGV where vision sensor is mounted can be controlled by the colorful sign like the traffic light. To recognize the sign for controlling the AGV, it is necessary to detect the sign and analyze the meaning of the this sign. Faster Regions with Convolutional Neural Network features (Faster R-CNN) is applied to detect the sign. To analyze the meaning of the color sign, RGB color space, which is well known and consists of primary colors, can be used. However, it is difficult to analyze the color sign by using only RGB color space. Therefore, the proposed method is designed for detecting and recognizing the color of the sign with CIE L*a*b* color space. To improve the effect of the analysis, RGB and CIE L*a*b* color space are combined. Based on RGB, CIE L*a*b* and Peak Signal-to-Noise Ratio (PSNR), the color of the region which is extracted by Faster R-CNN is determined by the fuzzy inference system. As experimental results, the proposed method can detect the sign and analyze the color of this sign.
The Exchange Relationship between Work-Family Enrichment and Affective Commitment: the Moderating Role of Gender

Abstract Workers’ perception that their job experience enriches their family life has been considered a mechanism that explains their positive attitudes toward the organization where they work. However, because women and men live their work and family differently, gender may condition this relationship between the work-family enrichment and workers’ attitudes. With a sample of 1885 workers from one Portuguese bank, with 802 women, the current study investigated the relationship between work-family enrichment and organizational affective commitment as well as the role of sex as a moderator of this relationship. The hypotheses were tested by using regression analysis. The results indicated that the perception held by workers that their work enriches their family is positively correlated with their affective commitment toward the organization. Furthermore, the data revealed that this relationship is stronger for women than for men. Study results have implications for management, particularly for human resource management, enhancing their knowledge about the relationship of work-family enrichment and workers’ affective commitment toward organization.
Stopband-Improved SIW Triplexer and Triple-Band Filters Using Alternately Cascaded Triple- and Single-Mode Cavities

Stopband-improved substrate-integrated waveguide (SIW) triplexer and triple-band filters are presented based on the novel realization method of coupling topologies composed of common triple-mode resonators coupled with multiple single-mode ones. Thanks to the common triple-mode cavities acting as T-junction proposed in our previous work, the three passbands can be flexibly controlled. The utilization of single-mode cavities not only simplifies the control of internal couplings but also supplies more degrees of freedom to stagger the higher order harmonics for stopband improvement. The implementation methodology for SIW triplexer and triple-band filters is presented and three examples including a third-order triplexer and two third- and fifth-order BPFs are synthesized, designed, fabricated, and tested with excellent performances and compact sizes. Good agreement is realized between the measured and the simulated results.
Reducing Write Amplification for Inodes of Journaling File System using Persistent Memory

Conventional journaling file systems, such as Ext4, guarantee data consistency by writing in-memory dirty inodes to block devices twice. The write back of inodes may contain up to 80% clean inode that is unnecessary to be written back, which caused severe write amplification problem and largely reduce performance since the size of an inode is several times less than the size of a basic unit for updating the block device. Emerging persistent memories (PMs), such as phase change memory, provide the possibility for storing the offset of inodes in memory persistently. In this paper, we propose an efficient scheme, Updating Frequency based Inode Aggregation (UFIA), to reduce the write amplification of dirty inodes using PM. The main idea of UFIA is to identify the frequently-updated inodes and reorganize them in adjacent physical locations on block device. Firstly, UFIA adopts PM as an inode mapping table for remapping logical inodes to any physical inodes. Secondly, we design an efficient algorithm for UFIA to identify and reorganize the frequently-updated inodes. We implement UFIA and integrate it into Ext4 (denoted by UFIA-Ext4) in Linux kernel 4.4. 4. The experiments are conducted with widely-used benchmark Filebench. Compared with original Ext4, the experimental results show that UFIA significantly reduces the write amplification of inodes and improves 54% of the performance on average.
A Write-Efficient Cache Algorithm based on Macroscopic Trend for NVM-based Read Cache

Compared with traditional storage technologies, non-volatile memory (NVM) techniques have excellent I/O performances, but high costs and limited write endurance (e.g., NAND and PCM) or high energy consumption of writing (e.g., STT-MRAM). As a result, the storage systems prefer to utilize NVM devices as read caches for performance boost. Unlike write caches, read caches have greater potential of write reduction because their writes are only triggered by cache updates. However, traditional cache algorithms like LRU and LFU have to update cached blocks frequently because it is difficult for them to predict data popularity in the long future. Although some new algorithms like SieveStore reduce cache write pressure, they still rely on those traditional cache schemes for data popularity prediction. Due to the bad long-term data popularity prediction effect, these new cache algorithms lead to a significant and unnecessary decrease of cache hit ratios. In this paper, we propose a new Macroscopic Trend (MT) cache replacement algorithm to reduce cache updates effectively and maintain high cache hit ratios. This algorithm discovers long-term hot data effectively by observing the macroscopic trend of data blocks. We have conducted extensive experiments driven by a series of real-world traces, and the results indicate that compared with LRU, the MT cache algorithm can achieve 15.28 times longer lifetime or less energy consumption of NVM caches with a similar hit ratio.
An Efficient Wireless Control Plane for Software Defined Networking in Data Center Networks

Software defined networking (SDN) has re-defined the way data center networks are deployed and functioned. The ability to separate the control plane and data plane have led to the simpler design and easier management. In SDN, the data plane and control plane are separately managed, so building an additional wired network to connect the control plane and the data plane can lead to a high cabling complexity. This paper proposes an end-to-end end wireless control plane architecture for SDN-enabled data center networks. In the proposed architecture, the switches form clusters that are wirelessly connected to the controller via access points (APs) and relay nodes (RNs). The switches use the 2.4/5-GHz band to connect with the APs, whereas the APs and the RNs are connected to the controller using the 60-GHz band. We have presented an analytical model to derive achievable data rates in our wireless control plane. We have also proposed two algorithms that allow an optimal number of a cluster size of the switches to be connected with the Controller via minimum number to APs/RNs such that the control traffic demands of the switches is guaranteed in interference constrained environment. Through extensive simulations, the results of our proposed architecture show that the cabling complexity in the control plane is reduced to zero and additional switches may be easily added in SDN data center. Thus, a pure wireless solution for building a control plane in a data center network is feasible.
Literature Review of WeChat Friends Circle Advertisement

In recent years, instant messaging tools have become one of the main channels for people to communicate, and WeChat is one of the most important members. Since WeChat was launched, monthly active users have exceeded 1 billion. In January 2015, WeChat friends circle advertisement was officially launched and used in the form of information flow advertisement, which users praised or criticized differently. Therefore, it is a problem that WeChat research and development team and advertisers must face to improve users' willingness to accept advertisements in the circle of friends. We used “WeChat” and “circle of friends” as key words to search in CNKI, and the number of articles retrieved was 205. After carrying out quantitative analysis and content analysis of relevant documents (2013 – 2018), it is found that most of the studies were carried out from the perspective of the communication mechanism, marketing strategy and comparative analysis of advantages and disadvantages of WeChat circle of friends. In addition, some studies are based on advertisement avoidance, user participation and willingness to accept. At the same time, we also take WeChat as the research background, integrate the development and changes of TAM (technology acceptance model), and summarize the basic situation of users' willingness to accept WeChat friends' circle advertisement in our country so as to provide more direction and space for the optimization research of WeChat friends' circle advertisement.
Identifying Participants' Characteristics Influencing Participant Estimation in Knowledge-Intensive Crowdsourcing

In knowledge-intensive crowdsourcing, estimating proper participants is an important way of ensuring tasks' outcomes. Multiple participants' characteristics have influence on participant estimation and should be considered comprehensively. Nevertheless, to the best of our knowledge, few studies particularly focused on investigating and proposing a common list of participants' characteristics. So this paper aims to investigate and identify a list of participants' characteristics that influence participant estimation in knowledge-intensive crowdsourcing. This study initially searches, filters, and analyses literatures related to the topic of this study, and subsequently extracts participants' characteristics. Furthermore, seven crowdsourcing marketplaces which are used widely to crowdsource knowledge-intensive tasks are investigated to acquire participants' characteristics presented in practical crowdsourcing systems. Combining those characteristics of participants obtained from the two aspects, the paper finally highlights five major participants' characteristics and their sub-characteristics.
Cost-Efficient, Portable, and Custom Multi-Subject Electroencephalogram Recording System

Multi-subject electroencephalogram (EEG) computing has attracted increasing interest in recent years. Commercial EEG headsets often lead to a space-required or cost-expensive setup for obtaining simultaneous recordings of multiple subjects. The sparsity or immobility of their electrode placements may compromise their realistic applicability. Thus, this work developed a cost-efficient, portable, and customizable system to mitigate these constraints. The developed system implemented two core hardware infrastructures, including an event broadcaster and a dry electrode-compatible EEG amplifier by assembling entirely low-cost, off-the-shelf electronic components. The broadcaster allowed distribution of event markers to multiple EEG amplifiers concurrently, whereas the amplifier transmitted the digitized, event-synchronized EEG signals wirelessly. By conducting an oddball event-related potential (ERP) experiment with simultaneous recordings of three subjects on 10 days, the system reliably captured the time- and phase-locked ERP components (e.g., N100 and P300 amplitudes) by single-subject, multi-subject, and multi-day analytical approaches. The practicality and stability of the proposed system was empirically demonstrated in terms of the signal quality, EEG-event synchronization, and inter-amplifier coordination for a multi-subject setup. This work sheds light on how to economically facilitate and scale up multi-subject EEG computing for fundamental research and for brain-computer interface (BCI) applications in a larger subject population.
A Comparative Analysis of N-Gram Deep Neural Network Approach to Classifying Human Perception on Dengvaxia

The increasing use of social media platform like Twitter provides opportunity for information dissemination to the public. The Dengvaxia controversy in the Philippines negatively affected the public’s perception towards vaccination. It has been noted that due to this incident, many parents have decided not to have their children vaccinated due to fear of endangering them [2]. This resulted to children contracting other diseases like measles due to the lack of immunization [1] [2]. The preference to not have newborns undergo vaccination program remains a threat to public health. Using publicly accessible tweets, this study aims to understand health perceptions of the public in relation to Dengvaxia. A deep neural network approach using n-gram vectorization is used in comparison to the Doc2Vec neural network classifier to identify tweets containing personal perception on health. It was discovered that not only does the bigram model perform better in classifying than the Doc2Vec model with a performance measure of 86.25% accuracy, 0.85 precision, 0.86 ROC and 0.85 F1 score, but also it is able to identify clearer and more diverse topic using LDA topic modeling in comparison with unigram and trigram model. This method allows the monitoring of public perception and acceptance towards the implementation of a new medication or vaccination especially after the Dengvaxia scandal that the Philippines experienced.
Automatic Energy Extraction Methods for EEG Channel Selection

This paper presents an automatic selected channel method for improving brain-computer interface (BCI) performance. Finding an effective channel composition in a BCI system is important for parsing the complexity of these systems. In this study, several statistical methods based on an energy extraction method were used to automatically detect the composition of selected channels. We introduce four techniques for automatic channel selection to optimize the energy extraction method, such as mean, high mean, box, and high in box. The performance of all of the proposed techniques was evaluated based on performance accuracy, compression ratio, and channel mapping on a brain scalp. The performance of the proposed BCI framework was compared against a BCI framework that used a manual technique as the energy extraction method. The proposed BCI framework system used a conventional common spatial pattern (CSP) to extract features from two-class motor imagery EEG signals before employing extreme learning machine (ELM) to classify the features of the EEG signal. As a result, the proposed automatic channel selection methods were found effective in finding optimal channels and provided better performance accuracy. In general, the proposed method improved the conventional BCI performance by up to 16% accuracy and 87% channel compression size. Besides that, the automatic technique also yielded better BCI performance accuracy of up to 5% compared to the BCI system that used the manual technique of energy extraction as its EEG channel selection method.
Contrasting resting-state fMRI abnormalities from sickle and non-sickle anemia

Sickle cell disease (SCD) is a chronic blood disorder that is often associated with acute and chronic cerebrovascular complications, including strokes and impaired cognition. Using functional resting state magnetic resonance images, we performed whole-brain analysis of the amplitude of low frequency fluctuations (ALFF), to detect areas of spontaneous blood oxygenation level dependent signal across brain regions. We compared the ALFF of 20 SCD patients to that observed in 19 healthy, age and ethnicity-matched, control subjects. Significant differences were found in several brain regions, including the insula, precuneus, anterior cingulate cortex and medial superior frontal gyrus. To identify the ALFF differences resulting from anemia alone, we also compared the ALFF of SCD patients to that observed in 12 patients having comparable hemoglobin levels but lacking sickle hemoglobin. Increased ALFF in the orbitofrontal cortex and the anterior and posterior cingulate cortex and decreased ALFF in the frontal pole, cerebellum and medial superior frontal gyrus persisted after accounting for the effect of anemia. The presence of white matter hyperintensities was associated with depressed frontal and medial superior frontal gyri activity in the SCD subjects. Decreased ALFF in the frontal lobe was correlated with decreased verbal fluency and cognitive flexibility. These findings may lead to a better understanding of the pathophysiology of SCD.
Hardware-Accelerated Energy-Efficient Synchronization and Communication for Ultra-Low-Power Tightly Coupled Clusters

Parallel ultra low power computing is emerging as an enabler to meet the growing performance and energy efficiency demands in deeply embedded systems such as the end-nodes of the internet-of-things (IoT). The parallel nature of these systems however adds a significant degree of complexity as processing elements (PEs) need to communicate in various ways to organize and synchronize execution. Naive implementations of these central and non-trivial mechanisms can quickly jeopardize overall system performance and limit the achievable speedup and energy efficiency. To avoid this bottleneck, we present an event-based solution centered around a technology-independent, light-weight and scalable (up to 16 cores) synchronization and communication unit (SCU) and its integration into a shared-memory multicore cluster. Careful design and tight coupling of the SCU to the data interfaces of the cores allows to execute common synchronization procedures with a single instruction. Furthermore, we present hardware support for the common barrier and lock synchronization primitives with a barrier latency of only eleven cycles, independent of the number of involved cores. We demonstrate the efficiency of the solution based on experiments with a post-layout implementation of the multicore cluster in a 22 nm CMOS process where the SCU constitutes less than 2 % of area overhead. Our solution supports parallel sections as small as 100 or 72 cycles with a synchronization overhead of just 10 %, an improvement of up to 14× or 30× with respect to cycle count or energy, respectively, compared to a test-and-set based implementation.
Online Extremism Discovering through Social Network Structure Analysis

The activity of extremist organizations on the Internet is continuously growing with the increase of Web’s usage for means of communication. Therefore analysis of radical members in social networks provides important information on how to prevent them propagate ideology and recruiting new members in the future. But nowadays terrorists often use confidential chats and private threads to communicate, thus it’s quite hard to detect them using only the public messages they generate. In fact, it is usually known that some users of social networks are dangerous, another are innocent, and no information is available about the remaining users. In this paper, we propose an approach for detecting radical users of social network among unknown ones by analyzing their relationships and features as of vertices of social graph without usage of any information about text content they generate. We find that the proposed method is very promising and may be efficiently used for real-time monitoring systems and future terrorism and extremism research.
Texture Analysis of Non-Small Cell Lung Cancer on Unenhanced CT and Blood Flow Maps: a Potential Prognostic Tool

The presence of tumour heterogeneity makes the clinical oncological practice very challenging, since introduces a great variability in tumours' response to available therapies. For this reason, in the last decade, quantifying the salient features of the intra-tumoural heterogeneity has gained a great attention, also leading to a re-emerging of the texture analysis. Tumour heterogeneity represents the complex biology of tumour micro-environment, characterised by both spatial and temporal variability, increased by the presence of chaotic blood vessels within tumour tissue. Computed Tomography (CT) has always been considered one of the reference technologies for morphological analysis of organs and tissues, permitting to capture the “in vivo” spatial tumour heterogeneity. The need to also detect hemodynamic tumour features has stimulated the use of CT perfusion (CTp), a promising functional imaging technique in the oncological field. CTp allows detecting the presence of tumour abnormal hemodynamic patterns, by analysing the tissue temporal variations occurring after an intravenous administration of contrast medium. This work presents the extraction of meaningful statistical and texture features from both baseline CT images and perfusion maps of lung tumours, which could work as prognostic image-based biomarkers.
Block-Flushing: A Block-based Washing Algorithm for Programmable Microfluidic Devices

Programmable Microfluidic Devices (PMDs) have emerged as a new architecture for next-generation flow-based biochips. These devices can be dynamically reconfigured to execute different bioassays flexibly and efficiently owing to their two-dimensional regularly-arranged valve structure. During execution of a bioassay or between the execution of multiple bioassays, some areas on the PMD, however, become contaminated and must be cleaned by washing them with a buffer flow before they are reused. In this paper, we propose a novel block-based washing technique called block flushing. In this method, contaminated areas are first collected according to given patterns and flushed as a whole to increase washing efficiency. Simulation results show that with this technique the proposed method can achieve on average 28% improvement in reducing washing time compared with two other baseline solutions.