Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient SDN Multicast Architecture for Dynamic Industrial IoT Environments

Large-scale industrial IoT services appear in smart factory domains such as factory clouds which integrate distributed small factories into a large virtual factory with dynamic combination based on orders of consumers. A smart factory has so many industrial elements including various sensors/actuators, gateways, controllers, application servers, and IoT clouds. Since there are complex connections and relations, it is hard to handle them in point-to-point manner. In addition, many duplicated traffics are exchanged between them through the Internet. Multicast is believed as an effective many-to-many communication mechanism by establishing multicast trees between sources and receivers. There are, however, some issues for adopting multicast to large-scale industrial IoT services in terms of QoS. In this paper, we propose a novel software-defined network multicast based on group shared tree which includes near-receiver rendezvous point selection algorithm and group shared tree switching mechanism. As a result, the proposed multicast mechanism can reduce the packet loss by 90% compared to the legacy methods under severe congestion condition. GST switching method obtains to decreased packet delay effect, respectively, 2%, 20% better than the previously studied multicast and the legacy SDN multicast.
Efficient Topic Modeling on Phrases via Sparsity

Topic modeling on phrases is important in understanding documents by providing interpretable topics. But existing methods are not as efficient as the topic modeling methods on words, which may limit their potential application.Towards providing a more efficient method, we propose a novel topic model SparseTP, which (1) models the words and phrases by linking them in Markov Random Field when necessary; (2) provides a well-formed lower bound of the model for Gibbs sampling; (3) utilizes the sparse distribution of words and phrases on topics to speed up the inference. The experiments demonstrate that it can achieve the high efficiency without sacrificing the effectiveness.
Highly Efficient 2D/3D Hybrid Perovskite Solar Cells via Low‐Pressure Vapor‐Assisted Solution Process

The fabrication of multidimensional organometallic halide perovskite via a low‐pressure vapor‐assisted solution process is demonstrated for the first time. Phenyl ethyl‐ammonium iodide (PEAI)‐doped lead iodide (PbI2) is first spin‐coated onto the substrate and subsequently reacts with methyl‐ammonium iodide (MAI) vapor in a low‐pressure heating oven. The doping ratio of PEAI in MAI‐vapor‐treated perovskite has significant impact on the crystalline structure, surface morphology, grain size, UV–vis absorption and photoluminescence spectra, and the resultant device performance. Multiple photoluminescence spectra are observed in the perovskite film starting with high PEAI/PbI2 ratio, which suggests the coexistence of low‐dimensional perovskite (PEA2MAn−1PbnI3n+1) with various values of n after vapor reaction. The dimensionality of the as‐fabricated perovskite film reveals an evolution from 2D, hybrid 2D/3D to 3D structure when the doping level of PEAI/PbI2 ratio varies from 2 to 0. Scanning electron microscopy images and Kelvin probe force microscopy mapping show that the PEAI‐containing perovskite grain is presumably formed around the MAPbI3 perovskite grain to benefit MAPbI3 grain growth. The device employing perovskite with PEAI/PbI2 = 0.05 achieves a champion power conversion efficiency of 19.10% with an open‐circuit voltage of 1.08 V, a current density of 21.91 mA cm−2, and a remarkable fill factor of 80.36%.
A Direct Phase-Tracking Doppler Radar Using Wavelet Independent Component Analysis for Non-Contact Respiratory and Heart Rate Monitoring

A continuous wave Doppler radar, operating as a phase-locked-loop in phase demodulator configuration, is proposed and in vivo demonstrated for noncontact vital signs monitoring. The radar architecture exhibits a unique precision in tracking the phase modulation caused by human cardiopulmonary activity from which heartbeat and respiration can simultaneously be extracted. The single mixer architecture is immune to the null point and does not require small-angle approximation conditions, which distinguishes it from pre-existing other approaches. This enables the proposed radar to behave highly linear, with very precise detection of phase modulations induced by any kind of movement, independently from amplitude and speed. After simulations and technical tests to validate functionality and safety of the proposed architecture, a practical setup was demonstrated on human volunteers. Wavelet independent component analysis was applied to successfully retrieve respiratory and heart rate information from the radar baseband signal.
Emojis-Based Sentiment Classification of Arabic Microblogs Using Deep Recurrent Neural Networks

Machine-learning based sentiment classification has gained increasing popularity for analyzing online content in social media. A new generation of artificial neural networks is deep learning, which has been successfully applied in several domains. In this study, we empirically evaluate two state-of-the-art models of deep recurrent neural networks to detect sentiment polarity of Arabic microblogs. We considered both unidirectional and bidirectional Long Short-Term Memory (LSTM) and its simplified variant Gated Recurrent Unit (GRU). Moreover, due to the complexities and challenges facing the Arabic language to model short dialectical text, which is commonly used in microblogs, we aim to assess non-verbal features extracted from a dataset of 2091 microblogs. We also compared the performance to baseline traditional learning methods and deep neural networks. The experimental results reveal that LSTM and GRU based models significantly outperform other classifiers with a slight difference between them with best results attained when using bidirectional GRU.
Qualification and Application of a Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometric Method for the Determination of Adalimumab in Rat Plasma

A liquid chromatography–quadrupole time-of-flight (Q-TOF) mass spectrometric method was developed for early-stage research on adalimumab in rats. The method consisted of immunoprecipitation followed by tryptic digestion for sample preparation and LC-QTOF-MS/MS analysis of specific signature peptides of adalimumab in the positive ion mode using electrospray ionization. This specific signature peptide is derived from the complementarity-determining region (CDR) of adalimumab. A quadratic regression (weighted 1/concentration), with an equation y = ax2 + bx + c, was used to fit calibration curves over the concentration range of 1–100 μg/mL for adalimumab. The qualification run met the acceptance criteria of ±25% accuracy and precision values for quality control (QC) samples. This qualified LC-QTOF-MS/MS method was successfully applied to a pharmacokinetic study of adalimumab in rats as a case study. This LC-QTOF-MS/MS approach would be useful as a complementary method for adalimumab or its biosimilars at an early stage of research.
Design and fabrication of photo-sensitive thin-film transistors with IGZO and organic photo-absorber

This paper presents theoretical design and experimental results of photo-sensitive thin-film transistors (TFTs) based on an InGaZnO layer covered with an organic or perovskite photo-absorber layer. The photo-absorbing layer is of a narrow bandgap and efficiently improves spectral response characteristics in visible light.
Optical Properties of Gyroid Structured Materials: From Photonic Crystals to Metamaterials

The gyroid is a continuous and triply periodic cubic morphology which possesses a constant mean curvature surface across a range of volumetric fill fractions. Found in a variety of natural and synthetic systems which form through self‐assembly, from butterfly wing scales to block copolymers, the gyroid also exhibits an inherent chirality not observed in any other similar morphologies. These unique geometrical properties impart to gyroid structured materials a host of interesting optical properties. Depending on the length scale on which the constituent materials are organised, these properties arise from starkly different physical mechanisms (such as a complete photonic bandgap for photonic crystals and a greatly depressed plasma frequency for optical metamaterials). This article reviews the theoretical predictions and experimental observations of the optical properties of two fundamental classes of gyroid structured materials: photonic crystals (wavelength scale) and metamaterials (sub‐wavelength scale).
Orofacial clefts and infections during pregnancy

Orofacial clefts (OFCs) are common congenital malformations of the lip, palate, or both caused by complex genetic and environmental factors. Specific antibodies against viruses influenza, rubella, cytomegalovirus, Epstein-Barr, parotitis and hepatitis B were investigated serologically in children with orofacial clefts and in their mothers. The results were compared with those obtained in control children and their mothers. Evaluation of the results and their statistical processing supports the assumption that infection during pregnancy may have occurred in the series studied induced by viruses of influenza, rubella, cytomegalovirus and possibly also by the Epstein-Barr virus. No association with the viruses of hepatitis B and parotitis was established.
A Regression-Based Approach Using Integer Linear Programming for Single-Document Summarization

Most of the existing approaches for extractive singledocument summarization of news articles rely on a single method to summarize all input documents. Recent work demonstrated that this is a significant limitation, since no summarization technique can achieve high performance for all input articles. In this context, this paper proposes a new regression-based approach using Integer Linear Programming (ILP) for single-document summarization. The proposed solution relies on a concept-based ILP method to generate multiple candidate summaries for each input article exploring different concept weighting methods and representation forms. Afterward, a regression model enriched with several extracted features at summary, sentence and ngram level is applied to select among the candidates the most informative summary based on an estimation of the traditional ROUGE-1 score. The investigated features are derived from indicators of content importance such as frequency, position, and coverage. Experiments conducted on the DUC 2001-2002 and CNN corpora show that the proposed method statistically outperforms other state-of-the-art extractive summarization approaches in most scenarios regarding ROUGE-1 and ROUGE-2 recall measures.
Dynamic mitigation of EV charging stations impact on active Distribution Networks with Distributed BESSs

Less secured and stressed power system operating conditions have motivated the grid integration of renewable energy generators. Nonetheless, intermittency of renewable energy sources along with the emerging charging requirements of highly-penetrating Electric Vehicles (EVs) necessitates looking for massive energy storage systems. This paper proposes a control algorithm to mitigate the dynamic impact of the EV Charging Stations (CSs) on Active distribution Networks (ADNs) depending on the distributed Battery Energy Storage Systems (BESSs). In the proposed scheme, voltage support and line congestion are targeted. The control algorithm relies on the dynamic model of the BESSs for their DC active power limits. A control scheme of the AC-DC and DC-DC converter stages for the EV CSs is proposed. In addition, the distributed BESSs, capable of peak-shaving and load-levelling of the ADN, are addressed. The effectiveness of the proposed scheme is validated throughout a modified IEEE test distribution system.
Modeling User-Item Profiles with Neural Networks for Rating Prediction

In recommender systems, the essential task is to predict the personalized rating of a user to a new item. To address this task, recommender systems usually employ matrix factorization model to predict ratings over a user-item rating matrix. However, this model severely suffers from the problem of data sparsity. Noting the large amount of user reviews available in the Internet, we exploit user preferences and item attributes contained in reviews to alleviate the problem. Specifically, we propose a Neural Profile-Aware Matrix Factorization model, namely NPMF, which incorporates the user and item profiles modeled with neural networks for rating prediction. We evaluate the performance of NPMF using three large-scale real-world datasets released in Yelp. The experimental results show that NPMF outperforms other mainstream rating prediction techniques and indeed alleviates the data sparsity problem.
An Effective Deep Learning Approach for Unobtrusive Sleep Stage Detection Using Microphone Sensor

Sleep plays a vital role in good health and well-being throughout human life. A great deal of studies have been done to detect sleep stages. Most of the current sleep monitoring systems are invasive to users, e.g. requiring users to wear a device during sleep. In this paper, we use microphone to detect sleep stages including deep sleep, light sleep and rapid eye movement (REM), and propose a convolutional neural network using spectrogram as input. This paper's contribution mainly concentrates on the following two aspects: First, microphone is unobtrusive for sleep detection. Second, we build the mapping between acoustic signal and sleep stages with little manual intervention to extract features. Performance of the proposed method is validated on a realistic environmental dataset containing 52 nights of 5 participants. Experimental results show that the accuracy of sleep stages detection is superior to the representative off-the-shelf applications. Besides, we propose to utilize the attention maps to visualize acoustic data to better understand the relationship between sound and sleep stages. Experimental results show that the model has the ability to effectively reduce noises in classification by ignoring the high-frequency sounds and white noises.
Pilot Distortion Attack and Zero-Startup-Cost Detection in Massive MIMO Network: From Analysis to Experiments

Accurate channel state information (CSI) is a key requirement for massive multiple-input multiple-output to achieve multi-fold increases in throughput and secrecy rate. Consequently, an adversary targeting the channel sounding process has the potential to significantly degrade performance. In this paper, we first present and model the pilot distortion attack, a simple but devastating jamming strategy in which the adversary distorts the access point’s (AP’s) CSI measurement of even a single client leading to denial of service for all clients associated with the AP. We then propose multiple-antenna carrier frequency offset estimate (MACE) as a countermeasure that exploits the AP’s antenna array to detect jamming with zero startup cost and zero additional network overhead. Our key insight is that with multiple antennas, the AP’s variance estimator of client carrier frequency offset significantly increases when there are jamming signals present. We build a test bed with a 72-antenna AP and collect over 3 000 000 over-the-air transmissions. Our results show that a single-antenna adversary jamming no more than 1/60 of the time and having no more transmit power than any client can cause over 23% reduction of achievable rate of all clients. Moreover, by setting a single detection threshold, MACE can achieve 0.97 true positive at 0.01 false positive for various client/adversary locations and for a wide range of signal-to-noise ratio (SNR) (5 ~ 35 dB) and signal-to-interference ratio (SIR) (−5 ~ 35 dB) with SNR – SIR $\geq5$ dB.
How Item Residual Heterogeneity Affects Tests for Differential Item Functioning

Differential item functioning (DIF) occurs when people with the same proficiency have different probabilities of giving a certain response to an item. The present study focused on an assumption implicit in popular methods for DIF testing that has received little attention in published literature (item residual homogeneity). The assumption is explained, a strategy for detecting violations of it (i.e., item residual heterogeneity) is illustrated with empirical data, and simulations are carried out to evaluate the performance of binary logistic regression, two-group item response theory (IRT), and the Mantel–Haenszel (MH) test in the presence of item residual heterogeneity. Results indicated that heterogeneity inflated Type I error and attenuated power for logistic regression, and attenuated power and produced biased estimates of the latent focal group mean and standard deviation for two-group IRT. The MH test was robust to item residual heterogeneity, probably because it does not use the logistic function.
Batched Incremental Structure-from-Motion

The incremental Structure-from-Motion (SfM) technique has advanced in both robustness and accuracy, but the efficiency and scalability remain its key challenges. In this paper, we propose a novel batched incremental SfM technique to tackle these problems in a unified framework, where two iteration loops are contained. The inner loop is a tracks triangulation loop, where a novel tracks selection method is proposed to find a compact subset of tracks for the bundle adjustment (BA). The outer loop is a camera registration loop, where a batch of cameras are simultaneously added to alleviate the drifting risk and reduce the running times of BA. By the tracks selection and batched camera registration, we find these two iteration loops converge fast. Extensive experiments demonstrate that our new SfM system performs similarly or better than many of the state-of-the-art SfM systems in terms of camera calibration accuracy, while is more efficient, robust and scalable for large-scale scene reconstruction.
Multi-classifier system for authorship verification task using word embeddings

Authorship Verification is considered as a topic of growing interest in research, which has shown excellent development in recent years. We want to know if an unknown document belongs to the documents set known to an author or not. Classical text classifiers often focus on many human designed features, such as dictionaries, knowledge bases and special tree kernels. Other studies use the N-gram function that often leads to the curse of dimensionality. Contrary to traditional approaches, this article proposes a new scheme of Machine Learning model based on fusion of three different architectures namely, Convolutional Neural Networks, Recurrent-Convolutional Neural Networks and Support Vector Machine classifiers without human-designed features. Word2vec based Word Embeddings is proposed to learn the best word representations for automatic authorship verification. Word Embeddings provides semantic vectors and extracts the most relevant information about raw text with a relatively small dimension. As well as the classifiers generally make different errors on the same learning samples which results in a combination of several points of view to maintain relevant information contained in different classifiers. The final decision of our system is obtained by combining the results of the three models using the voting method.
Candidate gene biodosimetry markers of exposure to external ionizing radiation in human blood: A systematic review

Purpose To compile a list of genes that have been reported to be affected by external ionizing radiation (IR) and to assess their performance as candidate biomarkers for individual human radiation dosimetry. Methods Eligible studies were identified through extensive searches of the online databases from 1978 to 2017. Original English-language publications of microarray studies assessing radiation-induced changes in gene expression levels in human blood after external IR were included. Genes identified in at least half of the selected studies were retained for bio-statistical analysis in order to evaluate their diagnostic ability. Results 24 studies met the criteria and were included in this study. Radiation-induced expression of 10,170 unique genes was identified and the 31 genes that have been identified in at least 50% of studies (12/24 studies) were selected for diagnostic power analysis. Twenty-seven genes showed a significant Spearman’s correlation with radiation dose. Individually, TNFSF4, FDXR, MYC, ZMAT3 and GADD45A provided the best discrimination of radiation dose < 2 Gy and dose ≥ 2 Gy according to according to their maximized Youden’s index (0.67, 0.55, 0.55, 0.55 and 0.53 respectively). Moreover, 12 combinations of three genes display an area under the Receiver Operating Curve (ROC) curve (AUC) = 1 reinforcing the concept of biomarker combinations instead of looking for an ideal and unique biomarker. Conclusion Gene expression is a promising approach for radiation dosimetry assessment. A list of robust candidate biomarkers has been identified from analysis of the studies published to date, confirming for example the potential of well-known genes such as FDXR and TNFSF4 or highlighting other promising gene such as ZMAT3. However, heterogeneity in protocols and analysis methods will require additional studies to confirm these results.
SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions

We introduce a principled approach for simultaneous mapping and clustering (SMAC) for establishing consistent maps across heterogeneous object collections (e.g., 2D images or 3D shapes). Our approach takes as input a heterogeneous object collection and a set of maps computed between some pairs of objects, and outputs a homogeneous object clustering together with a new set of maps possessing optimal intra- and inter-cluster consistency. Our approach is based on the spectral decomposition of a data matrix storing all pairwise maps in its blocks. We additionally provide tight theoretical guarantees for the accuracy of SMAC under established noise models. We also demonstrate the usefulness of our approach on synthetic and real datasets.
Assessing the Impact of Temperature and Supply Voltage Variations in Near-threshold Circuits using an Analytical Model

In this paper, we develop an analytical model based on Enz Krummenacher Vittoz (EKV) current equations to assess the impact of temperature and supply voltage (VDD) variations in circuits operating in near-threshold voltage (NTV) regime. Using the proposed model, we derive parameters that can be optimized to reduce the impact of these variations on devices operating in the NTV regime and highlight the dominant role of the inversion coefficient of the EKV equations. Further, we show that, instead of operating circuits such as a CMOS inverter very close to the threshold voltage (VTH.), it is beneficial to operate these circuits 3 - 4kT/q above the VTH. At these voltages, the impact of the temperature variations on the delay is minimized and the impact of VDD variations on delay is 0.7x lower than when operated at VDD =VTH. Additionally, compared to the super-threshold operation, the power consumption reduces by 5x and the delay increases by 5x. The results presented in this paper can be employed in estimating the increase in the time margins required when a circuit is migrated from the super-threshold operation to the near-threshold operation.
Note on the Determination of the Offset Lines in Doherty Power Amplifiers

This letter shows how to find by inspection, the values of the characteristic impedances and the electrical lengths of the offset lines of the main and auxiliary amplifiers constituting a Doherty power amplifier. This letter focuses on the easiest determination of these offset lines using the ABCD matrix coupled with the formalism of image parameters to describe the output matching circuits of the main and auxiliary amplifiers.