Miracle-dz/UniFAR-FTUs huggingface dataset:
SNR-Invariant Multitask Deep Neural Networks for Robust Speaker Verification

A major challenge in speaker verification is to achieve low error rates under noisy environments. We observed that background noise in utterances will not only enlarge the speaker-dependent i-vector clusters but also shift the clusters, with the amount of shift depending on the signal-to-noise ratio (SNR) of the utterances. To overcome this SNR-dependent clustering phenomenon, we propose two deep neural network (DNN) architectures: hierarchical regression DNN (H-RDNN) and multitask DNN (MT-DNN). The H-RDNN is formed by stacking two regression DNNs in which the lower DNN is trained to map noisy i-vectors to their respective speaker-dependent cluster means of clean i-vectors and the upper DNN aims to regularize the outliers that cannot be denoised properly by the lower DNN. The MT-DNN is trained to denoise i-vectors (main task) and classify speakers (auxiliary task). The network leverages the auxiliary task to retain speaker information in the denoised i-vectors. Experimental results suggest that these two DNN architectures together with the PLDA backend significantly outperform the multicondition PLDA model and mixtures of PLDA, and that multitask learning helps to boost verification performance.
Graphene/Organic Semiconductor Heterojunction Phototransistors with Broadband and Bi‐directional Photoresponse

A graphene–semiconductor heterojunction is very attractive for realizing highly sensitive phototransistors due to the strong absorption of the semiconductor layer and the fast charge transport in the graphene. However, the photoresponse is usually limited to a narrow spectral range determined by the bandgap of the semiconductor. Here, an organic heterojunction (C60/pentacene) is incorporated on graphene to realize a broadband (405–1550 nm) phototransistor with a high gain of 5.2 × 105 and a response time down to 275 µs. The visible and near‐infrared parts of the photoresponsivity (9127 A W−1@650 nm and 1800 A W−1@808 nm) come from the absorption of the organic layer and the graphene, respectively. For the first time, a bi‐directional (positive and negative) photoresponse is demonstrated at different wavelengths, due to the opposite charge transfer direction of the photoexcited carriers enforced by the unique band alignment. Such tunability will enable new functionalities such as large‐scale real‐time optical image and infrared focal plane array detection in the future.
rFilter: A Scalable and Space-efficient Membership Filter

In this paper, we present a probabilistic data structure for membership filter, called rFilter. The rFilter is an extension of the Bloom filter data structure. The rFilter is a simple, yet powerful membership filter among its kind. The rFilter requires constant time and very low space complexity. In addition, the rFilter is highly scalable and space-efficient as compared to other variants of Bloom filter. The rFilter avoids complex hashing overhead. The rFilter boosts up the space performance by 16× or 93.75% over integer representation of an array. Besides, the rFilter reduces 4× or 75% over character representation of an array. Moreover, the rFilter drastically reduces the chances of "false positive" over the conventional method.
Smart Phone Based Facial and Text Recognition Application (RICO)

In our everyday lives, more people are being diagnosed with assorted types of diseases; both mentally and physically. One of them being a life changing mental disease called Alzheimer. Within time and as people grow in age, more and more people are being diagnosed with that disease. Alzheimer disease is a type of disorder that affects the mental functions of the brain resulting in memory loss, which causes its patients to suffer from difficulty in recognizing faces they are familiar with. Moreover, patients tend to have problems interacting with the people surrounding them. Since there is no certain cure for this dementia, one of the ways we could contribute to help patients get along with this disease is by proposing a smart phone based facial and text recognition application (RICO). Rico does not aim to only help Alzheimer patients but can also help people with weak eyesight by recognizing and reading text. Rico has the ability to help early stage Alzheimer's patients identify people they interact with on daily basis. Rico is an application uses smart phone's camera to recognize faces, and then notify the patient. The application displays a brief description regarding the person's relation with the patient. The database contains familiar faces that the user most likely will interact with. Rico's goal is to ease the patient's life and let him/her adapt with the atmosphere without letting feeling excluded.
Hindi-English Code-Switching Speech Corpus

Code-switching refers to the usage of two languages within a sentence or discourse. It is a global phenomenon among multilingual communities and has emerged as an independent area of research. With the increasing demand for the code-switching automatic speech recognition (ASR) systems, the development of a code-switching speech corpus has become highly desirable. However, for training such systems, very limited code-switched resources are available as yet. In this work, we present our first efforts in building a code-switching ASR system in the Indian context. For that purpose, we have created a Hindi-English code-switching speech database. The database not only contains the speech utterances with code-switching properties but also covers the session and the speaker variations like pronunciation, accent, age, gender, etc. This database can be applied in several speech signal processing applications, such as code-switching ASR, language identification, language modeling, speech synthesis etc. This paper mainly presents an analysis of the statistics of the collected code-switching speech corpus. Later, the performance results for the ASR task have been reported for the created database.
Auto-Sorting System Toward Smart Factory Based on Deep Learning for Image Segmentation

Machine part sorting is important and monotonous in smart factory. In this paper, an auto-sorting system is proposed based on the deep learning method. In the proposed system, an industrial objection detection network combined with a robotic arm controlling system is designed to automatically and efficiently complete machine part sorting. Region-based full convolutional network (R-FCN) is applied for locating and recognizing different types of images of industrial object models. After comparison and simulation analysis, it illustrated that the R-FCN model trained with enough labeled data can efficiently and accurately recognize the object from the images captured by visual sensors. Furthermore, with enough data, the network can be robust to view angle rotation both vertically and horizontally, and a small part of overlapping of object will not mislead the judgment of the network in most situations. The case study results illustrate that the position and type of objects can be successfully detected. The code will be available publicly at https://github.com/tianwangbuaa/.
REMT: A Real-Time End-to-End Media Data Transmission Mechanism in UAV-Aided Networks

In recent years, UAVs have received much attention in both the military and civilian fields for monitoring, emergency relief and searching tasks. UAVs are considered a new technology to obtain data at high altitudes when equipped with sensors. This technology is vital to the success of next-generation monitoring systems, which are expected to be reliable, real-time, efficient and secure. However, due to the bandwidth limitations in UAV-aided networks, the size of the transmitted data is a crucial factor for real-time media data transmission requirements, especially for national defense. To address this issue, in this article, we propose a realtime end-to-end media data transmission mechanism with an unsupervised deep neural network. The proposed mechanism transmutes the media data captured by UAVs into latent codes with a predefined constant size and transmits the codes to the ground console station (GCS) for further reconstruction. We use a real-word dataset containing millions of samples to evaluate the proposed mechanism which achieves a high transmission ratio, low resource usage and good visual quality.
IONN: Incremental Offloading of Neural Network Computations from Mobile Devices to Edge Servers

Current wisdom to run computation-intensive deep neural network (DNN) on resource-constrained mobile devices is allowing the mobile clients to make DNN queries to central cloud servers, where the corresponding DNN models are pre-installed. Unfortunately, this centralized, cloud-based DNN offloading is not appropriate for emerging decentralized cloud infrastructures (e.g., cloudlet, edge/fog servers), where the client may send computation requests to any nearby server located at the edge of the network. To use such a generic edge server for DNN execution, the client should first upload its DNN model to the server, yet it can seriously delay query processing due to long uploading time. This paper proposes IONN (Incremental Offloading of Neural Network), a partitioning-based DNN offloading technique for edge computing. IONN divides a client's DNN model into a few partitions and uploads them to the edge server one by one. The server incrementally builds the DNN model as each DNN partition arrives, allowing the client to start offloading partial DNN execution even before the entire DNN model is uploaded. To decide the best DNN partitions and the uploading order, IONN uses a novel graph-based algorithm. Our experiments show that IONN significantly improves query performance in realistic hardware configurations and network conditions.
Machine Learning-based Query Augmentation for SPARQL Endpoints

Linked Data repositories have become a popular source of publicly-available data. Users accessing this data through SPARQL endpoints usually launch several restrictive yet similar consecutive queries, either to find the information they need through trial-and-error or to query related resources. However, instead of executing each individual query separately, query augmentation aims at modifying the incoming queries to retrieve more data that is potentially relevant to subsequent requests. In this paper, we propose a novel approach to query augmentation for SPARQL endpoints based on machine learning. Our approach separates the structure of the query from its contents and measures two types of similarity, which are then used to predict the structure and contents of the augmented query. We test the approach on the real-world query logs of the Spanish and English DBpedia and show that our approach yields high-accuracy prediction. We also show that, by caching the results of the predicted (More)
Maximizing Social Welfare Considering the Uncertainty of Wind Power Plants Using a Distributed Consensus-based Algorithm

In this paper, a distributed consensus-based algorithm is introduced for social welfare maximization problems in the presence of responsive loads and Wind Turbines (WTs) in smart grids. This distributed algorithm eliminates the need for a central controller in the system. Consequently, it prevents the curse of dimensionality, the computational burden and the communication congestion which are some drawbacks of using centralized algorithms for controlling smart grids. The effect of uncertainty of output power of WTs generation is another discussed issue. Modeling this uncertainty is investigated using Unscented Transformation (UT) method. In MATLAB simulation, the performance of this distributed algorithm is illustrated in the presence of demand response in some multi-agent systems. Additionally, the impact of WTs uncertainty is inspected in a 29-node system.
Does Investment in Digital Technologies Yield Digital Business Value? The Digital Investment Paradox and Knowledge Creation as Enabling Capability

: This paper explores whether the investments in Digital Technologies relate to Business Value in organizations and the role of Knowledge Creation. To evaluate this, data collected from Japanese Small and Medium Enterprises from “Competitive IT Strategy SME Selection 100” list of 3 years was analysed by correlation, regression and general linear model analysis. The direct effect that investment in Digital Technologies had on Business Value was observed for Learning & Growth objectives. The influence that the four processes of Knowledge Creation (SECI Model) had was explored and found that Combination process was positively related with the investment in Digital Technologies for Financial and Learning & Growth objectives. Externalization had a negative relationship with the investment in Financial objectives. Although not verified statistically, a trend showed organizations with higher Knowledge Creation Capabilities gained higher benefits from investment in Digital Technologies as the investment increased and vice versa. Although the limitations of this study are related to the population characteristics and responses’ reliability, it was considered that the potential insights were valuable enough to overcome such limitations. With this empirical study the concern of “Digital Investment Paradox” is raised and the debate is initiated with Knowledge Creation as an enabling capability.
Cross-Project Defect Prediction For Web Application Using Naive Bayes (Case Study: Petstore Web Application)

Predicting defect in software is a complicated process and time-consuming. AI-Based software defect predictor can predict 75% defect in software and help developer team to detect and to fix defect module before performing unit testing/system testing by quality assurance. Some research tried to construct prediction model using other project datasets, which is called cross-project defect prediction. Nevertheless, the project should have the same software metric attribute. Recently, web application takes a crucial part in human life, for that reason assuring the quality of web application is very serious. This research will implement defect prediction on the petstore web application with other project datasets. CK OO metric is employed as the parameter. Naive bayes algorithm is an effortless and successful algorithm for predicting defect on software. The objective of this research is to apply naive bayes algorithm in cross-project defect prediction for the web application using pandas and scikit-learn. The outcome of this research is naive bayes algorithm has an good accuracy level of 72.30% – 89.30% and slightly low false alarm around by 5% – 26.67%. However, it has low precision and recall score, around 12,5% – 25% and 20%–60%. Then, naive bayes algorithm predicting more defect module on software than code review.
Endotoxin Contributes to Artificial Loosening of Prostheses Induced by Titanium Particles

Background Aseptic loosening of orthopedic implants caused by wear particles is a major cause of joint replacement failure. However, the mechanism of aseptic loosening has not yet been defined. The present study explored whether endotoxin adherent on the titanium (Ti) particles contributes to aseptic loosening. Material/Methods Limulus amebocyte lysate detection was conducted to detect the levels of endotoxin adhered to the Ti particles. A mouse air pouches model was established and mice were divided into 4 groups and injected with phosphate-buffered saline (PBS) or Ti particles suspensions (0.1, 1, 10 mg/mL), following detection of the number of macrophages and the level of endotoxin. Scanning electron microscopy (SEM) was used to characterize the microstructures of Ti particles adhered with endotoxin. Results In vitro experiments showed that the level of endotoxin adhered to the Ti particles was significantly increased after adding LPS back to these “endotoxin-free” particles. In vivo experiments showed that Ti particles injection significantly increased the number of macrophages and the level of endotoxin. Conclusions In conclusion, these results suggest that adherent endotoxin may play an important role in aseptic loosening induced by Ti particles.
Recommending Refactoring Solutions Based on Traceability and Code Metrics

Software refactoring has been extensively used to rectify the design flaws and improve software quality without affecting its observable behaviors. For a given code smell, it is common that there exist multiple refactoring solutions. However, it is challenging for developers to select the best one from such potential solutions. Consequently, a number of approaches have been proposed to facilitate the selection. Such approaches compare and select among alternative refactoring solutions based on their impact on metrics of source code. However, their impact on the traceability between source code and requirements is ignored although the importance of such traceability has been well recognized. To this end, we select among alternative refactoring solutions according to how they improve the traceability as well as source code design. To quantify the quality of traceability and source code design we leverage the use of entropy-based and traditional coupling and cohesion metrics respectively. We virtually apply alternative refactoring solutions and measure their effect on the traceability and source code design. The one leading to greatest improvement is recommended. The proposed approach has been evaluated on a well-known data set. The evaluation results suggest that on up to 71% of the cases, developers prefer our recommendation to the traditional recommendation based on code metrics.
TDLAS Detection of Propane/Butane Gas Mixture by Using Reference Gas Absorption Cells and Partial Least Square Approach

Propane/butane gas mixtures are frequently used in the petroleum industry and sensors that provide early warning of the concentrations of such mixtures are essential for avoiding explosion hazards. Tunable diode laser absorption spectroscopy (TDLAS) underpins this paper, exploiting infrared absorption spectroscopy and providing the potential to provide a highly accurate, in situ and intrinsically safe method for the detection of propane/butane gas mixtures. In consideration of the significant spectral overlap of the absorption spectral features of propane and butane, an experimental approach using reference gas cells has been proposed to remove the severe absorption spectral overlapping that occurs by using known samples of both propane and butane gas in the reference gas cells. The partial least squares method was used to calculate the concentrations of propane and butane individually in the mixture. The spectral overlapping can be effectively removed, resulting in all the fitting correlation coefficients for the spectral extraction being higher than 0.96. A field test carried out using simulated gas samples shows that near both the first and second alarm values set, high accuracy can be obtained in the measurement, with all the relative errors in the calculated concentrations of propane and butane (in the gas mixture samples) below 3% lower explosion limit, which meets the requirement of the detection standards in the petroleum industry. This paper provides an experimental verification which then supports this being an effective TDLAS-based field sensor system for the petroleum industry to be used in practice.
Electronics and Packaging Intended for Emerging Harsh Environment Applications: A Review

Several industrial applications require specific electronic systems installed in harsh environments to perform measurements, monitoring, and control tasks such as in space exploration, aerospace missions, automotive industries, down-hole oil and gas industry, and geothermal power plants. The extreme environment could be surrounding high-, low-, and wide-range temperature, intense radiation, or even a combination of above conditions. We review, in this paper, the main leading applications that demand advanced technologies to fit the unconventional requirements of extreme operating conditions, discussing their main merits and limits compared to established and emerging technologies in this field, including silicon (Si), silicon on insulator (SOI), silicon germanium (SiGe), silicon carbide (SiC) as well as III–V semiconductors particularly the gallium nitride (GaN) semiconductor. In spite of successfully exceeding extreme conditions borders by developing advanced semiconductor devices dedicated for harsh environments, especially in high-temperature applications, the packaging challenges are still limiting the reliability of the developed technologies. Those challenges are examined in this review in terms of limitations and proposed solutions.
Targeted delivery of immune therapeutics to lymph nodes prolongs cardiac allograft survival

The targeted delivery of therapeutic drugs to lymph nodes (LNs) provides an unprecedented opportunity to improve the outcomes of transplantation and immune-mediated diseases. The high endothelial venule is a specialized segment of LN vasculature that uniquely expresses peripheral node addressin (PNAd) molecules. PNAd is recognized by MECA79 mAb. We previously generated a MECA79 mAb–coated microparticle (MP) that carries tacrolimus. Although this MP trafficked to LNs, it demonstrated limited therapeutic efficacy in our transplant model. Here, we have synthesized a nanoparticle (NP) as a carrier of anti-CD3, and optimized the conjugation strategy to coat the NP surface with MECA79 mAb (MECA79-anti-CD3-NP) to enhance LN accumulation. As compared with nonconjugated NPs, a significantly higher quantity of MECA79-NPs accumulated in the draining lymph node (DLN). Many MECA79-NPs underwent internalization by T cells and dendritic cells within the LNs. Short-term treatment of murine cardiac allograft recipients with MECA79-anti-CD3-NP resulted in significantly prolonged allograft survival in comparison with the control groups. Prolonged graft survival following treatment with MECA79-anti-CD3-NP was characterized by a significant increase in intragraft and DLN Treg populations. Treg depletion abrogated the prolongation of heart allograft survival. We believe this targeted approach of drug delivery could redefine the methods of administering immune therapeutics in transplantation.
Tri-methylation of histone H3 lysine 4 facilitates gene expression in ageing cells

Transcription of protein coding genes is accompanied by recruitment of COMPASS to promoter-proximal chromatin, which deposits di- and tri-methylation on histone H3 lysine 4 (H3K4) to form H3K4me2 and H3K4me3. Here we determine the importance of COMPASS in maintaining gene expression across lifespan in budding yeast. We find that COMPASS mutations dramatically reduce replicative lifespan and cause widespread gene expression defects. Known repressive functions of H3K4me2 are progressively lost with age, while hundreds of genes become dependent on H3K4me3 for full expression. Induction of these H3K4me3 dependent genes is also impacted in young cells lacking COMPASS components including the H3K4me3-specific factor Spp1. Remarkably, the genome-wide occurrence of H3K4me3 is progressively reduced with age despite widespread transcriptional induction, minimising the normal positive correlation between promoter H3K4me3 and gene expression. Our results provide clear evidence that H3K4me3 is required to attain normal expression levels of many genes across organismal lifespan.
Study of the Efficiency of Half-Width Substrate Integrated Waveguide Leaky-Wave Antennas in FR4

A study of the efficiency of a planar leaky-wave antenna in half-width substrate integrated waveguide technology with low-cost and lossy material is presented. Theoretical results show the limitations in the performance of leaky-wave antennas using low-cost substrates in terms of radiation, dissipation, spillover and aperture efficiencies. It is shown that, for a given lossy substrate, the maximum achievable radiation efficiency is related to the dissipation rate. Also, a maximum useful antenna length is reported for a given substrate.