Miracle-dz/UniFAR-FTUs huggingface dataset:
FooDNet: Toward an Optimized Food Delivery Network Based on Spatial Crowdsourcing

This paper builds a Food Delivery Network (FooDNet in short) using spatial crowdsourcing (SC). It investigates the participation of urban taxis to support on demand take-out food delivery. Unlike existing SC-enabled service sharing systems (e.g., ridesharing), the delivery of food in FooDNet is more time-sensitive and the optimization problem is more complex regarding high-efficiency, huge-number of delivery needs. In particular, two on demand food delivery problems under different situations are studied in our work: (1) for O-OTOD, the food is opportunistically delivered by taxis when carrying passengers, and the optimization goal is to minimize the number of selected taxis to maintain a relatively high incentive to the participated drivers; (2) for D-OTOD, taxis dedicatedly deliver food without taking passengers, and the aim is to minimize the number of selected taxis (i.e., to raise the reward for each participant) and the total traveling distance to reduce the cost. A two-stage approach, including the construction algorithm and the Adaptive Large Neighborhood Search (ALNS) algorithm based on simulated annealing, is proposed to solve the problem. We have conducted extensive experiments based on the real-world datasets, including city-wide restaurant data, cell tower data, and the large-scale taxi trajectory data with 10,000 taxis in the city of Chengdu, China. Experimental results demonstrate that our proposed algorithms are more effective and efficient than baselines, fulfilling the food delivery service using a smaller number of taxis within the given time.
A Maximum Likelihood Approach to Masking-based Speech Enhancement Using Deep Neural Network

The minimum mean squared error (MMSE) is usually adopted as the training criterion for speech enhancement based on deep neural network (DNN). In this study, we propose a probabilistic learning framework to optimize the DNN parameter for masking-based speech enhancement. Ideal ratio mask (IRM) is used as the learning target and its prediction error vector at the DNN output is modeled to follow statistically independent generalized Gaussian distribution (GGD). Accordingly, we present a maximum likelihood (ML) approach to DNN parameter optimization. We analyze and discuss the effect of shape parameter of GGD on noise reduction and speech preservation. Experimental results on the TIMIT corpus show the proposed ML-based learning approach can achieve consistent improvements over MMSE-based DNN learning on all evaluation metrics. Less speech distortion is observed in ML-based approach especially for high frequency units than MMSE-based approach.
On Deep Learning based algorithms for Detection of Diabetic Retinopathy

Diabetic retinopathy (DR) is one of the leading causes of avertible blindness worldwide. Early detection of the disease can help to save the vision of diabetic patients. Presence of exudates, hemorrhages, and microaneurysms indicate an unhealthy eye image. Deep learning models have triumphed in image recognition, object detection and biomedical signal classification. Convolution neural network based DR detection techniques are fast evolving and can identify complex features and thus can accurately classify even severe cases. The presented paper investigates the recent work done in diabetic retinopathy detection using deep learning and collating the milestones achieved to guide researchers working in this domain to the future trend.
Emphasis Detection for Voice Dialogue Applications Using Multi-channel Convolutional Bidirectional Long Short-Term Memory Network

Emphasis detection is important for user intention understanding in human-computer interaction scenario. Techniques have been developed to detect the emphatic words in speech, but challenges still exist in Voice Dialogue Applications (VDAs): the tremendous non-specific speakers and their various expressions. In this work, we present a novel approach to automatically detect emphasis in VDAs by using multi-channel convolutional bi -directional long short-term memory neural networks (MC-BLSTM), which can learn various expressions of large amounts of speakers and long span temporal dependencies across speech trajectories. In particular, we first use a multi-channel convolutional component in the proposed approach to extract high-level representation of input acoustic features for emphasis detection. The experimental results on a 3400 real-world dataset collected from Sogou 11http://yy.sogou. com Voice Assistant outper-form current state-of-the-art baseline systems (+6.2% in terms of F1-measure on average).
Automatic segmentation of esophageal cancer pathological sections based on semantic segmentation

The cancer area segmentation of esophageal histopathology images is a crucial step in determining the stage of esophageal cancer. This task is very important. However, manual segmentation will cost a lot of time. The rise of computational pathology has led to the development of automatic methods for cancer area detection. In the automatic segmentation problem, a well-labeled dataset is the most important part. One of the main contributions of this paper is to establish a dataset contains 1388 patches (958 Normal and 430 Abnormal containing tumor cells), marked with cancer, all of which are manually labeled and supervised by professional pathologists. We test the currently popular networks on our dataset, such as DeeplabV3, FCN+ResNet, Unet and so on. And FCN+ResNet achieves the best performance on our dataset with the highest Mean IoU (85.06%) and Pixel Acc (92.63%).
Detecting "Pump & Dump Schemes" on Cryptocurrency Market Using An Improved Apriori Algorithm

With the popularity of Bitcoin, a cryptocurrency market emerged. However, because of insufficient supervision, the market attracts scams, for example, pump and dump (P&D) scheme, a famous fraudulent behavior in stock markets, has been found rampant in the market. To help deal with this issue, as a preliminary study, this paper proposes an improved apriori algorithm to detect user groups which may involve in P&D schemes. The validity of the algorithm is verified by using the leaked transaction history of Mt. Gox Bitcoin exchange. Furthermore, by exploring some of the detected user groups, many abnormal trading behaviors in the exchange found. These findings provide new insights into the behavior of users in the cryptocurrency market, thus leading to meaningful implications for policymakers, investors, and managers dealing with the cryptocurrency market.
FIRST-PASSAGE PERCOLATION ON THE RANDOM GRAPH

We study first-passage percolation on the random graph Gp(N) with exponentially distributed weights on the links. For the special case of the complete graph, this problem can be described in terms of a continuous-time Markov chain and recursive trees. The Markov chain X(t) describes the number of nodes that can be reached from the initial node in time t. The recursive trees, which are uniform trees of N nodes, describe the structure of the cluster once it contains all the nodes of the complete graph. From these results, the distribution of the number of hops (links) of the shortest path between two arbitrary nodes is derived. We generalize this result to an asymptotic result, as N → ∞, for the case of the random graph where each link is present independently with a probability pN as long as NpN/(log N)3 → ∞. The interesting point of this generalization is that (1) the limiting distribution is insensitive to p and (2) the distribution of the number of hops of the shortest path between two arbitrary nodes has a remarkable fit with shortest path data measured in the Internet.
Culture of blastomeres from in vitro‐matured, fertilized, and cultured bovine embryos

A culture system was devised to study the differentiation of bovine blastomeres. Blastomeres (2–13 per well) from embryos produced by in vitro maturation, fertilization, and culture of oocytes obtained from slaughterhouse ovaries were cultured for 10 days in 24‐well culture plates on feeder layers in blastomere culture medium (BCM: equal parts tissue culture medium 199 and low‐glucose Dulbecco's modified Eagle's medium with 10% fetal bovine serum). Ovine embryonic fibroblasts and STO cells were superior to bovine and mouse embryonic fibroblasts as mitotically inactivated feeder cells. Over five studies in which four blastomeres from an embryo were added to each culture well, an average of one colony per well formed from the blastomeres. The colonies continued to grow throughout the culture period, and most colonies resembled trophectoderm in their cellular characteristics, although some cultures contained a mixture of trophectoderm and endoderm. When the number of blastomeres cultured in each well was varied from 2–8, the number of colonies formed was proportional to the number of blastomeres added. Blastomeres from day 5 and day 6 embryos produced fewer colonies than did those from day 4 embryos, perhaps as a result of differentiation and tighter blastomere adhesion resulting in damage during their separation. The absence of serum did not alter the number of colonies formed. A number of growth factors, including LIF, OM, PDGFα, and FGF4, had no effect on the number of colonies, the size of colonies, or their alkaline phosphatase staining score beyond that provided by the feeder layer or serum when present. Blastomeres did not form colonies in the absence of feeder layers. Mol. Reprod. Dev. 48:238–245, 1997. © 1997 Wiley‐Liss, Inc.
Presentation Attack Detection for Smartphone Based Fingerphoto Recognition Using Second Order Local Structures

Fingerprint recognition on smartphone provides a good alternative over traditional security measures such as lock-patterns and pin. However, such fingerprint systems have some inherent problems such as the fact that user will leave their latent fingerprint on the sensor, and the limited sensor area. Additionally, fingerprint sensors impact on the cost and form factor of the device. Hence, in literature, the camera based approaches such as fingerphoto recognition systems got the attention of many researchers and manufacturers. However, such systems are highly vulnerable to presentation attacks such as photo-prints, display and replay attacks. To countermeasure these attacks, we propose a robust presentation attack detection scheme based on the features extracted from the maximum response images obtained from the convolution of second order Gaussian derivatives and the input images at multiple scales. The proposed scheme has achieved the detection performance of BPCER of 1.8%, 0.0% and 0.66% at APCER=10% for the presentation attack instrument species i.e., print-photo, display and replay attacks respectively.
Frame Selection in SI-DNN Phonetic Space with WaveNet Vocoder for Voice Conversion without Parallel Training Data

In this paper, we propose a frame selection approach to voice conversion with speaker independent deep neural network (SIDNN) and Kullback-Leibler divergence (KLD). The acoustic difference between source and target speaker is equalized with SI-DNN in the ASR senone phonetic space. KLD is used as an ideal distortion measure to select the corresponding target frame given the source frame. Acoustic trajectory of the selected frames is rendered with maximum probability trajectory generation algorithm. WaveNet based vocoder is applied on the converted acoustic trajectory to get the final speech waveform. From the subjective results we find that 1) the proposed method can achieve better performance than the phonetic cluster based selection method [16]; 2) by applying WaveNet vocoder the naturalness and speaker similarity can be significantly improved compared with linear predictive coding (LPC) based vocoder; 3) WaveNet vocoder trained only with spectral features i.e., line spectrum pairs (LSP) can better maintain the pitch pattern towards target speaker than WaveNet vocoder trained with both spectral features i.e., LSP and prosodic features (F0 and Unvoiced/Voiced flag).
DRAM Effects on the Embedded Processor Performance

The importance of DRAM, which has a great influence on the performance of an embedded processor constituting an embedded system, has been increasing day by day, and research on DRAM has been actively conducted in industry and academia. When evaluating embedded processor performance through simulations, it is important to have a more accurate DRAM model in order to obtain reliable results. In this paper, an embedded processor simulator that can interwork with DRAM simulator has been developed. In order to analyze the effects of the DRAM model which operates correctly on a cycle-by-cycle basis, the MiBench embedded benchmark program is used.
Compact Dual-Polarized Continuous Transverse Stub Array With 2-D Beam Scanning

A dual-polarized (DP) flat antenna array with passive 2-D beam-scanning capability is presented in this paper. The array consists of a DP continuous transverse stub (CTS) array, a flat Risley prism (FRP), and a line source generator. Two sets of CTSs, being constituted by the substrate-integrated waveguide (SIW), share a common aperture and are arranged orthogonally for the DP operation. A novel compact long line source is proposed for exciting this DP-CTS array. The FRP is realized by two linear phase progression phase-shifting surfaces (LPP-PSSs) and is placed above the DP-CTS array. The beam-scanning capability in the azimuth and in the elevation can be realized by rotating simultaneously the two LPP-PSSs in the same and in the opposite directions, respectively. A six-element array prototype is simulated and fabricated. According to the measured results, beam-scanning ranges of 40° in the elevation and 360° in the azimuth are achieved with the maximum gain of 17.8 dBi. The novel contributions from this paper include: 1) a novel compact long line source and 2) the first passive DP 2-D beam-scanning flat antenna array. This array can be extended easily to a higher gain or a circular polarization application.
The cell cycle program of polypeptide labeling in Chlamydomonas reinhardtii

The cell cycle program of polypeptide labeling in syndhronous cultures of wild-type Chlamydomonas reinhardtii was analyzed by pulse-labeling cells with 35SO4 = or [3H]arginine at different cell cycle stages. Nearly 100 labeled membrane and soluble polypeptides were resolved and studied using one-dimensional sodium dodecyl sulfate (SDS)- polyacrylamide gel electrophoresis. The labeling experiments produced the following results. (a) Total 35SO4 = and [3H]arginine incorporation rates varied independently throughout the cell cycle. 35SO4 = incorporation was highest in the mid-light phase, while [3H]arginine incorporation peaked in the dark phase just before cell division. (b) The relative labeling rate for 20 of 100 polypeptides showed significant fluctuations (3-12 fold) during the cell cycle. The remaining polypeptides were labeled at a rate commensurate with total 35SO4 = or [3H]arginine incorporation. The polypeptides that showed significant fluctuations in relative labeling rates served as markers to identify cell cycle stages. (c) The effects of illumination conditions on the apparent cell cycle stage-specific labeling of polypeptides were tested. Shifting light-grown asynchronous cells to the dark had an immediate and pronounced effect on the pattern of polypeptide labeling, but shifting dark-phase syndhronous cells to the light had little effect. The apparent cell cycle variations in the labeling of ribulose 1,5-biphosphate (RUBP)-carboxylase were strongly influenced by illumination effects. (d) Pulse-chase experiments with light-grown asynchronous cells revealed little turnover or inter- conversion of labeled polypeptides within one cell generation, meaning that major polypeptides, whether labeled in a stage-specific manner or not, do not appear transiently in the cell cycle of actively dividing, light-grown cells. The cell cycle program of labeling was used to analyze effects of a temperature-sensitive cycle blocked (cb) mutant. A synchronous culture of ts10001 was shifted to restrictive temperature before its block point to prevent it from dividing. The mutant continued its cell cycle program of polypeptide labeling for over a cell generation, despite its inability to divide.
Image Demosaicking Using Densely Connected Convolutional Neural Network

In this paper, we propose image demosaicking model using densely connected convolutional neural network. Recently, deep neural networks show improved results in image processing field compared with conventional algorithms. However, they often suffer vanishing-gradient problem which makes models hard to be trained. To solve this problem, we applied densely connected convolutional neural network. More than that, our proposed network doesn't need any initial interpolation which can reduce computational complexity. Finally, we applied sub-pixel interpolation layer which can generate demosaicked output image efficiently and accurately. Experimental results show that our proposed model outperformed conventional methods.
Radioresistance of prostate cancer cells with low proteasome activity

Prostate cancer is frequently treated with radiotherapy. While treatment results are in general excellent, some patients relapse and current systemic therapies are not curative, thus, underlining the need for novel targeted therapies. Proteasome inhibitors have been suggested as promising new agents against solid tumors including prostate cancer but initial results from clinical trials are disappointing.
Dual channel Electrodermal activity sensor for motion artifact removal in car drivers’ stress detection

In this paper we present a dual channel sensor for electrodermal activity measurement, with particular attention to the drivers’ stress detection. The sensor captures the elec-trodermal signals that are present on the hands of the driver, transmits them via WiFi to a laptop and then the data are processed. In particular, we developed a novel algorithm for the removal of motion artifacts that arise when the driver moves the hands on the steering wheel. We performed several kinds of tests: first in laboratory, then on a professional driving simulator and finally in a real car in city traffic. The algorithm has been compared to several well known algorithms for signal separation. We identified, as an indicator of performances, the spectral flatness of the outputs. In this application, the proposed method outperformed the benchmark algorithms.
Effect of ZnO on Stainless Steel Electrode for Piezeoelectric Application

Flexible piezoelectric generator can be used for energy harvesting from the human movement, walking, breathing or fingertip pressure. In this paper we have used ZnO nanoparticle composite with PDMS on flexible stainless steel (SS) substrate for energy harvesting application. Piezoelectric device performance was increased when SS substrate was modified with ZnO nanowires grown by ultrasonic spray pyrolysis. X-Ray Diffraction and field emission scanning electron microscope studies show that ZnO nanowires were grown on SS substrate and they are Wurtzite in nature. Capacitance value of the devices found to be 80 and 110 pF respectively and relative permittivity was found to be 5.32 and 5.57 respectively at 100 Hz. Responses of the devices were found to be 15.6 VP-P and 27.2 VP-P from the device due to impact of the fingertips.
Study and Analysis of Nonlinear Effects in Optical Systems Using Spectral Technologies

The influence of nonlinear effects in fiber-optic transmission lines in the dispersion control mode was studied, on the basis of which methods for calculating the performance indicators optical information transmission systems and algorithms for determining parameters single-mode optical fibers were proposed. Analytical expressions are obtained, allowing to evaluate the efficiency of the functioning optical signal transmission systems based on the technology spectral and dense spectral channel compression on the wavelength.
Experimental Studies of Network Traffic of Mobile Devices with Android OS

As part of the study of mobile device traffic, the formation of an experimental database of network traffic of selected mobile applications, which can be used for training and testing classifiers using machine learning methods, occupies an important place.To automate the process of studying mobile application traffic classification algorithms, a software package has been developed that allows you to automatically collect network traffic packets from mobile devices and save them to a database; group network traffic packets into streams; upon user request, generate data sets with specified characteristics (number of specific application flows: background traffic; generate a data set based on the already created set with the addition of new flows, excluding repetitions). To collect traffic from mobile devices running the Android operating system, an application was developed that uses the application programming interface to create virtual private networks, collects network traffic packets, identifies the source application and sends them via HTTP to server software.Using the client and server software, the database created was filled with traffic from 18 main applications. During the data collection, 71,667 streams and 6,989,991 packets were received.
Hybrid generative/discriminative scene classification strategy based on latent dirichlet allocation for high spatial resolution remote sensing imagery

In order to capture the high-level concepts in high spatial resolution remote sensing (HSR) imagery, scene classification based on a latent Dirichlet allocation (LDA) model, a generative topic model, is a practical method to bridge the semantic gaps between the low-level features and the high-level concepts of HSR imagery. In the previous work, LDA has been considered as a scene classifier, namely C-LDA, and multiple LDA models for each scene class are built separately, where the scene class is determined by a maximum likelihood rule. The C-LDA strategy disregards the correlations between the generative topic spaces of the different scene classes. In this paper, two novel strategies of scene classification based on LDA are proposed to consider the correlations between the generative topic spaces of the different scene classes by sharing the topic spaces for all the scene classes. One of the proposed strategies utilizes LDA as part of the classifier, namely P-LDA, which generates the topic space from all the training images. A discriminative classifier (e.g., support vector machine, SVM) is also employed as the other classification part of P-LDA. The other proposed strategy employs LDA as the topic feature extractor, namely F-LDA, which generates the topic space from all the training and test images, and utilizes a discriminative classifier to classify the topic features. The experimental results using aerial orthophotographs show that the performances of the two proposed strategies for scene classification based on LDA are better than the traditional C-LDA method.
LSTM-Based Pitch Range Estimation from Spectral Information of Brief Speech Input

In human speech communication, pitch can be normalized automatically by listeners through a subjective estimation of the speaker’s overall pitch range, even from a very brief speech input. In speech technologies, pitch range used to be estimated by direct analysis of F0 values from a lengthy speech input, but a reliable estimation from a brief speech input has yet to be solved. In this study, we proposed a novel method of estimating pitch range from the spectral structure of a very brief speech input, using the recurrent neural network with long short-term memory (RNN-LSTM) to mimic the perceptual process of human listeners. Our experiments showed that the model gave the best estimation when speech input was as short as 300500ms, and in this condition the estimation was more reliable than the conventional method of direct F0 analysis. Thus, the validity of the proposed model was verified.