Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequence-to-sequence Modelling for Categorical Speech Emotion Recognition Using Recurrent Neural Network

To model the categorical speech emotion recognition tasks in a sequential approach, the first challenge is how to transfer the categorical label for each utterance into a label sequence. To settle this, we make a hypothesis that an utterance is consisting of emotional and non-emotional segments alternatively, and these non-emotional segments correspond to silent regions, short pauses, transits between phonemes, fricative phonemes, etc. With this hypothesis, we propose to treat an utterance, ’s label sequence as a chain of two kinds of states: emotional states denoting emotional frames and Nulls denoting non-emotional frames. Then, we exploit a connectionist temporal classification based recurrent neural network (CTC-RNN) to automatically label and align an utterance’s emotional segments with emotional labels, while non-emotional segments with non-emotional labels. Experimental results on the IEMOCAP corpus demonstrate the effectiveness of our proposed method compared to state-of-the-art emotion recognition algorithms.
Emphatic Speech Synthesis and Control Based on Characteristic Transferring in End-to-End Speech Synthesis

End-to-end text-to-speech (E2E TTS) synthesis has achieved great success. This work investigates the emphatic speech synthesis and control mechanisms in the E2E framework and proposes an E2E-based method for transferring emphasis characteristic between speakers. Characteristic differences between emphatic and neutral speech are learned from a smallscale corpus containing parallel neutral and emphasis speech utterances recorded by one speaker and further transferred to another speaker so that we can generate emphatic speech with latter speakers voice. Emphasis embedding is injected to the encoder of the extended E2E TTS model to capture the aforementioned differences; while the decoder and attention module are used to decode those differences into synthetic neutral / emphatic speech. Speaker codes linked to the decoder and attention module provide the E2E model the ability for characteristic transferring between speakers. To control the emphatic strength, an encoder memory manipulation mechanism is proposed. Experimental results indicate the effectiveness of our proposed model.
Database Performance Comparison of Xen, KVM and OSv Using Cassandra

V16 technologies have promoted the fast development of large-scale information systems across all over the world. The distributed database access also has been improved to adapt the non-centralized virtual machine. In this paper, we utilize the typical distributed database Cassandra to evaluate the performance of different virtualization technologies. The evaluation selects KVM, Xen and OSv as the target virtualization platforms to test the read/write latency in Cassandra database. The result shows that OSv has the shortest read and write latency and reaches the best performance. Furthermore, we compare the architecture of the target virtualization platforms to analyze the relationship between applications and virtualization architectures. The analysis illustrate that more software-defined abstract levels in the virtualization architecture lead to worse system performance, because the level between the application and hardware becomes deeper, and then the program execution flow becomes longer. Finally, we demonstrate that the virtualization platform with thinner architecture levels has a better performance in the distributed data access, due to the specified full-virtuallzation, tidy hierarchical level, removal of duplicate protection and software-defined abstract.
An Empirical Study of Emotion Analysis with Different Distributed Representation Methods for Chinese Microblogs

Distributed representation of text has the ability to effectively express the meaning of text. Up to now, most studies have adopted distributed representation methods for emotion analysis on Chinese microblogs without systematically comparing and analyzing the performance of them with different parameters. This paper conducts an empirical study for fine-grained emotion categorization on Chinese microblogs. Firstly, we collect a labeled corpus. It is processed into four types of experimental dataset based on different text granularity, i.e., the sentence-level dataset with characters (SLwC), the sentence-level dataset with words (SLwW), the paragraph-level dataset with characters (PLdC) and the paragraph-level dataset with words (PLdW). Secondly, five models of distributed representation (the average of word vectors, PV-DM, PV-CBOW, LSTM, and BiLSTM) are chosen. These methods are compared on the above four types of experimental datasets respectively. Finally, support vector machine (SVM) acts as an additional classifier to correctly classify users’ emotions in microblogs. The results indicate: 1) BiLSTM performs the best for generating emotional feature representation. Moreover, it performs the best on the paragraph-level dataset by combining with SVM; 2) Five models achieves better performance on paragraph-level dataset for emotion classification than sentence-level dataset whereas words as tokens of text are superior to those of characters 3) All models get better performance while the dimension of words between 300 with 500, especially for the LSTM-based with SVM methods.
Improving Lesk by Incorporating Priority for Word Sense Disambiguation

There exists a scarcity of work done in choosing the optimal WSD algorithm as applicable for different situations and even less work has been done in applying WSD techniques to low-resourced languages like Bengali. This paper presents three modified versions of the Lesk algorithm along with the original version, as applied to Bengali. The results obtained from experimentation, particularly our dependency tree based Lesk variant proves to be highly robust for handling the sense disambiguation task in Bengali.
Optical classification of neoplastic colorectal polyps – a computer-assisted approach (the COACH study)

Abstract Background and aims: Clinical data suggest that the quality of optical diagnoses of colorectal polyps differs markedly among endoscopists. The aim of this study was to develop a computer program that was able to differentiate neoplastic from non-neoplastic polyps using unmagnified endoscopic pictures. Methods: During colonoscopy procedures polyp photographies were performed using the unmagnified high-definition white light and narrow band image mode. All detected polyps (n = 275) were resected and sent to pathology. Histopathological diagnoses served as the ground truth. Machine learning was used in order to generate a computer-assisted optical biopsy (CAOB) approach. In the test phase pictures were presented to CAOB in order to obtain optical diagnoses. Altogether 788 pictures were available (602 for training the machine learning algorithm and 186 for CAOB testing). All test pictures were also presented to two experts in optical polyp characterization. The primary endpoint of the study was the accuracy of CAOB diagnoses in the test phase. Results: A total of 100 polyps (of these 52% neoplastic) were used in the CAOB test phase. The mean size of test polyps was 4 mm. Accuracy of the CAOB approach was 78.0%. Sensitivity and negative predictive value were 92.3% and 88.2%, respectively. Accuracy obtained by two expert endoscopists was 84.0% and 77.0%. Regarding accuracy of optical diagnoses CAOB predictions did not differ significantly compared to experts (p = .307 and p = 1.000, respectively). Conclusions: CAOB showed good accuracy on the basis of unmagnified endoscopic pictures. Performance of CAOB predictions did not differ significantly from experts’ decisions. The concept of computer assistance for colorectal polyp characterization needs to evolve towards a real-time application prior of being used in a broader set-up.
Ultrafast Genomic Database Search using Layered Locality Sensitive Hashing

In this article, we will demonstrate Layered Locality Sensitive Hashing in genomic sequence comparison. Locality Sensitive Hashing based algorithms have already been proved to be successful for approximate nearest neighbor search in high dimensional data. Genomic database search is the primary task for homology detection and motif identification. However, the huge genome size and unknown repetitive regions make the task even more difficult. To tackle this problem we have introduced layered locality sensitive hashing for large scale genomic comparisons. As it turns out, the proposed method reduces the search time by 93.6%, while producing results almost as good as the exact ones.
A 0.5-V 12-bit SAR ADC Using Adaptive Time-Domain Comparator With Noise Optimization

This paper presents a 0.5-V 12-bit low-voltage power-efficient successive-approximation register (SAR) analog-to-digital converter (ADC) using an adaptive time-domain (ATD) comparator with noise optimization. To be power efficient with different residual input levels (<inline-formula> <tex-math notation="LaTeX">$\Delta V_{\mathrm {in}}$ </tex-math></inline-formula>) during conversion, the proposed ATD comparator automatically adjusts its input-referred noise performance rather than consuming the same power for each bit conversion. Considering the noise requirement of 12-bit resolution, the proposed ATD technique effectively reduces the comparator power consumption by 50% compared to the conventional approach. Moreover, a differential threshold window (DTW) technique is also developed to provide the optimized time-domain threshold for lowest figure-of-merit (FoM) performance with a self-adjusted (<inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {ctrlp}}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {ctrln}}$ </tex-math></inline-formula>), depending on process–voltage–temperature (PVT) variation. The test chip occupies a core area of 0.109 mm<sup>2</sup> in Taiwan Semiconductor Manufacturing Company (TSMC) 90-nm CMOS technology. With a 0.5-V supply voltage, the prototype consumes 810 and 1425 nW at 100 and 250 kS/s, respectively. The achieved effective number of bits and signal-to-noise and distortion ratio with Nyquist-rate input are 10.71/10.3 bit and 66.3/63.8 dB, respectively. The resultant Walden FoM and Schreier FoM are 4.82/4.52 fJ/conversion step and 174.2/173.23 dB, respectively.
Comparison of polyglycolic acid, polycaprolactone, and collagen as scaffolds for the production of tissue engineered intestine.

Cell-seeded scaffolds play critical roles in the production of tissue engineered intestine (TEI), a potential strategy for the treatment of short bowel syndrome. The current study compares polyglycolic acid (PGA), polycaprolactone (PCL), and collagen as scaffolds for TEI production. Tubular PGA scaffolds were prepared from nonwoven BIOFELT® . Tubular PCL scaffolds were fabricated by electrospinning. Tubular collagen scaffolds were prepared using CollaTape, a wound dressing material. Both PGA and collagen were coated with poly-l-lactic acid (PLLA) to improve scaffold mechanical properties. Pore size, porosity, microstructure, mechanical properties (suture retention strength and ultimate compressive force) were determined. The scaffolds were first seeded with crypt stem cells isolated from 1 to 3 day old rat pups and then implanted into the peritoneal cavity of nude rats. After 4 weeks of in vivo incubation, these cell-seeded scaffolds were harvested for assessment of the TEI produced. Of the three materials compared, PLLA coated tubular PGA scaffolds had the appropriate pore size, mechanical properties and degradation rate leading to the production of TEI with an architecture similar to that of native rat intestine. © 2018 Wiley Periodicals, Inc. J. Biomed. Mater. Res. Part B, 2018. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater 107B: 750-760, 2019.
Effects of star anise (Illicium verum Hook.f.) essential oil on laying performance and antioxidant status of laying hens

To investigate the effects of dietary supplementation of star anise oil (SAO) on performance and antioxidant status of laying hens, a total of 864 Hy-Line brown laying hens at 26 wk of age were randomly allocated to 4 treatments with 6 replicates of 36 birds. Dietary treatments were non-star anise oil supplementation and supplemented with SAO at the level of 200, 400, and 600 mg/kg diet. The birds were fed the diets for 56 d. Average egg weight, average daily feed intake (ADFI), egg mass, laying rate, and feed conversion of each replicate were measured. Blood and liver samples from 12 birds were obtained, 72 eggs were picked out, per treatment at day 28 and day 56 of the experiment, and eggs stored for 56 d, to determine antioxidant status in serum, liver, and yolk. All laying hens had similar average egg weight, egg mass, laying rate, and feed conversion in day 29 to day 56 or the entire period of the experiment but significant difference on ADFI in day 1 to day 56. However, increasing diet concentration of SAO tended to improve (P < 0.10) egg mass, average egg weight, and ADFI in day 1 to day 28. Supplementation of SAO linearly increased (P < 0.05) activities of total superoxide dismutase (T-SOD) (day 28 and day 56) and glutathione peroxidase (GSH-PX) (day 56) in serum, GSH-PX (day 28 and day 56) in liver and total antioxidant capacity (T-AOC) (day 56) in serum and liver, but linearly reduced (P < 0.05) concentrations of malondialdehyde (MDA) (day 28 and day 56) in liver. Supplementation of SAO linearly increased (P < 0.05) T-SOD activity at day 14 and day 28, reduced (P < 0.05) MDA concentration at day 42 and day 56 of the experiment in yolk. Increasing content of SAO linearly (P < 0.05) increased T-SOD activity in yolk of eggs stored at day 0, 14, 28, 42, and 56, decreased MDA content of eggs stored at day 42 and 56, whether laying hens fed diets for 28 or 56 d. Dietary supplementation of SAO enhanced laying performance and overall antioxidant status of laying hens in a dose-dependent manner.
Examining the key genes and pathways in hepatocellular carcinoma development from hepatitis B virus-positive cirrhosis

To identify the key genes and pathways in the development of hepatocellular carcinoma (HCC) from hepatitis B virus (HBV)-positive liver cirrhosis, differentially expressed genes (DEGs) between HCC and liver cirrhosis tissue samples from the GSE17548 gene expression profile dataset were screened. A total of 1,845 DEGs were identified, including 1,803 upregulated and 42 downregulated genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) network analyses were performed. It was identified that the ‘cell cycle’ and ‘progesterone-mediated oocyte maturation’ KEGG pathways were significantly enriched in the DEGs. In addition, the high expression of the hub genes from the PPI network (including cyclin dependent kinase 1, cyclin B1, cyclin B2, mitotic arrest deficient 2 like 1, BUB1 mitotic checkpoint serine/threonine kinase and cyclin A2; P=0.00116, 0.00021, 0.04889, 0.00222, 0.00015 and 0.00647, respectively) was associated with a decrease in overall survival for patients with HCC as identified using survival and expression data from The Cancer Genome Atlas. The identified hub genes and pathways may help to elucidate the molecular mechanisms of HCC progression from HBV-positive liver cirrhosis. Additionally, they may be useful as therapeutic targets or serve as novel biomarkers for HCC prognosis prediction.
A 1.2-V 108.9-dB A-Weighted DR 101.4-dB SNDR Audio  $\Sigma\Delta$  ADC Using a Multi-Rate Noise-Shaping Quantizer

Along with the trend to reduce the power consumption of mobile devices, interface circuits such as analog-to-digital converters (ADCs) should also be designed to operate reliably at low supply voltages without compromising production yield. We present a high-fidelity audio <inline-formula> <tex-math notation="LaTeX">${\Sigma \Delta }$ </tex-math></inline-formula> ADC suitable for low-voltage mobile applications, based on a new multi-bit quantizer. Its effective resolution is increased economically by augmenting an internal low-resolution quantizer with the proposed multi-rate noise-shaping technique. Fabricated in a 0.13-<inline-formula> <tex-math notation="LaTeX">${ \mu }\textbf {m}$ </tex-math></inline-formula> CMOS technology, our ADC uses 3.5 mW at 1.2 V, and achieved an A-weighted DR of 108.9 dB and a peak signal-to-noise and distortion ratio of 101.4 dB.
Secured Privacy Data using Multi Key Encryption in Cloud Storage

In the distributed systems security of data is a vital functionality, sharing of information in cloud storage is a provident technique. Nowadays, the main challenges is data security in cloud computing. To overcome this problem we proposed a novel Dynamic Privacy Aggregate Key Re-Encryption (DPAK-RE) algorithm. The DPAK-RE algorithm provides security to sharing the information in the cloud. We describe a secure data sharing with other users in cloud storage. Data corner is authorized to the admittance approach related with individual information that is to be stored within jurisdiction. The authenticate users can aggregate several groups of top-secret keys and makes them as enfold together a solitary key and potential of entire keys being aggregated. This aggregate key is Re-encrypted and generates the personal key as a constant key size. This Personal key provides more secure of the privacy information for data sharing in cloud storage. The execution investigation and the security confirmation demonstrate that the proposed scheme can accomplish more effective and secure information sharing in a cloud environment.
Sparse Representation Based Discriminant Locality Preserving Dictionary Learning via Volterrafaces for Face Recognition

In this paper, we present a new face classification system that combines Volterrafaces, discriminant locality preserving criterion, and dictionary learning. This method first divides the original image into sub-blocks of the same size, and then the discriminant analysis model based on the two-dimensional Volterra kernel is used to find the nonlinear function mapping for each sub-block. Next, we use the proposed sparse representation based discriminant locality preserving dictionary learning method to individually optimize and classify the sub-features of each projection axis. Finally, the final classification result of the image is determined by a majority vote.
Real-Time RGB-D SLAM with Points and Lines

S50 is regarded as one of the core technology for automatic robots and self-driving cars, which has proven its value and gain much attention recent years. However, challenges still exist and problems such as changeable illumination and low-textured environment make SLAM algorithms unavailable, especially for feature-based visual SLAM. In this paper, a real-time RGB-D SLAM with points and lines is proposed, which has increased the accuracy and availability of SLAM algorithm in such situations. Specifically, our work is built upon ORB-SLAM, which is currently the state-of-the-art visual SLAM algorithm. The extracting method of point features in it is modified to reduce the influence of illumination changing. And instead of extracting only points, lines are extracted simultaneously for better localization. Furthermore, the idea of stability toward points and lines is proposed, and its measurement will be used in the bundle adjustment. Finally, the experiment shows that our approach has better accuracy and availability compared to the original ORB-SLAM.
Opinion Mining on Product Review Based on PM-LDA

An updated framework based on LDA is provided in this paper to extract features from online user reviews which are in Chinese. This model is an extension of the LDA by introducing the concepts of multi-gram and part of speech into it and it is named PM-LDA. Through this model, features are generated as topics and topic labels can be generated as the sentence that has the max topic probability. Topics in PM-LDA are divided into two different types. The one is some general features such as product brand, color or producing area, and the other is those latent characteristics which customers may be more interested in. Part of speech is used to get the feature object and feature opinion separately, to make it more accurate. Several experiments are carried out to help to evaluate this model and it is indicated that this method has improved performance both in accuracy and the quality of the topic model itself.
A 5.35-mW 10-MHz Single-Opamp Third-Order CT  $\Delta\Sigma$  Modulator With CTC Amplifier and Adaptive Latch DAC Driver in 65-nm CMOS

This paper reports a continuous-time (CT) third-order delta-sigma modulator that features a single amplifier biquad (SAB) and a passive integrator to simplify the circuit to just one power hungry operation amplifier (opamp). Such setup not only relaxes the gain and bandwidth requirement of the opamp design but also enhances the overall modulator stability which enables a power-efficient loop filter implementation. We used an SAR architecture in the quantizer with an advanced feedback technique to alleviate its speed penalty. By incorporating the proposed CT complementary (CTC) opamp and an adaptive latch scheme in the DAC driver, the modulator attains a signal bandwidth of 10 MHz with 79.6-dB signal-to-noise and distortion ratio (SNDR) while only consuming 5.35 mW from 1.2- and 1.8-V power supplies. The prototype has a dynamic range of 84.5 dB and a Schreier FoM of 177.2 dB with an active area of 0.033 mm2.
The Rule of Three: Abstractive Text Summarization in Three Bullet Points

Neural network-based approaches have become widespread for abstractive text summarization. Though previously proposed models for abstractive text summarization addressed the problem of repetition of the same contents in the summary, they did not explicitly consider its information structure. One of the reasons these previous models failed to account for information structure in the generated summary is that standard datasets include summaries of variable lengths, resulting in problems in analyzing information flow, specifically, the manner in which the first sentence is related to the following sentences. Therefore, we use a dataset containing summaries with only three bullet points, and propose a neural network-based abstractive summarization model that considers the information structures of the generated summaries. Our experimental results show that the information structure of a summary can be controlled, thus improving the performance of the overall summarization.
Analysis of Accumulator Effect on the Suppression of Fluid Pulsation

I294 the electro-hydraulic vibration generator with high accuracy, because of the periodic fluctuation of load flow, the pressure fluctuation of the system will be caused, so that the control accuracy of the vibration generator will be reduced, and the hydraulic components and pipelines will also be impacted. The inhibiting mechanism of fluid pulsation using hydraulic accumulator was firstly analyzed on the base of accumulator modelling. The AMESim was applied to establish the simulation model of electrohydraulic servo vibration generator. The simulations were carried out to verify the mathematical model of accumulator absorbing fluid pulsation, respectively the pressure fluctuations with and without accumulator, the pressure fluctuation in different accumulator volume, and the pressure fluctuation in different vibration frequency. The simulation results show that the accumulator has a good attenuation effect on the system pressure pulsation in the range of certain frequency and volume.