Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring Prosodic Transfer in Vector Space by Weighted Tonal Events

Prosodic transfer plays a significant role in forming stereo-typed foreign accent, so measuring the transfer of non-native speakers is of great importance to language learning. Recent researches are trying to build intonation models to describe the transfer using f0 and break differences between the target and the gold standard. This paper advances a method to compute prosodic distance in vector space by the weights of each prosodic event abstraction using ToBI labels produced by AuToBI. This is an attempt to evaluate prosodic transfer in a highly generalized level that could match cognitive reality. N-gram models of weighted tonal events exhibits separability of Ningbo speakers from US, and among learners of varying prosodic capacity in high dimensional vector space. Thus, it proves a right direction to measure prosodic transfer with discrete representations of intonation.
of Human Neutrophils Exocytosis of Specific and Tertiary Granules Protein Receptor Interaction, in the -Ethylmaleimide-Sensitive Factor Attachment N Protein Receptor/R-Soluble -Ethylmaleimide-Sensitive Factor Attachment N Protein-2, Through Q-Soluble Role of Vesicle-Associated Membrane

We have examined the role of the R-soluble N -ethylmaleimide-sensitive factor attachment protein receptor (SNARE) synapto-brevin-2/vesicle-associated membrane protein (VAMP)-2 in neutrophil exocytosis. VAMP-2, localized in the membranes of speciﬁc and gelatinase-containing tertiary granules in resting human neutrophils, resulted translocated to the cell surface following neutrophil activation under experimental conditions that induced exocytosis of speciﬁc and tertiary granules. VAMP-2 was also found on the external membrane region of granules docking to the plasma membrane in activated neutrophils. Speciﬁc Abs against VAMP-2 inhibited Ca 2 (cid:1) and GTP- (cid:2) -S-induced exocytosis of CD66b-enriched speciﬁc and tertiary granules, but did not affect exocytosis of CD63-enriched azurophilic granules, in electropermeabilized neutrophils. Tetanus toxin disrupted VAMP-2 and inhibited exocytosis of tertiary and speciﬁc granules. Activation of neutrophils led to the interaction of VAMP-2 with the plasma membrane Q-SNARE syntaxin 4, and anti-syntaxin 4 Abs inhibited exocytosis of speciﬁc and tertiary granules in electropermeabilized neutrophils. Immunoelectron microscopy showed syntaxin 4 on the plasma membrane contacting with docked granules in activated neutrophils. These data indicate that VAMP-2 mediates exocytosis of speciﬁc and tertiary granules, and
Kinematic Covariance Based Abnormal Gait Detection

This paper proposes an approach for automatic detection of abnormal human gait. We use an improved skeleton data covariance based gait assessment approach. Low-limbs flexion angles are derived using skeletons computed from data acquired by the Kinect sensor. Then for each gait sequence, we calculate a covariance matrix from the obtained angles data. The matrices are used as features for two classification schemes: a normal gait model-based and a k-NN-based. The resulting descriptor is compact, does not require prior temporal segmentation and shows competitive results on available pathological gait datasets.
Microbial metal resistance and metabolism across dynamic landscapes: high-throughput environmental microbiology

Multidimensional gradients of inorganic compounds influence microbial activity in diverse pristine and anthropogenically perturbed environments. Here, we suggest that high-throughput cultivation and genetics can be systematically applied to generate quantitative models linking gene function, microbial community activity, and geochemical parameters. Metal resistance determinants represent a uniquely universal set of parameters around which to study and evaluate microbial fitness because they represent a record of the environment in which all microbial life evolved. By cultivating microbial isolates and enrichments in laboratory gradients of inorganic ions, we can generate quantitative predictions of limits on microbial range in the environment, obtain more accurate gene annotations, and identify useful strategies for predicting and engineering the trajectory of natural ecosystems.
Design of an Endfire Microstrip Antenna for Aircraft Collision Avoidance System

Traffic alert and collision avoidance system provides a solution to reduce the threat of mid-air collisions between civil aircraft. The endfire antenna is very much essential in such airborne system. In this letter, an antenna using planar elements is being developed, and the design of a prototype model for printed endfire microstrip antenna with four feeding ports is presented. An aerodynamically shaped conformal metamaterial lens-based radome is also designed to encapsulate the antenna inside an enclosure. Here, the suppression of surface wave in the antenna is taken place, which resulted in gain enhancement and also reduction of sidelobes. Hence, the shape of the radiation pattern is tuned as per requirement. The proposed antenna is capable of transmitting the electromagnetic waves toward $\text{360}^{\circ }$ surveillance region of the aircraft with proper excitation of its feeding ports. The proposed antenna and its radome have been designed and subsequently simulated using CST Microwave Studio EM tool. The antenna fabrication and measurement are also carried out to validate the simulated antenna performance and other antenna characteristics. Quite excellent similarity is attained among all the simulated and experimental results.
Research on Dungan speech synthesis based on Deep Neural Network

Dungan language is a language used by the Dungan people in Central Asia. It is a special variant of Mandarin language in Central Asia. At present, the research on Dungan language is mainly in linguistics, and the research on Dungan language speech synthesis is still in a blank field. This paper realized a Dungan speech synthesis by using deep learning method to train a set of Deep Neural Network (DNN)-based acoustic models. Dungan language is then synthesized by predicting the acoustic features with the language features of Dungan language as network input. Experimental results show that the proposed method can synthesize high naturalness and high intelligibility Dungan speech.
SREP: An Energy Efficient Relay Protocol for Wireless Sensor Networks

While wireless sensor networks continue to break new grounds in applications, favored by technological innovations, energy efficiency continues to stagnate. Duty cycling remains the most popular and effective technique used to improve energy efficiency and thus lifetime of the network. Nevertheless, duty cycling imposes temporary unavailability on the network leading to deterioration of quality of service. To take care of this rather contradicting reality, this paper proposes Sleep Relay Protocol (SREP). Network nodes are divided into sets according to their location and the sets sleep in relay within a duty cycle period. Two set formation algorithms are proposed at initiation of our proposal. We extensively test our proposal with NS2 and observe further gain in energy without deterioration of availability. Comparing our proposal with Adaptive staggered protocol confirms almost no deterioration in packet delivery ratio (PDR). Moreover, there is improvement in latency.
Max Margin Cosine Loss for Speaker Identification on Short Utterances

Speaker identification has made extraordinary progress owing to the advancement of deep neural networks. Speaker feature discrimination is a vital term in speaker recognition. However, the traditional softmax loss usually lacks the power of discrimination. To address this problem, this paper explores a novel loss function, namely max margin cosine loss (MMCL). To be specific, we realize the function by L2 normalizing both features and weight vectors in the softmax loss, together with a cosine margin term to maximize the decision margin in the angular space. In addition, max margin constraint, as one regularization term, is incorporated into the proposed loss function. Experimental results demonstrate the effectiveness of our proposed max margin cosine loss and superiority over pervious losses. For example, on 2s condition, MMCL reduces the equal error rate by 10.63% relatively compared to additive angular margin cosine loss (AMCL), while AMCL has already obtained 6.37% relative reduction than softmax loss.1
Nakagami-m distribution of RSSI in shadowing pathloss model for indoor localization

Bluetooth low energy (BLE) beacons can be easily deployed to be used by a wide range of indoor localization techniques. BLE-beacon-based indoor localization techniques use the received signal strength indicator (RSSI) of the beacon signals. The variations of RSSI in an indoor environment is unpredictable and make the indoor localization challenging. Earlier researches considered that the distribution of RSSI in shadowing pathloss model is a normal (Gaussian) in the logarithmic scale. In an indoor environment, signals arrive at the receiver via various paths each having RSSI with Gaussian distribution. In this paper, we show that the aggregated magnitude of the indoor multipath signals at the receiver can also be modeled with Nakagami-m distribution. We investigate the performance of a BLE-based indoor localization technique using the maximum likelihood estimation (MLE) and the shadowing pathloss model with Nakagami-m distribution. We compare the location estimation performance when Gaussian distribution is used. We also collect data from a BLE test-bed and analyze the distribution of the RSSI measurements. The root means square error of the estimated position error for Nakagami-m based MLE solution is consistently lower than that of the Gaussian-based technique. In the computational time comparison, Nakagami-m distribution also shows a better performance speeding up the location estimation process by almost eight times.
Design of Chinese Depression Spoken Speech Corpus Based on Psychological Self-related Processing

In recent years, speech-based depression detection has gained some initial interest in China, but the lack of publicly available Chinese depression corpus leads to the difficulty of comparing its features and results. According to psychological theory, the pervasive hopelessness and high risk of suicide in depressive patients suggested that the self might be abnormal among them. It highlights many of the advantages of self-related processing can contribute to corpus design. In this paper, a novel design idea of Chinese depression spoken speech corpus based on psychological self-related processing is outlined in this paper. To do so, we therefore introduce some novel methods such as different psychological self and psychological experimental paradigms. The data and collection method have been used in our study.
Optogenetic Modulation of a Minor Fraction of Parvalbumin-Positive Interneurons Specifically Affects Spatiotemporal Dynamics of Spontaneous and Sensory-Evoked Activity in Mouse Somatosensory Cortex in Vivo

Abstract Parvalbumin (PV) positive interneurons exert strong effects on the neocortical excitatory network, but it remains unclear how they impact the spatiotemporal dynamics of sensory processing in the somatosensory cortex. Here, we characterized the effects of optogenetic inhibition and activation of PV interneurons on spontaneous and sensory‐evoked activity in mouse barrel cortex in vivo. Inhibiting PV interneurons led to a broad‐spectrum power increase both in spontaneous and sensory‐evoked activity. Whisker‐evoked responses were significantly increased within 20 ms after stimulus onset during inhibition of PV interneurons, demonstrating high temporal precision of PV‐shaped inhibition. Multiunit activity was strongly enhanced in neighboring cortical columns, but not at the site of transduction, supporting a central and highly specific role of PV interneurons in lateral inhibition. Inversely, activating PV interneurons drastically decreased spontaneous and whisker‐evoked activity in the principal column and exerted strong lateral inhibition. Histological assessment of transduced cells combined with quantitative modeling of light distribution and spike sorting revealed that only a minor fraction (˜10%) of the local PV population comprising no more than a few hundred neurons is optogenetically modulated, mediating the observed prominent and widespread effects on neocortical processing.
Investigation of Stacked Deep Neural Networks and Mixture Density Networks for Acoustic-to-Articulatory Inversion

Acoustic-to-articulatory inversion predicting articulatory movement based on the acoustic signal is useful for many applications like talking head, speech recognition, and education. DNN based technologies have achieved the state-of-the-art performance in the area. This paper investigates different stacked network architectures for acoustic-to-articulatory inversion. Two levels of DNNs or mixture density networks (MDNs) can be connected using different types of auxiliary features, including bottleneck features, directly generated features, and predicted articulatory features via MLPG algorithm extracted from the first level network. For the experiments, stacked systems using DNNs, time-delay DNNs (TDNNs), RNNs and MDNs were evaluated on both the MNGU0 English EMA database and AIMSL Chinese EMA database. Finally, on the default configurations of MNGU0 data using LSF acoustic features, the proposed stacked system using feed-forward MDNs with ellipsoid variance and MLPG generated features got 0.718mm in RMSE, which is similar to the RNN and RNN-MDN BLSTM systems with slower and more difficult training stage.
CODECOD: Crowdsourcing Platform for Code Smell Detection

Finding code smells in a program code must be done as soon as possible to improve the software maintainability. Nowadays, various automatic code smell detection tools have been developed. However, to increase the quality, the role of humans who do manual detection is still needed. Therefore, in this work, we develop a platform called CODECEOD, which involves crowd to detect code smells. This platform implements crowdsourcing method by decomposing requested tasks, in the form of uploaded source codes, into microtasks to enable the distribution of tasks to multiple workers. To guarantee the quality of the detection results, we introduced a quality assurance method called Find, Vote, Verify. Based on the evaluation involving software engineers, CODECOD is capable to detect more code smells with high accuracy compared to an automatic tool. Moreover, we also show that the proposed Find, Vote, Verify technique delivers an improved accuracy compared to the traditional output-agreement quality assurance technique.
Research on the Bias Network of Energy-Efficient Single Flux Quantum Circuits

Superconducting single flux quantum (SFQ) circuits have been shown to offer huge advantages that are ideal for high performance computing applications. These circuits can operate at very high frequencies (tens of GHz) but consume very low power (from nanowatts to microwatts per gate). Energy-efficient SFQ (ERSFQ) circuits, evolved from traditional rapid SFQ (RSFQ) circuits, were invented with the goal of elimination of static power dissipation. When compared with RSFQ circuits, ERSFQ circuits require a new biasing network that includes biasing Josephson junction(s), biasing inductor(s), and feeding Josephson transmission line(s). Some general suggestions on how to design the network have been offered, but a detailed analysis and optimization scenario for the network is still to be provided. In this paper, we first optimize the previously reported DC-biased two-Josephson junction per bit RSFQ shift register circuit, which can be used to provide a more robust, energy-efficient, and high memory capacity storage unit. The simulated power dissipation of 16-bit RSFQ shift register is 0.45 μW per bit when operating at 21.7 GHz. We then convert the RSFQ-based shift register into an ERSFQ structure and investigate the effects of the ERSFQ bias network on the margins of the ERSFQ circuit. Finally, we propose a more comprehensive optimization method for the ERSFQ biasing network.
The human long noncoding RNA lnc‐IL7R regulates the inflammatory response

Long noncoding RNAs (lncRNAs), once thought to be transcriptional noise, have been recently shown to regulate a variety of biological processes. However, there is not much knowledge regarding their roles in the inflammatory response. In this study, we performed human lncRNA microarray assays and identified a number of lncRNAs that demonstrated altered expression in response to LPS stimulation. Of these lncRNAs, lnc‐IL7R, which overlaps with the 3′untranslated region (3′UTR) of the human interleukin‐7 receptor α‐subunit gene (IL7R) gene, was significantly upregulated in LPS‐treated cells. Functionally, lnc‐IL7R was capable of diminishing the LPS‐induced inflammatory response, demonstrated by elevated expression of LPS‐induced E‐selectin, VCAM‐1, IL‐6, and IL‐8 in lnc‐IL7R knockdown cells. Mechanistically, we found that lnc‐IL7R knockdown diminished trimethylation of histone H3 at lysine 27 (H3K27me3), a hallmark of silent transcription, at the proximal promoters of the inflammatory mediators. Our data suggest that lnc‐IL7R contributes another layer of complexity in regulation of the inflammatory response.
Comparison and Analysis on Typical Network Representation Learning Algorithms

Large-scale complex networks show complex nonlinear relationships among objects, such as the social relationships in the real world, the citation relationship among papers and the interactions among proteins in biology. The analysis of complex network systems make it possible to reveal network structures, information disseminating laws, and communication patterns. Network representation learning (NRL) algorithms focus on mapping the original network structure information to a low-dimensional vector space through a series of operations under the premise of maximally retaining the network structure. In order to analyze current representative NRL algorithms effectively to provide valuable references for other researchers, we built an experimental platform to perform and test the NRL algorithms based on matrix factorization, the NRL algorithms based on shallow neural network and the NRL algorithms based on deep neural network, with datasets on Collaboration Network, Social Network and Citation Network. We implemented a series of comprehensive experiments, based on metrics include precision@k, micro-F1 and macro-F1. Our experiments include network reconstruction, vertex classification, and link prediction, and show readers principles, performances and applications of typical NRL algorithms.
Improving NILM by Combining Sensor Data and Linear Programming

Knowing the plug-level power consumption of each appliance in a building can lead to drastic savings in energy consumption. Non-Intrusive Load Monitoring (NILM) is a method for disaggregating power loads in a building to the single appliance level, without using direct sensors or electric meters attached to each device. This paper addresses the issues of NILM inaccuracy in the context of industrial or commercial buildings, by combining data from a low-cost, general-purpose, wireless sensor network. A novel approach to tackle this issue is proposed, using a simplex-based solver, to estimate the power load values of the steady states on sliding windows of data with varying size. In this paper, we show the principle of the approach and demonstrate its interest, limited complexity, and ease of use.
Phase sensitive X-ray imaging: Towards medical applications

The present work tackles the question on the evidence of X-ray phase contrast imaging with regard to a potential application in medical diagnostics. For this study the Talbot-Lau interferometer method by means of X-ray diffraction gratings was used. Specimens of human organs (e.g., adrenal gland, liver, blood vessels, etc.) were prepared and tomographic measurements were performed. The grating interferometer was developed and optimized for higher fractional Talbot orders, i.e., n=12, in order to improve the phase sensitivity of the system. The measurements with the grating interferometer simultaneously deliver two independent projection image data sets, one reflects the X-ray attenuation coefficient, equivalent to radiography or CT, and the second one uses the refraction index, which corresponds to the phase contrast. Both data sets were reconstructed which allows for the direct comparison of the two contrast mechanisms. It was shown that the achieved phase sensitivity is sufficient to reveal anatomical details and morphology in soft tissue. Moreover, it was shown that the two contrast mechanisms can deliver complementary information. For instance, specific features, such as blood vessels, are rendered more distinct by the phase contrast images than by the attenuation images.
Emotional speech synthesis based on DNN and PAD emotional state model

An emotional speech synthesis method based on deep neural network (DNN) and Pleasure-Arousal-Dominance (PAD) emotional state model is proposed. Firstly, the PAD model is used to annotate the emotional state of multi-speaker and multi-emotion speech corpus by calculating the distance between the marked emotional point of speech corpus and the typical emotional point. Secondly, the DNN-based emotional speech synthesis method is used to generate acoustic features. Thirdly, the prosodic features of synthesized emotion speech are modified by using PAD model. Finally, the target emotional speech is synthesized by vocoder. The subjective evaluation results show that comparing with hidden Markov model (HMM)-based and DNN-based method, the proposed method can achieve better performance. Objective tests also demonstrate that the spectrum of the emotional speech synthesized by the proposed method is much closer to the original speech.
Impact of Assessment of Empirical Studies Reliability: A Revisited Study

Empirical studies are one of the main inputs of Evidence Based Software Engineering to generate a body of knowledge. For this reason, it is relevant to know the reliability of the results obtained by applying empirical methods before the aggregation of evidence. In a previous paper, results of empirical studies were added to confirm the influence of contextual attributes without evaluating their reliability. This article reviews that previous work but considering in the addition of results its reliability. For this reazon, an evaluation method, created by the authors was used to assess the mitigation of threats to validity of empirical studies. The results of this study show that when considering the reliability of empirical studies, the previous results vary significantly. This impact validation of evaluation method can help to be adopted and improved in empirical software engineering research.
An Energy-Efficient IoT node for HMI applications based on an ultra-low power Multicore Processor

Developing wearable sensing technologies and unobtrusive devices is paving the way to the design of compelling applications for the next generation of systems for a smart IoT node for Human Machine Interaction (HMI). In this paper we present a smart sensor node for IoT and HMI based on a programmable Parallel Ultra-Low-Power (PULP) platform. We tested the system on a hand gesture recognition application, which is a preferred way of interaction in HMI design. A wearable armband with 8 EMG sensors is controlled by our IoT node, running a machine learning algorithm in real-time, recognizing up to 11 gestures with a power envelope of 11.84 mW. As a result, the proposed approach is capable to 35 hours of continuous operation and 1000 hours in standby. The resulting platform minimizes effectively the power required to run the software application and thus, it allows more power budget for high-quality AFE.