Miracle-dz/UniFAR-FTUs huggingface dataset:
TRACKING CREAK FROM EARLY TO LATE ADULTHOOD: A PANEL STUDY FROM THE NORTH EAST OF ENGLAND

This study investigates how creak and F0 change across the life-span in a panel of six English speakers from the North East of England, recorded between two and three times. Across timepoint, we observe a general decrease in F0 for all but one speaker. However, creak follows complex patterns as speakers age. In early adulthood, all speakers exhibit creak, and show evidence that creak can be recruited to indicate turn-finality. Towards the end of their working lives, speakers reduce their proportion of creak dramatically, and show little evidence of its previous turn-yielding function. In older age, creak reappears, and a subset of speakers show renewed use of its turn-yielding function. These findings suggest that both global and interactional uses of creak change over the life-span, and suggest that— for some communities—creak may be age-graded.
Dependency graph for short text extraction and summarization

ABSTRACT A sheer amount of text generated from microblogs and social media brings huge opportunities to the text mining applications. Many techniques such as sentiment analysis and opinion mining are proven effective to deliver insights from documents. However, most of these textual data are in the form of short and fragmented texts which are difficult to visually extract due to the sparsity issue and the context in the content is often unknown. Naive while widely used models, term frequency and the bag-of-words never considered the semantic relationship between the words, making the results relatively difficult to interpret. A well-known technique in text mining like topic model may provide a general ‘at glance’ understanding but can be difficult to interpret or to understand. One alternative is to aggregate words in a semantical order and generates an output of human-understandable sentences. In this paper, we address this direction by proposing the belief graph data model that joins short texts by inducing the part-of-speech tagging to maintain the order and to preserve the context of the content. Extensive experiments showed that our approach improves the overall qualitative evaluation of text understanding compared to the previous state of the art text mining techniques.
ACOUSTIC ANALYSES OF MANDARIN TONES PRODUCED BY BILINGUAL ELEMENTARY STUDENTS IN CANADA

In Canada, children can learn Mandarin in bilingual schools as a heritage language (HL) or second language (L2). Many Canadian students have greater difficulty acquiring Mandarin tones than other aspects of the language. The present study investigates bilingual students’ productions of Mandarin citation tones. Pitch contours are modelled using generalized additive mixed models for HL and L2 learners across grades to examine the roles of speech input. Results suggest that early home language experiences and recent school learning experiences interact with the tone targets and impact students’ tone productions differently. These results provide evidence on Mandarin phonetic learning in Canada among bilingual students from diverse backgrounds and expand the evidence for L2 phonetic learning theories in the suprasegmental domain among child learners of non-English languages.
Selective harmonics elimination using whale optimisation algorithm for a single‐phase‐modified source switched multilevel inverter

This study presents a new single-phase multilevel inverter topology along with whale optimisation algorithm-based switching technique. The proposed multilevel inverter requires a significantly reduced number of power semiconductor devices as compared with some of the prominent multilevel inverters. The proposed work is able to outperform the existing techniques involving popular optimisation techniques in terms of inverter performance, the rate of convergence and computational overhead. Detailed simulation analysis has been carried out for the proposed multilevel inverter on MATLAB/Simulink environment. An 11-level proposed inverter prototype has been developed and experimental results have been provided to verify the performance of the proposed multilevel inverter.
PHONETIC REALIZATION OF NARROW FOCUS BY CHINESE EFL LEARNERS

Focus bears the function of information transmission and is regarded as a notion closely related to the discourse function of prosody. This study takes 20 Chinese EFL learners and 8 native English speakers as the subjects to investigate whether the difference between Mandarin and English focus realization patterns affect the narrow focus realization of Chinese EFL learners. Findings are as follows. (1) Learners of lower English proficiency exhibit similar phonetic realization to Mandarin Chinese, with a post-focus intensity decrease in both initial and medial focus sentences and a significant post-focus duration reduction than English speakers. (2) Learners show new features not found in English narrow focus phonetic realization, that is, the loss of post-focus pitch range compression (PFC) in lower English proficiency learners, and the significantly higher pitch rising of final-focus words than the other focus positions in higher English proficiency learners.
FINAL LENGTHENING ACROSS VARIOUS SOUND CLASSES IN A LANGUAGE DOCUMENTATION CORPUS OF LOWER SORBIAN

This paper investigates segmental lengthening before speech pauses in Lower Sorbian, an endangered Slavic language. The paper compares duration of vowels, sonorants, fricatives and other obstruents at various distances to speech pauses, based on a sample of 15,000 word tokens from a corpus of spontaneous speech. Results indicate lengthening of segments directly adjacent to boundaries for vowels, sonorants, and fricatives, but not for stops and affricates. The effect of lengthening was stronger for more sonorous sounds (vowels, sonorants) than for less sonorous sounds (fricatives). No evidence for lengthening of stressed vowels in (ante-)penultimate syllables was found, suggesting a narrow domain of lengthening that does not extend beyond the final syllable
Feature-specific perceptual sensitivity to within-category variations

This study investigates perceptual sensitivity to types of within-category variation that are produced systematically by speakers. The systematic subcategorical increase of certain features is sometimes hypothesized to be targeted at perceptual benefit. We ask, then, whether there is an asymmetry with respect to the perceptual salience of the increase vs. the decrease of phonetic features. In particular, we examine the within-category perception of voice onset time (VOT) and coarticulatory vowel nasality in English. Two perceptual experiments, an AX discrimination task and an AXB perceptual similarity task, with words with manipulated VOT and nasality were conducted. Results revealed a perceptual asymmetry of within-category variations for VOT: participants were more accurate in discriminating stimulus pairs containing increased (vs. decreased) VOT, and were less likely to identify increased VOT stimuli as similar to a natural VOT token. On the other hand, no such asymmetries were observed for coarticulatory vowel nasality in either experiment.
Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations

Across five studies, we present the preliminary technical validation of an infant-wearable platform, LittleBeats™, that integrates electrocardiogram (ECG), inertial measurement unit (IMU), and audio sensors. Each sensor modality is validated against data from gold-standard equipment using established algorithms and laboratory tasks. Interbeat interval (IBI) data obtained from the LittleBeats™ ECG sensor indicate acceptable mean absolute percent error rates for both adults (Study 1, N = 16) and infants (Study 2, N = 5) across low- and high-challenge sessions and expected patterns of change in respiratory sinus arrythmia (RSA). For automated activity recognition (upright vs. walk vs. glide vs. squat) using accelerometer data from the LittleBeats™ IMU (Study 3, N = 12 adults), performance was good to excellent, with smartphone (industry standard) data outperforming LittleBeats™ by less than 4 percentage points. Speech emotion recognition (Study 4, N = 8 adults) applied to LittleBeats™ versus smartphone audio data indicated a comparable performance, with no significant difference in error rates. On an automatic speech recognition task (Study 5, N = 12 adults), the best performing algorithm yielded relatively low word error rates, although LittleBeats™ (4.16%) versus smartphone (2.73%) error rates were somewhat higher. Together, these validation studies indicate that LittleBeats™ sensors yield a data quality that is largely comparable to those obtained from gold-standard devices and established protocols used in prior research.
SOLUBILITY ENHANCEMENT OF POORLY WATER-SOLUBLE DRUG BY SPHERICAL CRYSTALLIZATION TECHNIQUE

In the world of pharmacy, Kawashima created the spherical crystallization process for increasing the size of drugs in 1986. This article investigates the use of spherical crystallization as a method for enhancing the solubility of a poorly water-soluble drug. The data show that the use of this technique significantly improves the solubility of the drug, making it more bioavailable and easier to administer. Along with this, other applications of spherical crystallization technique are also discussed like masking the bitter taste of drugs and increasing the flowability and compressibility of drugs among others. Several methods that can be used to achieve spherical crystallization of drugs are also highlighted. This article also discusses the potential for this method to be applied to other poorly water-soluble drugs, highlighting its potential as a widely applicable solution for improving drug solubility and the factors that influence the outcome of spherical crystallization. The studies that have been conducted on the use of spherical crystallization for enhancing the solubility of poorly water-soluble drugs have also been discussed.
INTONATIONAL PREFERENCES FOR LEXICAL CONTRAST AND VERUM FOCUS

This paper explores the intonational marking and functional relatedness of lexical contrast and verum focus in German. Lexical contrast, such as “She teaches” vs. “She learns” is commonly expressed by an L+H* accent, although L*+H is also attested. Verum focus highlights the truth of a proposition, such as ”She does learn”, by an accent, reportedly H*, on the finite verb. In a rating study, we investigated the appropriateness of H*, L+H* and L*+H on the two focus types. Listeners preferred L+H* on lexical contrast and L*+H on verum focus. Interestingly, in both conditions L+H* was preferred over H*. Hence, both lexical contrast and verum focus require prominent accents, i.e. rising accents with a large pitch excursion, but can also be realized by two distinct intonation patterns. These results contribute to the debate on the nature of verum focus suggesting a relatedness to contrastive focus
High Input Impedance Single-Resistor Tuned Variable-gain Allpass Filter with Low Phase-error

Two new single-CFA first-order allpass filter configurations are presented wherein the gain and phase-response is tunable by single resistor. The filter phase-error (θe) as caused by the device parasitic capacitors (Cy,z), are shown to be extremely low with appropriate design. Effects of roll-off characteristics of the port coefficients and parasitic components of the CFA are analysed. Filter response and it’s in extension the implemented oscillators have been satisfactorily substantiatedwith simulation, along with hardware experiments, covering a pole-frequency range of 1 ≤ fo (MHz) ≤ 9.6.
Development of a Hand Motion Tracking based Teleoperation System for Imitation learning

Imitation learning is emerging as one promising approach for robots to acquire skills. Since imitation learning provides a way to learn policies by imitating the behavior of experts, it requires a sufficient amount of sophisticated expert behavior trajectories. However, current interfaces, such as kinesthetic teaching or remote manipulation, have significant limitations in efficiently collecting diverse demonstration data. To address this issue, this work proposes an alternative interface for imitation that simplifies the demonstration acquisition process using a hand tracking solution while facilitating the transfer of human actions to the robot. Performance results show that the proposed system is effective for imitative learning, not only imitating expert demonstrations with low latency, but also helping to collect elaborate demonstration data.
Speed Control of IPMSM Based on Series Connection Leading Correction Linear Active Disturbance Rejection Controller

In the interior permanent magnet synchronous motor drive system, the conventional linear active disturbance rejection controller (LADRC) has coupling problems with tracking and antidisturbance under different operating conditions and various complex disturbance conditions due to the influence of bandwidth. To solve abovementioned problem, a series connection leading correction LADRC (SCLC-LADRC) is proposed in this article. As the core of SCLC-LADRC, the SCLC linear extended state observer (SCLC-LESO) is composed of the total disturbance transfer function of the conventional linear extended state observer and a series connection leading correction network. As a result, the estimation range of the LESO for total disturbance is expanded, and the problem of system oscillation caused by excessive observer bandwidth is also solved. Finally, the validity and availability of the proposed method are proved through theoretical analysis and experiments.
A Compact Design of SPAD Detector with Quenching Circuit for Reduced Dark Count Rate

We present a compact design of a Single-Photon Avalanche Diode (SPAD) detector with a Passive Quenching Active Reset (PQAR) circuit for reducing the Dark Count Rate (DCR). Fabricated in 0. 11μm CMOS Technology, the single pixel includes the SPAD device, quenching circuit, and a 10-bit counter. The integration of the PQAR circuit effectively suppresses the dark counts and improves the overall performance of the SPAD detector. By applying an excess bias of 2.5V, we achieved a DCR of 150cps.
A Novel Direct Sequence Spread Spectrum-based TDMA VLC Network in Open Light Field

Visible light communication (VLC) has been considered as a promising technology for indoor high-speed and low-latency communication services. Although transmitter lens is usually required to enhance the transmission rate and distance, this approach hinders the uniformity of indoor illumination and the mobility of communication in a practical VLC scenario. Thus, it is compelling necessity to investigate the VLC network without transmitter lens. However, due to severe attenuation of the channel and inescapable noise interference of the photoelectric conversion device, the signal-to-noise ratio (SNR) at the receiver tends to be low in the open light field. To address this issue, a novel direct sequence spread spectrum (DSSS)based time division multiple access (TDMA) VLC network with time-frequency synchronization scheme is proposed to reduce the signal power requirement and enhance the anti-interference capacity. Moreover, an experimental VLC test-bed in open light field is constructed to verify the efficiency of the proposed scheme. The experimental results demonstrate that the VLC test-bed with only 3 W LED transmit power can support smooth transmission of 720p high definition multimedia in indoor environment.
Insight the Effect of Standard and Non-Standard Lightning Impulse Voltage on Dry-type Nomex Insulation with Temperature Variation

In this study, a standard and five different nonstandard lightning impulse voltages are employed to investigate their effects on the Nomex paper based dry-type insulation and its nano-composite materials. It is important to study the detrimental effect of both standard and non-standard lightning impulses on the insulation dielectrics for design interest. Therefore, the reliability of the insulation material can be assured through the standard as well as few non-standard impulse tests. In addition, design engineers need to understand how insulation behaves when exposed to non-standard impulses. In this context, seven test samples with Nomex paper insulation and nano-composites-based varnish are prepared in the laboratory for the investigation. Therefore, the polarization and depolarization currents (PDC) of the test samples are measured and the equivalent circuit parameters of the test insulation samples are estimated, i.e., extended Debye model. Hence, the different impulses are applied on the insulation model to investigate the effect of the different impulse responses on different insulation samples at different temperatures.
Alleviating Deduplication-oriented Fragmentation of SSDs by Considering File Hotness and Popularity

Data deduplication is a highly efficient method for data storage, providing significant benefits in terms of storage utilization and cost reduction in large-scale storage systems. This research introduces a novel approach to dynamic chunk allocation in deduplication systems, aiming to minimize fragmentation and enhance read performance by leveraging supported SSD commands. The write strategy is intelligently determined based on the popularity of chunks within each die, optimizing overall system performance. Additionally, we introduce a read/write unit based on the plane level to harness the internal parallelism of SSDs. Through extensive simulations, our proposed design demonstrates an average performance improvement of 18.8%, highlighting our approach’s effectiveness and impact.
Bayesian Optimization for Parameter Tuning in Placement-Aware Logic Synthesis

Bayesian optimization has been studied for parameter tuning in EDA software, where the key components are Gaussian process (GP) that models the objective function and an acquisition function that selects the optimal point. In this paper, we address the practical use of Bayesian optimization for placement-aware logic synthesis. We select only parameters that have a positive impact on circuit performance and evaluate different GP kernels and acquisition functions for better optimization results. The experimental results indicate that Matérn52 kernel and upper confidence bound (UCB) acquisition function achieve 13.3% higher frequency than the baseline without parameter optimization. Matérn52 is about 0.5% better than other kernels, and UCB is about 1.0% better than other functions.
Low-Power Counters using Pathfinding Technique

This paper proposed a novel pathfinding method to implement low-power binary counters. It performs unique data shifting in custom designed pathfinding array to generate output. The pathfinding counters are built in Cadence Virtuoso using Samsung $65 \mathrm{~nm}$ technology with $1 \mathrm{~V}$ supply. The simulation results show better power efficiency compared to other digital counters. The 7:3 counter spends only $6.1 \mu \mathrm{W}$ power with $2.26 \mathrm{~ns} \cdot \mu \mathrm{W}$ PDP. And $15: 4$ pathfinding counter dissipates $20 \mu \mathrm{W}$ and has $12.8 \mathrm{~ns} \cdot \mu \mathrm{W}$ PDP. The pathfinding counter has great potential be implemented in neural engines to achieve superb power and area efficiency.
Analysis of Exploratory Traces in Programming for Identifying Students Struggling in Construction of Program Logic

Opportunities for programming education have been increasing, and enrichment of guidance is needed. To provide precise guidance in programming exercises, instructors need to successfully grasp learners' impasses. However, this is difficult in exercises at universities since few instructors need to deal with many learners. In this research, we focus on the characteristic traces left by a learner repeatedly writing and erasing source code in a program (exploratory traces). Our goal is to develop methods for automatically estimating students stumbling in construction of program logic on the basis of the analysis of exploratory traces. This paper mainly describes the methods for analyzing exploratory traces along with examples. We also describe experiments applying our methods to actual programming and discuss their effectiveness on the basis of their results.
Numerical Characterization of a 5-Layer (Pt/Ta/TaO/AlO/W) RRAM Device

Resistive random-access memory (RRAM) is a potential kind of memory to replace flash memory in next generation. RRAM has attracted a lot of research interest on its basic structure and material. To investigate further on RRAM, detailed parameter effects on device performance must be studied to predict and improve experimental device characteristics. This paper, based on a bipolar switching and five-layer tantalum RRAM device numerical simulation, presents the influence of several significant physical parameters and structure dimensions on the I–V characteristics. The numerical model is tuned according to the I-V data from experimental device with fixed dimension and then only one parameter is adjusted at one time to find the influence on the RRAM operation and I–V characteristics and to provide an understanding of RRAM device.
Adaptive Mode-Switching for Write-amplification Reduction of SMR Disks

As storage capacity demands escalate, Shingled Magnetic Recording (SMR) disks have grown increasingly popular in the storage device market [1]. However, the overlapping tracks inherent to SMR disk drives cause write amplification during random writes, a phenomenon that degrades performance relative to traditional Perpendicular Magnetic Recording (PMR) disk drives [2]. While Skylight has offered a solution to this issue [7], we present a novel approach in MSMR, a mode-switching scheme for SMR disks. MSMR optimizes storage space by intelligently allocating data to either PMR or SMR based on predicted update frequency. This adaptive strategy enables disks to meet storage capacity needs and performance efficiency objectives. Upon evaluation, MSMR has exhibited a significant reduction in write amplification, averagely lowering total writes by 99.39% compared to Native SMR. Thus, MSMR presents an efficient solution for boosting performance while minimizing write amplification.
A Comparison of One-class and Two-class Models for Ransomware Detection via Low-level Hardware Information

Recent years have witnessed a dramatic growth in ransomware attacks. Even though many tools have been developed to help combat against these attacks, new varieties of ransomware keep emerging and hence are difficult to keep track of with the traditional signature detection method. On the other hand, neural networks have been a popular technique that can be used to help enhance ransomware detection accuracy. Long Short-Term Memory (LSTM) network, in particular, is able to learn the temporal aspect of the time-series data which is suitable for the online behavioral analysis. In this paper, we compared the anomaly detection models trained with LSTM semi-supervised learning method against the LSTM model trained with the supervised learning method for ransomware detection, utilizing low-level hardware information. Overall, we are able to detect ransomware attacks with a detection accuracy of 98.60% for the supervised learning two-class model and 89.65% for the semi-supervised one-class model. Both models achieve a very high detection rate across multiple ransomware families with recall rates of 99.70% and 93.00% for two-class and one-class models, respectively. The supervised learning model demonstrates exceptional capability in detecting unseen ransomware attacks, which demonstrates the ability to overcome the limitation of static signature detection by performing live analysis of the system behavior. The model is able to retain a recall rate of 99.52% on average when facing against ransomware variety it has not seen during training. We hope the proposed methods shed light on our fight against ransomware.