Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of End-to-End Models for Joint Speaker and Speech Recognition

In this paper, we investigate the eﬀectiveness of using speaker information on the performance of speaker-imbalanced automatic speech recognition (ASR). We identify major speakers and combine other speakers who have a small size of speech, and make a systematic comparison of three methods that use speaker information for ASR including speaker attribute augmentation (SAug), multi-task learning (MTL), and adversarial learning (AL). We conduct experiments on a large spontaneous speech corpus of the Extraordinary Chambers in the Courts of Cambodia (ECCC) and an open Khmer text-to-speech corpus. As a result, we ﬁnd that the use of speaker clustering information improves ASR performance including new speakers. Moreover, AL achieves better performance and more robustness in the speaker-independent setting compared to the other methods. It reduces errors of the baseline model by 4.32%, 5.46%, and 16.10% for the closed test, open test, and out-of-domain test, respectively.
A Transonic Mixed Flow Compressor for an Extreme Duty

This paper describes the design of a transonic mixed flow compressor stage for an extreme duty, with an extremely high flow coefficient (Φ) of 0.25 and a high isentropic pressure rise coefficient (ψ) of 0.56. The impeller design makes use of modern aerodynamic practice from radial and transonic axial compressors, whereby the aerodynamic blade shape involved arbitrary surfaces on several spanwise sections. Some aspects of the aerodynamic optimization of the design were limited by mechanical considerations, but nevertheless the test data obtained on a prototype stage demonstrates that acceptable performance levels can be achieved at these extreme design conditions, although map width enhancement devices were needed to obtain an acceptable operating range. The test data is compared with CFD predictions to demonstrate the validity of the design methods used.Copyright © 2014 by ASME
Improving the Survival Time of Multiagents in Social Dilemmas through Neurotransmitter-Based Deep Q-Learning Model of Emotions

In multiagent systems, social dilemmas often arise whenever there is a competition over the limited resources. The major challenge is to establish cooperation among intelligent virtual agents for solving the situations of social dilemmas. In humans, personality and emotions are the primary factors that lead them toward a cooperative environment. To make agents cooperate, they have to become more like humans, that is, believable. Therefore, we hypothesize that emotions according to the personality give birth to believability, and if believability is introduced into agents through emotions, it improves their survival rate in social dilemma situations. The existing researches have introduced different computational models to introduce emotions in virtual agents, but they lack emotions through neurotransmitters. We have proposed a neurotransmitters-based deep Q-learning computational model in multiagents that is a suitable choice for emotion modeling and, hence, believability. The proposed model regulates the agents' emotions by controlling the virtual neurotransmitters (dopamine and oxytocin) according to the agent's personality. The personality of the agent is introduced using OCEAN model. To evaluate the proposed system, we simulated a survival scenario with limited food resources in different experiments. These experiments vary the number of selfish agents (higher neuroticism personality trait) and the selfless agents (higher agreeableness personality trait). Experimental results show that by adding the selfless agents in the scenario, the agents develop cooperation, and their collective survival time increases. Thus, to resolve the social dilemma problems in virtual agents, we can make agents believable through the proposed neurotransmitter-based emotional model. This proposed work may help in developing nonplayer characters (NPCs) in games.
Port throughput forecasting using ARIMA and OLS regression: case study : Gwangyang port in Korea

Title of Dissertation: Port throughput forecasting using ARIMA and OLS Regression (case study: Gwangyang port in Korea) Degree: Master of Science Predicting future cargo volume is the most basic step in establishing midto long-term strategies for ports. In this regard, forecasting the volume of cargo is important for Gwangyang Port, which needs to establish master strategy to improve the competitiveness of container terminals, which are suffering from stagnant cargo volume and intensifying competition between container terminals. Therefore, in this study, the ARIMA model, which is a representative univariate method, and the OLS regression model, which is a multivariate method, were used to confirm which method is suitable for predicting the throughput of Gwangyang port. And, in this process, important variables were identified for the change in Gwangyang port throughput, and the future cargo volume was predicted. As a result, it was found that the OLS regression model is more suitable for forecasting the Gwangyang port throughput, and in this process, it was confirmed that government consumption, China’s imports, and the Korean exchange rate were important variables for the change in cargo volume. In addition, the cargo volume of Gwangyang port was predicted to be stable without significant change.
Research on Compensation of Power Generation Benefit in Cascade Hydropower Stations

The compensation of cascade hydropower stations is discussed.The allocation model of power generation benefit in cascade hydropower stations is proposed under multiple power generation subjects.The compensation weights of each hydropower station are confirmed based on total generating capacity.According to the weighting coefficient,the increased power output and increased benefits of each station are re-allocated.As a result,the benefits of hydropower stations will be optimized because each station changes its goal from price competition to whole benefits improvement.Furthermore,time compensation is also discussed in order to make reasonable compensation.
Robust flight Control Law Design of Heavy Cargo Airdrop for Transport Aircraft

The aerodynamic effects due to heavy loads airdropping are investigated.The disturbance could be appropriately modeled with consideration of the variations of the aircraft mass characteristics,namely the significant change of the center of gravity during extraction process and the sudden reduction of the overall weight.The standard robust H∞ control method is adopted to design flight control law during the airdrop operation.Simulation results show that the designed control law achieved both good transient and steady state performances,compared to the linear quadratic regulator.
IFRS adoption and evaluation of total asset contribution to financial performance of listed insurance companies in Nigeria and South Africa

The international diversity which form the basis of differences observed in the financial reports produced by various countries of the world have hindered cross border investment opportunities hence, the need for harmonization of accounting standards and the eventual convergence on International Financial Reporting Standard (IFRS). This study seeks to evaluate the effect of IFRS adoption and contribution of total assets to financial performance of listed insurance companies in Nigeria Stock Exchange (NSE) market and Johannesburg Stock Exchange(JBS) market, South Africa between 2001 and 2018. Ex-post facto research design was adopted and secondary data were obtained from the audited financial statements of the companies. Purposive sampling technique was used to select 5 insurance companies out of the population of 25 quoted on NSE market. While 5 were also selected from the population of 8 leading insurance companies operating in Johannesburg. Pooled OLS model with the aid of stata computer software were used to analyze the data. The findings reveals that, the total assets of insurance companies in both Nigeria and South Africa have significant contributions to the profit after tax at P≥0.000 at both pre and post adoption of IFRS. Furthermore, the extent of similarities of local accounting standard to IFRS determines the level of changes in dependent variable explained by the independent variable. Hence, for Nigeria, Adjusted R is 14% while South Africa is 82%. The study will assist the accounting standard setters (IASB) and insurance regulatory bodies by providing them with veritable tools to formulate appropriate standards and policies to tackle issues that may arise from post adoption of IFRS. It is a useful contribution to the body of knowledge upon which the foundation for future research in this area can be laid.
Design of a new reflectometric system for real time plasma position control on the RFX-mod2 device

RFX-mod2 (R = 2.0 m, a = 0.49 m), the upgraded version of the previous RFXmod fusion device, will be equipped with a new reflectometry system specifically designed for plasma position control purposes. Featuring a high temporal and spatial resolution and being suitable for harsh fusion reactor experimental conditions (long pulses, high neutron fluxes), reflectometry has been proposed as a good candidate for this task. On RFX-mod2 the diagnostic system will consist of four bistatic ultrafast independent reflectometric units working in the frequency range (16–26.5 GHz) and installed in four different poloidal locations at the same toroidal angle: two on the equatorial plane (high field side/low field side) and two at the vertical top/bottom ports. Standard pyramidal horns will be installed in the external midplane and in the vertical ports, while parabolic hoghorn reflectors have been designed for the internal midplane. Different technical solutions for the integration in the machine, as the additive manufacturing for the antenna production, are presented. Despite the differences with respect to the application on large Tokamaks like DEMO, the RFX-mod2 plasma position reflectometer can contribute to test on a simple machine some of the issues related to the development of a reflectometry based plasma position and shape control.
Incremental Fermi Large Area Telescope Fourth Source Catalog

We present an incremental version (4FGL-DR3, for Data Release 3) of the fourth Fermi Large Area Telescope (LAT) catalog of γ-ray sources. Based on the first 12 years of science data in the energy range from 50 MeV to 1 TeV, it contains 6658 sources. The analysis improves on that used for the 4FGL catalog over eight years of data: more sources are fit with curved spectra, we introduce a more robust spectral parameterization for pulsars, and we extend the spectral points to 1 TeV. The spectral parameters, spectral energy distributions, and associations are updated for all sources. Light curves are rebuilt for all sources with 1 yr intervals (not 2 month intervals). Among the 5064 original 4FGL sources, 16 were deleted, 112 are formally below the detection threshold over 12 yr (but are kept in the list), while 74 are newly associated, 10 have an improved association, and seven associations were withdrawn. Pulsars are split explicitly between young and millisecond pulsars. Pulsars and binaries newly detected in LAT sources, as well as more than 100 newly classified blazars, are reported. We add three extended sources and 1607 new point sources, mostly just above the detection threshold, among which eight are considered identified, and 699 have a plausible counterpart at other wavelengths. We discuss the degree-scale residuals to the global sky model and clusters of soft unassociated point sources close to the Galactic plane, which are possibly related to limitations of the interstellar emission model and missing extended sources.
Model Agnostic Interpretability for Multiple Instance Learning

In Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag. A bag label is often only determined by a handful of key instances within a bag, making it difficult to interpret what information a classifier is using to make decisions. In this work, we establish the key requirements for interpreting MIL models. We then go on to develop several model-agnostic approaches that meet these requirements. Our methods are compared against existing inherently interpretable MIL models on several datasets, and achieve an increase in interpretability accuracy of up to 30%. We also examine the ability of the methods to identify interactions between instances and scale to larger datasets, improving their applicability to real-world problems.
Luck, Reason, and the Price–Pareto type-2 Distribution

We consider a version of the D.J.Price’s model for the growth of a bibliographic network, where in each iteration a constant number of citations is randomly allocated according to a weighted combination of accidental (uniformly distributed) and preferential (rich-get-richer) rules. Instead of relying on the typical master equation approach, we formulate and solve this problem in terms of the rank-size distribution. We show that, asymptotically, such a process leads to a Pareto-type 2 distribution with an appealingly interpretable parametrisation. We prove that the solution to the Price model expressed in terms of the rank-size distribution coincides with the expected values of order statistics in an independent Paretian sample. We study the bias and the mean squared error of three well-behaving estimators of the underlying model parameters. An empirical analysis of a large repository of academic papers yields a good fit not only in the tail of the distribution (as it is usually the case in the power law-like framework), but also across the whole domain. Interestingly, the estimated models indicate higher degree of preferentially attached citations and smaller share of randomness than previous studies.
The reflected entropy in the GMMG/GCFT flat holography

We extend the reflected entropy to the bipartite state in a two dimensional Galilean conformal field theory (GCFT2) which is dual to the asymptotically flat spacetime described by the generalized minimal massive gravity (GMMG). To this end we consider the Renyi entanglement entropy formula that can be related to the reflected entropy by some manipulations of the replica technique. In a limited case, we find the reflected entropy is twice the minimal entanglement wedge cross section (EWCS) and this is an evidence on the relation between the reflected entropy and the EWCS in the flat holography scenario.
Variable-Length Stop-Feedback Codes With Finite Optimal Decoding Times for BI-AWGN Channels

In this paper, we are interested in the performance of a variable-length stop-feedback (VLSF) code with m optimal decoding times for the binary-input additive white Gaussian noise channel. We first develop tight approximations to the tail probability of length-n cumulative information density. Building on the work of Yavas et al., for a given information density threshold, we formulate the integer program of minimizing the upper bound on average blocklength over all decoding times subject to the average error probability, minimum gap and integer constraints. Eventually, minimization of locally optimal upper bounds over all thresholds yields the globally minimum upper bound and the above method is called the two-step minimization. Relaxing to allow positive real-valued decoding times activates the gap constraint. We develop gap-constrained sequential differential optimization (SDO) procedure to find the optimal, gap-constrained, real-valued decoding times. In the error regime of practical interest, Polyanskiy's scheme of stopping at zero does not help. In this region, the achievability bounds estimated by the two-step minimization and gap-constrained SDO show that Polyanskiy’s achievability bound for VLSF codes can be approached with a small number of decoding times.
Constant-time one-shot testing of large-scale graph states

Fault-tolerant measurement-based quantum computation (MBQC) with recent progress on quantum technologies leads to a promising scalable platform for realizing quantum computation, conducted by preparing a large-scale graph state over many qubits and performing single-qubit measurements on the state. With fault-tolerant MBQC, even if the graph-state preparation suffers from errors occurring at an unknown physical error rate, we can suppress the effect of the errors. Verifying graph states is vital to test whether we can conduct MBQC as desired even with such errors. However, problematically, existing state-of-the-art protocols for graph-state verification by fidelity estimation have required measurements on many copies of the entire graph state and hence have been prohibitively costly in terms of the number of qubits and the runtime. We here construct an efficient alternative framework for testing graph states for fault-tolerant MBQC based on the theory of property testing. Our test protocol accepts with high probability when the physical error rate is small enough to make fault-tolerant MBQC feasible and rejects when the rate is above the threshold of fault-tolerant MBQC. The novelty of our protocol is that we use only a single copy of the N -qubit graph state and single-qubit Pauli measurements only on a constant-sized subset of the qubits; thus, the protocol has a constant runtime independently of N . Furthermore, we can immediately use the rest of the graph state for fault-tolerant MBQC if the protocol accepts. These results achieve a significant advantage over prior art for graph-state verification in the number of qubits and the total runtime. Consequently, our work offers a new route to a fast and practical framework for benchmarking large-scale quantum state preparation.
Dynamic origin-destination matrix estimation for urban rail transit based on averaging strategy

A dynamic Origin-Destination(O-D)matrix model based on least square approach is proposed for urban rail transit using moving-average strategy.Statistic analysis of historic automatic fare collection records shows that,in continuous periods,there is a strong average O-D flow relevance between priori passenger flow and current passenger flow.Therefore,a dynamic O-D matrix estimation model is established based on moving-average strategy.This model can utilize the priori flow information more efficiently.A passenger flow arrival coefficient based on the distribution of OD travel time is defined to characterize the dynamics of traffic flow,thus establishing the relationship between O-D flow and in-and-out flow at stations.Finally,a case analysis of Beijing rail transit network is carried out.Results show that,comparing with existing model,the accuracy of the proposed model is improved about 15% ~20% from the viewpoint of average relative deviation in a day.The proposed model can improve the accuracy of dynamic O-D matrix estimation in short time range for urban rail transit greatly.
Pan More Gold from the Sand: Refining Open-domain Dialogue Training with Noisy Self-Retrieval Generation

Real human conversation data are complicated, heterogeneous, and noisy, from which building open-domain dialogue systems remains a challenging task. In fact, such dialogue data still contains a wealth of information and knowledge, however, they are not fully explored. In this paper, we show existing open-domain dialogue generation methods that memorize context-response paired data with autoregressive or encode-decode language models underutilize the training data. Different from current approaches, using external knowledge, we explore a retrieval-generation training framework that can take advantage of the heterogeneous and noisy training data by considering them as “evidence”. In particular, we use BERTScore for retrieval, which gives better qualities of the evidence and generation. Experiments over publicly available datasets demonstrate that our method can help models generate better responses, even such training data are usually impressed as low-quality data. Such performance gain is comparable with those improved by enlarging the training set, even better. We also found that the model performance has a positive correlation with the relevance of the retrieved evidence. Moreover, our method performed well on zero-shot experiments, which indicates that our method can be more robust to real-world data.
Deployment Algorithm of Mobile Sensing Nodes Based on Evolutionary Optimization

Optimal deployment of sensor nodes in mobile sensor networks directly influences energy consumption of the network,surveillance ability of the monitoring area and network lifetime.The deployment problem is modeled as a multi-objective optimization model in terms of network coverage and energy consumption,a genetic algorithm based solution for sensor deployment is then proposed in a centralized manner.We evaluate our approach by applying it to a certain kind of initially central deployment model,and compare its performance with that of Vector based algorithm,Voronoi based algorithm and Boundary expansion with virtual force algorithm,and the simulation results show that the proposed algorithm achieves the maximum coverage,while guarantees network connectivity and the minimum energy consumption,which thereby prolongs the network lifetime.
An Exploration of Captioning Practices and Challenges of Individual Content Creators on YouTube for People with Hearing Impairments

Deaf and Hard-of-Hearing (DHH) audiences have long complained about caption qualities for many online videos created by individual content creators on video-sharing platforms (e.g., YouTube). However, there lack explorations of practices, challenges, and perceptions of online video captions from the perspectives of both individual content creators and DHH audiences. In this work, we first explore DHH audiences' feedback on and reactions to YouTube video captions through interviews with 13 DHH individuals, and uncover DHH audiences' experiences, challenges, and perceptions on watching videos created by individual content creators (e.g., manually added caption tags could create additional confidence and trust in caption qualities for DHH audiences). We then discover individual content creators' practices, challenges, and perceptions on captioning their videos (e.g., back-captioning problems) by conducting a YouTube video analysis with 189 captioning-related YouTube videos, followed by a survey with 62 individual content creators. Overall, our findings provide an in-depth understanding of captions generated by individual content creators and bridge the knowledge gap mutually between content creators and DHH audiences on captions.
Domain-Invariant Representation Learning from EEG with Private Encoders

Deep learning based electroencephalography (EEG) signal processing methods are known to suffer from poor test-time generalization due to the changes in data distribution. This becomes a more challenging problem when privacy-preserving representation learning is of interest such as in clinical settings. To that end, we propose a multi-source learning architecture where we extract domain-invariant representations from dataset-specific private encoders. Our model utilizes a maximum-mean-discrepancy (MMD) based domain alignment approach to impose domain-invariance for encoded representations, which outperforms state-of-the-art approaches in EEG-based emotion classification. Furthermore, representations learned in our pipeline preserve domain privacy as dataset-specific private encoding alleviates the need for conventional, centralized EEG-based deep neural network training approaches with shared parameters.