Miracle-dz/UniFAR-FTUs huggingface dataset:
End-to-End Language Diarization for Bilingual Code-Switching Speech

We propose two end-to-end neural conﬁgurations for language diarization on bilingual code-switching speech. The ﬁrst, a BLSTM-E2E architecture, includes a set of stacked bidirectional LSTMs to compute embeddings and incorporates the deep clustering loss to enforce grouping of languages belonging to the same class. The second, an XSA-E2E architecture, is based on an x-vector model followed by a self-attention encoder. The former encodes frame-level features into segment-level embeddings while the latter considers all those embed-dings to generate a sequence of segment-level language labels. We evaluated the proposed methods on the dataset obtained from the shared task B in WSTCSMC 2020 and our handcrafted simulated data from the SEAME dataset. Experimental results show that our proposed XSA-E2E architecture achieved a relative improvement of 12.1% in equal error rate and a 7.4% relative improvement on accuracy compared with the baseline algo-rithm in the WSTCSMC 2020 dataset. Our proposed XSA-E2E architecture achieved an accuracy of 89.84% with a baseline of 85.60% on the simulated data derived from the SEAME dataset.
Discussion of ‘gene expression programming to predict local scour using laboratory and field data’ by praveen Rathod & V.L. Manekar (2020)

estimation A model-independent sensitivity analysis performed to select influential parameters in the modeling of the local scouring Rathod and Manekar measured experimental data of the real-time scour using ultrasonic transducers in a recirculating sediment transport tilting flume. They also used Gene Expression Programming (GEP) approach to develop an model and compared its results with those of five available empirical models.
Toward natural approaches in restoration: Experiments of co‐evolving physical and biological structures in a self‐organizing step‐pool channel

Although the science and practice of river restoration has progressed in recent years toward more natural approaches, few example projects of headwater step‐pool channels exist in the literature. This study examined a restoration design in the headwaters of Wildcat Creek (Berkeley, California, USA) that allowed the creek to form its own stable morphology, instead of utilizing an engineered form. The creation of an experimental reach—where natural flows redistributed randomly placed clasts into step‐pool sequences after construction of an initial plane‐bed channel—afforded opportunity to test hypotheses regarding a co‐evolution of self‐organizing geomorphic and ecological responses over time. During three seasons following the initial restoration, we documented how the streambed self‐organized into step‐pool sequences by constructing plan maps and surveying changes in channel morphology. Concurrently, we sampled benthic macroinvertebrates in step, pool, and intermediate run habitats in the experimental reach and in an upstream reference reach. Results showed how natural flows created a characteristic step‐pool pattern quickly following the second post‐construction storm season. Taxonomic and functional metrics revealed that benthic macroinvertebrate communities assembled at a similarly rapid pace, becoming more distinct and diverse among habitat types as the step‐pool morphology developed. Multivariate ordination revealed that the benthic macroinvertebrate community in the experimental reach became more similar to that in the reference reach over time. Findings suggest a rapid morphologic and ecological co‐organization in the development of a complex stable step‐pool channel, giving promise for natural approaches in future restoration designs of “letting the river do the work,” to the extent afforded by local socioeconomic factors.
EOQ Model for Exponentially Deteriorating Items with Planned Backorders without Differential Calculus

Abstract We study the Economic Order Quantity (EOQ) model for deteriorating items with planned backorders. In the exponentially deteriorating items model, the inventory deterioration rate is proportional to the inventory level, which leads to an exponentially decreasing inventory level over time, obtained by solving an ordinary differential equation. Due to polynomial and exponential terms in the total cost function, an exact closed form solution is not possible. Therefore, an approximation of the total cost function has to be used. In this paper, we propose a concise and intuitive method to determine the inventory level function without using differential equations, and a method to determine the optimal solution without derivatives, based on an accurate approximation of the total cost function. Our approximation is novel and intuitive and numerical experiments demonstrate the accuracy of the closed form solution based on our approximation.
Prediction and evaluation of fatigue life via modified energy method considering surface processing

To predict fatigue life more accurately, we consider the relationship between static toughness and fatigue toughness in fatigue failure process, the numerical model of total dissipated plastic strain energy (TDPSE) and fatigue life is established. And considering the effect of surface roughness and surface processing coefficient on fatigue life, the life prediction method of TDPSE is modified. In addition, the fatigue life of Non-Masing and Masing materials are predicted by TDPSE and modified TDPSE method, respectively. Compared with TDPSE method, the life estimated by the modified TDPSE method are closer to the test results, and the modified TDPSE method lays a technical foundation for the development of mechanical components.
Immunological and Pathophysiological Outcomes of Helminth Infections and Type 2 Diabetes Comorbidity Studies in Humans and Experimental Animals—A Scoping Review

Animal and human studies have demonstrated that helminth infections are associated with a decreased prevalence of type 2 diabetes mellitus (T2DM). Lack of exposure to helminth infections has been postulated to be one mechanism to explain the markedly increased prevalence of T2DM in developed countries. However, there is still paucity of information regarding the immunological interactions between helminth infections and T2DM. The study aimed at reviewing peer-reviewed articles on host immune and pathophysiological outcomes from human and laboratory animal studies of helminth infections and T2DM comorbidity. A literature search was carried out in Google Scholar, PubMed, and EBSCOhost databases using the following keywords; immune responses OR immune modulation of helminth infections OR parasites infections AND Type 2 diabetes comorbidity in humans AND experimental/laboratory animals. Results showed that helminth infections provided some degree of protection from the pathology associated with T2DM by modulating the surrounding cytokine and chemokine milieu in humans and animals. Whilst there is some evidence regarding the protective effects of helminth infections to T2DM in cases of comorbidity, there is paucity of research in both laboratory animals and humans, with reference to the immunological and pathophysiological mechanisms which occur during comorbidity, and these constitute gaps for future research.
Audio-Visual Multi-Talker Speech Recognition in a Cocktail Party

Speech from microphones is vulnerable in a complex acoustic environment due to noise and reverberation, while the cameras are not. Thus, utilizing the visual modality in the “cock-tail party” scenario with multi-talkers has become a promising and popular approach. In this paper, we have explored the incorporating of visual modality into the end-to-end multi-talker speech recognition task. We propose two methods based on the modality fusion position, which are encoder-based fusion and decoder-based fusion. And for each method, advanced audio-visual fusion techniques including attention mechanism and dual decoder have been explored to ﬁnd the best usage of the visual modality. With the proposed methods, our best audio-visual multi-talker automatic speech recognition (ASR) model gets almost ∼ 50.0% word error rate (WER) reduction compared to the audio-only multi-talker ASR system.
Human-Centered AI

We identify three specific areas of focus to advance human-centered AI:• Designers and systems must understand the context of use and sense changes over time: Successful AI Engineering depends on the team’s ability to identify and articulate the desired system outcome and understand human and contextual factors affecting the outcome. The system itself must be able to learn when shifts in context have occurred. What are the best ways to maintain clarity around operational intent and mechanisms for adapting and evolving systems based on dynamic contexts and user needs? • Development of tools, processes, and practices to scope and facilitate human-machine teaming: Implementation of AI systems entails high levels of interdependence between human and machine. Adoption of AI systems requires the primary users to interact with and understand systems, gaining appropriate levels of trust. Every AI system needs to be designed to recognize boundaries and unfamiliar scenarios, and to provide transparency regarding its limitations. • Methods, mechanisms, and mindsets to engage in critical oversight: AI systems learn through data and observations, rather than being explicitly programmed for a deterministic outcome. Critical and reflective oversight by organizations, teams, and individuals that create and use AI systems is needed to uphold ethical principles and proactively consider the risks of bias, misuse, abuse, and unintended consequences through design, development, and ongoing deployment.For each area, we identify ongoing work as well and challenges and opportunities in developing and deploying AI systems with confidence.
The Crowdfunding Model, Collective Intelligence, and Open Innovation

In recent years, crowdfunding has attracted the attention of tech startups. It has become a good alternative way to readily raise funds, especially during the early startup stages. However, in the case of mass intelligence, it is quite difficult to ensure the accuracy and reliability of knowledge. Individual investors who are not experts in science and technology often face difficulties investing in technology companies. In this regard, a new type of collective intelligence formed by accredited professionals needs to be attempted. This paper explores an alternative crowdfunding model for enhancing access to technology investments by the general population through an investor acceptance model. We developed an investor acceptance model to examine how the crowdfunding model involving scientists and engineers is adopted by individual investors using survey data from the general population. The results revealed that individual investors have a positive attitude towards investing through the crowdfunding model when they perceive that the information provided by a group of scientific experts is useful. We found that the perceived usefulness of the information from scientists and engineers is affected by the perceived quality of the information and perceived credibility of the scientists and engineers. We also suggest a basic concept for the crowdfunding model utilizing the collective intelligence of scientists and engineers for tech startups. The results could suggest a policy direction for promoting innovation.
Short-term forecasting of monthly water consumption in hyper-arid climate using recurrent neural networks

Freshwater supply is a major challenge in regions with limited water resources and extremely arid climatic conditions. The objective of this study is to model the monthly water demand in Kuwait using the nonlinear autoregressive with exogenous input (NARX) neural network approach. The country lacks conventional surface water resources and is characterized by extremely arid climate. In addition, it has one of the fastest growing populations. In this study, linear detrending is performed on the water consumption time series for the period from January 1993 to December 2018 to eliminate the influence of population growth before application to the NARX model. Monthly temperature data are selected as exogenous input to the NARX model, because they are strongly associated with the water consumption data. Correlation analyses are performed to determine the input and feedback delays of the NARX model. The results demonstrate that the recurrent NARX model is efficient and robust for forecasting the short-term water demand, with a Nash-Sutcliffe (NS) coefficient of 0.837 in the validation period. Seasonal model assessment shows that the model performs best during the critical summer season. The NARX-based recurrent model is established as a powerful and promising tool for predicting urban water demand. Thus, it can efficiently aid the development of resilient water supply plans.
A Review of 3D Object Detection for Autonomous Driving of Electric Vehicles

In recent years, electric vehicles have achieved rapid development. Intelligence is one of the important trends to promote the development of electric vehicles. As a result, autonomous driving system is becoming one of the core systems of electric vehicles. Considering that environmental perception is the basis of intelligent planning and safe decision-making for intelligent vehicles, this paper presents a survey of the existing perceptual methods in vehicles, especially 3D object detection, which guarantees the reliability and safety of vehicles. In this review, we first introduce the role of perceptual module in autonomous driving system and a relationship with other modules. Then, we classify and analyze the corresponding perception methods based on the different sensors. Finally, we compare the performance of the surveyed works on public datasets and discuss the possible future research interests.
Lexical Modeling of ASR Errors for Robust Speech Translation

Error propagation from automatic speech recognition (ASR) to machine translation (MT) is a critical issue for the (still) dominant cascade approach to speech translation. To ro-bustify MT to ill-formed inputs, we propose a technique to ar-tiﬁcially corrupt clean transcripts so as to emulate noisy automatic transcripts. Our Lexical Noise model relies on estimating from ASR data: i) the probability distribution of the possible edit operations applicable to each word, and ii) the probability distribution of possible lexical substitutes for that word. Corrupted data generated from these probabilities are paired with their original clean counterpart for MT adaptation via ﬁne-tuning. Contrastive experiments on three language pairs led to three main ﬁndings. First, on noisy transcripts, the adapted models outperform MT systems ﬁne-tuned on synthetic data corrupted with previous noising techniques, approaching the upper bound performance obtained by ﬁne-tuning on real ASR data. Second, the increased robustness does not come at the cost of performance drops on clean test data. Third, and crucial from the application standpoint, our approach is domain/ASR-independent: noising patterns learned from a given ASR system in a certain domain can be successfully applied to robustify MT to errors made by other ASR systems in a different domain.
Supreme Audit Institutions and Sustainability of Public Finance. Links and Evidence along the Economic Cycles

Against the backdrop of concerns for diminishing the vulnerabilities of the economies of the Member States, the EU has adopted measures to strengthen budgetary discipline and control of the public deficit. In this context, the responsibility of government institutions has increased, not only in ensuring the sustainability of public finances but also in direct or indirect cooperation for good economic governance. From this perspective, this study aims to assess the impact of macroeconomic variables and those associated with supreme audit institutions on the sustainability of public finances measured by the size and dynamics of government deficit and gross public debt. Additionally, the impact of the same variables on governmental effectiveness and control of corruption has also been assessed. The data collected from secondary sources and panel data models were used to conduct an empirical study of the EU Member States which covered the 2002–2019 period and the sub-periods, divided as follows: pre-crisis, crisis, and post-crisis. The results of the study show that supreme audit institutions, through their organizational structure, the nature of their activities, and professionalism, may contribute to the reduction of public deficit and gross public debt and, implicitly, to higher efficiency and control of corruption. The results of analyses for the sub-periods show that ISAs played a more important role in reducing government deficit during crisis and post-crisis periods. By confirming or rejecting the results of the few studies that have been conducted so far, this study provides additional evidence that fills the gaps in the literature.
Multi‐agent reinforcement learning via knowledge transfer with differentially private noise

In multi‐agent reinforcement learning, transfer learning is one of the key techniques used to speed up learning performance through the exchange of knowledge among agents. However, there are three challenges associated with applying this technique to real‐world problems. First, most real‐world domains are partially rather than fully observable. Second, it is difficult to pre‐collect knowledge in unknown domains. Third, negative transfer impedes the learning progress. We observe that differentially private mechanisms can overcome these challenges due to their randomization property. Therefore, we propose a novel differential transfer learning method for multi‐agent reinforcement learning problems, characterized by the following three key features. First, our method allows agents to implement real‐time knowledge transfers between each other in partially observable domains. Second, our method eliminates the constraints on the relevance of transferred knowledge, which expands the knowledge set to a large extent. Third, our method improves robustness to negative transfers by applying differentially exponential noise and relevance weights to transferred knowledge. The proposed method is the first to use the randomization property of differential privacy to stimulate the learning performance in multi‐agent reinforcement learning system. We further implement extensive experiments to demonstrate the effectiveness of our proposed method.
Effects of Cultivation Management on Pearl Millet Yield and Growth Differed with Rainfall Conditions in a Seasonal Wetland of Sub-Saharan Africa

The production of pearl millet (Pennisetum glaucum (L.) R.Br. ) is important in Namibia, in sub-Saharan Africa, owing to the prevailing low precipitation conditions. Most fields supporting crop production in northern Namibia are located in a network of seasonal wetlands. The aim of the present study was to evaluate the effects of ridging and fertilizer application on the yield and the growth of pearl millet in the seasonal wetlands under different rainfall conditions. The study was conducted for two years (2017–2018) in the experimental fields in northern Namibia, and yield, yield components, and growth parameters were evaluated in relation to the application of different fertilizers (manure and mineral) with and without ridge-furrows. Manure fertilizer application presented the highest yield in 2018, whereas mineral fertilizer application showed the highest yield in 2017. The proportion of rainfall was the highest during the mid-growth period in 2017, and the reproductive stage in 2018. Thus, pearl millet plants under manure fertilization overcame damage resulting from waterlogging stress during the seed setting stage by improving the soil and plant nutrient conditions. In contrast, the plants under mineral fertilization were more tolerant to large amounts of rain during the mid-growth period. In this study, yield was mainly determined by total dry weight, and it was closely related to panicle density in both years. Therefore, we concluded that fertilizer application, including additional fertilizer based on the growth diagnostic, could be important for improving crop production in seasonal wetlands.
Versatile IT-system architecture for smart manufacturing solutions: the example for green manufacturing

ABSTRACT In the last decade, a rising number of opportunities for IT-based solutions in manufacturing have emerged. Often these opportunities are linked to terms like Industry 4.0, Industrial Internet of Things, Cyber-Physical Production Systems or Artificial Intelligence in Manufacturing and address different fields of action to achieve improvements in manufacturing. These include increased productivity, improved energy and resource efficiency, predictive maintenance or quality management support. However, current implementations of corresponding improvement measures often lack a common structure of the underlying IT systems, so that each of the measures is often planned and implemented as a stand-alone solution. Possible consequences include redundancies within the IT infrastructure and software functions as well as high efforts for later extensions. Against this background, an approach for a system architecture to implement different IT-based support measures based on a shared versatile system has been developed in order to gain synergy potentials and foster the reuse of software functions. Furthermore, an approach for the planning and implementation is given. This includes the design of the system architecture as well as the conception and implementation of the IT-based improvement measures. An exemplary application case within a factory for pneumatic valves demonstrates the applicability of the approaches.
Deep learning for processing and analysis of remote sensing big data: a technical review

ABSTRACT In recent years, the rapid development of Earth observation technology has produced an increasing growth in remote sensing big data, posing serious challenges for effective and efficient processing and analysis. Meanwhile, there has been a massive rise in deep-learning-based algorithms for remote sensing tasks, providing a large opportunity for remote sensing big data. In this article, we initially summarize the features of remote sensing big data. Subsequently, following the pipeline of remote sensing tasks, a detailed and technical review is conducted to discuss how deep learning has been applied to the processing and analysis of remote sensing data, including geometric and radiometric processing, cloud masking, data fusion, object detection and extraction, land-use/cover classification, change detection and multitemporal analysis. Finally, we discussed technical challenges and concluded directions for future research in deep-learning-based applications for remote sensing big data.
Sparse Superimposed Coding for Short-Packet URLLC

Sparse vector coding (SVC) is emerging as a key enabler for short-packet ultrareliable and low-latency communications (URLLCs), since it displays good block error rate (BLER) performance and can achieve low transmission latency. In this article, we propose an SVC-based sparse superimposed transmission (SVC-ST) coding scheme to further enhance the BLER performance of the SVC scheme. At the encoding side, a portion of transmission bits is represented by nonzero position indices of the sparse vector. The remaining bits are equally split into multiple streams and then mapped into the nonzero positions of sparse vector via quadrature amplitude modulation (QAM) with constellation rotation (CR). We afterward adopt the multipath matching pursuit-based soft decoding (MMP-SD) to recover the transmission packet. The BLER and bit error rate (BER) analyses of the SVC-ST scheme demonstrate the validity and rationality of our study. Moreover, we find from the simulation results that the proposed SVC-ST scheme outperforms SVC and its enhanced version (ESVC) schemes in terms of BLER and latency performance.