Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Myanmar Speech Synthesis with Linguistic Information and LSTM-RNN

Recently, Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) has become an attractive architecture in speech synthesis for its ability to learn long time-dependencies. Contextual linguistic information is an important feature for nat-uralness in speech synthesis and using that feature in various speech synthesis models improves the quality of the synthesized speeches for languages. In this paper, LSTM-RNN was applied in Myanmar speech synthesis, and the importance of contextual linguistic features and the effect of applying explicit tone information in different architectures of LSTM-RNN was examined using our proposed Myanmar question set. Experiments of LSTM-RNN, and a hybrid system of DNN and LSTM-RNN, i.e., four feedforward hidden layers followed by two LSTM-RNN layers, were done on Myanmar speech synthesis and compared with the baseline DNN. Both objective and subjective evaluations show that the hybrid of DNN and LSTM-RNN sys-tem gives more satisﬁable synthesized speeches for Myanmar language than the LSTM-RNN and baseline DNN systems.
Rheumatology nursing : a creative approach

Section 1 Setting the Scene: the Essence of Rheumatology Nursing / the Rheumatic Conditions - An Overview /Immunology and Investigative Techniques. Section 2 Addressing the Patient's Problems: Assessing Rheumatic Patients / Psychological Aspects / Body. Image and Sexuality / the Social Implications of Rheumatic Disease / Pain and Stiffness / Fatigue and Sleep / Skin and Nutrition / the Team Approach to Mobility and Self Care. Section 3 Therapeutic Interventions: Medication in Rheumatic Disease / Complementary Therapeutic Interventions/ Surgical Interventions / Patient Education / Seamless Care.
Spatially Cooperative Caching and Optimization for Heterogeneous Network

Compromised by the ever increasing amount of mobile data traffic generated by mobile devices, edge servers are expected to provide caching resources for users so as to enable low-latency content delivery and release the burden on backhaul network. Considering the limited caching capacities of edge servers, it is essential to design an efficient content placement strategy to increase the data offloading ratio, i.e., the proportion of requested contents that are delivered via edge caching rather than backhaul links. In this paper, we propose a spatially cooperative caching strategy for a two-tier heterogeneous network consisting of edge servers and caching helpers aiming at reducing the storage space taken by duplicate contents on caching helpers and increasing hit probability. We develop an analysis framework for the proposed spatially cooperative caching strategy by using tools from stochastic geometry and maximize the hit probability by optimizing the caching probabilities of contents on edge servers and caching helpers. Particular, we derive the closed-form solution of the optimization problem for unit cache size. The simulation results are identical with the analytical ones and the outperformance is observed with the optimal caching probability derived in this paper compared with the existing caching strategies.
Government Funding and Nonprofit Revenues in China: A Cross-Regional Comparison

Abstract This study examines the various effects of government funding on nonprofit revenues in China through a cross-regional comparison. We argue that central government funding increases nonprofit revenues by improving nonprofits’ legitimacy/reputation and competitive capacities. Based on a dataset of 2,058 questionnaires and 65 interviews with nonprofits receiving central government funding to participate in social service projects from 2013 to 2017, we find that having more central government funding enables nonprofits to raise more revenues through local government funding, private donations, and commercial revenues. A cross-regional comparison shows that central government funding has more effects on nonprofit revenues in the non-western region than in the western region. Our interviews suggest that resource accessibility and marketization institutions determine the various effects of central government funding on nonprofit revenues in western and non-western regions. The findings reveal nuances in the effects of government funding on nonprofit revenues through a cross-regional comparison under authoritarianism.
The Patient's Guide to Preventing Medical Errors

Dedication Acknowledgments Introduction Disclaimer The Error Factor Communication Vulnerabilities Why the Silence? Organized Structures within Hospitals Who Is Watching the Hospitals? Making Safer Choices, What Do the Numbers Mean? Rapid Advancements in Medicine Old Designs, Prone to Errors, System Failures, and the Human Factor Pictures: Too Close for Comfort Beyond Medical Malpratice Disclosure Toward a New Safety Culture: How the Change Will Happen The Patient Partnership: What Needs to Change Hospitals on the Mend What You Can Do to Protect Yourself Patient Help: BE SAFE Resources: Who You Can Contact, Patient Safety Resources References Index
Developing and Analyzing a Spanish Corpus for Forensic Purposes

In this paper, the methods for developing a database of Spanish writing that can be used for forensic linguistic research are presented, including our data collection procedures. Specifically, the main instrument used for data collection has been translated into Spanish and adapted from Chaski (2001). It consists of ten tasks, by means of which the subjects are asked to write formal and informal texts about different topics. To date, 93 undergraduates from Spanish universities have already participated in the study and prisoners convicted of gender-based abuse have participated. A twofold analysis has been performed, since the data collected have been approached from a semantic and a morphosyntactic perspective. Regarding the semantic analysis, psycholinguistic categories have been used, many of them taken from the LIWC dictionary (Pennebaker et al., 2001). In order to obtain a more comprehensive depiction of the linguistic data, some other ad-hoc categories have been created, based on the corpus itself, using a double-check method for their validation so as to ensure inter-rater reliability. Furthermore, as regards morphosyntactic analysis, the natural language processing tool ALIAS TATTLER is being developed for Spanish. Results shows that is it possible to differentiate non-abusers from abusers with strong accuracy based on linguistic features.
The SJTU Robust Anti-Spoofing System for the ASVspoof 2019 Challenge

The robustness of an anti-spooﬁng system is progressively more important in order to develop a reliable speaker veriﬁcation sys-tem. Previous challenges and datasets mainly focus on a spe-ciﬁc type of spooﬁng attacks. The ASVspoof 2019 edition is the ﬁrst challenge to address two major spooﬁng types - logical and physical access. This paper presents the SJTU’s submitted anti-spooﬁng system to the ASVspoof 2019 challenge. Log-CQT features are developed in conjunction with multi-layer convolutional neural networks for robust performance across both sub-tasks. CNNs with gradient linear units (GLU) activations are utilized for spooﬁng detection. The proposed system shows consistent performance improvement over all types of spooﬁng attacks. Our primary submissions achieve the 5 th and 8 th positions for the logical and physical access respectively. Moreover, our contrastive submission to the PA task exhibits better generalization compared to our primary submission, and achieves a comparable performance to the 3 rd position of the challenge.
The experiences of non-traditional students utilizing student support services: a qualitative study

The purpose of this phenomenological study was to examine the impact of student support services on the engagement and satisfaction of undergraduate non-traditional students at Kansas State University. For the purposes of this study, student support services were defined as non-academic departments or offices providing support services to students. The research questions were: (1) How do the experiences of non-traditional students, when utilizing the services of university student support staff, impact their engagement and satisfaction? (2) How do non-traditional students regard their experiences with student support services at Kansas State University? (3) What types of programming do student support services at Kansas State University provide for non-traditional students? (4) What level of involvement with student support service functions do non-traditional students find as enhancing their learning experience? Following the completion of in-depth interviews of 20 non-traditional students, five student support staff members and observation of five student support staff offices, the researcher identified the emergence of six themes related to the engagement and satisfaction of non-traditional students: (1) commitment student’s perceptions of efforts to engage and satisfy them through a commitment of university resources, (2) expectations demonstration of investment in the students and validation of their presence on campus by communicating clear expectations, (3) support provision and utilization of support services that promote student success (4) involvement perceptions of the relationship between student and support staff and opportunities for academic and social integration, (5) learning efforts to enhance the learning experiences of non-traditional students, (6) feedback – gathering of suggestions for change and improvement to the non-traditional student experience. These themes captured the experiences of non-traditional students while utilizing student support services. Students who develop or experience a positive connection with a student support staff member or faculty member reported that those experiences leave them feeling engaged in the life of the university and satisfied with their student role. Non-traditional students were confused about what student support services were available and how to utilize them. When students seek the help of support staff, the student often left feeling as though the staff do not understand their needs. Student support service offices did not differentiate between programs specifically for non-traditional and traditionally-aged students. Students could identify student support service staff as connectors as playing the role of; academic advisor, admissions representative, financial aid advisor, housing staff member, faculty member or others who take the opportunity to engage with the student. THE EXPERIENCES OF NON-TRADITIONAL STUDENTS UTILIZING STUDENT SUPPORT SERVICES: A QUALITATIVE STUDY
Diversification of export territories of family businesses as a tool of their further development

Family business is the largest global source of jobs in the private sector, whose multigenerational nature strengthens the stability of individual economies. A competitive small and medium-sized enterprise (SME) sector into which family businesses are classified is an essential prerequisite for the full-fledged integration of any economy into the global economic space. For the Czech economy, the importance of foreign trade is increasing, and is dependent on the capabilities of companies to expand to foreign markets. The goal of this article is to identify involvement of the generation of successors to export activities of family business, focusing on diversifying export territories in relation to structure of the industry A secondary goal is to discuss the demand mechanisms for SMEs/family business oriented toward export. The scientific hypotheses defined are focused on demonstrating a dependency between the diversification of export territories, the involvement of the generation of successors in the management of the company and structure of industry. Authors have demonstrated that family businesses managed by the first generation of founders export primarily to the territories of Slovakia, Germany, and the EU. Companies where the next generation contributes to management diversify territorial risk and also export outside the EU countries. A significant correlation was demonstrated between automotive industry and Germany, Slovakia and country outside the EU and mechanical engineering and country outside the EU. The uniqueness of this article lies in the topicality of the real transition of Czech family businesses to the next generation, which carries out foreign trade to promote the further development and sustainability of the family business for future generations.
Application of Decision Support Technology for Conceptual Cost Estimation

Conceptual cost estimates are often made at the beginning of the project when project scope is not yet well defined. Hence, predicting the conceptual costs on time, with high accuracy, presents a considerable challenge. One potential solution is to more effectively utilize historical data via integration with predictive analytical models. In this project, a decision support system was developed which predicts conceptual costs of construction projects and supports decision-making for long-term capital planning in public universities. The prototype system was developed based on historical data for roofing projects at the University of Alabama. We collected this historical data via a web-based data entry form subsystem. The developed system uses ridge regression models to train historical data. This system has a user-friendly interface and supports what-if analysis, allowing the user to see multiple scenarios of the estimation. The system also encompasses capabilities to forecast the effects of inflation on multi-year projects. Subsequent validation has demonstrated improvement in the resulting accuracy of the conceptual estimates.
pyMARS: automatically reducing chemical kinetic models in Python

Chemically reacting fluid flows occur in a diverse range of scientific and engineering fields, including combustion and fire, atmopsheric and oceanic fluid flows, electrochemical devices, heterogeneous catalysis, materials processing, and astrophysical fluid dynamics. Numerical simulations of reacting fluid flows rely on accurate chemical kinetic models to describe the participating chemical species and elementary reactions through which they interact. However, as models grow in detail they also grow in size, adding more species and reactions to capture intermediates and pathways. In the field of combustion, kinetic models for molecules relevant to transportation fuels (e.g., gasoline, diesel, jet fuel) can contain thousands of species and tens of thousands of elementary reactions (T. Lu & Law, 2009). Incorporating such models into multidimensional computational fluid dynamics simulations is practically impossible, due to the associated computational expense.
Load balancing method for KDN-based data center using neural network

The growth of cloud application services delivered through data centers with varying traffic demands unveils limitations of traditional load balancing methods. Aiming to attend evolving scenarios and improve the overall network performance, this paper proposes a load balancing method based on an Artificial Neural Network (ANN) in the context of Knowledge-Defined Networking (KDN). KDN seeks to leverage Artificial Intelligence (AI) techniques for the control and operation of computer networks. KDN extends Software-Defined Networking (SDN) with advanced telemetry and network analytics introducing a so-called Knowledge Plane. The ANN is capable of predicting the network performance according to traffic parameters paths. The method includes training the ANN model to choose the path with least load. The experimental results show that the performance of the KDN-based data center has been greatly improved.
OpenSketch

Product designers extensively use sketches to create and communicate 3D shapes and thus form an ideal audience for sketch-based modeling, non-photorealistic rendering and sketch filtering. However, sketching requires significant expertise and time, making design sketches a scarce resource for the research community. We introduce OpenSketch, a dataset of product design sketches aimed at offering a rich source of information for a variety of computer-aided design tasks. OpenSketch contains more than 400 sketches representing 12 man-made objects drawn by 7 to 15 product designers of varying expertise. We provided participants with front, side and top views of these objects, and instructed them to draw from two novel perspective viewpoints. This drawing task forces designers to construct the shape from their mental vision rather than directly copy what they see. They achieve this task by employing a variety of sketching techniques and methods not observed in prior datasets. Together with industrial design teachers, we distilled a taxonomy of line types and used it to label each stroke of the 214 sketches drawn from one of the two viewpoints. While some of these lines have long been known in computer graphics, others remain to be reproduced algorithmically or exploited for shape inference. In addition, we also asked participants to produce clean presentation drawings from each of their sketches, resulting in aligned pairs of drawings of different styles. Finally, we registered each sketch to its reference 3D model by annotating sparse correspondences. We provide an analysis of our annotated sketches, which reveals systematic drawing strategies over time and shapes, as well as a positive correlation between presence of construction lines and accuracy. Our sketches, in combination with provided annotations, form challenging benchmarks for existing algorithms as well as a great source of inspiration for future developments. We illustrate the versatility of our data by using it to test a 3D reconstruction deep network trained on synthetic drawings, as well as to train a filtering network to convert concept sketches into presentation drawings. We distribute our dataset under the Creative Commons CC0 license: https://ns.inria. fr/d3/OpenSketch.
Implementation of Data Mining Analysis to Determine the Tuna Fishing Zone Using DBSCAN Algorithm

Abstract— The aim of this study is to map the tuna fishing zones based on the daily fish catch data from the Hindian Ocean. With the study, it is expected to deliver a potential tuna fishing zones mapping, where it is based on the number of catch along with its spatial data. The study utilized a data mining approach with DBSCAN algorithm as the method to cluster the data. The study yields information that the Bigeye tuna is dominated the catch in the west monsoon, while Yellowfin tuna dominated the catch in the east monsoon. Based on the trial using the DBSCAN algorithm, we know that the optimal Eps and MinPts value are 1.5 and 5 respectively to generate a convergence cluster. Key words: Data Mining, DBSCAN Algorithm, Spatial Analysis, Clustering, Rapidminer. Abstrak—Tujuan dari penelitian ini adalah untuk memetakan zona penangkapan tuna berdasarkan data tangkapan ikan harian dari Samudra Hindia. Dengan studi ini, diharapkan untuk memberikan pemetaan zona penangkapan ikan tuna potensial, di mana ia didasarkan pada jumlah tangkapan bersama dengan data spasialnya. Penelitian ini menggunakan pendekatan penambangan data dengan algoritma DBSCAN sebagai metode untuk mengelompokkan data. Studi ini menghasilkan informasi bahwa tuna Bigeye mendominasi tangkapan di musim barat, sedangkan tuna Yellowfin mendominasi tangkapan di musim timur. Berdasarkan percobaan menggunakan algoritma DBSCAN, kita tahu bahwa nilai Eps dan MinPts yang optimal adalah 1,5 dan 5 masing-masing untuk menghasilkan cluster konvergensi. Kata kunci: Data Mining, DBSCAN Algorithm, Spatial Analysis, Clustering, Rapidminer
Memory Bandwidth Regulation for Multiframe Task Sets

Timing analysis of safety-critical real-time embedded systems should be free of both optimistic and pessimistic aspects. The multiframe model was devised to eliminate the pessimism in the schedulability analysis of systems with tasks whose worst-case execution times vary from job to job, according to known patterns. However, this model is optimistic and unsafe for multicores with shared memory controllers, since it ignores memory contention, and existing approaches to stall analysis based on memory regulation are very pessimistic if straightforwardly applied. This paper remedies this by adapting existing stall analyses for memory-regulated systems of conventional Liu-and-Layland tasks to the multiframe model. Experimental evaluations with synthetic task sets (and different task and memory budget assignment heuristics) show up to 85% higher scheduling success ratio for our analysis, compared to the frame-agnostic analysis, enabling higher platform utilisation without compromising safety. We also explore implementation aspects, such as how to speed up the analysis and how to trade off accuracy with tractability.
Real Time Estimation of rigid body orientation based on inertial sensors measurements

This paper presents a programmable DSP board developed for real-time estimation of rigid body orientation. This work takes place in a research project carried out by students. The project consists of unmanned aerial vehicle’s (UAV) attitude estimation. This estimation is processed by an Inertial Reference System (IRS) including the TMS320F2812 processor, low cost integrated Micro Electro Mechanical Systems (iMEMS) such as accelerometers and gyroscopes, temperature sensors, and magnetometers. Gyroscopes provide angular velocities that can be integrated to yield orientation. Accelerometers and magnetometers, pointing to gravity direction and north direction enables drift compensation. So the system performs a drift-free attitude estimation using quaternions for rotations representation. One benefit of this system is that it enables high integration by exploiting the processor’s capacities for sensors interfacing. Moreover it takes advantage of the high performance processor to achieve a real time estimation of the body’s orientation. For that digital processing includes signals filtering, measurements compensation and a lot of trigonometric functions for Euler angles computing. The system performances can be measured using a PC for results visualisation. The system has been designed to integrate in the future GPS component and pressure sensor. These components will allow more accurate attitude estimation and positioning.
Attention-Based DenseUnet Network With Adversarial Training for Skin Lesion Segmentation

Automatic segmentation of skin lesions in dermoscopy images is a challenging task due to the large size and shape variations of the lesions, the existence of various artifacts, the low contrast between the lesion and the surrounding skin. In this paper, we propose a novel Attention Based DenseUnet network (referred as Att-DenseUnet) with adversarial training for skin lesion segmentation. Att-DenseUnet is a Generative Adversarial Network which contains two major components: Segmentor and Discriminator. In the Segmentor module, we propose an architecture which is similar to DenseNet in the down-sampling path to ensure maximum multi-scale skin lesions information transfer between layers in the network at dense scale range, meanwhile, we design an attention module to automatically focus on the skin lesion features and suppress the irrelevant artifacts features in the output feature maps of the DenseBlocks. In the Discriminator module, we employ adversarial feature matching loss to train the Segmentor stably, force the Segmentor to extract multi-scale discriminative features, and guide the attention module focusing on the multi-scale skin lesions. A novel loss function of the Segmentor is proposed which combines the jaccard distance loss with the adversarial feature matching loss introduced by the Discriminator. We trained the proposed Att-DenseUnet on ISBI2017 dataset. The test results show that our approach gains the state-of-the-art performance, especially for JAC (0.8045) and SEN (0.8734) scores which are significantly improved by 2.2% and 1.9%, respectively, also our network is robust to different datasets, and gains the lowest time cost which make our network suitable for clinical application.