Perturbation-based QE: An Explainable, Unsupervised Word-level Quality Estimation Method for Blackbox Machine Translation

Quality Estimation (QE) is the task of predicting the quality of Machine Translation (MT) system output, without using any gold-standard translation references. State-of-the-art QE models are supervised: they require human-labeled quality of some MT system output on some datasets for training, making them domain-dependent and MT-system-dependent. There has been research on unsupervised QE, which requires glass-box access to the MT systems, or parallel MT data to generate synthetic errors for training QE models. In this paper, we present Perturbation-based QE - a word-level Quality Estimation approach that works simply by analyzing MT system output on perturbed input source sentences. Our approach is unsupervised, explainable, and can evaluate any type of blackbox MT systems, including the currently prominent large language models (LLMs) with opaque internal processes. For language directions with no labeled QE data, our approach has similar or better performance than the zero-shot supervised approach on the WMT21 shared task. Our approach is better at detecting gender bias and word-sense-disambiguation errors in translation than supervised QE, indicating its robustness to out-of-domain usage. The performance gap is larger when detecting errors on a nontraditional translation-prompting LLM, indicating that our approach is more generalizable to different MT systems. We give examples demonstrating our approach{\u27}s explainability power, where it shows which input source words have influence on a certain MT output word

Perturbation-based QE: An Explainable, Unsupervised Word-level Quality Estimation Method for Blackbox Machine Translation

Quality Estimation (QE) is the task of predicting the quality of Machine Translation (MT) system output, without using any gold-standard translation references. State-of-the-art QE models are supervised: they require human-labeled quality of some MT system output on some datasets for training, making them domain-dependent and MT-system-dependent. There has been research on unsupervised QE, which requires glass-box access to the MT systems, or parallel MT data to generate synthetic errors for training QE models. In this paper, we present Perturbation-based QE - a word-level Quality Estimation approach that works simply by analyzing MT system output on perturbed input source sentences. Our approach is unsupervised, explainable, and can evaluate any type of blackbox MT systems, including the currently prominent large language models (LLMs) with opaque internal processes. For language directions with no labeled QE data, our approach has similar or better performance than the zero-shot supervised approach on the WMT21 shared task. Our approach is better at detecting gender bias and word-sense-disambiguation errors in translation than supervised QE, indicating its robustness to out-of-domain usage. The performance gap is larger when detecting errors on a nontraditional translation-prompting LLM, indicating that our approach is more generalizable to different MT systems. We give examples demonstrating our approach's explainability power, where it shows which input source words have influence on a certain MT output word.Comment: Accepted to MT Summit 202

Cooperative underlay cognitive radio assisted NOMA: secondary network improvement and outage performance

In this paper, a downlink scenario of a non-orthogonal multiple access (NOMA) scheme with power constraint via spectrum sensing is considered. Such network provides improved outage performance and new scheme of NOMA-based cognitive radio (CR-NOMA) network are introduced. The different power allocation factors are examined subject to performance gap among these secondary NOMA users. To evaluate system performance, the exact outage probability expressions of secondary users are derived. Finally, the dissimilar performance problem in term of secondary users is illustrated via simulation, in which a power allocation scheme and the threshold rates are considered as main impacts of varying system performance. The simulation results show that the performance of CR-NOMA network can be improved significantly

E2EG: End-to-End Node Classification Using Graph Topology and Text-based Node Attributes

Node classification utilizing text-based node attributes has many real-world applications, ranging from prediction of paper topics in academic citation graphs to classification of user characteristics in social media networks. State-of-the-art node classification frameworks, such as GIANT, use a two-stage pipeline: first embedding the text attributes of graph nodes then feeding the resulting embeddings into a node classification model. In this paper, we eliminate these two stages and develop an end-to-end node classification model that builds upon GIANT, called End-to-End-GIANT (E2EG). The tandem utilization of a main and an auxiliary classification objectives in our approach results in a more robust model, enabling the BERT backbone to be switched out for a distilled encoder with a 25% - 40% reduction in the number of parameters. Moreover, the model's end-to-end nature increases ease of use, as it avoids the need of chaining multiple models for node classification. Compared to a GIANT+MLP baseline on the ogbn-arxiv and ogbn-products datasets, E2EG obtains slightly better accuracy in the transductive setting (+0.5%), while reducing model training time by up to 40%. Our model is also applicable in the inductive setting, outperforming GIANT+MLP by up to +2.23%.Comment: Accepted to MLoG - IEEE International Conference on Data Mining Workshops ICDMW 202

Equitable Access and Public Attitudes to Vaccination for Internal Migrants in Vietnam

This mixed methods participatory study explores equity and fairness in access to Vietnam’s Covid-19 vaccination programme, when the Covid-19 vaccine was scarce, with a focus on internal migrant workers. At the beginning of the pandemic large numbers of Vietnamese migrants from rural areas lost their jobs. Migrants are vital to the Vietnamese economy. Many factories that produce goods for export employ internal migrants. Before the pandemic, these workers faced inequalities of access to available health services and nutritious food. Although the Vietnamese state aspires to universal access to health, internal migrant workers living outside their village do not have long-term household book registration, which is the key to access many public services including health care and prevention. We found that migrant workers, especially those working in the export zones where factories produce for export, did have access to vaccination. However, there are intersectional inequalities between internal migrants based on other characteristics such as (dis)ability. Policies that give priority to economic productivity disadvantage those who are not considered vital or essential from that perspective. The delegation of some of the vaccination access decisions to local authorities, for example village headmen, allowed for flexibility based on local contexts and needs. However, the capacity of implementers at grass-root level to respond to emerging situations was not identified clearly in implementing guidelines. This resulted in a lack of transparency in local decision-making. We recommend establishing an independent body with representatives from various groups to monitor policy implementation and decision-making for vaccination and emergency preparedness for future outbreaks. More research is needed to explore the social acceptability of medical technologies and medical interventions, especially in prolonged epidemics such as the Covid-19 pandemic.Foreign, Commonwealth & Development Offic

MÔ PHỎNG ẢNH HƯỞNG CỦA MỰC NƯỚC BIỂN DÂNG ĐẾN BIẾN ĐỘNG ĐỊA HÌNH ĐÁY VÙNG VEN BỜ CỬA SÔNG MÊ KÔNG

This paper presents some study results on morphological change in the coastal region of Mekong River under the influences of sea level rise. In order to set up the models, measured data were collected, systematically and homogeneously processed to create open boundary conditions (time-serial data) for the model. Open sea boundary conditions of the model were created by NESTING method. The model (Delft3D model) was set up with 4 layers in Sigma coordinate. The results of model were validated, showing a fairly good agreement with measured data (water elevation, currents, and suspended sediment concentration) at some places in the study area. Results of some scenarios of simulation (dry and flood season) show the sea level rise due to climate change could make a reduction in the seaward sediment transport and increase its settling around estuaries. As a result, sea level rise causes an increase in the accreted rate of sandbars in southern estuary of Mekong river coastal area. The influences of sea level rise on Mekong river coastal bed topography are prevailing in the region of about 7 - 10 km seawards. Further 10 km from the coast, influences of sea level rise on coastal morphology are not significant.Bài báo trình bày các kết quả nghiên cứu dự báo biến động địa hình ở vùng ven bờ châu thổ sông Mê Kông dưới ảnh hưởng của nước biển dâng. Để thiết lập mô hình tính, các chuỗi số liệu quan trắc đã được thu thập, xử lý hệ thống và đồng bộ cho các điều kiện biên (sông, biển) của mô hình dạng chuỗi số liệu (time serial data). Các biên mở phía biển của mô hình được tạo ra bằng phương pháp lưới lồng (NESTING) từ mô hình có miền tính rộng hơn ở phía ngoài. Mô hình Delft3D với 4 lớp độ sâu theo hệ tọa độ Sigma đã được thiết lập và kiểm chứng cho thấy có sự phù hợp với số liệu đo đạc. Kết quả dự báo trong mùa cạn và mùa lũ đã cho thấy sự dâng cao mực nước biển do biến đổi khí hậu làm hạn chế sự phát tán của dòng trầm tích về phía biển và tập trung di chuyển quanh các cửa sông. Qua đó làm tăng tốc độ bồi tại các bãi bồi khu vực phía ngoài các cửa sông phía nam của vùng ven bờ châu thổ sông Mê Kông. Những ảnh hưởng do dâng cao mực nước biển đến địa hình đáy ven bờ châu thổ sông Mê Kông phổ biến diễn ra trong phạm vi khoảng 7 -       10 km từ cửa sông ra phía ngoài. Ở phía ngoài 10 km từ bờ ra, ảnh hưởng do dâng cao mực nước đến địa hình đáy hầu như không đáng kể

Exploiting Secrecy Performance of Uplink NOMA in Cellular Networks

Funding Information: This work was supported in part by the Air Force Office of Scientific Research under Award FA9550-20-1-0090, and in part by the National Science Foundation under Grant CNS-2034218.Peer reviewedPublisher PD

Survey on Vietnamese teachers’ perspectives and perceived support during COVID-19

The COVID-19 pandemic has caused unprecedented damage to the educational system worldwide. Besides the measurable economic impacts in the short-term and long-term, there is intangible destruction within educational institutions. In particular, teachers – the most critical intellectual resources of any schools – have to face various types of financial, physical, and mental struggles due to COVID-19. To capture the current context of more than one million Vietnamese teachers during COVID-19, we distributed an e- survey to more than 2,500 randomly selected teachers from two major teacher communities on Facebook from 6th to 11th April 2020. From over 373 responses, we excluded the observations which violated our cross-check questions and retained 294 observations for further analysis. This dataset includes: (i) Demographics of participants; (ii) Teachers' perspectives regarding the operation of teaching activities during the pandemic; (iii) Teachers' received support from their schools, government bodies, other stakeholders such as teacher unions, and parents' associations; and (iv) teachers' evaluation of school readiness toward digital transformation. Further, the dataset was supplemented with an additional question on the teachers' primary source of professional development activities during the pandemic

Takagi-Sugeno fuzzy unknown input observers to estimate nonlinear dynamics of autonomous ground vehicles : theory and real-time verification

We address the simultaneous estimation problem of the lateral speed, the steering input and the effective engine torque, which play a fundamental role in vehicle handling, stability control and fault diagnosis of autonomous ground vehicles. Due to the involved longitudinal-lateral coupling dynamics and the presence of unknown inputs (UIs), a new nonlinear observer design technique is proposed to guarantee the asymptotic estimation performance. To this end, we make use of a specific Takagi-Sugeno (TS) fuzzy representation with nonlinear consequents to exactly model the nonlinear vehicle dynamics within a compact set of the vehicle state. This TS fuzzy modeling not only allows reducing significantly the realtime computational effort in estimating the vehicle variables but also enables an effective way to deal with unmeasured nonlinearities. Moreover, via a generalized Luenberger observer structure, the UI decoupling can be achieved without requiring a priori UI information. Using Lyapunov stability arguments, the UI observer design is reformulated as an optimization problem under linear matrix inequalities, which can be effectively solved with standard numerical solvers. The effectiveness of the proposed TS fuzzy UI observer design is demonstrated with realtime hardware-in-the-loop experiments