The Global Dissemination of China’s Poverty Alleviation Experience: Enhancing Human Rights through Poverty Reduction

China primarily utilizes the member state reporting system and the Universal Periodic Review (UPR) to showcase its success in poverty alleviation. However, there is still room for improvement, such as incomplete statistical data and information, a lack of transparency and explanation regarding domestic policies, and the use of Chinese-style language in reports that can be challenging for foreign audiences to comprehend. Therefore, China should fully acknowledge and earnestly work towards improvement. Solutions include consolidating achievements in poverty alleviation, refining and enriching experiences in poverty reduction, enhancing understanding of the significance of both international human rights monitoring mechanisms, establishing a unified and efficient poverty reduction information and statistics system, and promoting a consensus to further advance the great rejuvenation of the Chinese nation and the creation of a community with a shared future for all mankind

A hybrid framework of iterative MapReduce and MPI for molecular dynamics applications

Developing platforms for large scale data processing has been a great interest to scientists. Hadoop is a widely used computational platform which is a fault-tolerant distributed system for data storage due to HDFS (Hadoop Distributed File System) and performs fault-tolerant distributed data processing in parallel due to MapReduce framework. It is quite often that actual computations require multiple MapReduce cycles, which needs chained MapReduce jobs. However, Design by Hadoop is poor in addressing problems with iterative structures. In many iterative problems, some invariant data is required by every MapReduce cycle. The same data is uploaded to Hadoop file system in every MapReduce cycle, causing repeated data delivering and unnecessary time cost in transferring this data. In addition, although Hadoop can process data in parallel, it does not support MPI in computing. In any Map/Reduce task, the computation must be serial. This results in inefficient scientific computations wrapped in Map/Reduce tasks because the computation can not be distributed over a Hadoop cluster, especially a Hadoop cluster on a traditional high performance computing cluster. Computational technologies have been extensively investigated to be applied into many application domains. Since the presence of Hadoop, scientists have applied the MapReduce framework to biological sciences, chemistry, medical sciences, and other areas to efficiently process huge data sets. In our research, we proposed a hybrid framework of iterative MapReduce and MPI for molecular dynamics applications. We carried out molecular dynamics simulations with the implemented hybrid framework. We improved the capability and performance of Hadoop by adding a MPI module to Hadoop. The MPI module enables Hadoop to monitor and manage the resources of Hadoop cluster so that computations incurred in Map/Reduce tasks can be performed in a parallel manner. We also applied the local caching mechanism to avoid data delivery redundancy to make the computing more efficient. Our hybrid framework inherits features of Hadoop and improves computing efficiency of Hadoop. The targeting application domain of our research is molecular dynamics simulation. However, the potential use of our iterative MapReduce framework with MPI is broad. It can be used by any applications which contain single or multiple MapReduce iterations, invoke serial or parallel (MPI) computations in Map phase or Reduce phase of Hadoop

International Soft Law Governance of Artificial Intelligence: Advantages, Approaches, and Credibility

Given the transnational nature of data and algorithms associated with artificial intelligence, individual countries often face challenges in developing an effective governance system. Ensuring a healthy, stable, and sustainable AI ecosystem requires international communication, cooperation, and interaction. However, in the emerging and fast-changing interdisciplinary field of AI, internationally binding laws like treaties and agreements cannot be easily established overnight. Multilateral negotiations and local consultations are necessary, as each provision must be carefully considered and weighed, a process that is time-consuming and slow in yielding results. Therefore, it is clear that non-coercive soft law methods are necessary to achieve rapid international consensus and facilitate action. Soft law intervention provides practical space in the current landscape of Internet governance and is both necessary and feasible. At the very least, soft law can initiate the process of legalizing Internet governance. The application of soft law can encourage countries to reach some foundational and framework agreements, enabling practical and effective preparations for future, more comprehensive agreements that may be reached

Twice Dynamic Consolidation — An Unusual Application in Treating Liquefiable Saturated Sandy Loam Deposits

More innovative methods of ground treatment have replaced traditional methods. Dynamic consolidation, for example, has been applied widespreadly in China. Presented in this paper is a case history of adopting the twice dynamic consolidation method to improve liquefiable saturated loesslike sandy loam deposit, since the site for the Aluminum Material Company which is located at the suburb of Talyuan, China, Is geotechnically adverse. In this project the effective depth of improvement increased significantly. Judged from the ln-situ investigation and laboratory triaxial shear test, the liquefaction potential was eliminated as predicted

ELECTRICITY GENERATION CHARACTERISTICS OF AN ANAEROBIC FLUIDIZED BED MICROBIAL FUEL CELL

Anaerobic fluidized bed microbial fuel cell (AFBMFC) was developed to investigate the effect of fluidization behaviors on the electrogenesis capacity. Waste water and active carbon were used as liquid and solid phase, respectively. The fuel cell was started up successfully using anaerobic activated sludge as inoculums. The power density is increased with increasing circular liquid velocity up to 450 mW·m-2. High COD remove rate reached 93% after five days operation. Meanwhile, the effects of cathode area on the electrogenesis capacity of AFB MFC were also investigated

GeoLocator: a location-integrated large multimodal model for inferring geo-privacy

Geographic privacy or geo-privacy refers to the keeping private of one's geographic location, especially the restriction of geographical data maintained by personal electronic devices. Geo-privacy is a crucial aspect of personal security; however, it often goes unnoticed in daily activities. With the surge in the use of Large Multimodal Models (LMMs), such as GPT-4, for Open Source Intelligence (OSINT), the potential risks associated with geo-privacy breaches have intensified. This study develops a location-integrated GPT-4 based model named GeoLocator and designs four-dimensional experiments to demonstrate its capability in inferring the locational information of input imageries and/or social media contents. Our experiments reveal that GeoLocator generates specific geographic details with high accuracy and consequently embeds the risk of the model users exposing geospatial information to the public unintentionally, highlighting the thread of online data sharing, information gathering technologies and LLMs on geo-privacy. We conclude with the broader implications of GeoLocator and our findings for individuals and the community at large, by emphasizing the urgency for enhanced awareness and protective measures against geo-privacy leakage in the era of advanced AI and widespread social media usage.Comment: 16pages, 2 figure

Leveraging phone-level linguistic-acoustic similarity for utterance-level pronunciation scoring

Recent studies on pronunciation scoring have explored the effect of introducing phone embeddings as reference pronunciation, but mostly in an implicit manner, i.e., addition or concatenation of reference phone embedding and actual pronunciation of the target phone as the phone-level pronunciation quality representation. In this paper, we propose to use linguistic-acoustic similarity to explicitly measure the deviation of non-native production from its native reference for pronunciation assessment. Specifically, the deviation is first estimated by the cosine similarity between reference phone embedding and corresponding acoustic embedding. Next, a phone-level Goodness of pronunciation (GOP) pre-training stage is introduced to guide this similarity-based learning for better initialization of the aforementioned two embeddings. Finally, a transformer-based hierarchical pronunciation scorer is used to map a sequence of phone embeddings, acoustic embeddings along with their similarity measures to predict the final utterance-level score. Experimental results on the non-native databases suggest that the proposed system significantly outperforms the baselines, where the acoustic and phone embeddings are simply added or concatenated. A further examination shows that the phone embeddings learned in the proposed approach are able to capture linguistic-acoustic attributes of native pronunciation as reference.Comment: Accepted by ICASSP 202

An ASR-free Fluency Scoring Approach with Self-Supervised Learning

A typical fluency scoring system generally relies on an automatic speech recognition (ASR) system to obtain time stamps in input speech for either the subsequent calculation of fluency-related features or directly modeling speech fluency with an end-to-end approach. This paper describes a novel ASR-free approach for automatic fluency assessment using self-supervised learning (SSL). Specifically, wav2vec2.0 is used to extract frame-level speech features, followed by K-means clustering to assign a pseudo label (cluster index) to each frame. A BLSTM-based model is trained to predict an utterance-level fluency score from frame-level SSL features and the corresponding cluster indexes. Neither speech transcription nor time stamp information is required in the proposed system. It is ASR-free and can potentially avoid the ASR errors effect in practice. Experimental results carried out on non-native English databases show that the proposed approach significantly improves the performance in the "open response" scenario as compared to previous methods and matches the recently reported performance in the "read aloud" scenario.Comment: Accepted by ICASSP 202