Reviewerly/exHarmony huggingface dataset:
Interpreting Patient Descriptions using Distantly Supervised Similar Case Retrieval

Biomedical natural language processing often involves the interpretation of patient descriptions, for instance for diagnosis or for recommending treatments. Current methods, based on biomedical language models, have been found to struggle with such tasks. Moreover, retrieval augmented strategies have only had limited success, as it is rare to find sentences which express the exact type of knowledge that is needed for interpreting a given patient description. For this reason, rather than attempting to retrieve explicit medical knowledge, we instead propose to rely on a nearest neighbour strategy. First, we retrieve text passages that are similar to the given patient description, and are thus likely to describe patients in similar situations, while also mentioning some hypothesis (e.g.\ a possible diagnosis of the patient). We then judge the likelihood of the hypothesis based on the similarity of the retrieved passages. Identifying similar cases is challenging, however, as descriptions of similar patients may superficially look rather different, among others because they often contain an abundance of irrelevant details. To address this challenge, we propose a strategy that relies on a distantly supervised cross-encoder. Despite its conceptual simplicity, we find this strategy to be effective in practice.
Interactive Model of Rural Tourism and New Socialist Countryside Construction Using Deep Learning Technology

China has proposed two major measures to address the "three rural issues": the first is to abolish the agricultural tax, which has been in place for over 2000 years; the second is to propose the construction of a new socialist countryside, which would mark the end of the old era and the beginning of the new. As a result, this paper employs in-depth learning technology to enhance rural tourism development and the creation of a new socialist countryside. This paper investigates deep-learning-based rural tourism and the creation of a new socialist countryside. Because MSE and MAE reflect the prediction error score, the lower the value, the better the recommendation accuracy. The MSE value of the machine learning algorithm is 2.456, the MSE value of the data mining algorithm is 2.324, and the MSE value of the convolution neural network algorithm is 2.102, when the number reaches 80. It can be concluded that the convolution neural network algorithm proposed in this paper has the lowest MSE and MAE values of the three methods, implying that the convolution neural network algorithm is the best of the three. The use of the convolution neural network algorithm to implement the scientific concept of development and the construction of a new socialist countryside is an important part of creating a harmonious society that fully meets the central government's objectives and requirements for the construction of a new socialist countryside.
Feasibility of Central Bank Digital Currency for Blockchain-Based Zakat in Indonesia

Distrust to zakat institutions is one of the causes zakat funds not reaching the potential amount in Indonesia. Blockchain technology, with its capability of tracing, can provide transparency information. The Blockchain-based Zakat can potentially overcome distrust issues to zakat institutions. However, the legal status of cryptocurrency is still skeptical. This study aims to see the feasibility of CBDC can be implemented in blockchain-based Zakat in Indonesia. The method used in this research is the qualitative method. An extensive literature review method was utilized to identify and analyze the relevant literature in order to propose the model. This study suggests Blockchain-based Zakat with the use of CBDC and BAZNAS as the zakat agencies.
Facilitating Development of Higher Education Informatization Using Blockchain Technology

The development of education informatization is crucial to improve the quality of higher education and promote the innovation of education, thus to cultivate innovative talents that meet the requirements of the modern information society. However, the current education informatization construction still faces problems such as insufficient top-level design, unconnected data sharing, and risks of privacy leakage. To this end, this article combines the emerging blockchain technology to discuss the positive function of blockchain in the establishment of education informatization, the possible opportunities for education informatization in the future, and some important challenges that will be faced. Specifically, from the perspective of higher education information system, we analyze the crucial issues in the current information system architecture including postgraduate training, teaching management, supervisor management, project information management, etc., and present the security and scalability issues of the system. On this basis, we propose the potential solutions based on blockchain and analyze the positive effects brought by blockchain technology in terms of security protection and data sharing. Finally, we summarize several major future issues that still need to be overcome in the convergence of blockchain and informatization of higher education.
Event‐sourced, observable software architectures: An experience report

Abstract The speeding growth of the IT market and the spreading of disruptive technologies are leading towards more and more risky operations in need of constant upkeep, monitoring as well as proactive orchestration. On the one hand, the property allowing a system to be catered by automated monitoring and healing technology is defined as observability . On the other hand, appropriate design principles to manifest observability were originally referred as event sourcing by its inventor Martin Fowler and warrant for the aforementioned sustainable software operations. Both event sourcing and observability are complex to leverage on and design for. In an effort to understand more on both concepts, we offer an experience report on their practical use, featuring: (1) a rigorous definition of software architecture observability and a set of principles to design for observability using augmented forms of well‐known design patterns in line with event sourcing; and (2) an impact analysis in the context of a case study. Our study reveals several interesting notions around the concept of observability but our findings also make explicit new architecture trade‐offs that software architects and stakeholders need to consider as first‐class architecture‐level concerns.
Platform on Online Learning for Students with Mental Retardation

This study discusses the use of platforms in online learning for students with special needs. Students with special needs have participated in online learning in the last 2 years following government policies regarding the implementation of learning during the pandemic. The learning process certainly has its own challenges because of the different conditions of the students. Various adjustments that must be made are constrained by existing limitations. The rapid development of various learning platforms does not seem to have much contribution in the implementation of learning for students with special needs. This research was conducted in several schools in Java which represent the research topic. Data were collected through interviews with 3 groups of informants consisting of teachers, parents, and students. The data is supported by the results of observations and documentation of the implementation of online learning which is followed by students with special needs. The results show that in general there are 3 platforms used in online learning for students with special needs. The platforms used are Whatsapp„ Google Workspace, and Youtube. the three platforms are used in the process of delivering material, storing and categorizing learning documents, and used in the learning evaluation process. The selection of the platform was based on several considerations, namely: it is familiar to students, easy to use, and does not require a large quota.&#x0D; Keywords: Platform; Online Learning; Student with Special Needs
Personalized Abstractive Opinion Tagging

An opinion tag is a sequence of words on a specific aspect of a product or service. Opinion tags reflect key characteristics of product reviews and help users quickly understand their content in e-commerce portals. The task of abstractive opinion tagging has previously been proposed to automatically generate a ranked list of opinion tags for a given review. However, current models for opinion tagging are not personalized, even though personalization is an essential ingredient of engaging user interactions, especially in e-commerce. In this paper, we focus on the task of personalized abstractive opinion tagging. There are two main challenges when developing models for the end-to-end generation of personalized opinion tags: sparseness of reviews and difficulty to integrate multi-type signals, i.e., explicit review signals and implicit behavioral signals. To address these challenges, we propose an end-to-end model, named POT, that consists of three main components: (1) a review-based explicit preference tracker component based on a hierarchical heterogeneous review graph to track user preferences from reviews; (2)a behavior-based implicit preference tracker component using a heterogeneous behavior graph to track the user preferences from implicit behaviors; and (3) a personalized rank-aware tagging component to generate a ranked sequence of personalized opinion tags. In our experiments, we evaluate POT on a real-world dataset collected from e-commerce platforms and the results demonstrate that it significantly outperforms strong baselines.
College Student Social Dynamic Analysis and Educational Mechanism Using Big Data Technology

With the advancement of the "big data" technology, college students inadvertently purchase personal advice while taking advantage of the exciting Internet to access information quickly and easily. In order to objectively achieve the real office of college students' material enlightenment penetration in the mobile-friendly network, we choose the popular mobile social network, and we apply the natural clustering algorithm rules to segment the college students. Further, we identify college students, based on which we construct information leakage and apply the risk assessment design. The comprehensive entrepreneurial evaluation of the microblog platform combined with the user's mobile complaints is utilized to conduct a psychological analysis on the key components and key communication channels of college students' complaint leakage. We obtain ticket data using the social prospect method and refer to four dogmatic characteristic elements of query motivation. And we also collect dimensions through surrogate analysis. Based on the reference feature factor, four different user groups are rapidly moved. Dining, social, teaching and large users, and the model features of various usage profiles are described in combination with eight categories of user feature importance. The |objective is to improve college students' awareness of social network and mobile partner network mobile information to a certain extent. We attempt to protect college students from fraud and installation plans, to standardize the management of online social platform of advertisements, and to progressively promote the disposal of network movable property due to security changes.
Fine-grained modeling and optimization for intelligent resource management in big data processing

Big data processing at the production scale presents a highly complex environment for resource optimization (RO), a problem crucial for meeting performance goals and budgetary constraints of analytical users. The RO problem is challenging because it involves a set of decisions (the partition count, placement of parallel instances on machines, and resource allocation to each instance), requires multi-objective optimization (MOO), and is compounded by the scale and complexity of big data systems while having to meet stringent time constraints for scheduling. This paper presents a MaxCompute-based integrated system to support multi-objective resource optimization via fine-grained instance-level modeling and optimization. We propose a new architecture that breaks RO into a series of simpler problems, new fine-grained predictive models, and novel optimization methods that exploit these models to make effective instance-level recommendations in a hierarchical MOO framework. Evaluation using production workloads shows that our new RO system could reduce 37-72% latency and 43-78% cost at the same time, compared to the current optimizer and scheduler, while running in 0.02-0.23s.
Golden Retriever

In this work, we present the Golden Retriever, a system leveraging state-of-the-art visio-linguistic models (VLMs) for real-time text-image retrieval. The unique feature of our system is that it can focus on words contained in the textual query, i.e., locate and high-light them within retrieved images. An efficient two-stage process implements real-time capability and the ability to focus. Therefore, we first drastically reduce the number of images processed by a VLM. Then, in the second stage, we rank the images and highlight the focussed word using the outputs of a VLM. Further, we introduce a new and efficient algorithm based on the idea of TF-IDF to retrieve images for short textual queries. One of multiple use cases where we employ the Golden Retriever is a language learner scenario, where visual cues for "difficult" words within sentences are provided to improve a user's reading comprehension. However, since the backend is completely decoupled from the frontend, the system can be integrated into any other application where images must be retrieved fast. We demonstrate the Golden Retriever with screenshots of a minimalistic user interface.
Why do Semantically Unrelated Categories Appear in the Same Session?

Session-based recommendation has recently attracted more and more research efforts. Most existing approaches are intuitively proposed to discover users' potential preferences or interests from the anonymous session data. This apparently ignores the fact that these sequential behavior data usually reflect session user's potential demand, i.e., a semantic level factor, and therefore how to estimate underlying demands from a session has become a challenging task. To tackle the aforementioned issue, this paper proposes a novel demand-aware graph neural network model. Particularly, a demand modeling component is designed to extract the underlying multiple demands of each session. Then, the demand-aware graph neural network is designed to first construct session demand graphs and then learn the demand-aware item embeddings to make the recommendation. The mutual information loss is further designed to enhance the quality of the learnt embeddings. Extensive experiments have been performed on two real-world datasets and the proposed model achieves the SOTA model performance.
Using Recommender Systems to Help Revitalize Local News

American local newspapers have been experiencing a large loss of reader retention and business within the past 15 years due to the proliferation of online news sources and social media. This has led to a disturbing trend where local journalism and local news outlets are being forced out of business often leaving whole communities without a key source of credible information. This trend has a potentially broad societal impact as these key anchors of local trust and democracy are slowly becoming extinct. Local media companies are starting to shift from an advertising-supported business model to one based on subscriptions to mitigate their financial crises. But with strong competition from a variety of online news sources, these companies need to increase user engagement by providing significant added value. Providing more personalized content in the local context may be one way that these companies can succeed in this effort. Recommender system technologies are the primary enabling mechanisms for delivering such personalized content. However, using standard machine learning models that focus on users’ global preferences is not appropriate in this context because the local preferences of users exhibit some specific characteristics which do not necessarily match their long-term or global preferences in the news. The overall goal of this research is to develop predictive models that more effectively derive user engagement through automatic personalization. Effective recommender systems may be among the tools that can help reverse the current decline of interest in local news sources. Our research explores approaches to learning localized models from user interaction data with news articles, particularly in news categories where there is intense local interest and there is a significant difference between users’ global and local news preferences. Specifically, we propose using such localized models in a session-based recommender system where the system can switch between users’ global and local preference models automatically when warranted. We report experiments performed on a news dataset from a local newspaper show that these local models, particularly the Life-and-Culture news category, do indeed provide more accuracy and effectiveness for personalization which, in turn, may lead to more user engagement with local news content.
An End-to-End Classifier Based on CNN for In-Air Handwritten-Chinese-Character Recognition

A convolutional neural network (CNN) has been successfully applied to in-air handwritten-Chinese-character recognition (IAHCCR). However, the existing models based on CNN for IAHCCR need to convert the coordinate sequence of a character into images. This conversion process increases training and classifying time, and leads to the loss of information. In order to solve this problem, we propose an end-to-end classifier based on CNN for IAHCCR in this paper, which, to knowledge, is novel for online handwritten-Chinese-character recognition (OLHCCR). Specifically, our model based on CNN directly takes the original coordinate sequence of an in-air handwritten-Chinese-character as input, and the output of the full connection layer is pooled by global average pooling to form a fixed-size feature vector, which is sent to softmax for classification. Our model can not only directly process coordinate sequences such as the models based on recurrent neural network (RNN), but can also obtain the global structure information of characters. We conducted experiments on two datasets, IAHCC-UCAS2016 and SCUT-COUCH2009. The experimental results show a comparison with existing CNN models based on image processing or RNN-based methods; our method does not require data augmentation techniques nor an ensemble of multiple trained models, and only uses a more compact structure to obtain higher recognition accuracy.
Item Similarity Mining for Multi-Market Recommendation

Real-world web applications such as Amazon and Netflix often provide services in multiple countries and regions (i.e., markets) around the world. Generally, different markets share similar item sets while containing different amounts of interaction data. Some markets are data-scarce and others are data-rich and leveraging those data from similar and data-rich auxiliary markets could enhance the data-scarce markets. In this paper, we explore multi-market recommendation (MMR), and propose a novel model called M$^3$Rec to improve all markets recommendation simultaneously. Since items play the role to bridge different markets, we argue that mining the similarities among items is the key point of MMR. Our M^3Rec preprocess two global item similarities: intra- and inter- market similarities. Specifically, we first learn the second-order intra-market similarity by adopting linear models with closed-form solutions, and then capture the high-order inter-market similarity by the random walk. Afterward, we incorporate the global item similarities for each local market. We conduct extensive experiments on five public available markets and compare with several state-of-the-art methods. Detailed experimental results demonstrate the effectiveness of our proposed method.
Learning to Rank Instant Search Results with Multiple Indices

At Xfinity, an instant search system provides a variety of results for a given query from different sources. For each keystroke, new results are rendered on screen to the user, which could contain movies, television series, sporting events, music videos, news clips, person pages, and other result types. Users are also able to use the Xfinity Voice Remote to submit longer queries, some of which are more open-ended. Examples of queries include incomplete words which match multiple results through lexical matching (i.e., "ali"), topical searches ("vampire movies"), and more specific longer searches ("Movies with Adam Sandler"). Since results can be based on lexical matches, semantic matches, item-to-item similarity matches, or a variety of business logic driven sources, a key challenge is how to combine results into a single list. To accomplish this, we propose merging the lists via a Learning to Rank (LTR) neural model which takes into account the search query. This combined list can be personalized via a second LTR neural model with knowledge of the user's search history and metadata of the programs. Because instant search is under-represented in the literature, we present our learnings from research to aid other practitioners.
Educational Soundings: a Cryptogenealogy of Sounding Differently

This essay presents an imaginative ‘cryptogenealogy’ of four different educational soundscapes spanning several thousand years of Western history. It begins with a return to the Odysseus myth, and in particular Odysseus’s sonic struggle with the Sirens. The article then charts the echoes of this foundational image through two educational spaces—the study and the classroom—as they appear in the Western tradition of educational thinking. Grounding the analysis in historical documents by Comenius and Vives, the essay demonstrates how educational soundscapes are haunted by Odysseus, and in particular themes of embodiment and disembodiment, location and dislocation. The cryptogenealogy then culminates with a turn to the postdigital, which brings disembodiment and displacement to their absolute dominance, and how these sonic tendencies might be interrupted to produce a new educational acoustics beyond the sacrifices traced throughout the cryptogenealogy.
Towards Multi-Method Support for Product Search and Recommending

Today, online shops offer a variety of components to support users in finding suitable items, ranging from filters and recommendations to conversational advisors and natural language chatbots. All these methods differ in terms of cognitive load and interaction effort, and, in particular, in their suitability for the specific user. However, it is often difficult for users to determine which method to use to reach their goal. Moreover, as the settings are not propagated between the methods, there is a lack of support for switching components. In this paper, we study the reasons for using the different components in more detail and present an initial proposal for a multi-method approach that provides a more seamless experience, allowing users to freely and flexibly choose from all available methods at any time.