Reviewerly/exHarmony huggingface dataset:
Evolution of Space Habitat Designs for Commercial Applications

Recent developments in the space sector by state agencies and industries demonstrate that space commerce, driven by scientific and technological advancements, is and will continue impacting many aspects of our personal, institutional and business lives and society in general. Satellite telecommunications companies, supported by commercial launch operations, connect people together globally, offer access that is close to limitless to information sources, allowing growth of industries worldwide. Commercial space activities are already expanding from satellite infrastructures to human spaceflight demanding advanced design solutions for orbital facilities that respond to diverse needs of growing commercial sector. This paper will present trends and tendencies in architectures for commercial space activities based on designs developed at the Sasakawa International Centre for Space Architecture by its graduate students. Some of the discussed work was developed for master thesis studies along with other projects that were sponsored by the industry and agency grants. In the summary, the paper will emphasize critical design aspects, define system architecture related problems and derived from them design requirements. Examples include: Artificial Gravity Science and Excursion Vehicle (AGSEV) (sponsored by Bigelow Aerospace) and related to that project Commercial Space Development Plan, Life and Biological Sciences Orbital Facility (LaBS) (curated by the former astronaut and former Assistant Director to the NASA Johnson Space Center (JSC)), Excalibur-Almaz Commercial station (Excalibur-Almaz), and sponsored Lunar Habitat (Boeing sponsored).
Exploring the Applications of Frequency Modulation to Secure CubeSats from Eavesdropping, Jamming, and Interference

The advancement of space vehicles and the expansion of use-cases for Earth-centered orbits has sparked a proliferation of nanosatellite deployments. Concurrently, offensive non-kinetic weaponry is becoming increasingly accessible and potent. This paper postulates that CubeSats will become a primary target for adversarial cyber and electromagnetic activities (CEMA). Existing obstacles to nanosatellite security engineering are assessed in conjunction with prior attempts to harden nanosatellites from malicious cyber and electronic payloads. A frequency modulation-based architecture is subsequently proposed to safeguard CubeSats from non-kinetic anti-satellite (ASAT) weaponry.
Signal Reconstruction for Secure Imagery Communications

We propose an image reconstruction pipeline that can intelligently reproduce an original image from a fraction of its original pixels. Specifically, we propose using the convolutional neural network U-Net to reconstruct “degraded” imagery to its raw, original form. We define degraded imagery as imagery that has been reduced in context by a defined percentage; essentially, a percentage of pixels are zeroed out of or taken out of the image. We investigated (1) the performance of U-Net for image reconstruction and (2) the effect of increasing levels of image degradation for various trained models and the observation of its effects on the reconstructed images. In this work, all research is simulated, meaning we are not analyzing an application of this method on real systems in real time, but are merely studying the method digitally for its reconstruction ability. The experiments include five trained models, using the U-Net architecture and similar training parameters, which are individually trained on different levels of pixel reduction. The investigation includes both a qualitative and quantitative analysis to display the comparison between the original imagery and the reconstructed imagery. Results show that each trained model can handle different levels of reduction within a range and are able to reconstruct context-limited images (images with greater than 75% of the pixels removed). We have been exploring this technology from the context of communication between collaborating systems. Areas that may be of interest for this technology is additional encryption for cybersecurity of an incoming/outgoing signal and efficient communication-linking between systems. The publicly available xView dataset is used in our experiments and analysis.
Information Extraction from Social Media

Information extraction (IE) is a common sub-area of natural language processing that focuses on identifying structured data from unstructured data. One application domain of IE is Information Retrieval (IR), which relies on accurate and high-performance IE to retrieve high quality results from massive datasets. Another example of IE is to identify named entities in a text. For example, in the the sentence "Katy Perry lives in the USA", Katy Perry and USA are named entities of types of PERSON and LOCATION, respectively. Also, identify the sentiment expressed in a text is another instance of IE: in the sentence, "This movie was awesome", the expressed sentiment is positive. Finally, IE is concerned with identifying various linguistic aspects of text data, e.g., part of speech of words, noun phrases, dependency parses, etc., which can serve as features for additional IE tasks. This tutorial introduces participants to a) the usage of Python based, open-source tools that support IE from social media data (mainly Twitter), and b) best practices for ensuring the responsible use of IE and research data. Participants will learn and practice various lexical, semantic, and syntactic IE techniques that are commonly used for analyzing tweets. Participants will also be familiarized with the landscape of publicly available social media data (including popular NLP and IE benchmarks) and methods for collecting and preparing them for analysis. Furthermore, participants will be trained to use a suite of open source tools (SAIL for active learning, TwitterNER for named entity recognition, TweetNLP for transformer based NLP, and SocialMediaIE for multi task learning), which utilize advanced machine learning techniques (e.g., deep learning, active learning with human-in-the-loop, multi-lingual, and multi-task learning) to perform IE on their own or existing datasets. Participants will also learn how social contexts of text production and usage of results can be integrated into IE systems to improve these systems and to consider the role of time in improving social media IE quality. Finally, participants will learn about the governance of social media data for research purposes. The tools introduced in the tutorial will focus on the three main stages of IE, namely, collection of data (including annotation), data processing and analytics, and visualization of the extracted information. More details can be found at: https://socialmediaie.github.io/tutorials/
Geographical Address Models in the Indian e-Commerce

Unambiguous customer addresses are important for the e-Commerce companies in timely and accurate delivery of shipments. In many developing countries a prescribed structure is not usually followed in practice. It is observed that the customer addresses contain additional text such as instructions to the delivery team. Further not every address has an associated geolocation information in many countries. Thus address understanding, address classification, and clustering of similar but noisy addresses are critical. In addition to last mile delivery solutions, the models help reduce buyer fraud and understanding returns. In view of noisy nature of customer addresses and non-availability of associated geolocation, the above problems are effectively solved with the help of NLP. The proposed talk traces the challenges in the Indian addresses in the context of e-commerce, solution approaches, and their extensions during the last 8 years. The talk is based on the author's own experience, his publications as well as developments in this topic across the industry over these years.
MASR: A Model-Agnostic Sparse Routing Architecture for Arbitrary Order Feature Sharing in Multi-Task Learning

Multi-task learning (MTL) has experienced rapid growth in recent years. A typical way of conducting MTL with deep neural networks (DNNs) is either establishing a sort of global feature sharing mechanism across all tasks or assigning each task an individual set of parameters with cross-connections. However, these existing approaches leverage DNNs only to share features of a certain order. Several modelsdemonstrated that explicitly modeling feature sharing with both low-order and high-order features can boost performance. To this end, we propose a model-agnostic sparse routing architecture called MASR, which emphasizes arbitrary order feature sharing for multi-task learning. It is able to choose specific orders of features to route for a given task through learnable latent variables. Moreover, MASR is model-agnostic and can be combined with existing MTL models to share features of both low-order and high-order. Extensive experimental results on several real-world datasets not only confirm the significant improvement of MASR performed to existing MTL models but also outperform existing hybrid architectures in terms of AUC metric.
JavaScript&amp;Me, A Tool to Support Research into Code Transformation and Browser Security

Doing research into code variations and their applications to browser security is challenging. One of the most important aspects of this research is to choose a relevant dataset on which machine learning algorithms can be applied to yield useful results. Although JavaScript code is widely available on various sources, such as package managers, code hosting platforms, and websites, collecting a large corpus of JavaScript and curating it is not a simple task. We present a novel open-source tool that helps with this task by allowing the automatic and systematic collection, processing, and transformation of JavaScript code. These three steps are performed by independent modules, and each one can be extended to incorporate new features, such as additional code sources, or transformation tools, adding to the flexibility of our tool and expanding its usability. Additionally, we use our tool to create a corpus of around 270k JavaScript files, including regular, minified, and obfuscated code, on which we perform a brief analysis. The conclusions from this analysis show the importance of properly curating a dataset before using it in research tasks, such as machine learning classifiers, reinforcing the relevance of our tool.
KRAF: A Flexible Advertising Framework using Knowledge Graph-Enriched Multi-Agent Reinforcement Learning

Bidding optimization is one of the most important problems in online advertising. Auto-bidding tools are designed to address this problem and are offered by most advertising platforms for advertisers to allocate their budgets. In this work, we present a Knowledge Graph-enriched Multi-Agent Reinforcement Learning Advertising Framework (KRAF). It combines Knowledge Graph (KG) techniques with a Multi-Agent Reinforcement Learning (MARL) algorithm for bidding optimization with the goal of maximizing advertisers' return on ad spend (ROAS) and user-ad interactions, which correlates to the ad platform revenue. In addition, this proposal is flexible enough to support different levels of user privacy and the advent of new advertising markets with more heterogeneous data. In contrast to most of the current advertising platforms that are based on click-through rate models using a fixed input format and rely on user tracking, KRAF integrates the heterogeneous available data (e.g., contextual features, interest-based attributes, information about ads) as graph nodes to generate their dense representation (embeddings). Then, our MARL algorithm leverages the embeddings of the entities to learn efficient budget allocation strategies. To that end, we propose a novel coordination strategy based on a mean-field style to coordinate the learning agents and avoid the curse of dimensionality when the number of agents grows. Our proposal is evaluated on three real-world datasets to assess its performance and the contribution of each of its components, outperforming several baseline methods in terms of ROAS and number of ad clicks.
Discriminative Language Model via Self-Teaching for Dense Retrieval

Dense retrieval (DR) has shown promising results in many information retrieval (IR) related tasks, whose foundation is high-quality text representations for effective search. Taking the pre-trained language models (PLMs) as the text encoders has become a popular choice in DR. However, the learned representations based on these PLMs often lose the discriminative power, and thus hurt the recall performance, particularly as PLMs consider too much content of the input texts. Therefore, in this work, we propose to pre-train a discriminative language representation model, called DiscBERT, for DR. The key idea is that a good text representation should be able to automatically keep those discriminative features that could well distinguish different texts from each other in the semantic space. Specifically, inspired by knowledge distillation, we employ a simple yet effective training method, called self-teaching, to distill the model's knowledge constructed when training on the sampled representative tokens of a text sequence into the model's knowledge for the entire text sequence. By further fine-tuning on publicly available retrieval benchmark datasets, DiscBERT can outperform the state-of-the-art retrieval methods.
Intent Disambiguation for Task-oriented Dialogue Systems

Task-Oriented Dialogue Systems (TODS) have been widely deployed for domain specific virtual assistants at contact centres to route customers' calls or deliver information needs of the customer in a conversational interaction. TODS employ natural language understanding components in order to map user commands to a set of pre-defined intents. However, Contact Centre users often fail to formulate their complex information needs in a single utterance which leads to formulating ambiguous user commands. This can negatively impact intent classification, and consequently customer satisfaction. To avoid feeding ambiguous user commands to the intent classifier of virtual assistants and help users in formulating their commands, we have implemented a solution that (1) identifies when a user is ambiguous and the virtual assistant should ask a clarification question, (2) disambiguates the user command and provides top-N most likely intents in a form of a clarification question. Our experimental result shows that our proposed intent disambiguation solution has a statistically significant improvement over a popularity based intent disambiguation model and an Intent Ranking Model of the Natural Language Understanding engine for a virtual assistant of a contact centre in terms of intent disambiguation accuracy and Mean Reciprocal Rank.
MomNet: Gender Prediction using Mechanism of Working Memory

In social media analysis, gender prediction is one of the most important tasks of user profiling. Web users often post messages in a timeline manner to record their living moments. These messages containing texts and images, constitute long multi-modal data that potentially represents the living style, preference, or opinion regarding users. Therefore, it is feasible to predict the gender of a user by utilizing such living moments. However, the rich modalities (time, length, text, and image) of living moments with difficult challenges have not been fully exploited by the research communities for practical applications. To this end, we propose a novel gender prediction framework based on user-posted living Moments MomNet). The MomNet mainly consists of a moment memory module and a central executive module inspired by the two characteristics of working memory theory. One is that humans can associate related information to facilitate memory. Our moment memory module aggregates similar uni-modal moments of a user to form different chunks and encode the chunks into moment memory representations. The other is that humans coordinate information from different modalities to make judgments. Our central executive module is designed to coordinate comprehensive attentions of moment memory representations from texts, images, and their combinations. Finally, a softmax classifier is used to predict gender. Extensive experiments conducted on a real-world public dataset show that our framework achieves 86.63% accuracy and outperforms all state-of-the-art methods in terms of accuracy.
A Multi-Interest Evolution Story

The query-based recommendation now is becoming a basic research topic in the e-commerce scenario. Generally, given a query that a user typed, it aims to provide a set of items that the user may be interested in. In this task, the customer intention (i.e., browsing or purchase) is an important factor to configure the corresponding recommendation strategy for better shopping experiences (i.e., providing diverse items when the user prefers to browse or recommending specific items when detecting the user is willing to purchase). Though necessary, this is usually overlooked in previous works. In addition, the diversity and evolution of user interests also bring challenges to inferring user intentions correctly.
LGP: Few-Shot Class-Evolutionary Learning on Dynamic Graphs

Graph few-shot learning aims to learn how to quickly adapt to new tasks using only a few labeled data, which transfers learned knowledge of base classes to novel classes. Existing methods are mainly designed for static graphs, while many real-world graphs are dynamic and evolving over time, resulting in a phenomenon of structure and class evolutions. To address the challenges caused by the phenomenon, in this paper, we propose a novel algorithm named Learning to Generate Parameters (LGP) to deal with few-shot class-evolutionary learning on dynamic graphs. Specifically, for the structure evolution, LGP integrates ensemble learning into a backbone network to effectively learn invariant representation across different snapshots within a dynamic graph. For the class evolution, LGP adopts a meta-learning strategy that can learn to generate the classified parameters of novel classes via the parameters of the base classes. Therefore, LGP can quickly adapt to new tasks on a combination of base and novel classes. Besides, LGP utilizes an attention mechanism to capture the evolutionary pattern between the novel and based classes. Extensive experiments on a real-world dataset demonstrate the effectiveness of LGP.
NEST

We outline a simulation-based study of the effect rapid population-scale concept drifts have on Collaborative Filtering (CF) models. We create a framework for analyzing the effects of macro-trends in population dynamics on the behavior of such models. Our framework characterizes population-scale concept drifts in item preferences and provides a lens to understand the influence events, such as a pandemic, have on CF models. Our experimental results show the initial impact on CF performance at the initial stage of such events, followed by an aggravated population herding effect during the event. The herding introduces a popularity bias that may benefit affected users, but which comes at the expense of a normal user experience. We propose an adaptive ensemble method that can effectively apply optimal algorithms to cope with the change brought about by different stages of the event.
Utilizing Contrastive Learning To Address Long Tail Issue in Product Categorization

Neural network models trained in a supervised learning way have become dominant. Although high performances can be achieved when training data is ample, the performance on labels with sparse training instances can be poor. This performance drift caused by imbalanced data is named as long tail issue and impacts many NN models used in reality. In this talk, we will firstly review machine learning approaches addressing the long-tail issue. Next, we will report on our effort on applying one recent LT-addressing method on the item categorization (IC) task that aims to classify product description texts into leaf nodes in a category taxonomy tree. In particular, we adopted a new method, which consists of decoupling the entire classification task into (a) learning representations using the K-positive contrastive loss (KCL) and (b) training a classifier on balanced data set, into IC tasks. Using SimCSE to be our self-learning backbone, we demonstrated that the proposed method works on the IC text classification task. In addition, we spotted a shortcoming in the KCL: false negative (FN) instances may harm the representation learning step. After eliminating FN instances, IC performance (measured by macro-F1) has been further improved.
Multi-Aggregator Time-Warping Heterogeneous Graph Neural Network for Personalized Micro-Video Recommendation

Micro-video recommendation is attracting global attention and becoming a popular daily service for people of all ages. Recently, Graph Neural Networks-based micro-video recommendation has displayed performance improvement for many kinds of recommendation tasks. However, the existing works fail to fully consider the characteristics of micro-videos, such as the high timeliness of news nature micro-video recommendation and sequential interactions of frequently changed interests. In this paper, a novel Multi-aggregator Time-warping Heterogeneous Graph Neural Network (MTHGNN) is proposed for personalized news nature micro-video recommendation based on sequential sessions, where characteristics of micro-videos are comprehensively studied, users' preference is mined via multi-aggregator, the temporal and dynamic changes of users' preference are captured, and timeliness is considered. Through the comparison with the state-of-the-arts, the experimental results validate the superiority of our MTHGNN model.
Leveraging Automated Search Relevance Evaluation to Improve System Deployment

Over the last year, a digital initiative has been focused on reengineering the search engine for kp.org, a health web portal serving over 12 million members. However, traditional software testing techniques that rely on limited use cases and consistent behavior are neither comprehensive nor specific for capturing complex user search behaviors. To support system deployment, we utilize information retrieval (IR) technologies to monitor search performance, identify areas of improvement and suggest actionable items. In this case study we share industrial experience on building an IR evaluation pipeline and its usage to inform deployment and improve system development. The work emphasizes domain specific challenges, best practices and lessons learned during system deployment in a healthcare setting. It features the ability of IR techniques to strengthen collaboration between data scientists, software engineers and product managers in making data-driven decisions.
Handling RDF Streams

RDF stream processing (RSP) has become a vibrant area of research in the Semantic Web community, which guarantees interoperability and opens up important applications. There have been efforts to extend RDF data and SPARQL query for representing streaming information and continuous querying functionalities. However, existing solutions will incur significant low throughput due to the recomputation of the results from scratch as the window slides. In this paper, we propose a novel graph-based framework, referred as IncTreeRDF, towards continuous SPARQL query evaluation over RDF data streams. Under the framework, the RDF data streams are modeled as streaming graphs; the SPARQL queries are translated into graph patterns and evaluated via continuous sub-graph pattern-matching over streaming RDF graphs. IncTreeRDF employs a query-centric auxiliary data structure called TStore to store some intermediate results, which supports fast incremental maintenance. Based on TStore, we can not only avoid re-computing matches of the query but also prune invalid updates. Besides, we define matching-free update, in which subgraph matching calculation can be avoided under this scenario. Extensive experimental results show that IncTreeRDF significantly outperforms existing competitors.