Reviewerly/exHarmony huggingface dataset:
Towards Understanding and Characterizing the Arbitrage Bot Scam In the Wild

This paper presents the first comprehensive analysis of an emerging cryptocurrency scam named "arbitrage bot" disseminated on online social networks. The scam revolves around Decentralized Exchanges (DEX) arbitrage and aims to lure victims into executing a so-called "bot contract" to steal funds from them. To entice victims and convince them of this scheme, we found that scammers have flocked to publish YouTube videos to demonstrate plausible profits and provide detailed instructions and links to the bot contract. To collect the scam at a large scale, we developed a fully automated scam detection system namedCryptoScamHunter, which continuously collects YouTube videos and automatically detects scams. Meanwhile,CryptoScamHunter can download the source code of the bot contract from the provided links and extract the associated scam cryptocurrency address. Through deployingCryptoScamHunter from Jun. 2022 to Jun. 2023, we have detected 10,442 arbitrage bot scam videos published from thousands of YouTube accounts. Our analysis reveals that different strategies have been utilized in spreading the scam, including crafting popular accounts, registering spam accounts, and using obfuscation tricks to hide the real scam address in the bot contracts. Moreover, from the scam videos we have collected over 800 malicious bot contracts with source code and extracted 354 scam addresses. By further expanding the scam addresses with a similar contract matching technique, we have obtained a total of 1,697 scam addresses. Through tracing the transactions of all scam addresses on the Ethereum mainnet and Binance Smart Chain, we reveal that over 25,000 victims have fallen prey to this scam, resulting in a financial loss of up to 15 million USD. Overall, our work sheds light on the dissemination tactics and censorship evasion strategies adopted in the arbitrage bot scam, as well as on the scale and impact of such a scam on online social networks and blockchain platforms, emphasizing the urgent need for effective detection and prevention mechanisms against such fraudulent activity.
A Study of Sentiment Analysis based on Specific 6-emotion Category for Thai Language

Social media such as Twitter, Facebook, Web logs and Review sites are indispensable tools for our customers’ communication sites. From a business perspective, it is important to improve customer satisfaction and customer insights by capturing and analyzing customer emotions in detail through social media, customer feedback from call centers, and questionnaire analysis. This paper presents an effective classification method for Thai. In order to solve the problem of linguistic difficulty in Thai, this method used sentiment analysis using 6-emotion as an aspect-based analysis method in addition to conventional sentiment analysis such as positive and negative. This paper describes the results of evaluating the usefulness of the 6-emotion analysis in helping to judge positive and negative in Thai sentences.
PD-PAn: Prefix- and Distribution-Preserving Internet of Things Traffic Anonymization

One of the features of network traffic in Internet of Things (IoT) environments is that various IoT devices periodically communicate with their vendor services by sending and receiving packets with unique characteristics through private protocols. This paper investigates semantic attacks in IoT environments. An IoT semantic attack is active, covert, and more dangerous in comparison with traditional semantic attacks. A compromised IoT server actively establishes and maintains a communication channel with its device, and covertly injects fingerprints into the communicated packets. Most importantly, this server not only de-anonymizes other IPs, but also infers the machine states of other devices (IPs). Traditional traffic anonymization techniques, e.g., Crypto-PAn and Multi-View, either cannot ensure data utility or is vulnerable to semantic attacks. To address this problem, this paper proposes a prefix- and distribution-preserving traffic anonymization method named PD-PAn, which generates multiple anonymized views of the original traffic log to defend against semantic attacks. The prefix relationship is preserved in the real view to ensure data utility, while the IP distribution characteristic is preserved in all the views to ensure privacy. Intensive experiments verify the vulnerability of the state-of-the-art techniques and effectiveness of PD-PAn.
The New Educational Models Based on Information and Communication Technology and Education 4.0

Technological advancement has had a positive impact on many industrial areas and various service sectors. Education is among these benefited sectors, due to the innovative processes that have been implemented in recent years, the technologies combined with the pedagogical, have taken an important turn in the way of teaching and learning. Information and communication technology (ICT) and Education 4.0 have had their repercussions on the educational system, which has allowed in recent years to see the transformation and development of innovative educational models using technological tools and resources to contribute to the teaching process-learning. In this research, a systematic review of the literature was carried out, to know the new educational models that have emerged based on ICT and Education 4.0, as dynamic resources within the educational system, which come to innovate the way of learning, understand, order, and undertake the processes necessary to achieve meaningful learning. These new models respond to the social and educational demands and transformations, such as what was experienced in the pandemic, where confinement opened the way for technology to provide school continuity, generating new challenges and challenges, but allowing digital tools to respond to the existing need.
Farm Friendly Chat Bot

This study presents the development of a chat room and a chatbot designed to facilitate discussions on prevalent farming issues among peers and experts. Its primary aim is to provide timely support to farmers in making informed decisions about their farming practices. To create a structured framework for these conversations, a standardized set of questions was formulated through consultations and surveys involving farmers, experts, and other stakeholders. The questions were analyzed to extract 'intents,' representing the specific information or assistance users might seek, and 'examples,' which are concrete instances users provide to express their particular intent. Additionally, 'entities' were identified to represent distinct objects or concepts related to these intents. The model was trained using the Artificial Intelligence Markup Language (AIML) to predict the intent based on the provided examples. This training process enhances the chatbot's ability to understand and respond to user queries effectively. Furthermore, the chatbot was deployed on a cloud platform, reducing the computational resources required on the client end. This approach ensures accessibility and usability for a broader user base without significant hardware constraints.
Theoretical Considerations and Applications for Social Robots in Virtual Reality

While Virtual Reality (VR) has been used by researchers to investigate human-robot interaction (HRI), these efforts were largely focused on investigating HRI with industrial robots. In this current paper, we identify this gap in the literature and suggest the benefits and the limitations of investigating HRI in VR, and offer use cases for interacting with social robots in the VR space. We identify some use cases where interacting with a social robot in VR may be beneficial over interacting with a human in VR, and offer future directions and applications for this topic.
Looking at Participant Engagement for Product Design Through a Critical Lens

Human factors focus on taking the users’ capabilities, limitations, and environment into consideration when developing products. Thus, it is essential to have diverse perspectives and voices when designing products to be used by a variety of users. However, this is not always done and can be a missed opportunity in developing inclusive products. In this panel, we bring together researchers from different sectors to discuss challenges and strategies to engage a diverse research population at different stages in the product design process. Topics include research planning and the design process; data collection methods; and community- and participant-level recruitment. We hope that by sharing our experiences, we can prepare others to have the conversations needed that will allow them to successfully approach these topics.
Contrastive Keyword Extraction from Versioned Documents

Versioned documents are common in many situations and play a vital part in numerous applications enabling an overview of the revisions made to a document or document collection. However, as documents increase in size, it gets difficult to summarize and comprehend all the changes made to versioned documents. In this paper, we propose a novel research problem of contrastive keyword extraction from versioned documents, and introduce an unsupervised approach that extracts keywords to reflect the key changes made to an earlier document version. In order to provide an easy-to-use comparison and summarization tool, an open-source demonstration is made available which can be found at https://contrastive-keyword-extraction.streamlit.app/
IUI: Intent-Enhanced User Interest Modeling for Click-Through Rate Prediction

Click-Through Rate (CTR) prediction is becoming increasingly vital in many industrial applications, such as recommendations and online advertising. How to precisely capture users' dynamic and evolving interests from previous interactions (e.g., clicks, purchases, etc.) is a challenging task in CTR prediction. Mainstream approaches focus on disentangling user interests in a heuristic way or modeling user interests into a static representation. However, these approaches overlook the importance of users' current intent and the complex interactions between their current intent and global interests. To address these concerns, in this paper, we propose a novel intent-enhanced user interest modeling for click-through rate prediction in large-scale e-commerce recommendations, abbreviated as IUI. Methodologically, different from existing works, we consider users' recent interactions to be inspired by their implicit intent and then leverage an intent-aware network to model their current local interests in a more precise and fine-grained manner. In addition, to obtain a more stable co-dependent global and local interest representation, we employ a co-attention network capable of activating the corresponding interest in global-level interactions and capturing the dynamic interactions between global- and local-level interaction behaviors. Finally, we incorporate self-supervised learning into the model training by maximizing the mutual information between the global and local representations obtained via the above two networks to enhance the CTR prediction performance. Compared with existing methods, IUI benefits from the different granularity of user interest to generate a more accurate and comprehensive preference representation. Experimental results demonstrate that the proposed model outperforms previous state-of-the-art methods in various metrics on three real-world datasets. In addition, an online A/B test deployed on the JD recommendation platforms shows a promising improvement across multiple evaluation metrics.
Interpretable Natural Language Understanding

In recent years, we have witnessed the shift of paradigms in Natural Language Processing (NLP) from fine-tuning large-scale pre-trained language models (PLMs) on task-specific data to prompt-based learning. In the latter, the task description is embedded into the PLM input, enabling the same model to handle multiple tasks. While both approaches have demonstrated impressive performance in various NLP tasks, their opaque nature makes comprehending their inner workings and decision-making processes challenging for humans.
Workshop on Enterprise Knowledge Graphs using Large Language Models

Knowledge graphs are used for organizing and connecting individual entities to integrate the information extracted from different data sources. Typically, knowledge graphs are used to connect various real-world entities like persons, places, things, actions, etc. For the knowledge graphs created using the enterprise data, the knowledge graph entities can be of different types-static entities (e.g., people, projects), communication entities (e.g., emails, meetings, documents), derived entities (e.g., rules, definitions, entities from emails), etc. The graphs are used to connect these entities with enriched context (as edges and node attributes) and used for powering various search and recommendations applications.
Knowledge Graphs for Knowing More and Knowing for Sure

Knowledge graphs have been conceived to collect heterogeneous data and knowledge about large domains, e.g. medical or engineering domains, and to allow versatile access to such collections by means of querying and logical reasoning. A surge of methods has responded to additional requirements in recent years. (i) Knowledge graph embeddings use similarity and analogy of structures to speculatively add to the collected data and knowledge. (ii) Queries with shapes and schema information can be typed to provide certainty about results. We survey both developments and find that the development of techniques happens in disjoint communities that mostly do not understand each other, thus limiting the proper and most versatile use of knowledge graphs.
Comparing Fine-Tuned Transformers and Large Language Models for Sales Call Classification: A Case Study

We present a research project carried out to enable a Call Categorization Service (CCS) for Dynamics 365 Sales Conversation Intelligence. CCS identifies prevalent types of sales calls based on their transcription, for the purpose of automating manual sales processes and making informed business decisions. We sift through R&D process, and provide clear evidence that purpose-focused fine-tuned transformers out-perform GPT-3 in this text classification task. Additionally we share: an efficient, non-trivial data annotation approach suited to the problem of finding data related to rare categories in a highly unbalanced data source; Considerations regarding zero-shot and in-context learning (i.e. few-shot learning) when using LLMs for classification and cost and performance analysis that opt in favor of fine-tuned transformers as well.
Vigil: Effective End-to-end Monitoring for Large-scale Recommender Systems at Glance

The success of large-scale recommender systems hinges upon their ability to deliver accurate and timely recommendations to a diverse user base. At Glance, we deliver snackable personalized content to the lock screens of 200M smartphones. In this context, continuous monitoring is paramount as it safeguards data integrity, detects drifts, addresses evolving user preferences, optimizes system downtime, and ultimately augments the system's effectiveness and user satisfaction. In this talk, we delve into Vigil, a set of monitoring practices developed to provide comprehensive end-to-end monitoring of recommender systems at Glance. These practices revolve around three key pillars: mitigating developer fatigue, ensuring precise predictions, and establishing a centralized monitoring framework. By adopting these practices, we have observed a 30% reduction in compute cost, a 26% drop in downtime, and a surge in developer productivity demonstrated by a 45% decrease in turnaround time.
The impact of organizational service climate on customer experience An exploratory study of the opinions of a sample of workers in travel and tourism companies in Baghdad Governorate

The current research aims to identify the nature of the relationship between the organizational service climate and the customer experience, as the research attempts to address a realistic problem represented in the companies’ need to create a positive customer experience. By distributing a questionnaire to the customers of travel and tourism companies in the province of Baghdad, it obtained (82) responses that were analyzed using the advanced statistical program Smart PLS as well as the statistical program SPSS v 25. The customer is in the company, and the research recommends a set of recommendations, the most important of which is that the company in question should do everything that would enhance the positive climates among the workers because it would bring many benefits to the company, including sharing knowledge and providing assistance formally and informally, as well as creating a positive experience for the customer.
RT2S: A Framework for Learning with Noisy Labels

We introduce Robust Training with Trust Scores (RT2S), a framework to train machine learning classifiers with potentially noisy labels. RT2S calculates a trust score for each training sample, which indicates the quality of its corresponding label. These trust scores are employed as sample weights during training and optionally during threshold optimization. The trust scores are generated from two sources: (i) the model's confidence in the observed label, leveraging out-of-fold prediction scores to detect anomalous labels in the training data, and (ii) the probability of the correct label, ascertained by a Large Language Model with the ability to identify biased label noise. We evaluate RT2S by training machine learning models on 6 product classification datasets that utilize low-quality labels generated by a rule-based classification engine acting as a surrogate labeler. Our experimental findings indicate that RT2S outperforms all baselines, and achieves an average accuracy improvement of 4.38% (max 7.18%) over rule-based classifiers in particular.
An Efficient Selective Ensemble Learning with Rejection Approach for Classification

Recent studies found that selective ensemble learning (e.g., dynamic ensemble selection) shows better predictive performance for classification tasks, compared to traditional static ensemble. However, there are some limitations of the available methods, such as high computational cost and multiple restrictions in base model ranking and aggregation (especially for class-imbalanced data modeling). Besides, the current methods make predictions for all data without measuring the credibility regarding different data patterns. This paper proposes a selective ensemble learning with rejection approach that aggregates base models from a different perspective. The approach introduces rejection measures to quantify base model credibility, and learns how to use the models according to their credibility on different sample patterns. It avoids the complexity in base model ranking and therefore is computationally more efficient than current methods. Any common evaluation metrics can be adopted in the selective ensemble strategy, which allows the developed model to handle class-imbalanced data properly. Also, a global rejection region is developed which indicates whether the ensemble model can provide reliable predictions for the targets. We implement the approach in the modeling of 12 datasets, including both class-imbalanced and class-balanced cases. Results show that the approach significantly reduces the inference time while showing promising performance, compared to 8 dynamic ensemble selection methods in the literature. Feature contributions and impacts of different rejection ratios on performance are also investigated to better demonstrate the approach.
Comparative Analysis of Open Source and Commercial Embedding Models for Question Answering

In this industry track presentation, we will provide a comprehensive tour of the best performing embedding models for question answering, as determined by the Massive Text Embedding Benchmark1. We showcase these models while also considering solutions offered by OpenAI and Cohere, renowned for their state-of-the-art performance. Through rigorous evaluations on internal Salesforce datasets tailored for Question Answering on Knowledge articles, we compare the performance of these models using standardized metrics.
Weight Matters: An Empirical Investigation of Distance Oracles on Knowledge Graphs

Distance computation is a bottleneck that limits the performance of many applications based on knowledge graphs (KGs). One common approach to improving online distance computation is to offline precompute certain information to be stored in an index called distance oracle. However, its effectiveness remains under-studied in the setting where edges are methodologically weighted to capture the structure and semantics of edge types in a KG. To fill the gap, in this paper, we present the first evaluation of representative distance oracles on KGs with commonly used edge weighting schemes. Our negative results and empirical justifications provide insights and a motivation for future studies of this unique setting.
Quantum Split Learning for Privacy-Preserving Information Management

Recently, research on quantum neural network (QNN) architectures has been attracted in various fields. Among them, the distributed computation of QNN has been actively discussed for privacy-preserving information management due to data and model distribution over multiple computing devices. Based on this concept, this paper proposes quantum split learning (QSL) which splits a single QNN architecture across multiple distributed computing devices to avoid entire QNN architecture exposure. In order to realize QSL design, this paper also proposes cross-channel pooling, which utilizes quantum state tomography. Our evaluation results verifies that QSL preserves privacy in classification tasks and also improves accuracy at most by 6.83% compared to existing methods.
SeqGen: A Sequence Generator via User Side Information for Behavior Sparsity in Recommendation

In real-world industrial advertising systems, user behavior sparsity is a key issue that affects online recommendation performance. We observe that users with rich behaviors can obtain better recommendation results than those with sparse behaviors in a conversion-rate (CVR) prediction model. Inspired by this phenomenon, we propose a new method SeqGen, in an effort to exploit user side information to bridge the gap between rich and sparse behaviors. SeqGen is a learnable and pluggable module, which can be easily integrated into any CVR model and no longer requires two-stage training as in previous works. In particular, SeqGen learns a mapping relationship between the user side information and behavior sequences, only on the basis of the users with long behavior sequences. After that, SeqGen can generate rich sequence features for users with sparse behaviors based on their side information, so as to alleviate the issue of user behavior sparsity. The generated sequence features will then be fed into the classifier tower of an arbitrary CVR model together with the original sequence features. To the best of our knowledge, our approach constitutes the first attempt to exploit user side information for addressing the user behavior sparsity issue. We validate the effectiveness of SeqGen on the publicly available dataset MovieLens-1M, and our method receives an improvement of up to 0.5% in terms of the AUC score. More importantly, we successfully deploy SeqGen in the commercial advertising system Xlight of Alipay, which improves the grouped AUC of the CVR model by 0.6% and brings a boost of 0.49% in terms of the conversion rate on A/B testing.