Reviewerly/exHarmony huggingface dataset:
Posteriori Trust: Behavior Driven Trust on a Network Graph at Scale

Mobile networks can connect everyone with a simple call or message and recent years have seen a dramatic increase in abuse through robocalling and messaging. This impacts everything from network optimization decisions to users being scammed. Users on any network often believe that there should be some level of intelligence that allows for the blocking and detection of abuse like robocalling, telemarketing, or scam/fraud schemes. A key failure in most approaches is the desire to maintain user privacy while collecting this kind of intelligence. We have designed a way to determine trust between two devices in a way that is driven by device behavior and not intrusive through surveys or requiring access to private contact lists. Instead, we observe encrypted network traffic to build a weighted graph by taking all traffic as inputs to create a weighted vector which in turn considers all other traffic on the network and scales to a single numeric representation of trust. This gets us to a weighted trusted graph that can be the foundation to many other important business use cases including fraud prevention, marketing, and churn. After exploring our trusted graph, a form of social graph with trusted weights, we found that our graph is hyper connected through hub nodes that have very high outdegree counts, many relationships. In other social graphs this might be referred to as influencers, but when we add the concept of trusted relationships, these might be unwanted influencers. We show that by setting the right trusted threshold, we can filter out ~80% of the relationships in our graph without losing the core social structures, suggesting that a potential super-majority of relationships are untrusted.
A Hybrid Score to Optimize Clustering Hyperparameters for Online Search Term Data

Clustering is a popular means of grouping trends in datasets. When applied to natural language data like queries to online search engines, clustering provides insights on user intent, discoverability of online data, and other topics of interest to digital marketers. However, the volume, variety, and evolving nature of the data makes manually reviewing and optimizing the hyperparameters of a clustering methodology tedious and expensive. Thus, we propose a simple optimization exercise leveraging a new metric we call the Hybrid Cluster Score (HCS), which combines three common clustering validity indices: the Silhouette Coefficient, the Calinski-Harabasz Score, and the Davies-Bouldin Index. By running multiple iterations of a clustering algorithm over a dataset with varying hyperparameters and calculating the HCS, we demonstrate that you can identify strong-performing hyperparameters that can be used to cluster data from the same source over future time periods. This method is used to optimize weekly clustering of websites’ search term data as part of a Yext search analytics product. Clusters produced via HCS-optimized hyperparameters are preferable for Yext’s product use case 79 percent of the time in human-in-the-loop testing vs. the preoptimization production state.
Fuzzy Law: Towards Creating a Novel Explainable Technology-Assisted Review System for e-Discovery

In the legal field, Technology-Assisted Review (TAR) systems for e-discovery are typically perceived as "black boxes" by practitioners, providing little to no insight into how the system makes its classification predictions. The lack of explainability in TAR systems for e-discovery renders their decisions opaque, making it difficult for attorneys to trust their recommendations and thus to discharge ethical obligations to clients. In addition, litigants cannot fully participate in the process if they cannot understand the relevance judgments, and jurists cannot make well-informed judgments on discovery matters. The Fuzzy ARTMAP algorithm is an explainable neural network architecture that permits the extraction of fuzzy If-Then rules from the model at any point in its training, the model is also geometrically interpretable, allowing a researcher or practitioner to understand what the model has learned up to that point.This paper evaluates the explainable Fuzzy ARTMAP algorithm for use in the TAR domain. Not only does it achieve suitable document classification performance for a TAR system, as measured by recall and recall-at-effort, but it also enables direct insight into how the algorithm decides relevance. This is in contrast to existing approaches for explainable TAR which only rely on extracting document snippets as post hoc explanations of why a document is relevant.In addition, the effect of different document features (tf-idf, word2vec, and GloVe) on recall performance is also evaluated. Performance is compared to AutoTAR, the state-of-the-art TAR algorithm which makes relevance predictions but is not able to provide any explanations about them. Experiments on the Reuters-21578 and 20Newsgroups corpora indicate robust recall performance overall and comparable or better metrics than AutoTAR in some circumstances.
Enhanced Video BERT for Fast Video Advertisement Retrieval

Recently, video BERT based on cross-modal attention has achieved excellent performance in many cross-modal tasks in academia. Nevertheless, the expensive computation cost of cross-modal attention makes video BERT impractical for large-scale search in industrial applications. Inspired by the success of the tree-based deep model (TDM) in the recommendation system, we present a enhanced video BERT (EVB). It provides a practical solution to deploy the heavy video BERT for the large-scale query-to-video search. The proposed EVB overcomes the limitation of TDM relying on global features, and makes the tree structure based on a global feature compatible with version BERT using a set of local features. What’s more, we proposes a similarity-based dynamic construction to integrate the optimization of model efficiency. The proposed EVB has been deployed in our video advertising platform and brings a considerable boost in CVR and CTR for advertisers.
Adaptive Method for Machine Learning Model Selection in Data Science Projects

Data science projects involve a machine learning (ML) process based on data, code, and models that change over time. For example, the datasets may increase in size and allow an ML model that requires larger datasets to be applied. However, the dynamic factors that influence model selection are not well understood and explicitly represented. This paper presents ongoing work on an adaptive method for ML model selection in big data science projects. The proposed method involves (i) identifying the factors that affect model selection based on heuristics proposed in the literature; and (ii) modeling the variability of these factors using a feature diagram and constraints that trigger adaptive reconfiguration, that is, changes in model selection due to changes in the variability factors. The applicability of the method is demonstrated through an illustrative use case. The proposed method can lead to an improved understanding of dynamic factors that influence model selection, how these factors explicitly affect the selection, and how the adaptive factors can be represented and automated. This improved understanding can result in a project model selection process that is less implicit and more efficient, more adaptive and explainable, and ultimately constitute a foundation for the creation of novel dynamic software product lines to support this process.
LAMPT: LAbel Mask-Predicted Transformer for Extreme Multi-label Text Classification

Extreme Multi-label text Classification ( XMC) is a task of recalling the most relevant labels for each given text from an extremely large-scale label set. It is emphasized that XMC is a more complex classification task because there are two main problems: large space of labels and the labels in XMC tasks tend to be correlated. Existing methods attempt to model label correlations by viewing the XMC task as a sequence generation problem however they still suffer from (1) using the slow serial decoding strategy where labels are predicted one-by-one. (2) needing to compare a mass of label ordering strategies in the decoding stage to achieve satisfied accuracy. In this work, we propose LAbel Mask-Predicted Transformer (LAMPT) to address the both issues, which is a novel non-autoregressive generation model that (1) enriches the input raw text representation with the additional label features by fully exploiting the label dependencies, (2) allows for efficient parallel decoding thanks to its non-autoregressive decoding formulation and mask-prediced training strategy. Experimental results demonstrate that single model performance is substantially enhanced by LAMPT. On a Wiki dataset with thirty-one thousand labels, LAMPT-XLNet accuracy has gained 1.6% relative improvement on P@3 over the LightXML-XLNet. Also, the P@1 of ensemble LAMPT is 90.00%, a significant i mprovement over the state-of-the-art ensemble LightXML (transformer-based) and AttentionXML (LSTM-based), which achieve 89.45% and 87.47%, respectively.
Hard Disk Fault Detection Method based on Temporal Convolutional Network

The data centre of a hard disk failure is usually irreparable for data center losses, and hard disk failure detection is also the focus of industry and academia research. Traditional methods mainly use machine learning and statistical concepts for hard disk fault detection, but due to differences in sample data and manual feature simulation, the time complexity and accuracy of fault detection are insufficient. Based on this, this paper presents a hard disk fault detection method based on Temporal Convolutional Network, builds a prediction network based on Temporal Convolutional Network, and uses real environment datasets to complete the model optimization. The experimental data show that there are obvious advantages in model size and prediction accuracy.
Low-resource CNN Accelerator Based on FPGA

Convolutional Neural Networks (CNNs) are deployed on the Internet of Things (IoT) edge devices with strict restrictions on resource and power consumption. Most of the work's optimization focuses on inference speed and average precision, ignoring resource and power consumption optimization. This paper proposes the strategies of weight quantization, parameter reordering, block convolution multiplexing, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{CONV}+\text{POOL}$</tex> ‘ to implement an FPGA-based CNN accelerator with low-resource and low-power. The CNN algorithm is designed in High Level Synthesis tool (HLS), which can realize the FPGA development with high-level language to achieve high development efficiency and flexibility. The experimental results show that our implementation has achieved 2.812W of low power consumption for executing YOLOV2 on the PYNQ-Z2, consuming only 2052Kb of on-chip BRAMs, which is better than the current work in terms of resource consumption. The performance is about 42.31GOP/s, which is much higher than CPU (ARM-A9).
Governance framework for voice spam detection and interception of telecom network

With the rapid development of 5G technology, the number of telecom network users is exploding, which brings great pressure to the governance of the whole network. Voice spam, such as fraud, harassment and other illegal voice calls, not only occupy communication resources, but also disturb people's daily life and social order. In light of this, considering different scenario elements, this paper proposes a governance framework that considers time, location and user attributes simultaneously to identify and intercept voice spam. Specifically, we firstly use decision tree algorithm to recognize and classify voice spam in telecom network. Then, based on the frequent itemset theory, the malicious phone IDs are further analyzed and revealed. The telephone numbers are divided into white list, grey list, personal blacklist and group blacklist. Through simulation analysis and practical application in Zhengzhou area, the framework proposed can accurately detect voice spam and timely remind users of potential risks, providing users with secure voice services.
Robust and Resource-efficient Machine Learning Aided Viewport Prediction in Virtual Reality

360-degree panoramic videos have gained considerable attention in recent years due to the rapid development of head-mounted displays (HMDs) and panoramic cameras. One major problem in streaming panoramic videos is that panoramic videos are much larger in size compared to traditional ones. Moreover, the user devices are often in a wireless environment, with limited battery, computation power, and bandwidth. To reduce resource consumption, researchers have proposed ways to predict the users’ viewports so that only part of the entire video needs to be transmitted from the server. However, the robustness of such prediction approaches has been overlooked in the literature: it is usually assumed that only a few models, pre-trained on past users’ experiences, are applied for prediction to all users. We observe that those pre-trained models can perform poorly for some users because they might have drastically different behaviors from the majority, and the pre-trained models cannot capture the features in unseen videos. In this work, we propose a novel meta learning based viewport prediction paradigm to alleviate the worst prediction performance and ensure the robustness of viewport prediction. This paradigm uses two machine learning models, where the first model predicts the viewing direction, and the second model predicts the minimum video prefetch size that can include the actual viewport. We first train two meta models so that they are sensitive to new training data, and then quickly adapt them to users while they are watching the videos. Evaluation results reveal that the meta models can adapt quickly to each user, and can significantly increase the prediction accuracy, especially for the worst-performing predictions.
Predictive Masking for Semi-Supervised Graph Contrastive Learning

Graph Contrastive Learning (GCL) has recently emerged to leverage contrastive loss as a pseudo-supervision signal for self-supervised learning. In order to introduce contrastive learning loss to graphs, existing GCL methods mostly focus on leveraging network topology or node similarity to classify a pair of nodes as same/different node pairs or close/distant node pairs. In this paper, we propose a semi-supervised graph contrastive learning framework, pmGCL, leveraging GCL to augment the performance of a classifier through a predictive masking approach. Specifically, a classifier is trained using a small number of labeled nodes to predict node labels. The label prediction results are then transformed into a binary prediction of whether two nodes have the same label or not for all node pairs. The converted result, serving as a binary masking matrix, will help the succeeding GCL learning to learn to pull nodes likely belonging to the same class to be closer and push the ones belonging to different classes to be further away from each other. Experiments and comparisons, with respect to different benchmark networks and label percentages, show that pmGCL consistently outperforms rival graph convolution neural network (GCN) and GCL baseline with a simple constraint posed on the problem.
Using Contextual Text Mining and Ontology Methods to Establish a Novel Technology Trend and Associative Analysis Framework for Sustainable Energy Development in Taiwan

In 2015, the United Nations proposed 17 Sustainable Development Goals, SDGs, as the guidelines for all countries in the world to promote sustainable development before 2030. Government Research Bulletin (GRB), the research projects and technical reports sponsored by government, which has long-term, numerous, complete research method, technology development and policy analysis information in Taiwan. Therefore, it is an important and effective way to explore SDG-related information from a large amount of GRB text. In this paper, a novel technologies trend and associative analysis framework which uses contextual text mining and ontology methods is proposed and applied to SDG 7, which "Affordable and Clean Energy". First, we integrate dictionary-based method and semantic textual similarity analysis algorithm to obtain a SDG 7 classifier which can exactly and quickly classify a large amount number of GRB text to SDG 7. Then, two major SDG 7 analysis procedures based on the classification results are implemented. One is using contextual text mining algorithm to obtain energy technologies trend information. The other is adopting ontology method to establish energy technologies associative analysis concept map. According to the analysis results mentioned above, we are able to efficiently incorporate the energy technology with long-term trend, energy technology associative information, and the most influential authors on the specify energy technology in order to generate a global strategy for continuous improvement in Taiwan.
The Data Recovery Service in NoSQL

Not Only SQL (NoSQL) is a critical technology that is scalable and provides flexible schemas, thereby complementing existing relational database technologies. Although NoSQL is flourishing, present solutions lack the features required by enterprises for critical missions. In this paper, we explore solutions to the data recovery issue in NoSQL. Data recovery for any database table entails restoring the table to a prior state or replaying (insert/update) operations over the table given a time period in the past. Recovery of NoSQL database tables enables applications such as failure recovery, analysis for historical data, debugging, and auditing. In this paper, we first identify the design and implementation issues with regard to the data recovery problem for NoSQL databases, including time length of recovery, fault tolerance, scalability, memory constraint, software compatibility, and quality of recovery. Particularly, our study emphasizes on columnar NoSQL databases. We then propose and evaluate four solutions to address the data recovery problem in NoSQL; each solution has its pros and cons. We implement our solutions based on Apache HBase, a popular NoSQL database in the Hadoop ecosystem widely adopted by industry. Our implementations are extensively benchmarked with an industrial NoSQL benchmark under real environments. Specifically, our research findings and implementations in this paper have been contributed to and integrated with Apache HBase for global distribution.
COMPA: A Comparative Retrieval and Analytical Engine for Consumer Products

Often people need to make comparisons in order to make selections and decisions. Increasingly such comparison activities are conducted online. For example, consumers typically read many reviews and compare various models before an online purchase. Since this comparison-based analytical and decision-making process often involves significant manual effort, it is greatly beneficial if the process can be largely automated and effectively performed by computers. In this research, we make an effort to design and implement COMPA, a web-based comparative retrieval and analytical engine in the context of consumer products that can effectively assist decision-making. COMPA takes two products A vs B as input, retrieves relevant online reviews, performs comprehensive analysis, and returns a comparison report including product comparison scores as well as supporting evidence.
Stereotype and Categorical Bias Evaluation via Differential Cosine Bias Measure

A vast range of Natural Language Processing (NLP) systems that are in use today have direct impact on humans. While machine learning models are expected to automatically infer world knowledge from historical texts, we should also be cognizant not to let NLP applications consume undesired societal stereotype bias back. Many bias evaluation measures have been designed and experimented to check whether unwanted stereo-type bias is present in the model or not. Upon performing various experiments, we found out that the most popular bias measures do not always indicate bias accurately. In addition to these experimental findings, we also propose our novel Differential Cosine Bias measure with examples of unwanted stereotype biases as well as necessary categorical bias that is based on knowledge. Our experiments show that our bias measure is a potential indicator of bias in NLP models compared to the popular bias evaluation measures.
Attention-Aware Multi-modal RNN for Deception Detection

Nowadays, various video data are springing up. In the field of human-centric video analysis, deception detection becomes a crucial issue to us. Thanks to AI development, automated deception detection has been researched for a while. Nonetheless, the mainstream techniques work as a black box, which is unexplainable. This paper presents an attention mechanism on visual and audio features, which makes the detection interpretable. Besides, we embrace the approach of multi-modal by combining visual, audio, and transcription features as an ensemble model, which can achieve 96% of accuracy.
Vehicle orientation detection based on synthetic data with image-to-image translation and domain adaptation

In this paper, the author describes the solution for the IEEE BigData Cup 2022 Challenge: Vehicle class and orientation detection in the real-world using synthetic images from driving simulators. This challenge’s task is to enhance the performance of the vehicle detection models, which are trained solely on photo-realistic images, in real-world scenarios. By applying image-to-image translation and domain adaptation technique, the proposed solution can improve the detection results on real-world images using models trained on synthetic data. Furthermore, the proposed method ranked 2nd in the leaderboard with final score of 0.4456.
A Fast On-Line ϵ-Approximation Algorithm for Mining Strongly Closed Itemsets

In this paper, we give a novel on-line ϵ-approximation algorithm for mining the set of strongly closed itemsets in a transaction stream, and also study its acceleration technique with several pruning methods such as look-ahead success detection for strongly closed itemsets. We also show some experimental results for evaluating the proposed algorithms.
Research on Query Optimization Method Based on Dameng

The optimization technology of database query performance is studied based on Dameng. Office OA applications are used as the test environment, and python scripts are used to batch insert large amounts of data. A large number of experiments have been carried out on the four technologies of data table index, select statement optimization, paging query and parallel query. The test tools developed on easyops platform and Google Chrome developer tools have been used to analyze the data of database response and page loading speed of the test results, respectively. With the two as parameters of the test results, the impact of the four technologies on the database query performance is discussed. The results show that appropriate data table index, optimization of “*” and “limit” in SQL query statements, ID limited paging query and parallel query technology can greatly improve the response speed of the database.