Reviewerly/exHarmony huggingface dataset:
GCN-based Explainable Recommendation using a Knowledge Graph and a Language Model

In this paper, we propose a novel graph convolutional network (GCN)-based recommendation method using both knowledge graph (KG) and review texts to solve the cold start problem and provide explainability. In our model, GCN-based collaborative filtering (CF) is parallelly performed on the user-item interaction graph and the KG to utilize the items’ additional information in the recommendation process. Also, we use a pretrained language model to generate embeddings from the user reviews and utilize them in the GCN embedding propagation process to reflect the users’ subjective sentiment and opinion. After the recommendation is performed, we generate the paths from the target user to recommended items by using the KG and embeddings from review texts. To prove the superiority of the proposed method, we conduct the experiment by comparing recommendation performance with baseline models. In the experiment, the proposed method outperformed the other models.
Explainable e-Discovery (XeD) Using an Interpretable Fuzzy ARTMAP Neural Network for Technology-Assisted Review

In the legal field, corporate civil matters often entail tens or hundreds of thousands of documents that need review for relevance. Technology-Assisted Review (TAR) systems utilize machine learning classification algorithms, such as logistic regression, SVM, and transformers, to retrieve all, or nearly all, relevant documents from the corpus under review. However, in these e-discovery scenarios, TAR systems are typically perceived as “black boxes” by practitioners; where the TAR system provides little or no insight into why a document is predicted to be relevant. This lack of explainability makes it difficult for attorneys to trust classifications from TAR systems, hinders litigants from participating fully as they cannot understand why documents are being classified as relevant, and relies on the costly interpretation of experts rather than the model itself for understanding.In contrast to these opaque methods, the Fuzzy ARTMAP algorithm is an explainable neural network architecture that is both geometrically interpretable and allows for the extraction of fuzzy If-Then rules from the model at any point in its training. This enables a practitioner or researcher multiple modes with which to understand what the model has learned up to that point, laying the foundation for Explainable e-Discovery (XeD).In this paper, the explainable Fuzzy ARTMAP neural network is extended to include fuzzy subsethood to rank documents for active learning and is then evaluated for use in the TAR domain with several corpora. In addition to achieving desirable performance for a TAR system, it also enables direct insight into how the algorithm decides relevance. This is in contrast to existing approaches for explainable TAR which rely on extracting document snippets as post hoc explanations of why a document is relevant. Additionally, we demonstrate the model’s interpretability with both textual and graphical representations of the learned model for a range of representations including tf-idf, GloVe, and Word2Vec.
Leveraging Knowledge Graphs for Matching Heterogeneous Entities and Explanation

Entity matching (EM), also known as record linkage, is crucial in data integration, cleaning, and knowledge base construction. Modern matching techniques leverage deep learning and pre-trained language models (PLMs) to effectively identify matching records, showcasing significant advancements over traditional methods. However, certain critical matching aspects have received limited attention in these techniques. They heavily rely on PLMs’ encodings and face challenges in integrating external sources of knowledge to enhance matching accuracy. Additionally, these techniques often lack transparency, impeding users’ understanding of the underlying rationale for matching decisions. Furthermore, they exhibit limitations and decreased performance in handling heterogeneous records from datasets with diverse schemas. This paper presents EXKG, a novel technique that addresses these challenges and effectively matches heterogeneous records with varying attributes. EXKG combines the power of knowledge graphs (KGs) and PLMs to perform record linkage while offering explanatory insights into the matching results. We demonstrate that EXKG achieves competitive performance through experimental studies compared to state-of-the-art matching techniques. As a by-product, our solution generates explanations that give end users a comprehensive understanding of the matching process. We evaluate the quality of these explanations by using a user study and show they empower end users to make informed decisions
Towards Large Language Model Organization: A Case Study on Abstractive Summarization

In this work we propose ”LLM organization”, an organizational structure-based LLM workflow for improving the performance of standard abstractive summarization techniques and mitigate unfaithful summary generation. We formulated the organizational structure-based LLM workflow as a directed acyclic graph (DAG), where each node corresponds to an LLM and each edge to a communication protocol. Our workflow is benchmarked on 5 datasets from various domains, using 7 evaluation metrics. The results indicate that LLM organization could mitigate unfaithfulness and increase the overall performance of abstractive summarization methods.
Detecting Credit Card Frauds Using Deep Learning and Face Detection

In this post-pandemic era, there has been a tremendous rise in online transactions. Along with this increase in frequency, there is an increase in fraudulent transactions. Additionally, a recent Nielsen Report estimates that over the next ten years, the card industry will lose 400 billion to fraud. Techniques of Machine learning are frequently used in classifying fraudulent credit card transactions because they operate under the notion that false patterns can be found by looking at previous transactions. But the process becomes difficult when it must take into consideration shifting customer behaviour and fraudsters' inventiveness in creating new fraud patterns. Systems for detecting fraud can use deep learning to uncover anomalies. With the ability to handle both big data and real-time data, deep learning is also superior to current machine learning techniques. We aim to provide a solution to solve this problem of credit card fraud detection by leveraging the technology of face detection and combining it with deep learning, thus proposing a two-step verification-based approach. Broadly, in the proposed approach, the user has to register using his card details and then capture his face. Then login to the system, enter the card details and the system will prompt for face capture once the face and the card details are captured they are verified with the already stored data in the database. If the verification passes then the transaction is successful otherwise the user is logged out of the system.
The Utility of Standards and Good Practice Guidelines for Records Professionals: comparing apples, oranges, and other fruits

The perceived usefulness of standards and good practice guidelines (S&GPG) for records professionals is often seen as ambiguous. Many professionals find the abundance of options overwhelming and confusing. Even after selecting seemingly suitable S&GPG, their direct benefits may not always be evident and can potentially restrict professional autonomy in certain situations. This article explores various approaches employed by records professionals to understand the complexity of S&GPG, such as simple listing or ontological representation. However, each approach has its own set of constraints. The article proposes an initial meta-framework that draws from insights of successful frameworks to provide preliminary categories. The purpose of this conceptual proposal is to assist records professionals understand the connections between S&GPG.
Exploiting Multiple Sequence Lengths in Fast End to End Training for Image Captioning

We introduce a method called the Expansion mechanism that processes the input unconstrained by the number of elements in the sequence. By doing so, the model can learn more effectively compared to traditional attention-based approaches. To support this claim, we design a novel architecture ExpansionNet v2 that achieved strong results on the MS COCO 2014 Image Captioning challenge and the State of the Art in its respective category, with a score of 143.7 CIDErD in the offline test split, 140.8 CIDErD in the online evaluation server and 72.9 AllCIDEr on the nocaps validation set. Additionally, we introduce an End to End training algorithm up to 2.8 times faster than established alternatives.
LoRA-like Calibration for Multimodal Deception Detection using ATSFace Data

Recently, deception detection on human videos is an eye-catching techniques and can serve lots applications. AI model in this domain demonstrates the high accuracy, but AI tends to be a non-interpretable black box. We introduce an attention-aware neural network addressing challenges inherent in video data and deception dynamics. This model, through its continuous assessment of visual, audio, and text features, pinpoints deceptive cues. We employ a multimodal fusion strategy that enhances accuracy; our approach yields a 92% accuracy rate on a real-life trial dataset. Most important of all, the model indicates the attention focus in the videos, providing valuable insights on deception cues. Hence, our method adeptly detects deceit and elucidates the underlying process. We further enriched our study with an experiment involving students answering questions either truthfully or deceitfully, resulting in a new dataset of 309 video clips, named ATSFace. Using this, we also introduced a calibration method, which is inspired by Low-Rank Adaptation (LoRA), to refine individual-based deception detection accuracy.
Subspace Interpolation and Indexing on Stiefel and Grassmann Manifolds as a Lightweight Inference Engine

Subspace Indexing with Interpolation (SIM-I) on Stiefel and Grassmann manifolds is proposed in this work. Given a partition of some original high-dimensional data set, SIM-I is constructed via two steps: in the first step we build linear affinity-aware subspace models based on each partition; in the second step we interpolate between several adjacent linear subspace models constructed in the first step using the “center of mass” calculation on Stiefel and Grassmann manifolds. Through these two steps, SIM-I builds a globally nonlinear and smoothly regularized low-dimensional embedding model of the original data set. Furthermore, given sufficiently many training samples on the data manifold either labelled by some pre-trained learning model such as Deep Neural Networks (DNNs) or provided with original natural labels, we first apply SIM-I on this data set and then perform nearest-neighbor classification on the resulting low-dimensional embedding. This helps us to build a Lightweight Inference Engine (LIE) carrying similar level of feature extraction by the pre-trained learning model. For DNNs, such LIE can be interpreted as some (nonstandard) shallow neural network with a wide first hidden layer. From this perspective, SIM-I provides a way to exchange deep network for wide but shallow ones and may provide some new insights to interpret DNNs.
MIAE: A Mobile Application Recommendation Method Based on a NTK Model

The emergence of more and more mobile applications in recent years has driven the development of application recommendation algorithms. However, many algorithms are limited to using users’ ratings for apps while considering other relevant information as marginal. In this paper, we consider the user’s app usage time and the user’s forum level in addition to the rating, explore the impact of different attributes and multidimensional attributes on the recommendation algorithms, and propose a model MIAE (Multi-info. Autoencoders for Recommendation) based on a Neural Tangent Kernel(NTK) that can accommodate multiple information dimensions. Specifically, our approach involves utilizing a fully-connected neural network with NTK parameterization. We introduce ridge regression as a regularization technique to convert the recommendation problem into a probabilistic framework. To obtain results, we employ a gradient descent method based on the NTK, enabling efficient optimization. We tested our approach using both comparison experiment and ablation experiment. In the comparison experiment, we used two representative algorithms, GLocal-K [1] and CosRec [2], as baselines. Quantitative results on our crawled taptap dataset show that our proposed NTK-based approach, MIAE, with one-dimensional information input outperforms representative algorithms, achieving high performance on various evaluation metrics. In our ablation experiment, we examined several variables, including the user’s rating of the app, app usage duration, and forum level. The results demonstrated that utilizing the individual variable ‘Level’ led to the highest recall and F1 scores. On the other hand, employing a combination of variables such as ‘Level + Duration’ resulted in the best precision and NDCG scores. This finding confirms the beneficial impact of both the user’s app usage duration and level on app recommendations.
Boosting Feedback: A Framework for Enhancing Ground Truth Data Collection

This study proposes a novel hybrid feedback approach called ”Boosting Feedback” to address the challenges of collecting correct data in machine learning research. Implicit feedback, derived from implicit behavioral logs, provides sufficient data quantity but may lack data quality due to various factors. Explicit feedback, obtained directly from users through surveys, offers high-quality data but is resource-intensive. The Boosting Feedback approach leverages implicit logs to augment the quantity of correct data from a single explicit feedback, doubling the available data by estimating opposite states from implicit logs. The method’s effectiveness is validated in actual recommendation experiment in the wild. Boosting Feedback offers a promising solution to improve data collection in machine learning research.
AI-Algorithmically-Generated Song with Lyrics

This is a song completely generated automatically by AI from lyrics using purely novel algorithms we proposed and does not require any form of training data. It follows the music theory conventions and guidelines. The algorithm selects from the most common time signatures in songs such as 2/4, 3/4, and 4/4, and does not provide solely a uniform rhythm but a variety of rhythms instead. Since the song utilized as a demonstration is generated from lyrics written by a child and for children, the rhythms are relatively simple. The melody is controlled within an interval from C4 to C5 so that it includes all of the notes within an octave and is also “singable” for the majority of the human population for at least a portion of the interval. Additionally, the algorithm can be used to generate multiple songs for one set of lyrics. The demonstrated song is designed to be sung by children due to its easy, comprehensible nature and simple melody. The tempo is manually set at 120 beats per minute and the time signature is automatically determined as 4/4 time by our algorithms.
Choosing the Right Time to Learn Evolving Data Streams

Continuous data generation over time presents new challenges for Machine Learning systems, which must develop real-time models due to memory and latency limitations. Streaming Machine Learning algorithms analyze data streams one sample at a time, progressively updating their models. However, is it necessary to utilize all the data for model updates? This paper introduces the Online Ensemble SPaced Learning (OE-SPL) strategy, an ensemble meta-strategy that combines online ensemble learning and the Spaced Learning heuristic to rapidly learn underlying concepts without using all samples. We evaluated OE-SPL on synthetic and real data streams containing various concept drifts, providing statistical evidence that OE-SPL achieves comparable performance to state-of-the-art ensemble models while recovering from multiple concept drift occurrences more efficiently, using less time and RAM-Hours.
Mining Rare Utility Patterns within Target Items

As a crucial subfield of pattern discovery, high utility rare itemset mining (HURIM) is developed to discover abnormal but significant patterns. HURIM plays a vital role in various scenarios, such as network security, disease detection, and biomedicine. However, the traditional HURIM algorithms ignore the users’ demands, which generates massive needless patterns. In general, target-based HURIM algorithms can discover more useful information that meets the needs of users than traditional HURIM algorithms. To this end, we propose a targeted HURIM algorithm called Mining Rare Utility Patterns within Target Items (TIRUP). TIRUP adopts two techniques (projection and merging technologies), to diminish the consumption of database scanning. To effectively improve the performance of TIRUP, this paper utilizes several strategies based on frequency, utility, and target factors. Finally, a series of experiments are conducted to demonstrate the efficiency of the proposed TIRUP algorithm, and the experimental results indicate that TIRUP is suitable for processing large-scale and dense datasets.
Performance of Null Handling in Array Databases

Array databases specialize in storage, management, and query processing on massive multidimensional array data such as satellite image time-series, weather forecast models, IoT sensor measurements, medical imaging data, etc. Some of the values in this data may be ”null” for a variety of reasons, such as unknown, known false, etc. The DBMS must handle null values correctly and efficiently. As of today, the effects of different data structures for representing null values on query processing in array databases have not been systematically studied. As a consequence, it is not clear what the optimal way for handling null values is. In this paper, we measure how four different methods for encoding null values perform across most common categories of array operations. The result is a comprehensive overview, publicly available in open source, providing relevant insights into the performance characteristics of these data structures allowing to discern which among them offers the most optimal approach for representing null values in an array processing context.
KF K-means: A High Performance K-means Implementation using Kernel Fusion

The K-means algorithm is one of the simplest and most universal clustering algorithms. Significant work has been carried out over several years to improve its performance in both academic and industrial applications. Researchers have optimized K-means not only on the algorithm level but also on the architecture level. Notably, GEMM, a rigorously studied matrix multiplication operation, has been used to speed up the Euclidean-distance calculations in the K-means algorithm. The Intel DAAL library currently provides a fast K-means implementation based on the Intel Math Kernel Library GEMM subroutine and low-level architecture information. However, in spite of utilizing the MKL GEMM subroutine and architecture properties, the performance of the state-of-the-art K-means implementation is still far from its hardware peak performance. This paper presents a faster fused-matrix K-means kernel that is superior to current K-means designs. Based on our experimental results, the fused matrix K-means kernel runs around 76% faster than the state-of-the-art Intel DAAL K-means algorithm and is able to achieve nearly double floating point performance on Intel x86-84 Ivy micro-architectures.
Effective Face Inpainting by Conditional Generative Adversarial Network

In the paper, we propose a two-stage face-inpainting approach based on conditional generative adversarial networks. In the first stage, a deep-learning model is trained for predicting face landmarks. It also dynamically adjusts the penalty value of the loss function based on the view-degree of a face to improve the ability of predicting high view-degree faces. In the second stage, masked face images and their corresponding face landmarks are concatenated to form the condition of training a conditional Generative Adversarial Network (GAN) for inpainting the masked face. If an input masked image is a nearly-frontal face, an additional procedure for face symmetry processing will be performed before the image is input into the inpainting model. The experimental results show that the proposed training method in the first stage can effectively enhance the robustness of the face-landmark prediction model and reduce the impact of data imbalance, thereby improving the effect of later face inpainting. They also show that the proposed face-inpainting model in the second stage can better maintain the geometric structures and symmetric outlooks of inpainted faces than previous ones.
IoT-Reg: A Comprehensive Knowledge Graph for Real-Time IoT Data Privacy Compliance

The proliferation of the Internet of Things (IoT) has led to an exponential increase in data generation, especially from wearable IoT devices. While this data influx offers unparalleled insights and connectivity, it also brings significant privacy and security challenges. Existing regulatory frameworks like the United States (US) National Institute of Standards and Technology Interagency or Internal Report (NISTIR) 8228, the US Health Insurance Portability and Accountability Act (HIPAA), and the European Union (EU) General Data Protection Regulation (GDPR) aim to address these challenges but often operate in isolation, making their compliance in the vast IoT ecosystem inconsistent. This paper presents the IoT-Reg ontology, a holistic semantic framework that amalgamates these regulations, offering a stratified approach based on the IoT data lifecycle stages and providing a comprehensive yet granular approach to IoT data handling practices. The IoT-Reg ontology aims to transform the IoT domain into a realm where regulatory controls are seamlessly integrated system components by emphasizing risk management, compliance, and the pivotal role of manufacturers’ privacy policies, ensuring consistent adherence, enhancing user trust, and promoting a privacy-centric IoT environment. We include the results of validating this framework against risk mitigation for Wearable IoT devices.
Mitigating Annotation Artifacts in Physical Commonsense Reasoning Benchmark

To achieve human-like AI, physical commonsense reasoning plays a pivotal role for machines to understand the real world. Thereby, physical commonsense reasoning benchmarks [1]–[3] have been developed to evaluate how well language models can understand the physical characteristics and the corresponding utilization. While pre-trained language models (PLMs) have shown remarkable performances on such benchmarks, recent studies [4] have discovered that these models tend to exploit artifacts in benchmarks as shortcuts for the over-estimated results, overpassing the actual reasoning. In this paper, we propose a novel crowdsouring MAA framework to mitigate the artifacts that leads to shortcut effect revealed by the stress test on partial input in physical commonsense reasoning, disclosing the real commonsense reasoning ability of the PLMs.