Reviewerly/exHarmony huggingface dataset:
Mining High Utility Itemset with Multiple Minimum Utility Thresholds Based on Utility Deviation

High Utility Itemset Mining (HUIM) is the task of extracting actionable patterns considering the utility of items such as profits and quantities. An important issue with traditional HUIM methods is that they evaluate all items using a single threshold, which is inconsistent with reality due to differences in the nature and importance of items. Recently, algorithms were proposed to address this problem by assigning a minimum item utility threshold to each item. However, since the minimum item utility (MIU) is expressed as a percentage of the external utility, these methods still face two problems, called “itemset missing” and “itemset explosion”. To solve these problems, this paper introduces a novel notion of Utility Deviation (UD), which is calculated based on the standard deviation. The U D and actual utility are jointly used to calculate the MIU of items. By doing so, the problems of “itemset missing” and “itemset explosion” are alleviated. To implement and evaluate the U D notion, a novel algorithm is proposed, called HUI-MMU-UD. Experimental results demonstrate the effectiveness of the proposed notion for solving the problems of “itemset missing” and “itemset explosion”. Results also show that the proposed algorithm outperforms the previous HUI-MMU algorithm in many cases, in terms of runtime and memory usage.
On Training and Verifying Robust Autoencoders

Autoencoders have become ubiquitous in machine learning thanks to their broad range of applications. Therefore we must be able to formally state and verify properties about their behaviour such as denoising or robustness. However, so far formal verification for autoencoders has almost not been addressed. Thus we introduce the first formal problem specification for robustness of autoencoders.Moreover we give a framework capable of proving the robustness property for autoencoders based on SMT solvers. Yet, because these SMT solvers are notoriously slow, the approach does not scale up to larger autoencoders. Therefore we describe a regularization scheme aimed both to increase the autoencoder’s robustness and to decrease the time required for verification.In our experiments we highlight the use of the new problem specification and compare our proposed regularization scheme to other, already existing ones. Using our approach, verification time becomes up to 21 times faster.
Join-Chain Network: A Logical Reasoning View of the Multi-head Attention in Transformer

Developing neural architectures that are capable of logical reasoning has become increasingly important for a wide range of applications (e.g., natural language processing). Towards this grand objective, we propose a symbolic reasoning architecture that chains many join operators together to model output logical expressions. In particular, we demonstrate that such an ensemble of join chains can express a broad subset of “tree-structured” first-order logical expressions, named <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{FOET}$</tex> which is particularly useful for modeling natural languages. To endow it with differentiable learning capability, we closely examine various neural operators for approximating the symbolic join-chains. Interestingly, we find that the widely used multi-head self-attention module in transformer can be understood as a special neural operator that implements the union bound of the join operator in probabilistic predicate space. Our analysis not only provides a new perspective on the mechanism of the pretrained models such as BERT for natural language understanding, but also suggests several important future improvement directions.
InQMAD: Incremental Quantum Measurement Anomaly Detection

Streaming anomaly detection refers to the problem of detecting anomalous data samples in streams of data. This problem poses challenges that classical and deep anomaly detection methods are not designed to cope with, such as conceptual drift and continuous learning. State-of-the-art flow anomaly detection methods rely on fixed memory using hash functions or nearest neighbors that may not be able to constrain high frequency values or remove seamless outliers and cannot be trained in an end-to-end deep learning architecture. We present a new incremental anomaly detection method that performs continuous density estimation based on random Fourier features and the mechanism of quantum measurements and density matrices. The method continuously updates the estimation of the density of normal samples, giving more importance to new samples and decreasing the importance of older samples. It can process potentially endless data and its update complexity is constant <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(1)$</tex> . A systematic evaluation against 12 state-of-the-art streaming anomaly detection algorithms and using 12 streaming datasets is presented.
Fast Hybrid Oracle-Explainer Approach to Explainability Using Optimized Search of Comprehensible Decision Trees

Explainability, Transparency, and Fairness are now recognized foundations that have altered the landscape of the artificial intelligence domain. Excellent performance is no longer enough if the decisions of a system can upset the life of a regular citizen. The stakeholders of a utilised system must know whether they can trust the results the system provides, for both ethical and legal reasons. This can be made possible with Explainable Artificial Intelligence (xAI). It helps detect errors that could compromise the effectiveness of an intelligent system. It can discover biases present in the data that could lead to unfair treatment. It also provides novel insights regarding the data and the investigated domain. The applicability and benefits of Explainable Artificial Intelligence are explicit; however, scalable and simple-to-use solutions are scarce. Therefore, this paper proposes a new approach to explainability called "Fast Hybrid Oracle-Explainer". This method is a significant extension and improvement over previous approaches utilizing Comprehensible Decision Trees, expanded with the Nearest Neighbors Search algorithm. Compared to its predecessors, the proposed method maintains a similar level of agreement while drastically reducing the time necessary to provide an explanation. Furthermore, it still offers concise and intuitive decision-tree-based explanations. This paper presents details of this new approach in the context of an Intrusion Detection System. The soundness, clarity, and performance of the proposed method are proven experimentally.
A Learning-based Framework for Multi-View Instance Segmentation in Panorama

The application of panoramas in computer vision has received a lot of attention due to its ability to represent information about the surrounding environment. Instance segmentation on panoramas can make machines better understand 3D scenes. However, there are few efforts have been made on detecting the instance for panoramas. The main challenge is that in a panorama, objects are subject to three variations: geometric distortion, edge discontinuity and minification of objects. To address the above issues, we propose an instance segmentation method for panoramas based upon the multi-view fusion. First, a set of sub-views are sampled from a panorama by a rectilinear projection, and then the Cascade Mask R-CNN is employed to perform instance segmentation on each sub-view. Subsequently, we reverse mapping the obtained instance segmentation results of sub-views back to the panorama. Finally, we combine the complementary advantages of large and small fields of view, and merge the instance segmentation results of the entire panorama with the reorganized instance segmentation results to obtain high-quality instance segmentation results for panoramas. The quantitative experiments illustrate that our proposed method obtains higher Mean Average Precision (0.28) than existing benchmark methods.
Gamifying Shooting Training in Cave Automatic Virtual Environment (CAVE)

Shooting is one of the sports that demonstrate accuracy, precision, consistency with the speed of the shooter. In fact, most of the games come with several limitations especially for shooting athlete as most of shooting games are only suitable for those who like playing video games but not for sport purposes. Therefore, this paper discussed on a development of shooting game by using virtual reality with Cave Automatic Virtual Environment (CAVE). CAVE allows people to experience three dimensional spaces in the virtual world along with a safe environment in a convenient way for any purposes. There are some phases has been carried out towards the development of this project started with the documentation and research of virtual reality by using CAVE followed by the implementation of virtual reality by using CAVE in games and ended with evaluation and user acceptance test performed to measure the performance of this project. The proposed VR game is expected to enhance the user experience through usability testing and user acceptance.
Online Air Pollution Inference using Concept Recurrence and Transfer Learning

Pollution from wood burners has profound health implications for the general population. Typically, monitoring the level of airborne particulate matter, PM <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</inf> , in these areas often requires making inferences about missing or corrupted readings. Air Quality inference in these cases often poses critical challenges. The factors can evolve over time, changing the distribution of data. Such changes in the distribution of data are known as concept drift. Moreover, air pollution inference for a location typically would require historical data to be collected for the location. We investigate five air quality studies in New Zealand rural towns. We explore two different research problems: (1) an adaptive recurrent drift algorithm to model recurrence patterns in PM <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</inf> levels for a town with the ability to recover after accuracy deterioration after a concept drift using an adaptive recurrent drift algorithm, and (2) transfer learning for the data stream whereby we reuse a pre-trained air pollution inference model from a town as the starting point for an air pollution inference model on another town. We further investigate the relationship between the changes we detected and changes within the prediction horizon. We showed that the average accuracy of the air quality inference for the five towns is between 70% and 94% using the recurrent drift algorithm. We also show that transfer learning was advantageous between two of the five towns.
The Conversational Short-phrase Speaker Diarization (CSSD) Task: Dataset, Evaluation Metric and Baselines

The conversation scenario is one of the most important and most challenging scenarios for speech processing technologies because people in conversation respond to each other in a casual style. Detecting the speech activities of each person in a conversation is vital to downstream tasks, like natural language processing, machine translation, etc. People refer to the detection technology of ”who speak when” as speaker diarization (SD). Traditionally, diarization error rate (DER) has been used as the standard evaluation metric of SD systems for a long time. However, DER fails to give enough importance to short conversational phrases, which are short but important on the semantic level. Also, a carefully and accurately manually-annotated testing dataset suitable for evaluating the conversational SD technologies is still unavailable in the speech community. In this paper, we design and describe the Conversational Short-phrases Speaker Diarization (CSSD) task, which consists of training and testing datasets, evaluation metric and baselines. In the dataset aspect, despite the previously open-sourced 180-hour conversational MagicData-RAMC dataset, we prepare an individual 20-hour conversational speech test dataset with carefully and artificially verified speakers timestamps annotations for the CSSD task. In the metric aspect, we design the new conversational DER (CDER) evaluation metric, which calculates the SD accuracy at the utterance level. In the baseline aspect, we adopt a commonly used method: Variational Bayes HMM x-vector system, as the baseline of the CSSD task. Our evaluation metric is publicly available at https://github.com/SpeechClub/CDER_Metric.
Improving Speech Recognition with Augmented Synthesized Data and Conditional Model Training

With recent advances in end-to-end text to speech (TTS), the quality of synthetic data has been significantly improved. Synthesized speech is becoming a feasible alternative to human speech for training speech recognizers. By using multi-speaker TTS, the synthesis architecture manipulations the combination of various speakers, prosodies and speaking styles. It helps to enrich the acoustic diversity and benefits the robustness of automatic speech recognition (ASR). However, the improvement of building acoustic model in ASR system with synthetic data is still limited due to the mismatch between the synthetic and the real data. Human speech is more natural and contains infoxrmation that not exist in synthetic data, such as ambient noise and frequency warping inspired by channel. In this paper, we propose two novel techniques to mitigate the problem: (i) Pre-train TTS model with large dataset, and then transfer it to each speaker for generating synthetic data which is more suitable on the ASR task. (ii) A conditional training method that improves the performance of augmenting real data with synthesized materials. Experimental results show that these methods can significantly improve the building of speech recognition systems using synthetic data. For example, the results on AISHELL-1 dataset show the proposed methods can achieve up to 41.7% relative error reduction in character error rate (CER) compared to the traditional method of building ASR model with synthetic data. Moreover, we observe up to 12.7% relative error reduction by augmenting human speech and synthesized speech with our conditional training method compared to naively using the human speech.
Implementation of the School Literacy Program and Its Influence on Reading Interest at Elementary School Students in Suburban of Bali

One of the important aspects of implementing the 21 st -century learning is reading literacy.Reading literacy is influenced by students' reading culture and reading interest.The purpose of this study was to identify (1) the implementation of the School Literacy Program in suburban elementary schools in Bali and (2) the effect of School Literacy Program implementation on students' reading interest in suburban elementary schools of Bali.To achieve this goal, this research used a mixed or combination type of research (mixed methodology).This study took suburban elementary schools in each district in Bali as the samples in which one school was chosen from each district.The total number of samples were 9 schools with 155 students.The data collection technique used in this research was a questionnaire.The questionnaire consisted of (1) the implementation of the School Literacy Program and (2) students' reading interests.The results of this study were that the implementation of the School Literacy Program in suburban elementary schools in Bali was carried out only at the habituation stage.It was done through reading activities for 15 min before the learning activities began.These activities utilized the reading corners in each class.The implementation of the School Literacy Program had a significant effect on students' reading interest in suburban elementary schools throughout the province of Bali.This was reflected in the value of linear regression analysis (t sig (0.000) < (0.05)..
Sandbox: a novel algorithm for object recognition using millimeter wave radar

Focusing on the millimeter wave (MMW) radar classification and recognition problem of objects on the road, such as pedestrians, cyclists, and cars, a sandbox algorithm based on a three-dimensional feature vector, including the target motion feature and electromagnetic (EM) scattering feature radar cross section (RCS), is proposed. And then, a new machine learning algorithm suitable for MMW radar target classification is proposed and verified in the self-test data set. Compared with conventional algorithms, such as k-nearest neighbor (KNN) and support vector machine (SVM), it has better classification accuracy and faster classification speed.
An HLS implementation of on-the-fly randomness test for TRNGs

In this paper, we present an FPGA implementation of a randomness test called the count-the-ones test, which can be used to assure or control the quality of true random number generators (TRNGs) on-the-fly. Since our implementation adopts fixed-point arithmeric operations, reducing their precision is a trade-off between the amount of hardware and computational error. We used high level synthesis (HLS) to assess this trade-off easily. Based on our evaluation, we focused on two versions of circuits. A high-precision circuit required 607 LUTs and 536 FFs for 0.06% of an average error ratio of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\chi^{2}$</tex> value, whereas a resource-saving circuit reduced the amount of hardware to 377 LUTs and 430 FFs with 1.48% of an error ratio.
Multi-instance Multi-label Learning Based on Parallel Attention and Local Label Manifold Correlation

Real-world applications always contain complex data objects with multiple semantics. The machine learning tasks generalized from these applications can be formulated as multi-instance multi-label learning (MIML) problems. Each object is represented by a bag of multiple instances and can be associated with multiple labels simultaneously. Extensive studies on the MIML have been conducted during the past few years. However, many of them do not consider local label correlations, which is critical in multi-label scenario. To address this problem, we propose the multi-instance multi-label learning based on parallel attention and local label manifold correlation (MIML-LLMC) algorithm. First, parallel multiple attention mechanisms convert each bag into a number of fusion vectors, one for each label. They are also utilized to find out which instances in the bag trigger respective labels to improve explanability. Second, local label manifold correlation vectors (LLMCs) are constructed based on label manifold generation and clustering. They provide a high level of representation of the label vectors. Third, each fusion vector is concatenated to the LLMC to predict each label independently. With the utilization of attention and LLMCs, MIML-LLMC is able to discover instance-label relations and exploit local label correlations simultaneously. Experiments reveal that our approach is highly competitive to the state-of-the-art MIML algorithms and yields reasonable results in understanding the relations between input patterns and output label semantics.
Moving Target Tracking Based on Shadow with Unsupervised Deep Track in Video-SAR

The moving target is always defocused and shifted out of the scene in image sequences in video Synthetic Aperture Radar (video-SAR), which makes moving target tracking be a formidable challenge. In this paper, a new moving target tracking method based on shadow with unsupervised deep track (UDT) is proposed. Our track network is built on the Siamese network framework and is trained on a series of unlabeled SAR image sequences with unsupervised learning way. Meanwhile, we use the trajectory detection technology to get more accurate tracking results. Finally., the processing results of the real data are provided to verify the effectiveness of the proposed method.
Multimodal Humor Detection Based on Cross-Modal Attention and Modal Maximum Correlation

Multimodal humor detection is an active research area that uses different combinations of modalities for humor understanding user-generated videos. Previous studies often use a single neural network to process multiple modalities together, which ignores the differences between different modalities. Moreover, because there is no effective and sufficient information interaction between neural networks, and thus the complementary information between different modalities is not captured. In this paper, we propose a new humor detection framework, CAMC, which first uses appropriate neural networks for different modalities and then models multimodal interactions at aligned time steps using cross-modal attention networks. A deep canonical correlation analysis is also used in the fusion phase to capture the maximum correlation between the different modalities. Experiments conducted on the publicly available and popular humor detection dataset UR-FUNNY show that our model has significant advantages over previous robust baselines.
Mining Valuable Fuzzy Patterns via the RFM Model

This paper aims to propose an effective algorithm to discover valuable patterns by applying the fuzzy method to the RFM model. RFM analysis is a common method in customer relationship management, through which we can identify valuable customer groups. By combining RFM analysis with frequent pattern mining, valuable RFM - patterns can be found from the RFM-pattern-tree, such as the RFMP-growth algorithm. Aiming to mine patterns that have quantitative relationships among items, we introduce the fuzzy method in the RFM model, and we present a fuzzy - Rfu - tree algorithm in which a new pruning strategy is proposed to prune candidate patterns. Experiments show the effectiveness of the new algorithm. The new algorithm guarantees a high overlap degree with the RFM-patterns gen-erated by RFMP-growth, with more valuable information (with additional fuzzy level) in the mined patterns.
The ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge (ICSRC): Dataset, Tracks, Baseline and Results

This paper summarizes the outcomes from the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge (ICSRC). We first address the necessity of the challenge and then introduce the associated dataset collected from a new-energy vehicle (NEV) covering a variety of cockpit acoustic conditions and linguistic contents. We then describe the track arrangement and the baseline system. Specifically, we set up two tracks in terms of allowed model/system size to investigate resource-constrained and -unconstrained setups, targeting to vehicle embedded as well as cloud ASR systems respectively. Finally we summarize the challenge results and provide the major observations from the submitted systems.
SAR Target Detection Based on Improved SSD with Saliency Map

A target detection method based on improved single shot multibox detector (SSD) is proposed to solve insufficient training samples for synthetic aperture radar (SAR) target detection. Firstly, a residual network is introduced in the feature extraction structure for extracting deep features of the target; then, the aspect ratio of default boxes is redesigned to match different sizes of target detection; finally, the existing dataset is expanded with small samples based on saliency map. The experimental results based on the simulated SAR data and the real SAR data prove that the proposed method has better performance in SAR target detection.