Reviewerly/exHarmony huggingface dataset:
Explainable Sequential Anomaly Detection via Prototypes

Sequential anomaly detection has received more and more attention because of its wide applications in various domains, such as debugging system failures via logs. Researchers have recently proposed many deep learning-based approaches for sequential anomaly detection. However, these approaches work as black-boxed models, not providing explanations for detected anomalies. On the other hand, explainability is a critical requirement to build trustworthiness in detection results. Moreover, domain experts would like to learn why a sequence is labeled as an anomaly. To overcome this challenge, in this paper, we propose a framework for Explainable Sequential Anomaly Detection (ESAD) in a semi-supervised setting. As there are various normal and abnormal behaviors in sequential data, ESAD derives multiple prototypes to describe diverse normal and abnormal sequences. Each prototype can encode one type of normal or abnormal behavior. Given a new sequence, if the sequence is similar to an abnormal prototype, the sequence will be detected as abnormal. After decoding the abnormal prototype as a prototypical sequence, domain experts can further understand the newly detected abnormal sequence by examining the prototypical sequence. We conduct experiments on one log dataset and two text datasets. Experimental results including quantitative and qualitative analysis on three datasets show the effectiveness of our model.
Self-supervised Bidirectional Prompt Tuning for Entity-enhanced Pre-trained Language Model

With the promotion of the pre-training paradigm, researchers are increasingly focusing on injecting external knowledge, such as entities and triplets from knowledge graphs, into pre-trained language models (PTMs) to improve their understanding and logical reasoning abilities. This results in significant improvements in natural language understanding and generation tasks and some level of interpretability. In this paper, we propose a novel two-stage entity knowledge enhancement pipeline for Chinese pre-trained models based on “bidirectional” prompt tuning. The pipeline consists of a “forward” stage, in which we construct fine-grained entity type prompt templates to boost PTMs injected with entity knowledge, and a “backward” stage, where the trained templates are used to generate type-constrained context-dependent negative samples for contrastive learning. Experiments on six classification tasks in the Chinese Language Understanding Evaluation (CLUE) benchmark demonstrate that our approach significantly improves upon the baseline results in most datasets, particularly those that have a strong reliance on diverse and extensive knowledge.
RCN:Rules-Constrained Network for Autonomous Driving Scene Recognition

Autonomous driving scene recognition is a challenging and promising task that aims to locate and classify the temporal regions where driving scenes may occur. Most of the existing methods use the methods of temporal action proposal generation: the temporal scene of autonomous driving is seen as an action for recognition. However, due to the complexity and similarity of scene conditions, the application of temporal action recognition methods to autonomous driving scenes is not as effective. To solve these difficulties, based on the fact that autonomous driving scenes are subject to certain rules, we propose a rule-assisted mechanism to help classification, which is based on the fact that the temporal order of autonomous driving scenes is governed by traffic rules and realistic situations, so that the classification results that violate the rules are cancelled out, thus improving the effectiveness of classification. In the rule-constrained approach, we propose two rule formulation methods, one is to manually formulate a perfect rule to which all the temporal order scenes must conform, and the other is to generate rules in the training dataset using the algorithm we propose. We conducted experiments on two autonomous driving datasets provided by CAC data centers, and in combination with existing action classifiers, our approach shows significant performance improvements and is scalable.
Defense Against Reconstruction Attacks in Split Federated Learning Through Decreasing Correlation Between Inputs and Activations

Split Federated Learning (SFL) is the most recent distributed training scheme. Compared to Federated Learning, SFL reduces the overhead of client while achieving better privacy protection. However, SFL attackers can still use the intermediate activations of client to reconstruct the original data containing sensitive information. To defend against this reconstruction attack, we propose to use distance correlation loss to reduce the overall correlation between input data and intermediate activations, and we further construct an effective and efficient dynamic channel pruning network to automatically sense the sensitive channels in intermediate activation and thus selectively obfuscate sensitive information. On CIFAR-10, FairFace and HAM10000 datasets, we carry out Auto-encoder attack and Model Inversion (MI) attack to the model based on ResNet-18. The experimental results show that compared with the existing methods, our method obtains better defense performance at a very low utility loss, thus achieving better privacy utility tradeoff. On CIFAR-10 dataset, our method obtains 0.033 reconstruction MSE (Mean Square Error) for both Auto-encoder attack and MI attack, with 0.019 and 0.014 gains than the existing SFL, respectively. Additionally, our method achieves 74.41% accuracy, only with 1% less accuracy than the existing SFL.
Energy Efficient Memory-based Inference of LSTM by Exploiting FPGA Overlay

The fourth industrial revolution (a.k.a. Industry 4.0) relies on intelligent machines that are fully autonomous and can diagnose and resolve operational issues without human intervention. Therefore, embedded computing platforms enabling the necessary computations for intelligent machines are critical for the ongoing industrial revolution. Especially field programmable gate arrays (FPGAs) are highly suited for such embedded computing due to their high performance and easy reconfigurability. Many Industry 4.0 applications, such as predictive maintenance, critically depend on real-time and reliable processing of time-series data using recurrent neural network models, especially long short-term memory (LSTM). Therefore, the FPGA-based acceleration of LSTM is imperative for many Industry 4.0 applications. Existing LSTM models for FPGAs incur significant resources and power and are not energy efficient. Moreover, prior works focusing on reducing latency and power mainly adhere to model pruning, which compromises the accuracy. Comparatively, we propose a memory-based energy-efficient inference of LSTM by exploiting overlay in FPGA. In our methodology, we pre-compute predominant operations and store them in the available embedded memory blocks (EMBs) of an FPGA. On-demand, these pre-computed results are accessed to minimize the necessary workload. Via this methodology, we obtained lower latency, lower power, and better energy efficiency than state-of-the-art LSTM models without any loss of accuracy. Specifically, when implemented on the ZynQ XCU104 evaluation board, a 3 x reduction in latency and 5 x reduction in power is obtained then the reference 16-bit LSTM model.
Efficient Repetition Coding for Deep Learning Towards Implementation Using Emerging Non-Volatile Memory with Write-Errors

Emerging non-volatile memory devices, such as resistive random access memory (ReRAM) and voltage-controlled magnetoresistive random access memory (VC-MRAM), promise low energy consumption for artificial intelligence applications. However, when implementing deep neural networks (DNNs) using such memory devices, write-error may cause millions of bit-flipping to DNN. This easily degrades the DNN performance. To address this problem, we propose a novel repetition coding for deep-learning (RC-DL), which is a repetition coding designed to protect IEEE 32-bit floating-point (FP32) DNN models. Compared to conventional repetition coding, the proposed RC-DL exploits FP32 non-uniform magnitude encoding by increasing the repeat rates to protect sensitive bit positions and reduce the repeat rates to insensitive bit positions. Hence, RC-DL uses a number of bits equivalent to a 3-bit repetition code while delivering the performance close to 11-bit repetition code. We perform extensive Monte Carlo simulations to simulate the write-error property with ImageNet 2012 pretrained models. The DNN models with RC-DL are shown to be operable in the extremely imperfect environment while delivering with only minor reductions in DNN performance.
Dual Relation-Aware Entity Alignment for Knowledge Graph

Entity alignment (EA) is a vital step for knowledge fusion, which aims to discover entities with the same meaning from the different knowledge graphs. Several researchers attempted to obtain enhanced entity embeddings by using the strategies that relation-aware entity embeddings for EA. However, they ignored the effect of aggregating different range neighborhoods of entities on relation embeddings. In this study, we propose a novel Dual Relation-aware Entity Alignment framework named DRAEA for EA. Specifically, we utilize entity embeddings based on 1-hop neighborhoods and 2-hop neighbor-hoods to represent different relation embeddings, respectively. Then we aggregate different relation embeddings back to entity embeddings by using the self-attention mechanism. Last, an effective entity-assisted global alignment strategy is designed to accomplish the EA tasks. Experimental results on three real-world datasets show that DRAEA outperforms the state-of-the-art methods.
Towards Multi-User Activity Recognition through Facilitated Training Data and Deep Learning for Human-Robot Collaboration Applications

Human-robot interaction (HRI) research is progressively addressing multi-party scenarios, where a robot interacts with more than one human user at the same time. Conversely, research is still at an early stage for human-robot collaboration The use of machine learning techniques to handle such type of collaboration requires data that are less feasible to produce than in a typical HRC setup. This work outlines scenarios of concurrent tasks for non-dyadic HRC applications. Based upon these concepts, this study also proposes an alternative way of gathering data regarding multi-user activity, by collecting data related to single users and merging them in post-processing, to reduce the effort involved in producing recordings of pair settings. To validate this statement, 3D skeleton poses of activity of single users were collected and merged in pairs. After this, such datapoints were used to separately train a long short-term memory (LSTM) network and a variational autoencoder (VAE) composed of spatio-temporal graph convolutional networks (STGCN) to recognise the joint activities of the pairs of people. The results showed that it is possible to make use of data collected in this way for pair HRC settings and get similar performances compared to using training data regarding groups of users recorded under the same settings, relieving from the technical difficulties involved in producing these data. The related code and collected data are publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The code repository is available at: https://github.com/francescosemeraro/Multi_User_through_Single_User.git. The data repository is available at: https://figshare.com/s/64bd023e968e6eb096f3..
Patch-wise Mixed-Precision Quantization of Vision Transformer

As emerging hardware begins to support mixed bit-width arithmetic computation, mixed-precision quantization is widely used to reduce the complexity of neural networks. However, Vision Transformers (ViT ViTs) require complex self-attention computation to guarantee the learning of powerful feature representations, which makes mixed-precision quantization of ViTs still challenging. In this paper, we propose a novel patch-wise mixed-precision quantization (PMQ) for efficient inference of ViT <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s$</tex> . Specifically, we design a lightweight global metric, which is faster than existing methods, to measure the sensitivity of each component in ViT <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s$</tex> to quantization errors. Moreover, we also introduce a pareto frontier approach to automatically allocate the optimal bit-precision according to the sensitivity. To further reduce the computational complexity of self-attention in inference stage, we propose a patch-wise module to reallocate bit-width of patches in each layer. Extensive experiments on the ImageNet dataset shows that our method greatly reduces the search cost and facilitates the application of mixed-precision quantization to ViTs.
Two-way Cross-domain Recommendation with Central Social Influence

As an effective method to solve the cold start problem of recommender systems, cross-domain recommendation has received more and more attention and research. Currently, most cross-domain recommendation models rely on the unidirectional knowledge transfer between the same users in the source and target domains. The rich information in the source domain is transferred to the target domain with sparse information to achieve better recommendation effect. However, the performance of cross-domain recommendation heavily depends on the number of overlapping active users, and these models cannot fully utilize the useful knowledge behind active users in a single domain. This limitation makes it difficult for the model to achieve ideal results in real-world scenarios. To solve the above problems and optimize cross-domain recommendation, we propose a Two-way Cross-domain Recommendation with Central Social Influence(CST-CDR) and the concept of super-user. Through the idea of clustering, user circles are formed centering on active super-users, so the potential common preferences of single-field active users, dual-field active users, and super-users are fully explored, alleviating the dependence of the model on overlapping and active users. At the same time, the cross-domain recommendation is extended to realize two-way information migration, so that users who are active in only one domain can have a preliminary preference judgment. Finally, the effectiveness of the proposed method is demonstrated on two real datasets.
Towards Better Word Importance Ranking in Textual Adversarial Attacks

Transformer models have been widely used in the filed of natural language processing due to their powerful learning ability. Nevertheless, recent studies have shown that transformer models are vulnerable to the maliciously crafted adversarial examples. In the challenging black box setting, main stream textual adversarial attacks typically consist of two steps: Word Importance Ranking (WIR) and word transformation. The attack performance is highly dependent on the ranking of words. Existing WIR methods are designed with heuristic rules, which lack theoretical guarantee and require a large amount of queries. To address this issue, we design a textual coalitional game and propose PWSHAP, which is a plug-and-in WIR method employing Shapley value to determine the significance of each word based on its impact on the classification. Through extensive experiments on three benchmark datasets and model architectures, we illustrate that the proposed PWSHAP achieve the-state-of-the-art attack success rate with significant fewer queries to the classification model. Meanwhile, the generated adversarial examples are more natural and coherent compared to the strong baselines.
Simulation of Chinese language and text information system processing mode based on hidden Markov model

With the popularization of computers, artificial intelligence technology has become more and more mature, among which speech recognition technology in artificial intelligence is favored by people. In the past few years, the acoustic model combined with the Gaussian mixture model and the hidden Markov model has always been in the leading position. In the field of speech recognition technology, because the development of speech data has gradually expanded, and the complexity of the data has also increased. The larger the data, the traditional data network model is slowly showing inadequacy. However, the deep neural network model is easy to deal with large and complex data modeling. This article combines the advantages of basic learning theory and speech recognition technology, and launches in-depth research on embedding learning theoretical knowledge into the field of speech recognition. Nowadays, large-scale text information databases relying on computers are becoming more and more important in linguistic research, and a large-scale corpus that fully reflects language facts and contains rich language information has been constructed. The establishment of the text information database system is long, from word segmentation, part-of-speech tagging to syntactic tagging to semantic tagging. Therefore, the characteristic of information system processing is that the systematic description depends on the application environment of understanding vocabulary and reasoning. According to different scenarios, the realization methods of semantic description roles are also different, and the description of semantic roles in correct scenarios is clearer, more detailed and systematic. Therefore, this article is of great significance for solving the semantic problem of using Chinese frame network for Chinese information processing in the context of speech recognition.
TETRAA - Trained and Selective Transmutation of Encoder-based Adversarial Attack

The adversarial attack is a pivotal field of research. These attacks can help the organization identify model vulnerabilities and save them from financial and reputation loss. Researchers have already explored several adversarial attacks on computer vision applications. However, these attacks fail when it comes to the transactional dataset-which has added constraints of limited attack attempts and scalability for large feature space containing both categorical merchant code and continuous transaction amount. The existing literature on the adversarial attack for the tabular domain is limited to numerical features only. To overcome these challenges, we introduce a novel Genetic attack algorithm TETRAA for black-box attacks that produce evolution-based specialized perturbations through encoder/autoencoder for each example, giving the advantage of query efficiency. A novel fitness function is formalized for handling the perturbation of categorical features under the constraints of realistic samples. Further, we improve the success rate and the norm of perturbation of generated samples using a binary search-based approach. Experiments show TETRAA requires significantly fewer model queries than several state-of-the-art black-box adversarial attacks ZOO, boundary attack, HopSkipJump attack, and ESPA, along with better success rate and norm of perturbation when tested on untargeted attacks.
Reducing the Cost of Cycle-Time Tuning for Real-World Policy Optimization

Continuous-time reinforcement learning tasks commonly use discrete steps of fixed cycle times for actions. As practitioners need to choose the action-cycle time for a given task, a significant concern is whether the hyper-parameters of the learning algorithm need to be re-tuned for each choice of the cycle time, which is prohibitive for real-world robotics. In this work, we investigate the widely-used baseline hyper-parameter values of two policy gradient algorithms—PPO and SAC—across different cycle times. Using a benchmark task where the baseline hyper-parameters of both algorithms were shown to work well, we reveal that when a cycle time different than the task default is chosen, PPO with baseline hyper-parameters fails to learn. Moreover, both PPO and SAC with their baseline hyper-parameters perform substantially worse than their tuned values for each cycle time. We propose novel approaches for setting these hyper-parameters based on the cycle time. In our experiments on simulated and real-world robotic tasks, the proposed approaches performed at least as well as the baseline hyper-parameters, with significantly better performance for most choices of the cycle time, and did not result in learning failure for any cycle time. Hyper-parameter tuning still remains a significant barrier for real-world robotics, as our approaches require some initial tuning on a new task, even though it is negligible compared to an extensive tuning for each cycle time. Our approach requires no additional tuning after the cycle time is changed for a given task and is a step toward avoiding extensive and costly hyper-parameter tuning for real-world policy optimization.
TADS: Temporal Autoencoder Dynamic Series Framework for Unsupervised Anomaly Detection

Deep learning models for time series anomaly detection are mainly divided into supervised learning, semi-supervised learning, and unsupervised learning. Unsupervised learning has grown in importance for identifying anomaly features in time series without labels. Unsupervised learning of anomaly detection needs to be improved in two aspects. One is the calculation time, the other is the anomaly score. Some excellent unsupervised learning algorithms utilize clustering algorithms to filter out normal features, which takes too much time. Moreover, these algorithms use a fixed-length window to calculate anomaly scores, leading to inaccuracies and fluctuations. This paper proposes a Temporal Autoencoder Dynamic Series (TADS) framework. Here are the original contributions of the paper. Firstly, in the process of network compression coding, an enhanced Channel Attention Mechanism is used to identify critical features. Secondly, it uses Dynamic Series to replace the fixed window which improves the inaccuracy and fluctuation and also avoids the random error behavior caused by parameter adjustment. In addition, it is unnecessary to use clustering algorithms to obtain normal features. Finally, on the public MIT-BIH dataset, the experimental results show that TADS can improve the F1 score from 79% to 91% and accuracy from 89% to 93%. Meanwhile, the calculation time and fluctuation decreased by 42 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and 19.8%, respectively. The experimental results demonstrate the performance of TADS for unsupervised anomaly detection on the MIT-BIH time series of ECG dataset through qualitative and quantitative methods.
A Mixture of Experts with Adaptive Semantic Encoding for Event Detection

Event Detection (ED) is a challenging but valuable task. It aims to identify the words that trigger the events in text and classify them into pre-defined types. The previous works utilize entity information as supplementary clues for ED. However, word semantics in different contexts always have subtle variations. Capturing the connection between entity information and changing semantics is challenging. To tackle the issue, we propose the Mixture of Experts (MoE) technique with context clues to adaptively model semantics. We term the framework as CMoE. Concretely, the CMoE simultaneously performs entity and event detection with the MoE technique which are flexible components embedded transformer block. Furthermore, we apply multi-task learning to mine the shared knowledge between entity and event. We conduct experiments on the public ACE 2005 and KBP 2017 datasets. The results show that our model achieves competitive performance on ACE 2005 without using external knowledge, yielding an improvement of about 2.3% F1-score for ED. More importantly, our model outperforms all State-of-The-Art models on KBP 2017.