Reviewerly/exHarmony huggingface dataset:
A Prediction-Augmented AutoEncoder for Multivariate Time Series Anomaly Detection

To ensure the normal running of IT systems, multivariate time series changing constantly related to system states need to be monitored to detect unexpected events and anomalies and further prevent adverse effects on the systems. With the rapid increase of complexity and scales of the systems, a more automated and accurate anomaly detection method becomes crucial. In this paper, we propose a new framework called Prediction-Augmented AutoEncoder (PAAE) for multivariate time series anomaly detection, which learns a better representation of normal data from the perspective of reconstruction and prediction. Predictive augmentation is introduced to constrain the latent space to improve the ability of the model to recognize anomalies. And a novel anomaly score is developed considering both the reconstruction errors and prediction errors to reduce false negatives. Extensive experiments prove that the introduction of prediction is effective and show the superiority and robustness of PAAE. Particularly, PAAE obtains at best 7.9% performance improvement on the SMAP dataset.
Automotive Augmented Reality: User Experience and Enabling Technology

Abstract AR HUD systems offer compelling benefits to drivers because of improvements in situational awareness and an increase in comfort with autonomous systems.
End-to-end speech recognition from raw speech: Multi time-frequency resolution CNN architecture for efficient representation learning

We propose a multi time-frequency (t-f) resolution CNN architecture for end-to-end speech recognition from raw speech waveform. We address issues related to the nature of front-end convolutional kernels and the kind of multi t-f spectrographic feature maps formed and the back-end convolutional processing of the feature maps within two tasks, namely, frame-wise phoneme classification and an encoder-decoder (with CTC-Attention loss) based continuous phoneme decoding. Our multi t-f resolution CNN (MT-CNN) architecture works with unconstrained learnt kernels and back-end 2D-convolutional layers to process the multi t-f spectrographic feature maps for these tasks. We contrast this architecture with two other variants in recent work - a multi-scale feature based system and the SincNet (which uses parameterized convolutional kernels constrained in the form of Sinc functions with learnable bandwidths). We show a consistent performance gain of the proposed multi t-f architecture over these two variants - a 3-8% accuracy (absolute gain) in the frame-wise classification task and 3% PER (absolute gain) in the continuous phoneme decoding task. These two performance gains together establish the effectiveness of the proposed architecture in using the multi t-f unconstrained variable length 1-D convolutional kernels, 2-D multi t-f spectrographic feature maps and the back-end 2-D convolution layers.
Rethinking Binary Hyperparameters for Deep Transfer Learning

The current standard for a variety of computer vision tasks using smaller numbers of labelled training examples is to fine-tune from weights pre-trained on a large image classification dataset such as ImageNet. The application of transfer learning and transfer learning methods tends to be rigidly binary. A model is either pre-trained or not pre-trained. Pre-training a model either increases performance or decreases it, the latter being defined as negative transfer. Application of L2-SP regularisation that decays the weights towards their pre-trained values is either applied or all weights are decayed towards 0. This paper re-examines these assumptions. Our recommendations are based on extensive empirical evaluation that demonstrate the application of a non-binary approach to achieve optimal results. (1) Achieving best performance on each individual dataset requires careful adjustment of various transfer learning hyperparameters not usually considered, including number of layers to transfer, different learning rates for different layers and different combinations of L2SP and L2 regularization. (2) Best practice can be achieved using a number of measures of how well the pre-trained weights fit the target dataset to guide optimal hyperparameters. We present methods for non-binary transfer learning including combining L2SP and L2 regularization and performing non-traditional fine-tuning hyperparameter searches. Finally we suggest heuristics for determining the optimal transfer learning hyperparameters. The benefits of using a non-binary approach are supported by final results that come close to or exceed state of the art performance on a variety of tasks that have traditionally been more difficult for transfer learning.
A Fully Unsupervised and Efficient Anomaly Detection Approach with Drift Detection Capability

Accurate detection of anomalies on dynamic data streams is challenging due to the high velocity, large volume, and particularly, its dynamicity property which exhibits concept drift. This may result in varying anomaly contexts over time. Some researches assume such dynamicity is seasonal and attempt to model the seasonality for anomaly detection. However, this becomes non-viable when the assumption is violated. In this paper, we propose MIRMAD, a simple, on-line, and ensemble-based anomaly detection algorithm that is able to overcome the above-mentioned challenges with the capability to identify drifting locations in the data stream and discard outdated data to minimize performance losses. Especially in an unsupervised environment, identifying the drift locations provides an additional level of information to analysts for informed decision-making. Empirical results on benchmark data sets have demonstrated that the fully unsupervised MIRMAD&#x2019;s performances are comparable to even semi-supervised approaches and yet runs at least 15 times faster than the compared methods on average. We further investigate MIRMAD&#x2019;s efficacy in a real-world case study and provided a detailed sensitivity analysis on different parameter settings.
Physical training online: transitioning towards digital pedagogies

This article discusses a case-study revolving around an experiment in transferring online a teaching component on physical theatre. It documents a response to the COVID-19 pandemic when actors, teachers, and students had to find ever more creative ways of working in the digital space. The article argues that working online does not need to refute those values and learning objectives that are typically embedded in performance training, such as interaction, collaboration, and experimentation and adaptation. The article also offers concrete suggestions aimed at facilitating the transition to online teaching.
Research on Deep Learning-Based Algorithm and Model for Personalized Recommendation of Resources

Abstract Resource recommendation system is a new type of management system, which uses personalized information to solve business needs such as customer consultation and product recommendation, and provides users with high quality services and achieves accurate marketing, so nowadays resource recommendation system has a pivotal role in modern resource management. In this paper, I study the algorithm and model of resource personalized recommendation based on deep learning, taking human resource recommendation as an example.
Deep neural network based learning to rank for address standardization

Address standardization is the process of converting and mapping free-form addresses into a standard structured format. For many business cases, the addresses are entered into the information systems by end-users. They are often noisy, uncompleted, and in different formatted styles. In this paper, we propose a deep learning-based approach to the address standardization challenge. Our key idea is to leverage a Siamese neural network model to embed raw inputs and standardized addresses into a single latent multi-dimensional space. Thus, the corresponding of the raw input address is the one with the highest-ranking score. Our experiments demonstrate that our best model achieved 95.41% accuracy, which is 6.6% improvement from the current state of the art.
Design and Verification of Process Discovery Based on NLP Approach and Visualization for Manufacturing Industry

When a consultant of a company that provides a smart factory solution consults with a customer, it is difficult to define the outline of the manufacturing process and create all activities within the process by case. It requires a large amount of resources from the company to perform a task. In this study, we propose a process discovery automation system that helps consultants define manufacturing processes. In addition, for process discovery, a fully attention-based transformer model, which has recently shown a strong performance, was applied. To be useful to consultants, we solved the black box characteristics of the deep learning model applied to process discovery and proposed a visualization method that can be used in the monitoring system when explaining the discovery process. In this study, we used the event log of the metal fabrication process to perform the modeling, visualization, and evaluation.
Energy efficient resource controller for Apache Storm

Summary Apache Storm is a distributed processing engine that can reliably process unbounded streams of data for real‐time applications. While recent research activities mostly focused on devising a resource allocation and task scheduling algorithm to satisfy high performance or low latency requirements of Storm applications across a distributed and multi‐core system, finding a solution that can optimize the energy consumption of running applications remains an important research question to be further explored. In this article, we present a controlling strategy for CPU throttling that continuously optimize the level of consumed energy of a Storm platform by adjusting the voltage and frequency of the CPU cores while running the assigned tasks under latency constraints defined by the end‐users. The experimental results running over a Storm cluster with 4 physical nodes (total 24 cores) validates the effectiveness of proposed solution when running multiple compute‐intensive operations. In particular, the proposed controller can keep the latency of analytic tasks, in terms of 99th latency percentile, within the quality of service requirement specified by the end‐user while reducing the total energy consumption by 18% on average across the entire Storm platform.
Chinese Paraphrase Dataset and Detection

Linguistic diversity is one of the core challenges faced by natural language processing technology, and paraphrase reflects the diversity of language. In recent years, Chinese paraphrase technology has received wide attention from academic and business researchers. There is a number of Chinese paraphrase datasets are published, and paraphrase detection evaluation tasks have been released. However, most of these Chinese paraphrase datasets have not been widely used. To resolve the above issue, we try to evaluate the value of current Chinese paraphrase datasets through paraphrase detection task, and whether the value of paraphrase is weakened in the era of pre-trained language models such as Bert. The experimental results show that the current Chinese paraphrase dataset has a significant enhancement effect on the paraphrase detection task. And the performances of paraphrase detection achieved by Bert can be further enhanced with paraphrase dataset. The result proves the value of Chinese paraphrase dataset on paraphrase detection task.
Revisiting Flipping Strategy for Learning-based Stereo Depth Estimation

Deep neural networks (DNNs) have been widely used for stereo depth estimation, which achieve great success in performance. In this paper, we introduce a novel flipping strategy for DNN on the stereo depth estimation task. Specifically, based on a common DNN for stereo matching, we apply the flipping operation for both input stereo images, which are further fed to the original DNN. A flipping loss function is proposed to jointly train the network with the initial loss. We apply our strategy to many representative networks in both supervised and self-supervised manners. Extensive experimental results demonstrate that our proposed strategy improves the performance of these networks.
Overcoming Rare-Language Discrimination in Multi-Lingual Sentiment Analysis

The digitalization of almost all aspects of our everyday lives has led to unprecedented amounts of data being freely available on the Internet. In particular social media platforms provide rich sources of user-generated data, though typically in unstructured form, and with high diversity, such as written in many different languages.Automatically identifying meaningful information in such big data resources and extracting it efficiently is one of the ongoing challenges of our time. A common step for this is sentiment analysis, which forms the foundation for tasks such as opinion mining or trend prediction. Unfortunately, publicly available tools for this task are almost exclusively available for English-language texts. Consequently, a large fraction of the Internet users, who do not communicate in English, are ignored in automatized studies, a phenomenon called rare-language discrimination.In this work we propose a technique to overcome this problem by a truly multi-lingual model, which can be trained automatically without linguistic knowledge or even the ability to read the many target languages. The main step is to combine self-annotation, specifically the use of emoticons as a proxy for labels, with multi-lingual sentence representations.To evaluate our method we curated several large datasets from data obtained via the free Twitter streaming API. The results show that our proposed multi-lingual training is able to achieve sentiment predictions at the same quality level for rare languages as for frequent ones, and in particular clearly better than what mono-lingual training achieves on the same data. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Risk prediction for repeated measures health outcomes: A divide and recombine framework

We propose a machine learning framework for risk prediction for binary response sequence observed over time, creating a trajectory for disease progression and regression. The proposed framework employs a divide and recombine technique using the relation between marginal, conditional, and joint probability models from probability theory. To demonstrate the framework, the data from the US Health and Retirement Study with seven follow-ups for the response, the activity of daily living index (ADL), and risk factors have been used. To assess the effects of the risk factors on ADL, the proposed framework adapted regressive logistic regression, logistic regression with the lasso, support vector machines, classification tree, random forests, and neural network models. The models are tuned and evaluated on the training and test data containing 75% and 25% of the cases, respectively. The test data accuracies varied from 92% to 95% across different follow-ups with high specificity and sensitivity. The accuracy, sensitivity, and specificity for the ensemble of the six models are found very high, all above 90%. Inclusion of interaction terms between the risk factors, risk factors and historical ADL, and historical ADL from different follow-ups in the regressive logistic model shows noticeable improvements in accuracy, sensitivity, and specificity. Adjusting the probability threshold for classification shows a considerable increase in sensitivity. The framework provides a general and flexible approach in addressing the issue of risk predictions for health-related response, which is repeated over time and longitudinal in nature. This method can be used in other applications to analyze big data.
360-Degree Image Classification and Viewport Prediction Using Deep Neural Networks

The growing popularity of 360-degree (360D) content demands for comfortable content consumption by the end users. A 360D image/video captures it surrounding and provides the end user with an enjoyable emissive experience of that place. However, not the whole scene is important for the user most of the time. That poses a challenge in viewing a 360D image/video on a 2D screen. The salience view in a 360D image is location based. In this article, the important viewport is predicted for 360D images in twenty-six categories of places using the Sun360 dataset. The proposed method classifies the equirectangular images based on place category using deep convolutional neural networks. From the classified images a set of viewports are generated by the transformation equation. Best viewport is predicted from the set using feature extraction and reverse image search.
Application of Digital Twin Theory for Improvement of Natural Gas Treatment Unit

This paper describes fundamental principles of Digital Twins theory and provides exact investigation results in application of Digital Twins theory in upstream branch of oil and gas industry, namely based on example of natural gas treatment plant’s performance increase. As one of key process units of natural gas treatment which allows to implement most powerful functions of digital twin gas sweetening unit with set of membranes is considered as object of investigation. On the base of membrane technology manipulated variables are defined as inputs to digital twin model. Some theoretical results as well as real references of model’s engine calculations are reflected in the paper. Details of technical dashboards to visualize calculated results of running model based on manipulated variables are presented including monthly key performance indicators report dashboard, process flow diagram dashboard and high-level management dashboard. Paper also demonstrates data flow between digital twin model and real process unit and also inside digital twin model.
Minimal linear codes constructed from functions

In this paper, we consider minimal linear codes by a general construction of linear codes from q-ary functions. First, we give necessary and sufficient conditions for codewords which are constructed by functions to be minimal. Second, as applications, we present three constructions of minimal linear codes. Constructions on minimal linear codes in this paper generalize some recent results in Ding et al. (IEEE Trans. Inf. Theory 64(10), 6536–6545, 2018); Heng et al. (Finite Fields Appl. 54, 176–196, 2018); Bartoli and Bonini (IEEE Trans. Inf. Theory 65(7), 4152–4155, 2019); Mesnager et al. (IEEE Trans. Inf. Theory 66(9), 5404–5413, 2020); Bonini and Borello (J. Algebraic Comb. 53, 327–341, 2021). In our three constructions, the conditions of functions are much looser than theirs.
Facial Recognition for Remote Electronic Voting – Missing Piece of the Puzzle or Yet Another Liability?

Reliable voter identification is one of the key requirements to guarantee eligibility and uniformity of elections. In a remote setting, this task becomes more complicated compared to voter identification at a physical polling station. In case strong cryptographic mechanisms are not available, biometrics is one of the available alternatives to consider. In this paper, we take a closer look at facial recognition as a possible remote voter identification measure. We cover technical aspects of facial recognition relevant to voting, discuss the main architectural decisions, and analyse some of the remaining open problems, including dispute resolution and privacy issues.
The rotation scheme of quantum states based on EPR pairs

In this paper, we give a further discussion of short-distance teleportation. We propose bidirectional, rotation and cyclic rotation teleportation schemes for short-distance participants, respectively. In our bidirectional transmission scheme, the quantum channel is still an EPR pair and an auxiliary qubit in the ground state [Formula: see text], and two participants can transmit an unknown single-qubit state to each other. In the rotation and cyclic rotation schemes, bidirectional transmission is performed between two adjacent participants in turn. The unknown state qubits of the participants collapse into the ground state after one bidirectional transmission, and can be used as auxiliary qubits in subsequent bidirectional transmission. After a complete state rotation, each participant has held the unknown state of the other participants, and the last one owned by the participant is still the original unknown state. Although the schemes we proposed are applicable to a small range of transmission, they have certain advantages in saving quantum resources.
FedSmarteum: Secure Federated Matrix Factorization Using Smart Contracts for Multi-Cloud Supply Chain

With increased awareness comes unprecedented expectations. We live in a digital, cloud era wherein the underlying information architectures are expected to be elastic, secure, resilient, and handle petabyte scaling. The expectation of epic proportions from the next generation of the data frameworks is to not only do all of the above but also build it on a foundation of trust and explainability across multi-organization business networks. From cloud providers to automobile industries or even vaccine manufacturers, components are often sourced by a complex, not full digitized thread of disjoint suppliers. Building Machine Learning and AI-based order fulfillment and predictive models, remediating issues, is a challenge for multi-organization supply chain automation. We posit that Federated Learning in conjunction with blockchain and smart contracts are technologies primed to tackle data privacy and centralization challenges. In this paper, motivated by challenges in the industry, we propose a decentralized distributed system in conjunction with a recommendation system model (Matrix Factorization) that is trained using Federated Learning on an Ethereum blockchain network. We leverage smart contracts that allow decentralized serverless aggregation to update local-ized items vectors. Furthermore, we utilize Homomorphic Encryption (HE) to allow sharing the encrypted gradients over the network while maintaining their privacy. Based on our results, we argue that training a model over a serverless Blockchain network using smart contracts will provide the same accuracy as in a centralized model while maintaining our serverless model privacy and reducing the overhead communication to a central server. Finally, we assert such a system that provides transparency, audit-ready and deep insights into supply chain operations for enterprise cloud customers resulting in cost savings and higher Quality of Service (QoS).
Steganography in RGB Images Using Adjacent Mean

Steganography is the practice of hiding information or data in a seemingly innocuous cover medium, such as message, file, image, audio, and video.In the past decades, many approaches of steganography in images were proposed for various applications.In social communication and the information highly exposed society, steganography requires high embedding capacity to transmit secret data efficiently.Generally, there is a trade-off between fidelity and embedding capacity.In this paper, we propose a novel and efficient data hiding algorithm in 24-bit color images with super high embedding capacity and acceptable peak signal-to-noise ratio (PSNR) using spatial-domain-adjacent mean.In the proposed algorithm, the embedding rate is about 7.4 bits per pixel (bpp) when the PSNR is nearly 30, and the embedding rate is about 8.88 bpp when the PSNR is nearly 25.The advantage of the proposed method is no need to transform data in another domain and without training data.Experiments also demonstrate the imperceptibility under some state-of-art steganalysis.The proposed steganography provides an efficient way to transmit sensitive information in the information highly exposed society.