Reviewerly/exHarmony huggingface dataset:
HORUS - Heuristic Object Recognition Unified System Using YOLO and CUDA

Abstract: The object detection based on deep learning is an important application in deep learning technology, which is characterized by its strong capability of feature learning and feature representation compared with the traditional object detection methods. The paper first makes an introduction of the classical methods in object detection and expounds the relation and difference between the classical methods and the deep learning methods in object detection. Then it introduces the emergence of the object detection methods based on deep learning and elaborates the most typical methods nowadays in the object detection via deep learning. In the statement of the methods, the paper focuses on the framework design and the working principle of the models and analyzes the model performance in the real-time and the accuracy of detection. Eventually, it discusses the challenges in the object detection based on deep learning and offers some solutions for reference.
Call for Papers: Special Issue of the Journal of Online Trust and Safety

Although the internet was predicted to undermine non-democratic regimes, numerous studies have shown that, instead, dictators have skillfully used the internet to strengthen their control over society.Autocrats have employed both overt and covert tactics to promote their narratives online, and have applied these tactics to both domestic and international audiences.A growing body of research shows that under some circumstances, autocrats' influence operations achieve their intended effectsshaping people's views and behavior in favor of the regime within and beyond the regime's borders.Non-state societal actors (e.g., citizens, firms, criminal groups) in autocracies also inflict online harms such as harassment, hate speech, and fraud, either under regime supervision or on their own.
Feature Mining of News Communication Topic Elements Based on BERT Model

In order to solve the problems of lack of standardization, fuzzy semantics and sparse features in news topic texts, a feature mining of news communication topic elements based on the BERT model is proposed.In the research, multi-layer fully connected layer feature extraction is performed on the output of the news topic text in the BERT model, and the final extracted text features are purified by the feature projection method to enhance the classification effect.Then the feature projection network is fused in the hidden layer inside the BERT model for feature projection, so as to enhance and purify the classification features through the feature projection of the hidden layer.Experiments are performed on Toutiao, Sohu News, THUC News-L, and THUC News-S datasets.The experimental results show that compared with the baseline BERT method, the two methods have better performance in terms of accuracy and macro-average F1 value, and the highest accuracy is 86.96%, 86.17%, 94. 40% and 93.73%, respectively, which verifies the feasibility and effectiveness of the proposed method.It is concluded that the proposed method for news topic text classification combining BERT and FP net is effective and efficient.
How Blockchain Technology can add value in Project Management Information System (PMIS)

This paper focuses on one of the rapid evolving technologies – Blockchain technology. The blockchain technology is one of the efficient technologies that will help the organizations in managing the various industry challenges related to data management. This research focuses on the use of blockchain technology that works of the principle of decentralization in the PMIS. In this context, a qualitative research has been conducted with the help of interviewers. The findings of the qualitative research indicated that blockchain technology has now become a major need of the project based organizations with the increasing threat of data loss, data tempering and attacks etc. The blockchain technology in PMIS will help in ensuring the integrity and value of the data along with ensuring a systematic communication among the stakeholders involved in the project. The findings also indicate that the blockchain will equally distribute the power among the stakeholder and improving the effectiveness of project in terms of meeting the time, cost and quality objectives. Lastly, the paper recommends that there is a critical need of blockchain experts and personnel who can effectively use this technology not only in PMIS but other areas as well such as the banking and finance. The training and development of the people in the area of implementing and integrating the blockchain technology with the information systems has an important relevance.
Special Issue of EJAM: The Mathematics in Renewable Energy

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A Unified Framework for Tuning Hyperparameters in Clustering Problems

Selecting hyperparameters for unsupervised learning problems is challenging in general due to the lack of ground truth for validation. Despite the prevalence of this issue in statistics and machine learning, especially in clustering problems, there are not many methods for tuning these hyperparameters with theoretical guarantees. In this paper, we provide a framework with provable guarantees for selecting hyperparameters in a number of distinct models. We consider both the subgaussian mixture model and network models to serve as examples of i.i.d. and non-i.i.d. data. We demonstrate that the same framework can be used to choose the Lagrange multipliers of penalty terms in semi-definite programming (SDP) relaxations for community detection, and the bandwidth parameter for constructing kernel similarity matrices for spectral clustering. By incorporating a cross-validation procedure, we show the framework can also do consistent model selection for network models. Using a variety of simulated and real data examples, we show that our framework outperforms other widely used tuning procedures in a broad range of parameter settings.
LDTSF: A Label-Decoupling Teacher-Student Framework for Semi-Supervised Echocardiography Segmentation

The accurate segmentation of the right and left ventricles with limited labeled data is a challenging task in echocardiographic data analysis. To fully leverage the easily accessible unlabeled data, we propose a label-decoupling teacher-student framework (LDTSF) based on semi-supervised learning. Specifically, the decoupled deep network within LDTSF jointly predicts pixel-wise segmentation maps, level set-based edge regression maps, target skeleton maps and target detail maps to focus on edge pixels. Several micro-task-transformable layers are used to map multi-task representations to a unified space in order to supervise the consistency among multiple tasks using massive unlabeled data. In addition, we first train a teacher model based on semi-supervised learning strategy, and then use the pseudo-labels generated by the teacher model together with the original labels to train a student model. Experiments on our self-collected 3D echocardiographic dataset and a publicly available MRI dataset show that our method outperforms state-of-the-art semi-supervised learning methods. The code will be available at: https://github.com/SwanKnightZJP/LDTSF.
TinyOOD: Effective out-of-Distribution Detection for TinyML

Tiny machine learning (TinyML) has emerged recently for resource constrained Internet of Things (IoT) devices. However, the deployed TinyML model cannot handle outof-distribution (OOD) inputs appropriately. While many high-accuracy OOD detection methods have emerged, they often ignore the limitations of the deployment environment. In this paper, we propose a novel effective out-of-distribution detection method for TinyML (TinyOOD), which exploits cascading early exit and channel-attention-based neural mean discrepancy (CA-NMD) for dynamic and efficient OOD detection on microcontroller units (MCUs). To demonstrate its effectiveness, we extensively evaluate TinyOOD using four public datasets, one as the in-distribution (ID) dataset and the others as the OOD datasets. Experiments demonstrate that TinyOOD significantly reduces the computations by up to 38.23% in inference while maintaining the performance of OOD detection.
Unsupervised Pre-Training for Data-Efficient Text-to-Speech on Low Resource Languages

Neural text-to-speech (TTS) models can synthesize natural human speech when trained on large amounts of transcribed speech. How-ever, collecting such large-scale transcribed data is expensive. This paper proposes an unsupervised pre-training method for a sequence-to-sequence TTS model by leveraging large untranscribed speech data. With our pre-training, we can remarkably reduce the amount of paired transcribed data required to train the model for the target downstream TTS task. The main idea is to pre-train the model to reconstruct de-warped mel-spectrograms from warped ones, which may allow the model to learn proper temporal assignment relation between input and output sequences. In addition, we propose a data augmentation method that further improves the data efficiency in finetuning. We empirically demonstrate the effectiveness of our proposed method in low-resource language scenarios, achieving outstanding performance compared to competing methods. The code and audio samples are available at: https://github.com/cnaigithub/SpeechDewarping
Automatic Segmentation of Nasopharyngeal Carcinoma in CT Images Using Dual Attention and Edge Detection

Nasopharyngeal carcinoma (NPC) is a malignant tumor with a high incidence. Accurate segmentation of the tumor region in Computed Tomography (CT) images of NPC is the key to treatment. However, the features of uneven grayscale values and hazy boundaries of NPC regions make accurate NPC segmentation particularly challenging. To address these problems, we propose an accurate and effective NPC segmentation method using Dual Attention and Edge Detection Convolutional Neural Network (DAED-Net). Firstly, we combine a 2.5D convolutional neural network with UNet++ and propose a new backbone called Dual-dimension Dense UNet (DD-UNet), which can extract more beneficial features from 3D images. Secondly, a Dual Attention Module (DAM) is proposed to help the model better segment the target region of NPC by efficiently collecting spatial and channel attention information from feature maps. Moreover, an Edge Detection Module (EDM) is introduced in the network to enhance the segmentation of the target contours. Finally, we evaluate the proposed DAED-Net on the public MICCAI 2019 StructSeg NPC dataset from different perspectives. Numerical and visual results show that the proposed method outperforms nine state-of-the-art segmentation methods and yields more accurate NPC segmentation results.
Text Classification In The Wild: A Large-Scale Long-Tailed Name Normalization Dataset

Real-world data usually exhibits a long-tailed distribution, with a few frequent labels and a lot of few-shot labels. The study of institution name normalization is a perfect application case showing this phenomenon: there are many institutions worldwide, with enormous variations of their names in the publicly available literature. In this work, we first collect a large-scale institution name normalization dataset containing over 25k classes whose frequencies are naturally long-tail distributed. We construct our test set from four different subsets: many-, medium-, and few-shot sets, as well as a zero-shot open set, which are meant to isolate the few-shot and zero-shot learning scenarios from the massive many-shot classes. We also replicate several important benchmarks on our data, covering a wide range from search-based methods to neural network methods. Further, we propose our specially pretrained, BERT-based model that shows better out-of-distribution generalization on few-shot and zero-shot test sets. Compared to other datasets focusing on the long-tailed phenomenon, our dataset has one order of magnitude more training data than the largest existing long-tailed datasets and is naturally long-tailed rather than manually synthesized. We believe it provides an important and different scenario to study this problem. To our best knowledge, this is the first natural language dataset that focuses on this long-tailed and open-set classification problem. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
E-Branchformer-Based E2E SLU Toward Stop on-Device Challenge

In this paper, we report our team’s study on track 2 of the Spoken Language Understanding Grand Challenge, which is a component of the ICASSP Signal Processing Grand Challenge 2023. The task is intended for on-device processing and involves estimating semantic parse labels from speech using a model with 15 million parameters. We use E2E E-Branchformer-based spoken language understanding model, which is more parameter controllable than cascade models, and reduced the parameter size through sequential distillation and tensor decomposition techniques. On the STOP dataset, we achieved an exact match accuracy of 70.9% under the tight constraint of 15 million parameters.
Distill-Quantize-Tune - Leveraging Large Teachers for Low-Footprint Efficient Multilingual NLU on Edge

This paper describes Distill-Quantize-Tune (DQT), a pipeline to create viable small-footprint multilingual models that can perform NLU on extremely resource-constrained Edge devices. We distill semantic knowledge from a large-sized teacher (transformer-based), that has been trained on huge amount of public and private data, into our Edge candidate (student) model (Bi-LSTM based) and further compress the student model using a lossy quantization method. We show that unlike monolingual models, in a multilingual scenario, post-compression finetuning on downstream tasks is not enough to recover the performance loss caused by compression. We design a fine-tuning pipeline to recover the lost performance using a compounded loss function consisting of NLU, distillation and compression losses. We show that pre-biasing the encoder with semantics learned on a language modeling task can further improve the performance when used in conjunction with DQT pipeline. Our best performing multilingual model achieves a size reduction of 85% and 99.2% when compared to uncompressed student and teacher models respectively. It outperforms the uncompressed monolingual models (by >30% on average) across all languages on our in-house data. We further validate our approach and see similar trends on the public MultiATIS++ dataset.
Fine-Grained Product Classification on Leaflet Advertisements

In this paper, we describe a first publicly available fine-grained product recognition dataset based on leaflet images. Using advertisement leaflets, collected over several years from different European retailers, we provide a total of 41.6k manually annotated product images in 832 classes. Further, we investigate three different approaches for this fine-grained product classification task, Classification by Image, by Text, as well as by Image and Text. The approach "Classification by Text" uses the text extracted directly from the leaflet product images. We show, that the combination of image and text as input improves the classification of visual difficult to distinguish products. The final model leads to an accuracy of 96.4% with a Top-3 score of 99.2%. We release our code at https://github.com/ladwigd/Leaflet-Product-Classification.
Finding Outliers in Gaussian Model-Based Clustering

Clustering, or unsupervised classification, is a task often plagued by outliers. Yet there is a paucity of work on handling outliers in clustering. Outlier identification algorithms tend to fall into three broad categories: outlier inclusion, outlier trimming, and \textit{post hoc} outlier identification methods, with the former two often requiring pre-specification of the number of outliers. The fact that sample Mahalanobis distance is beta-distributed is used to derive an approximate distribution for the log-likelihoods of subset finite Gaussian mixture models. An algorithm is then proposed that removes the least plausible points according to the subset log-likelihoods, which are deemed outliers, until the subset log-likelihoods adhere to the reference distribution. This results in a trimming method, called OCLUST, that inherently estimates the number of outliers.
The Role of Global and Local Context in Named Entity Recognition

Pre-trained transformer-based models have recently shown great performance when applied to Named Entity Recognition (NER). As the complexity of their self-attention mechanism prevents them from processing long documents at once, these models are usually applied in a sequential fashion. Such an approach unfortunately only incorporates local context and prevents leveraging global document context in long documents such as novels, which might hinder performance. In this article, we explore the impact of global document context, and its relationships with local context. We find that correctly retrieving global document context has a greater impact on performance than only leveraging local context, prompting for further research on how to better retrieve that context.
Plug-and-Play Multilingual Few-shot Spoken Words Recognition

As technology advances and digital devices become prevalent, seamless human-machine communication is increasingly gaining significance. The growing adoption of mobile, wearable, and other Internet of Things (IoT) devices has changed how we interact with these smart devices, making accurate spoken words recognition a crucial component for effective interaction. However, building robust spoken words detection system that can handle novel keywords remains challenging, especially for low-resource languages with limited training data. Here, we propose PLiX, a multilingual and plug-and-play keyword spotting system that leverages few-shot learning to harness massive real-world data and enable the recognition of unseen spoken words at test-time. Our few-shot deep models are learned with millions of one-second audio clips across 20 languages, achieving state-of-the-art performance while being highly efficient. Extensive evaluations show that PLiX can generalize to novel spoken words given as few as just one support example and performs well on unseen languages out of the box. We release models and inference code to serve as a foundation for future research and voice-enabled user interface development for emerging devices.
Towards Invertible Semantic-Preserving Embeddings of Logical Formulae

Logic is the main formal language to perform automated reasoning, and it is further a human-interpretable language, at least for small formulae. Learning and optimising logic requirements and rules has always been an important problem in Artificial Intelligence. State of the art Machine Learning (ML) approaches are mostly based on gradient descent optimisation in continuous spaces, while learning logic is framed in the discrete syntactic space of formulae. Using continuous optimisation to learn logic properties is a challenging problem, requiring to embed formulae in a continuous space in a meaningful way, i.e. preserving the semantics. Current methods are able to construct effective semantic-preserving embeddings via kernel methods (for linear temporal logic), but the map they define is not invertible. In this work we address this problem, learning how to invert such an embedding leveraging deep architectures based on the Graph Variational Autoencoder framework. We propose a novel model specifically designed for this setting, justifying our design choices through an extensive experimental evaluation. Reported results in the context of propositional logic are promising, and several challenges regarding learning invertible embeddings of formulae are highlighted and addressed.
A Serious Game for Simulating Cyberattacks to Teach Cybersecurity

With the rising number of cyberattacks, such as ransomware attacks and cyber espionage, educating non-cybersecurity professionals to recognize threats has become more important than ever before. However, traditional training methods, such as phishing awareness campaigns, training videos and assessments have proven to be less effective over time. Therefore, it is time to rethink the approach on how to train cyber awareness. In this paper we suggest an alternative approach -- a serious game -- to educate awareness for common cyberattacks. While many serious games for cybersecurity education exist, all follow a very similar approach: showing people the effects of a cyber attack on their own system or company network. For example, one of the main tasks in these games is to sort out phishing mails. We developed and evaluated a new type of cybersecurity game: an attack simulator, which shows the entire setting from a different perspective. Instead of sorting out phishing mails the players should write phishing mails to trick potential victims and use other forms of cyberattacks. Our game explains the intention of each attack and shows the consequences of a successful attack. This way, we hope, players will get a better understanding on how to detect cyberattacks.
HAISTA-NET: Human Assisted Instance Segmentation Through Attention

Instance segmentation is a form of image detection which has a range of applications, such as object refinement, medical image analysis, and image/video editing, all of which demand a high degree of accuracy. However, this precision is often beyond the reach of what even state-of-the-art, fully automated instance segmentation algorithms can deliver. The performance gap becomes particularly prohibitive for small and complex objects. Practitioners typically resort to fully manual annotation, which can be a laborious process. In order to overcome this problem, we propose a novel approach to enable more precise predictions and generate higher-quality segmentation masks for high-curvature, complex and small-scale objects. Our human-assisted segmentation model, HAISTA-NET, augments the existing Strong Mask R-CNN network to incorporate human-specified partial boundaries. We also present a dataset of hand-drawn partial object boundaries, which we refer to as human attention maps. In addition, the Partial Sketch Object Boundaries (PSOB) dataset contains hand-drawn partial object boundaries which represent curvatures of an object's ground truth mask with several pixels. Through extensive evaluation using the PSOB dataset, we show that HAISTA-NET outperforms state-of-the art methods such as Mask R-CNN, Strong Mask R-CNN, and Mask2Former, achieving respective increases of +36.7, +29.6, and +26.5 points in AP-Mask metrics for these three models. We hope that our novel approach will set a baseline for future human-aided deep learning models by combining fully automated and interactive instance segmentation architectures.
A Bootstrap Algorithm for Fast Supervised Learning

Training a neural network (NN) typically relies on some type of curve-following method, such as gradient descent (GD) (and stochastic gradient descent (SGD)), ADADELTA, ADAM or limited memory algorithms. Convergence for these algorithms usually relies on having access to a large quantity of observations in order to achieve a high level of accuracy and, with certain classes of functions, these algorithms could take multiple epochs of data points to catch on. Herein, a different technique with the potential of achieving dramatically better speeds of convergence, especially for shallow networks, is explored: it does not curve-follow but rather relies on 'decoupling' hidden layers and on updating their weighted connections through bootstrapping, resampling and linear regression. By utilizing resampled observations, the convergence of this process is empirically shown to be remarkably fast and to require a lower amount of data points: in particular, our experiments show that one needs a fraction of the observations that are required with traditional neural network training methods to approximate various classes of functions.