Reviewerly/exHarmony huggingface dataset:
IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation

Cell instance segmentation in Pap smear image remains challenging due to the wide existence of occlusion among translucent cytoplasm in cell clumps. Conventional methods heavily rely on accurate nuclei detection results and are easily disturbed by miscellaneous objects. In this paper, we propose a novel Instance Relation Network (IRNet) for robust overlapping cell segmentation by exploring instance relation interaction. Specifically, we propose the Instance Relation Module to construct the cell association matrix for transferring information among individual cell-instance features. With the collaboration of different instances, the augmented features gain benefits from contextual information and improve semantic consistency. Meanwhile, we proposed a sparsity constrained Duplicate Removal Module to eliminate the misalignment between classification and localization accuracy for candidates selection. The largest cervical Pap smear (CPS) dataset with more than 8000 cell annotations in Pap smear image was constructed for comprehensive evaluation. Our method outperforms other methods by a large margin, demonstrating the effectiveness of exploring instance relation.
Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents

Data availability is a bottleneck during early stages of development of new capabilities for intelligent artificial agents.We investigate the use of text generation techniques to augment the training data of a popular commercial artificial agent across categories of functionality, with the goal of faster development of new functionality.We explore a variety of encoderdecoder generative models for synthetic training data generation and propose using conditional variational auto-encoders.Our approach requires only direct optimization, works well with limited data and significantly outperforms the previous controlled text generation techniques.Further, the generated data are used as additional training samples in an extrinsic intent classification task, leading to improved performance by up to 5% absolute f-score in low-resource cases, validating the usefulness of our approach.
Analysis of Blockchain Technology for Higher Education

The authenticity of academic diplomas is a matter of great concern for employers and other authorities to verify the authenticity of academic degrees. The issuing institutions may have closed or fail to keep accurate records. In these situations, the verification of academic diplomas launches challenges. In a worldwide education market, many institutions are involved, and it becomes even harder to keep track and handle different approaches/procedures to verify the status and authenticity of diplomas. As in many other areas, the blockchain technology surges as a promising solution to the referred problem. In this paper, we propose an approach using this technology implemented in University Fernando Pessoa. We also discuss the new challenges in security and implementation raised by the usage of this technology.
Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations

Temporal models based on recurrent neural networks have proven to be quite powerful in a wide variety of applications, including language modeling and speech processing. However, training these models often relies on backpropagation through time (BPTT), which entails unfolding the network over many time steps, making the process of conducting credit assignment considerably more challenging. Furthermore, the nature of backpropagation itself does not permit the use of nondifferentiable activation functions and is inherently sequential, making parallelization of the underlying training process difficult. Here, we propose the parallel temporal neural coding network (P-TNCN), a biologically inspired model trained by the learning algorithm we call local representation alignment. It aims to resolve the difficulties and problems that plague recurrent networks trained by BPTT. The architecture requires neither unrolling in time nor the derivatives of its internal activation functions. We compare our model and learning procedure with other BPTT alternatives (which also tend to be computationally expensive), including real-time recurrent learning, echo state networks, and unbiased online recurrent optimization. We show that it outperforms these on-sequence modeling benchmarks such as Bouncing MNIST, a new benchmark we denote as Bouncing NotMNIST, and Penn Treebank. Notably, our approach can, in some instances, outperform full BPTT as well as variants such as sparse attentive backtracking. Significantly, the hidden unit correction phase of P-TNCN allows it to adapt to new data sets even if its synaptic weights are held fixed (zero-shot adaptation) and facilitates retention of prior generative knowledge when faced with a task sequence. We present results that show the P-TNCN's ability to conduct zero-shot adaptation and online continual sequence modeling.
Stacked Garbling for Disjunctive Zero-Knowledge Proofs

Zero-knowledge (ZK) proofs (ZKP) have received wide attention, focusing on non-interactivity, short proof size, and fast verification time. We focus on the fastest total proof time, in particular for large Boolean circuits. Under this metric, Garbled Circuit (GC)-based ZKP (Jawurek et al., [JKO], CCS 2013) remained the state-of-the-art technique due to the low-constant linear scaling of computing the garbling. We improve GC-ZKP for proof statements with conditional clauses. Our communication is proportional to the longest branch rather than to the entire proof statement. This is most useful when the number $$m $$ of branches is large, resulting in up to factor $$m \times $$ improvement over JKO. In our proof-of-concept illustrative application, prover $$\mathsf {P}$$ demonstrates knowledge of a bug in a codebase consisting of any number of snippets of actual C code. Our computation cost is linear in the size of the codebase and communication is constant in the number of snippets. That is, we require only enough communication for a single largest snippet! Our conceptual contribution is stacked garbling for ZK, a privacy-free circuit garbling scheme that can be used with the JKO GC-ZKP protocol to construct more efficient ZKP. Given a Boolean circuit $$\mathcal {C}$$ and computational security parameter $$\kappa $$ , our garbling is $$L\cdot \kappa $$ bits long, where L is the length of the longest execution path in $$\mathcal {C}$$ . All prior concretely efficient garbling schemes produce garblings of size $$|\mathcal {C} |\cdot \kappa $$ . The computational cost of our scheme is not increased over prior state-of-the-art. We implement our GC-ZKP and demonstrate significantly improved ( $$m \times $$ over JKO) ZK performance for functions with branching factor $$m $$ . Compared with recent ZKP (STARK, Libra, KKW, Ligero, Aurora, Bulletproofs), our scheme offers much better proof times for larger circuits (35- $$1000\times $$ or more, depending on circuit size and compared scheme). For our illustrative application, we consider four C code snippets, each of about 30–50 LOC; one snippet allows an invalid memory dereference. The entire proof takes 0.15 s and communication is 1.5 MB.
Towards Unsupervised Speech Recognition and Synthesis with Quantized Speech Representation Learning

In this paper we propose a Sequential Representation Quantization AutoEncoder (SeqRQ-AE) to learn from primarily unpaired audio data and produce sequences of representations very close to phoneme sequences of speech utterances. This is achieved by proper temporal segmentation to make the representations phoneme-synchronized, and proper phonetic clustering to have total number of distinct representations close to the number of phonemes. Mapping between the distinct representations and phonemes is learned from a small amount of annotated paired data. Preliminary experiments on LJSpeech demonstrated the learned representations for vowels have relative locations in latent space in good parallel to that shown in the IPA vowel chart defined by linguistics experts. With less than 20 minutes of annotated speech, our method outperformed existing methods on phoneme recognition and is able to synthesize intelligible speech that beats our baseline model.
AP-Loss for Accurate One-Stage Object Detection

One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously, with the former suffering much from extreme foreground-background class imbalance issue due to the large number of anchors. This paper alleviates this issue by proposing a novel framework to replace the classification task in one-stage detectors with a ranking task, and adopting the average-precision loss (AP-loss) for the ranking problem. Due to its non-differentiability and non-convexity, the AP-loss cannot be optimized directly. For this purpose, we develop a novel optimization algorithm, which seamlessly combines the error-driven update scheme in perceptron learning and backpropagation algorithm in deep networks. We provide in-depth analyses on the good convergence property and computational complexity of the proposed algorithm, both theoretically and empirically. Experimental results demonstrate notable improvement in addressing the imbalance issue in object detection over existing AP-based optimization algorithms. An improved state-of-the-art performance is achieved in one-stage detectors based on AP-loss over detectors using classification-losses on various standard benchmarks. The proposed framework is also highly versatile in accommodating different network architectures. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/cccorn/AP-loss</uri> .
Notes on Using Google Colaboratory in AI Education

We discuss our experiences using Google Colaboratory (Colab), a hosted version of Jupyter Notebooks, in undergraduate artificial intelligence (AI) courses at two universities. Colab was designed for AI and data science researchers to share reproducible experiments and explanations of techniques, but we have also found it well suited to classroom use. The primary benefit is that it provides students computational resources sufficient to run modern AI techniques interactively, and avoids students needing to separately configure software packages and dependencies, since they can run notebooks shared by the instructor. We briefly outline two of our notebooks, for teaching deep learning with Tensorflow, and reinforcement learning with OpenAI Gym.
Adversarial Self-supervised Learning for Semi-supervised 3D Action Recognition

We consider the problem of semi-supervised 3D action recognition which has been rarely explored before. Its major challenge lies in how to effectively learn motion representations from unlabeled data. Self-supervised learning (SSL) has been proved very effective at learning representations from unlabeled data in the image domain. However, few effective self-supervised approaches exist for 3D action recognition, and directly applying SSL for semi-supervised learning suffers from misalignment of representations learned from SSL and supervised learning tasks. To address these issues, we present Adversarial Self-Supervised Learning (ASSL), a novel framework that tightly couples SSL and the semi-supervised scheme via neighbor relation exploration and adversarial learning. Specifically, we design an effective SSL scheme to improve the discrimination capability of learned representations for 3D action recognition, through exploring the data relations within a neighborhood. We further propose an adversarial regularization to align the feature distributions of labeled and unlabeled samples. To demonstrate effectiveness of the proposed ASSL in semi-supervised 3D action recognition, we conduct extensive experiments on NTU and N-UCLA datasets. The results confirm its advantageous performance over state-of-the-art semi-supervised methods in the few label regime for 3D action recognition.
Anomaly Detection, Localization and Classification Using Drifting Synchrophasor Data Streams

With ongoing automation and digitization of the electric power system, several Phasor Measurement Units (PMUs) have been deployed for monitoring and control. PMU data can have multiple anomalies, and many of the researchers in the past have concentrated on training machine/deep learning algorithms offline for anomaly detection over PMU data (i.e., not in real-time). These machine/deep learning algorithms, when trained offline on a sample rather than a population of the dataset, fail to consider the dynamic behavior of the power grid in real-time, resulting in low accuracy. Considering the dynamic behavior of the power grid (e.g., change in load, generation, distributed energy resources (DERs) switching, network, controls), the definition of data anomalies varies in time and requires online training. A fundamental challenge is to enable online (i.e., real-time) training of machine/deep learning algorithms for anomaly detection over streaming PMU data. While machine/deep learning is often desirable to manage data streams, training a deep learning algorithm over streaming PMU data is nontrivial due to changes in data statistics caused by dynamic streaming data. This article proposes PMUNET: a novel device-level deep learning-based data-driven approach for anomaly detection, localization, and classification over streaming PMU data, using online learning and multivariate data-drift detection algorithm. Two variants of PMUNET, Dynamic data Change Driven Learning (DCDL) and Continuity Driven Learning (CDL), are proposed and compared. DCDL aims to train the deep learning algorithm whenever the definition of anomaly changes due to the power grid dynamics. On the other hand, CDL continuously trains the deep learning algorithm over the PMU data-stream. The experimental results verify that DCDL outperforms CDL and other efficient anomaly detection methods over multiple events such as faults and load/ generator/capacitor/DERs variations/switching for IEEE 14 and 39 Bus test system as well as real PMU industrial data. The result verifies that DCDL variant of PMUNET improves over existing approach with a gain of 2% - 10% in terms of accuracy, false-positive rate, and false-negative rate.
Domain Generalization with MixStyle

Though convolutional neural networks (CNNs) have demonstrated remarkable ability in learning discriminative features, they often generalize poorly to unseen domains. Domain generalization aims to address this problem by learning from a set of source domains a model that is generalizable to any unseen domain. In this paper, a novel approach is proposed based on probabilistically mixing instance-level feature statistics of training samples across source domains. Our method, termed MixStyle, is motivated by the observation that visual domain is closely related to image style (e.g., photo vs.~sketch images). Such style information is captured by the bottom layers of a CNN where our proposed style-mixing takes place. Mixing styles of training instances results in novel domains being synthesized implicitly, which increase the domain diversity of the source domains, and hence the generalizability of the trained model. MixStyle fits into mini-batch training perfectly and is extremely easy to implement. The effectiveness of MixStyle is demonstrated on a wide range of tasks including category classification, instance retrieval and reinforcement learning.
Big data analytics for default prediction using graph theory

With the unprecedented increase in data all over the world, financial sector such as companies and industries try to remain competitive by transforming themselves into data-driven organizations. By analyzing a huge amount of financial data, companies are able to obtain valuable information to determine their strategic plans such as risk control, crisis management, or growth management. However, as the amount of data increase dramatically, traditional data analytic platforms confront with storing, managing, and analyzing difficulties. Emerging Big Data Analytics (BDA) overcome these problems by providing decentralized and distributed processing. In this study, we propose two new models for default prediction. In the first model, called DPModel-1, statistical (logistic regression), and machine learning methods (decision tree, random forest, gradient boosting) are employed to predict company default. Derived from the first model, we propose DPModel-2 based on graph theory. DPModel-2 also comprises new variables obtained from the trading interactions of companies. In both models, grid search optimization and SHapley Additive exPlanations (SHAP) value are utilized in order to determine the best hyperparameters and make the models interpretable, respectively. By leveraging balance sheet, credit, and invoice datasets, default prediction is realized for about one million companies in Turkey between the years 2010–2018. The default rates of companies range between 3%-6% by year. The experimental results are conducted on a BDA platform. According to the DPModel-1 results, the highest AUC score is ensured by random forest with 0.87. In addition, the results are improved for each technique separately by adjusting new variables with graph theory. According to DPModel-2 results, the best AUC score is achieved by random forest with 0.89.
Beetle swarm optimization algorithm: Theory and application

In this paper, a new meta-heuristic algorithm, called beetle swarm optimization (BSO) algorithm, is proposed by enhancing the performance of swarm optimization through beetle foraging principles. The performance of 23 benchmark functions is tested and compared with widely used algorithms, including particle swarm optimization (PSO) algorithm, genetic algorithm (GA) and grasshopper optimization algorithm (GOA). Numerical experiments show that the BSO algorithm outperforms its counterparts. Besides, to demonstrate the practical impact of the proposed algorithm, two classic engineering design problems, namely, pressure vessel design problem and himmelblau?s optimization problem, are also considered and the proposed BSO algorithm is shown to be competitive in those applications.
A novel recurrent neural network approach in forecasting short term solar irradiance

Forecasting solar irradiance is of utmost importance in supplying renewable energy efficiently and timely. This paper aims to experiment five variants of recurrent neural networks (RNN), and develop effective and reliable 5-minute short term solar irradiance prediction models. The 5 RNN classes are long-short term memory (LSTM), gated recurrent unit (GRU), Simple RNN, bidirectional LSTM (Bi-LSTM), and bidirectional GRU (Bi-GRU); the first 3 classes are unidirectional and the last two are bidirectional RNN models. The 26 months data under consideration, exhibits extremely volatile weather conditions in Jinju city, South Korea. Therefore, after different experimental processes, 5 hyper-parameters were selected for each model cautiously. In each model, different levels of depth and width were tested; moreover, a 9-fold cross validation was applied to distinguish them against high variability in the seasonal time-series dataset. Generally the deeper architectures of the aforementioned models had significant outcomes; meanwhile, the Bi-LSTM and Bi-GRU provided more accurate predictions as compared to the unidirectional ones. The Bi-GRU model provided the lowest RMSE and highest R2 values of 46.1 and 0.958; additionally, it required 5.25*10-5 seconds per trainable parameter per epoch, the lowest incurred computational cost among the mentioned models. All 5 models performed differently over the four seasons in the 9-fold cross validation test. On average, the bidirectional RNNs and the simple RNN model showed high robustness with less data and high temporal data variability; although, the stronger architectures of the bidirectional models, deems their results more reliable.
Graph Neural Networks for Friend Ranking in Large-scale Social Platforms

Graph Neural Networks (GNNs) have recently enabled substantial advances in graph learning. Despite their rich representational capacity, GNNs remain under-explored for large-scale social modeling applications. One such industrially ubiquitous application is friend suggestion: recommending users other candidate users to befriend, to improve user connectivity, retention and engagement. However, modeling such user-user interactions on large-scale social platforms poses unique challenges: such graphs often have heavy-tailed degree distributions, where a significant fraction of users are inactive and have limited structural and engagement information. Moreover, users interact with different functionalities, communicate with diverse groups, and have multifaceted interaction patterns.
Initial teacher training for twenty-first century skills in the Fourth Industrial Revolution (IR 4.0): A scoping review

The Fourth Industrial Revolution (IR 4.0) is characterized by rapidly changing technologies and workforce demands. Educational systems seek to respond to these changes. Little is known about ways in which Teacher Training Institutions (TTI) are preparing preservice teachers to address these educational demands. This scoping review examines the high-quality literature with respect to initial teacher training activities and challenges, specifically focusing on 21st century skills and technology integration in the context of IR 4.0. The results show TTI requires coherence throughout the organization to effectively respond to shifting needs and contexts. The development of IR 4.0 technologies move swiftly, providing new opportunities for developing preservice teachers' 21st century skills. Such technologies could reframe the role of TTIs and teacher educators. Contrastingly, the pressure for TTI and teacher educators to maintain required skills increases alongside technologies. This scoping review concludes that research on this topic remains valuable and critical to further inform initial teacher training in IR 4.0 to facilitate the development of preservice teachers’ 21st century skills.
Self-Supervised Text-Independent Speaker Verification Using Prototypical Momentum Contrastive Learning

In this study, we investigate self-supervised representation learning for speaker verification (SV). First, we examine a simple contrastive learning approach (SimCLR) with a momentum contrastive (MoCo) learning framework, where the MoCo speaker embedding system utilizes a queue to maintain a large set of negative examples. We show that better speaker embeddings can be learned by momentum contrastive learning. Next, alternative augmentation strategies are explored to normalize extrinsic speaker variabilities of two random segments from the same speech utterance. Specifically, augmentation in the waveform largely improves the speaker representations for SV tasks. The proposed MoCo speaker embedding is further improved when a prototypical memory bank is introduced, which encourages the speaker embeddings to be closer to their assigned prototypes with an intermediate clustering step. In addition, we generalize the self-supervised framework to a semi-supervised scenario where only a small portion of the data is labeled. Comprehensive experiments on the Voxceleb dataset demonstrate that our proposed self-supervised approach achieves competitive performance compared with existing techniques, and can approach fully supervised results with partially labeled data.