Reviewerly/exHarmony huggingface dataset:
Interrelate Training and Searching: A Unified Online Clustering Framework for Speaker Diarization

For online speaker diarization, samples arrive incrementally, and the overall distribution of the samples is invisible. Moreover, in most existing clustering-based methods, the training objective of the embedding extractor is not designed specially for clustering. To improve online speaker diarization performance, we propose a unified online clustering framework, which provides an interactive manner between embedding extractors and clustering algorithms. Specifically, the framework consists of two highly coupled parts: clustering-guided recurrent training (CGRT) and truncated beam searching clustering (TBSC). The CGRT introduces the clustering algorithm into the training process of embedding extractors, which could provide not only cluster-aware information for the embedding extractor, but also crucial parameters for the clustering process afterward. And with these parameters, which contain preliminary information of the metric space, the TBSC penalizes the probability score of each cluster, in order to output more accurate clustering results in online fashion with low latency. With the above innovations, our proposed online clustering system achieves 14.48\% DER with collar 0.25 at 2.5s latency on the AISHELL-4, while the DER of the offline agglomerative hierarchical clustering is 14.57\%.
Exploiting Mixed Unlabeled Data for Detecting Samples of Seen and Unseen Out-of-Distribution Classes

Out-of-Distribution (OOD) detection is essential in real-world applications, which has attracted increasing attention in recent years. However, most existing OOD detection methods require many labeled In-Distribution (ID) data, causing a heavy labeling cost. In this paper, we focus on the more realistic scenario, where limited labeled data and abundant unlabeled data are available, and these unlabeled data are mixed with ID and OOD samples. We propose the Adaptive In-Out-aware Learning (AIOL) method, in which we employ the appropriate temperature to adaptively select potential ID and OOD samples from the mixed unlabeled data and consider the entropy over them for OOD detection. Moreover, since the test data in realistic applications may contain OOD samples whose classes are not in the mixed unlabeled data (we call them unseen OOD classes), data augmentation techniques are brought into the method to further improve the performance. The experiments are conducted on various benchmark datasets, which demonstrate the superiority of our method.
OA-FSUI2IT: A Novel Few-Shot Cross Domain Object Detection Framework with Object-Aware Few-Shot Unsupervised Image-to-Image Translation

Unsupervised image-to-image (UI2I) translation methods aim to learn a mapping between different visual domains with well-preserved content and consistent structure. It has been proven that the generated images are quite useful for enhancing the performance of computer vision tasks like object detection in a different domain with distribution discrepancies. Current methods require large amounts of images in both source and target domains for successful translation. However, data collection and annotations in many scenarios are infeasible or even impossible. In this paper, we propose an Object-Aware Few-Shot UI2I Translation (OA-FSUI2IT) framework to address the few-shot cross domain (FSCD) object detection task with limited unlabeled images in the target domain. To this end, we first introduce a discriminator augmentation (DA) module into the OA-FSUI2IT framework for successful few-shot UI2I translation. Then, we present a patch pyramid contrastive learning (PPCL) strategy to further improve the quality of the generated images. Last, we propose a self-supervised content-consistency (SSCC) loss to enforce the content-consistency in the translation. We implement extensive experiments to demonstrate the effectiveness of our OA-FSUI2IT framework for FSCD object detection and achieve state-of-the-art performance on the benchmarks of Normal-to-Foggy, Day-to-Night, and Cross-scene adaptation. The source code of our proposed method is also available at https://github.com/emdata-ailab/FSCD-Det.
ApproxASP – a Scalable Approximate Answer Set Counter

Answer Set Programming (ASP) is a framework in artificial intelligence and knowledge representation for declarative modeling and problem solving. Modern ASP solvers focus on the computation or enumeration of answer sets. However, a variety of probabilistic applications in reasoning or logic programming require counting answer sets. While counting can be done by enumeration, simple enumeration becomes immediately infeasible if the number of solutions is high. On the other hand, approaches to exact counting are of high worst-case complexity. In fact, in propositional model counting, exact counting becomes impractical. In this work, we present a scalable approach to approximate counting for answer set programming. Our approach is based on systematically adding XOR constraints to ASP programs, which divide the search space. We prove that adding random XOR constraints partitions the answer sets of an ASP program. In practice, we use a Gaussian elimination-based approach by lifting ideas from SAT to ASP and integrating it into a state of the art ASP solver, which we call ApproxASP. Finally, our experimental evaluation shows the scalability of our approach over the existing ASP systems.
Efficient Decoder for Learned Image Compression via Structured Pruning

We propose a structured pruning method to achieve a light-weighted decoder of learned image compression to accommodate various terminals. The structured pruning method identifies the effectiveness of each channel of decoder via gradient ascent and gradient descent while maintaining the encoder and entropy model. To our best knowledge, this paper is the first attempt to design a structured pruning method for universal pretrained learned image compression. Experimental results demonstrate that the proposed method can reduce about 40&#x0025; parameters and save 25&#x0025; inference time at the cost of 0.23 dB BD-PSNR and 4.33&#x0025; BD-rate change.
A Divide and Conquer Algorithm for Predict+Optimize with Non-convex Problems

The predict+optimize problem combines machine learning and combinatorial optimization by predicting the problem coefficients first and then using these coefficients to solve the optimization problem. While this problem can be solved in two separate stages, recent research shows end to end models can achieve better results. This requires differentiating through a discrete combinatorial function. Models that use differentiable surrogates are prone to approximation errors, while existing exact models are limited to dynamic programming, or they do not generalize well with scarce data. In this work we propose a novel divide and conquer algorithm based on transition points to reason over exact optimization problems and predict the coefficients using the optimization loss. Moreover, our model is not limited to dynamic programming problems. We also introduce a greedy version, which achieves similar results with less computation. In comparison with other predict+optimize frameworks, we show our method outperforms existing exact frameworks and can reason over hard combinatorial problems better than surrogate methods.
A Synthetic Prediction Market for Estimating Confidence in Published Work

Explainably estimating confidence in published scholarly work offers opportunity for faster and more robust scientific progress. We develop a synthetic prediction market to assess the credibility of published claims in the social and behavioral sciences literature. We demonstrate our system and detail our findings using a collection of known replication projects. We suggest that this work lays the foundation for a research agenda that creatively uses AI for peer review.
High-Accuracy Oral Squamous Cell Carcinoma Auxiliary Diagnosis System Based on EfficientNet

It is important to diagnose the grade of oral squamous cell carcinoma (OSCC), but the current evaluation of the biopsy slide still mainly depends on the manual operation of pathologists. The workload of manual evaluation is large, and the results are greatly affected by the subjectivity of the pathologists. In recent years, with the development and application of deep learning, automatic evaluation of biopsy slides is gradually being applied to medical diagnoses, and it has shown good results. Therefore, a new OSCC auxiliary diagnostic system was proposed to automatically and accurately evaluate the patients' tissue slides. This is the first study that compared the effects of different resolutions on the results. The OSCC tissue slides from The Cancer Genome Atlas (TCGA, n=697) and our independent datasets (n=337) were used for model training and verification. In the test dataset of tiles, the accuracy was 93.1% at 20x resolution (n=306,134), which was higher than that at 10x (n=154,148, accuracy=90.9%) and at 40x (n=890,681, accuracy=89.3%). The accuracy of the new system based on EfficientNet, which was used to evaluate the tumor grade of the biopsy slide, reached 98.1% [95% confidence interval (CI): 97.1% to 99.1%], and the area under the receiver operating characteristic curve (AUROC) reached 0.998 (95%CI: 0.995 to 1.000) in the TCGA dataset. When verifying the model on the independent image dataset, the accuracy still reached 91.4% (95% CI: 88.4% to 94.4%, at 20x) and the AUROC reached 0.992 (95%CI: 0.982 to 1.000). It may benefit oral pathologists by reducing certain repetitive and time-consuming tasks, improving the efficiency of diagnosis, and facilitating the further development of computational histopathology.
Analyzing the attitude of Indian citizens during the second wave of COVID-19: A text analytics study

The COVID-19 pandemic outbreak has created severe public health crises and economic consequences across the globe. This study used text analytics techniques to investigate the key concerns of Indian citizens raised in social media during the second wave of COVID-19.In this study, we performed a sentiment and emotion analysis of tweets to understand the attitude of Indian citizens during the second wave of COVID-19. Moreover, we performed topic modeling to understand the significant issues and concerns related to COVID-19.Our results show that most social media posts were in neutral tone, and the percentage of posts that showed positive sentiment was less. Furthermore, emotion analysis results show that 'Fear' and 'Surprise' were the prominent emotions expressed by the citizens. Topic modeling results reveal that 'High crowd' and 'political rallies' are the two primary topics of concern raised by Indian citizens during the second wave of COVID-19.Hence, Indian government agencies should communicate crisis information and combating strategies to citizens more effectively in order to minimize the fear and anxiety amongst the public.
Extraction of PE Online Teaching Resources With Positive Psychology Based on Advanced Intelligence Algorithm

Physical education (PE) teaching resources occupy a very important position in the teaching of PE theory. Especially in the context of the Internet era, how to effectively extract PE teaching resources from the Internet is very important for PE teachers. However, the quality of PE teaching resources on the Internet is uneven, if not correctly identified, it will bring harm to students' values. Therefore, it is very necessary to correctly identify the teaching resources of positive psychology. In the era of artificial intelligence, advanced intelligent algorithms provide a solution for the realization of this purpose. In this study, a text sentiment analysis model multi-layer-attention convolutional neural network (ACNN)-CNN based on hierarchical CNN is proposed, which combines the advantages of convolutional neural networks and the attention mechanism. In multi-layer-ACNN-CNN, position encoding information is added to the embedding layer to improve the accuracy of text sentiment classification. In order to verify the performance of the model, online PE teaching resources are extracted by a crawler system and the proposed model is used to classify the positive psychology of the teaching resources. By comparison, the proposed model obtained a better positive psychology classification effect in the experiment, which verifies that the model can extract text features more accurately, and is more suitable for emotion classification of long texts.
Low-resource Low-footprint Wake-word Detection using Knowledge Distillation

As virtual assistants have become more diverse and specialized, so has the demand for application or brand-specific wake words. However, the wake-word-specific datasets typically used to train wake-word detectors are costly to create. In this paper, we explore two techniques to leverage acoustic modeling data for large-vocabulary speech recognition to improve a purpose-built wake-word detector: transfer learning and knowledge distillation. We also explore how these techniques interact with time-synchronous training targets to improve detection latency. Experiments are presented on the open-source "Hey Snips" dataset and a more challenging in-house far-field dataset. Using phone-synchronous targets and knowledge distillation from a large acoustic model, we are able to improve accuracy across dataset sizes for both datasets while reducing latency.
An Analysis of Grammatical Error on Students’ Writing

The research was aimed to analyze the most common grammatical error on students’ writing. All categories of grammatical error types were classified and described based on Dulay on theory of Linguistic Category. The data source of this study was 26 students of the second grade of Nurul Jadid Islamic Senior High School (MANJ). In this study, observation, writing test, and documentation were used to collect data. The major findings of this study found 155 errors in the students’ writing composition. The omission error percentage is 44%. Meanwhile, addition error occurs in 27% errors, and the misformation error is 23%. The last, the misordering error is 6%. The result of the analysis shows that omission error is the most common error produced by the students. As a whole, students still committed errors in their writing. Teachers have to pay high attention to the problematic area of the grammar structure, so some pedagogical implications on the error analysis are suggested.
Analysis of the Information Security of Public Organizations in Ecuador

Public organizations have the responsibility by law to manage sensitive data that must be shared; the problem was found that organizations are very vulnerable and that computer attacks are becoming more and more effective and dangerous. As an objective of this research, the Information Security of public organizations in Ecuador was analyzed, the current status was known and improvements in its management were proposed. The deductive method was applied for the review and analysis of factors and variables that allow improving information security management in public organizations. The result was an Information security management model based on strategic planning, process and matrices for the evaluation of the information security management capacity. It was concluded that public organizations in Ecuador have a low level of information security management capacity, 70% are at a "Formative" level and 22% at a "Managed" level. To Manage Information Security in an optimal way, it is necessary to start from a strategic planning that allows directing the resources and capacities available to the achievement of the objectives established for the organization.
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This article determines all the solutions in the finite field F 2 4 n of the equation x 2 3 n + 2 2 n + 2 n − 1 + ( x + 1 ) 2 3 n + 2 2 n + 2 n − 1 = b . Specifically, we explicitly determine the set of b 's for which the equation has i solutions for any positive integer i . Such sets, which depend on the number of solutions i , are given explicitly and expressed nicely, employing the absolute trace function over F 2 n , the norm function over F 2 4 n relatively to F 2 n and the set of ( 2 n + 1 ) st roots of unity in F 2 4 n . The equation considered in this paper comes from an article by Budaghyan et al. ( [2] ) in which the authors have investigated novel approaches for obtaining alternative representations for functions from the known infinite APN families. In particular, they have been interested in determining the differential spectrum of some power functions among them is the one F ( x ) = x 2 3 n + 2 2 n + 2 n − 1 defined over F 2 4 n . The problem of the determination of such spectrum has led to a conjecture (Conjecture 27 in the preprint (2020) [2] for which an updated version will appear in 2022 at the IEEE Transactions Information Theory) stated by Budaghyan et al. As an immediate consequence of our results, we prove that the above equation has 2 2 n solutions for one value of b , 2 2 n − 2 n solutions for 2 n values of b in F 2 4 n and has at most two solutions for all remaining points b , leading to complete proof of the conjecture raised by Budaghyan et al. We highlight that the recent work of Li et al., in [9] gives the complete differential spectrum of F and also gives an affirmative answer to the conjecture of Budaghyan et al. However, we emphasize that our approach is interesting and promising by being different from Li et al. Indeed, on the opposite to their article, our technique allows to determine ultimately the set of b 's for which the considered equation has solutions as well as the solutions of the equation for any b in F 2 4 n .
Dichotomic Pattern Mining Integrated With Constraint Reasoning for Digital Behavior Analysis

Sequential pattern mining remains a challenging task due to the large number of redundant candidate patterns and the exponential search space. In addition, further analysis is still required to map extracted patterns to different outcomes. In this paper, we introduce a pattern mining framework that operates on semi-structured datasets and exploits the dichotomy between outcomes. Our approach takes advantage of constraint reasoning to find sequential patterns that occur frequently and exhibit desired properties. This allows the creation of novel pattern embeddings that are useful for knowledge extraction and predictive modeling. Based on dichotomic pattern mining, we present two real-world applications for customer intent prediction and intrusion detection. Overall, our approach plays an integrator role between semi-structured sequential data and machine learning models, improves the performance of the downstream task, and retains interpretability.
Clustering-Based Representation Learning through Output Translation and Its Application to Remote-Sensing Images

In supervised deep learning, learning good representations for remote-sensing images (RSI) relies on manual annotations. However, in the area of remote sensing, it is hard to obtain huge amounts of labeled data. Recently, self-supervised learning shows its outstanding capability to learn representations of images, especially the methods of instance discrimination. Comparing methods of instance discrimination, clustering-based methods not only view the transformations of the same image as “positive” samples but also similar images. In this paper, we propose a new clustering-based method for representation learning. We first introduce a quantity to measure representations’ discriminativeness and from which we show that even distribution requires the most discriminative representations. This provides a theoretical insight into why evenly distributing the images works well. We notice that only the even distributions that preserve representations’ neighborhood relations are desirable. Therefore, we develop an algorithm that translates the outputs of a neural network to achieve the goal of evenly distributing the samples while preserving outputs’ neighborhood relations. Extensive experiments have demonstrated that our method can learn representations that are as good as or better than the state of the art approaches, and that our method performs computationally efficiently and robustly on various RSI datasets.
Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing

In this paper, we firstly empirically find that existing models struggle to handle hard mentions due to their insufficient contexts, which consequently limits their overall typing performance.To this end, we propose to exploit sibling mentions for enhancing the mention representations.Specifically, we present two different metrics for sibling selection and employ an attentive graph neural network to aggregate information from sibling mentions.The proposed graph model is scalable in that unseen test mentions are allowed to be added as new nodes for inference.Exhaustive experiments demonstrate the effectiveness of our sibling learning strategy, where our model outperforms ten strong baselines.Moreover, our experiments indeed prove the superiority of sibling mentions in helping clarify the types for hard mentions.
Dynamic Grey Wolf Optimization Algorithm Based on Quasi-Opposition Learning

Although the structure of Grey Wolf Optimization algorithm (GWO) is simple and easy to understand, it has the disadvantage of unbalanced exploration and exploitation. To solve the problem, this paper puts forward a kind of learning strategy based on quasi-opposition learning and dynamic search strategy improved Grey Wolf Optimization algorithm. That is verified on the nine benchmark test functions, the experimental results proved that the improved Grey Wolf Optimization algorithm has better search performance, compared with the cuckoo search algorithm (CS) and particle swarm optimization (PSO) algorithm at the same time, by contrast, improved algorithm has the advantages of larger.