Reviewerly/exHarmony huggingface dataset:
Mining Rare Recurring Events in Network Traffic using Second Order Contrast Patterns

Data mining techniques such as contrast pattern mining provide a promising approach to detecting and characterizing changes in network traffic. However, a major challenge for network managers is how to prioritize their analysis of these changes, without being overwhelmed by uninformative patterns. In particular, some changes in traffic occur on a regular basis, such as system backups, and it is important to filter out these rare recurring events, so that network managers can focus on new events. In this paper we address the problem of identifying rare recurring events in network traffic, and we propose a novel solution to detecting new events based on the approach of mining second order contrast patterns. Based on an empirical evaluation using a variety of real traffic sources, we show that our method can achieve high accuracy and F1-Score in detecting new events. Our work demonstrates the importance of higher order contrast pattern mining in practice, and provides an effective method for finding such higher order patterns in large datasets.
A Survey of Text Games for Reinforcement Learning Informed by Natural Language

Abstract Reinforcement Learning has shown success in a number of complex virtual environments. However, many challenges still exist towards solving problems with natural language as a core component. Interactive Fiction Games (or Text Games) are one such problem type that offer a set of safe, partially observable environments where natural language is required as part of the Reinforcement Learning solution. Therefore, this survey’s aim is to assist in the development of new Text Game problem settings and solutions for Reinforcement Learning informed by natural language. Specifically, this survey: 1) introduces the challenges in Text Game Reinforcement Learning problems, 2) outlines the generation tools for rendering Text Games and the subsequent environments generated, and 3) compares the agent architectures currently applied to provide a systematic review of benchmark methodologies and opportunities for future researchers.
An Intense Investigation on Deep and Traditional Machine Learning Approaches for Better Sentiment Classification

The world has recently seen unprecedented growth of social networks that have enabled internet users to share and communicate their views in various areas of life. Analysis of opinion is one type of text mining that aims to discover human views and emotions about any particular event, or topic. In the experiment of this research, we perform document level sentiment analysis in the form of positive and negative sentiments using both the traditional models and recent deep learning models. Three traditional methods (i.e. Naive Bayes (NB), Logistic Regression (LR), and Voting Ensemble) and three deep learning layout (i.e. Single Convolutional Neural Network (CNN), Single CNN + FastText and Ensemble(2-Layer CNN + Gated Recurrent Unit (GRU))) have been employed for two different datasets to evaluate the opinion classification. In our experiment, accuracy, f1-score, and RMSE have been considered as evaluation parameters and the optimistic results show that for airline review dataset, Single CNN + FastText comes out with the highest performance whereas for hotel review dataset, 2-L CNN + GRU comes out with the highest performance.
Gaussian Prototype Rectification For Few-shot Image Recognition

Few-shot Learning aims to recognize novel categories with scarce training data, which is a challenging problem in computer vision. Many single-point prototype-based methods are noise-vulnerable and deviate from the expected prototypes in a low-data regime. To address these issues, we firstly model the prototype as a multidimensional Gaussian distribution. Such a vanilla Gaussian prototype gains no significant performance improvement in complicated datasets (e.g., miniImageNet, tiered-ImageNet) due to weakly representativity of the latent vectors, this paper further proposes to rectify it by mutual information. In a nutshell, we use a multidimensional Gaussian distribution with local mutual information estimation and maximization to represent each prototype. The distribution estimation learns to construct a dimension and class-dependent distance metric while local mutual information maximization rectifies the prototype and guarantees the representation's suitability for few-shot classification. Besides, to solve prototype deviation in the low-data regime, we propose the transductive Kalman (t-Kalman) fusion method. To the best of our knowledge, we are the first to give the theoretical derivation of Kalman data fusion for few-shot scenario. Extensive experiments show our method outperforms previous methods on many benchmark datasets under various settings. Ablation study shows the mutual information and t-Kalman bring in significant improvement on miniImageNet (6.15% and 5.16%) and tieredImageNet (9.25% and 4.76%) under 1-shot and 5-shot task, demonstrating the effectiveness of the proposed Gaussian prototype rectification.
Managing Uncertainty in Crowdsourcing with Interval-Valued Labels

Crowdsourcing has been an emerging machine learning paradigm. It collects labels from human crowds as inputs typically through the Internet. Due to limitations on knowledge, social-economic status, and other factors, participants may often have ambiguity in labeling some instances in practice. In this work, we propose interval-valued labels (IVLs), instead of commonly used binary-valued ones, to manage such kind of uncertainty in crowdsourcing. IVLs possess interval specific statistic and probabilistic properties. With them, this work presents an algorithm that is able to make an inference with a favorable matching probability as a main result. The algorithm also implies an index, which measures the overall uncertainty of collected IVLs quantitatively. Reported computational experiments further evidence that we may better manage uncertainty in crowdsourcing with IVLs than without.
Variational Hyper-encoding Networks

We propose a framework called HyperVAE for encoding distributions of distributions. When a target distribution is modeled by a VAE, its neural network parameters are sampled from a distribution in the model space modeled by a hyper-level VAE. We propose a variational inference framework to implicitly encode the parameter distributions into a low dimensional Gaussian distribution. Given a target distribution, we predict the posterior distribution of the latent code, then use a matrix-network decoder to generate a posterior distribution for the parameters. HyperVAE can encode the target parameters in full in contrast to common hyper-networks practices, which generate only the scale and bias vectors to modify the target-network parameters. Thus HyperVAE preserves information about the model for each task in the latent space. We derive the training objective for HyperVAE using the minimum description length (MDL) principle to reduce the complexity of HyperVAE. We evaluate HyperVAE in density estimation tasks, outlier detection and discovery of novel design classes, demonstrating its efficacy.
Robust Watermarking using Arnold and Hybrid Transform in Medical Images

The number of medical digital images continues to grow, thus requiring the protection of patient privacy. It is the reason watermarking on medical images has received special attention. This study proposes a watermarking scheme on the image using the Arnold cat map and hybrid transform (Fast Discrete Curvelet Transforms (FDCuT), Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD)). The Arnold Cat Map technique is applied to pre-processing to increase security and reduce the likelihood of targeted attacks. Experiments were carried out on six modalities: MRI image, x-Ray, CT scan, ultrasound, eye image, and fundus scan. The experimental results show that the watermarked image produces excellent imperceptibility with a peak signal-to-noise ratio (PSNR) above 50 dB, and the structural similarity index (SSIM) is close to 1. The value of bit error rate (BER) 0 and normalized correlation (NC) 1 are obtained in conditions without attack. This technique is resistant to various attacks, namely JPEG compression, adding noise, and filtering. Therefore, the proposed watermarking scheme has good imperceptibility and robustness to be applied in e-health applications.
An Enhanced Particle Filter for Uncertainty Quantification in Neural Networks

The brittleness of deep learning models is ailing their deployment in real-world applications, such as transportation and airport security. Most work focuses on developing accurate models that only deliver point estimates without further information on model uncertainty or confidence. Ideally, a learning model should compute the posterior predictive distribution, which contains all information about the model output. We cast the problem of density tracking in neural networks using Particle Filtering, a powerful class of numerical methods for the solution of optimal estimation problems in non-linear, non-Gaussian systems. Particle filters are a powerful alternative to Markov chain Monte Carlo algorithms and enjoy established convergence and performance guarantees. In this paper, we advance a particle filtering framework for neural networks, where the predictive output is a distribution. The mean of this distribution serves as the point estimate decision and its variance provides the model confidence in the decision. Our framework shows increased robustness under noisy conditions. Additionally, the predictive variance increases monotonically with decreasing signal-to-noise ratio (SNR); thus reflecting a lower confidence or higher uncertainty. This paper serves as a pioneering proof-of-concept framework that will allow the development of a theoretical understanding of robust neural networks.
Efficient Movement Compensation and Detection Algorithm using Blob Detection and Modified Kalman Filter

In various real time applications such as security and surveillance etc., detection of movement from video sequence is commonly used. In such applications, time required to detect the movement and its accuracy is very crucial. In this paper, an efficient motion compensation and detection algorithm using Blob detection and modified Kalman filter techniques is proposed. The method is mainly based on Kalman filtering technique which is modified to compensate and detect the unwanted movement caused by the camera. Also the shadow effect caused by the variation in the intensity of light and object is removed using thresholding technique. Accuracy of movement detection is improved by implementing the blob detection method. The experimental results obtained from the developed algorithm is compared with few methods existing in the literature for validation.
Wide Two-Degree-of-Freedom Static Laser Scanner with Miniaturized Transmission Mechanism and Piezoelectric Actuation

In recent years, laser scanners have attracted significant attention for applications such as laser radars. However, the establishment of a two-degree-of-freedom scanner that can quasi-statically drive a large mirror with a large deflection angle has proven to be challenging. In this paper, we propose a laser scanner design and fabrication method by combining two unimorph piezoelectric actuators composed of piezoelectric single-crystal Pb(In1/2Nb1/2)O3-Pb(Mg1/3Nb2/3)O3-PbTiO3 and a miniature translation-rotation conversion mechanism with flexible polyimide hinges. The size of the entire scanner was 32 mm × 12 mm × 10 mm. We successfully demonstrated that the scanner could achieve a large quasi-static mechanical deflection angle amplitude of 20.5° in two axes with a 6-mm-square mirror.
Ambiguity and conflict: Dissecting uncertainty in decision-making.

Making decisions is fundamental to how we navigate, survive, and thrive in our environment. The quality of information used to support decisions is rarely perfect. Many decisions are made under conditions of uncertainty, arising from ambiguous or conflicting information. Conflict and ambiguity, though conceptually distinct, both generate uncertainty, a commonality that has led to overlapping and inconsistent terminology in the literature. Evidence from human and animal research suggests a behavioral dissociation in responding to conflict and ambiguity. This dissociation can be studied through the implementation of spatial or operant tasks in rodents which find close parallels in gambling tasks in humans. Pharmacological manipulations in rodents and fMRI studies in humans further suggest a dissociation in the neural processing of conflict and ambiguity such that fronto-striato-parietal circuits may be most important for interpreting ambiguous information, while the ventral striatum and ventral hippocampus are critical for resolving conflicting information. Overall, the neural representation and resolution of conflict and ambiguity remain relatively understudied despite the fundamental importance of these processes to understanding decision-making. We highlight the need for further research to differentiate these related yet distinct processes through implementation of carefully designed behavioral tasks with neural circuit-dissection techniques and the potential to pursue translational research between rodents and humans. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
CKMorph: a comprehensive morphological analyzer for Central Kurdish

A morphological analyzer, a significant component of many natural language processing applications, especially for morphologically rich languages, divides an input word into all its composing morphemes and identifies their morphological roles. This paper introduces a comprehensive morphological analyzer for Central Kurdish (CK), also known as Sorani, a low-resourced language with rich morphology. Building upon the limited existing literature, we first assembled and systematically categorized an extensive collection of the morphological and morphophonological rules of the language. Additionally, we collected and manually labeled a generative lexicon containing nearly 10,000 verb, noun and adjective stems, named entities, and other types of word stems. We used these rule sets and resources to implement CKMorph Analyzer based on finite-state transducers. In order to provide a benchmark for future research, we collected, manually labeled, and publicly shared test sets for evaluating the accuracy and coverage of the analyzer. CKMorph was able to correctly analyze 95.9% of the first test set, containing 1000 CK words morphologically analyzed according to the context. Moreover, CKMorph gave at least one analysis for 95.5% of 4.22 M CK tokens of the second test set. The demonstration of the application and resources, including the CK verb database and test sets, are openly accessible at github.com/CKMorph.
Multi-Level Graph Representation for Big Data Arising in Science Mapping (Dagstuhl Seminar 21152)

This report documents the program and the outcomes of Dagstuhl Seminar 21152 Multi-Level Graph Representation for Big Data Arising in Science Mapping. The seminar brought together researchers coming from information visualization, psychology, cognitive science, human-computer interaction, graph drawing, computational geometry, and cartography with interests in the of to discuss novel graph mining and layout algorithms and their application to the development of science mapping standards and services. Due to the pandemic, this was a hybrid event with only 5 in-person participants and 25 by-zoom participants from over ten different countries and at least five different time-zones. There were three overview talks and four special presentations (evening webinars) from different communities represented in the seminar. Abstracts of these talks and presentations are collected in this report. Three working groups formed around open research problems related to the seminar topic and we report about their findings.
Adaptation of Morphosyntactic Taggers

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Human Activity Recognition using A Improved Model Based on Multi-Head CNN-LSTM

Recent deep learning methods have been used to obtain good performance for Human Activity Recognition (HAR) task. In this paper, three fully convolutional blocks form Multi-Head Convolutional Neural Network (Multi-Head CNN), which abstracts the high-level features of sensor data. An additional convolutional layer (ACL) is used to make up for the missing low-level features and augmented with each head CNN, which is expressed as a NCNN. Then a Bidirectional-LSTM(BiLSTM) follows each NCNN to form each head CNN+BiLSTM. The output of all three parallel CNN+BiLSTMs is concatenated and conveyed into a dense layer, which is followed by an output layer with six neurons on the basis of the number of output classes. Compared to other models, the experimental results show that the proposed architecture can achieve better performance, which prevent overfitting and get a very good accuracy.
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification

Traditionalhand-crafted linguisticallyinformed features have often been used for distinguishing between translated and original non-translated texts.By contrast, to date, neural architectures without manual feature engineering have been less explored for this task.In this work, we (i) compare the traditional feature-engineering-based approach to the feature-learning-based one and (ii) analyse the neural architectures in order to investigate how well the hand-crafted features explain the variance in the neural models' predictions.We use pre-trained neural word embeddings, as well as several end-to-end neural architectures in both monolingual and multilingual settings and compare them to feature-engineering-based SVM classifiers.We show that (i) neural architectures outperform other approaches by more than 20 accuracy points, with the BERT-based model performing the best in both the monolingual and multilingual settings; (ii) while many individual hand-crafted translationese features correlate with neural model predictions, feature importance analysis shows that the most important features for neural and classical architectures differ; and (iii) our multilingual experiments provide empirical evidence for translationese universals across languages.
Comparative Study of Sentiment Analysis Techniques

Sentiment Analysis (SA) is an active area of study in the field of text mining. SA is the computational treatment of thoughts, emotions and literary subjectivity. This paper addresses a detailed summary of the latest update in this area. The related fields of SA (transfer learning, emotional detection, and resource building) that attracted researchers have recently been explored. The paper provides a description of the various approaches to sentiment classification and methods used for sentiment analysis. Starting from this summary, the paper introduces a classification of methods with respect to features, advantages and limitations.
A Relation-Guided Attention Mechanism for Relational Triple Extraction

Relational triples are the essential parts of knowledge graphs, which can be usually found in natural language sentences. Relational triple extraction aims to extract all entity pairs with semantic relations from sentences. Recent studies on triple extraction focus on the triple overlap problem where multiple relational triples share single entities or entity pairs in a sentence. Besides, we find sentences may contain implicit relations, and it is challenging for most existing methods to extract implicit relational triples whose relations are implicit in the sentence. In this paper, we propose a relation-guided attention mechanism (RGAM) for relational triple extraction. Firstly, we extract subjects of all possible triples from the sentence, and then identify the corresponding objects under target relations with relation guidance. We utilize relations as prior knowledge instead of regarding relations as classification labels, and apply attention mechanism to obtain fine-grained relation representations, which guide extracted subjects to find the corresponding objects. Our approach (RGAM) can not only learn multiple dependencies in each triple, but also be suitable for extracting implicit relational triples and handling the overlapping triple problem. Extensive experiments show that our model achieves state-of-the-art performance on two public datasets NYT and WebNLG, which demonstrates the effectiveness of our approach.
ICT Performance and Robustness Investigations: Some Ethical and Human Behavior Aspects

There is, in general, no doubt regarding the ethical and human behaviour aspects of the research work of any type. The researchers are thought of working for society and are low paid. However, in the practice of science and technology, they should work, first of all, for system designers who need their support in designing, developing and implementing the systems under investigation. Unfortunately, this is not a popular case in the area of Information and Communication Technology (ICT), where the support of the system and network researchers for the system and network designers, developers and operators is very limited.Considering that, the team of authors of the book (the team) decided to present the design experiences gained during designing, implementing and operating some line of the computer systems and networks in Poland (the country). The country was selected purposefully: Due to the political reasons, various embargos were imposed on the importation of modern equipment and methods of the computer industry, and the team needed very severely good support from the researchers to fulfil the design and development tasks successfully. However, in the ICT domain, which is a relatively new study and, thus, needing the significant support of science in every country, this support was a minute one in practice. In well developed countries, possessing a surplus of hardware and software components, the need for the support was also observed but could be bypassed through using a surplus of supplies or by learning from the design errors. This bypass was much less available in the country in severe economic conditions and the political conditions of the so-called Cold War.The objective of this book is to present the line of the ICT systems and networks under design and operation from the late 1960s and finishing when this book was written, and to present the requirements for the system and the basic support available from this science. The research aspects under consideration were, first of all, the performance evaluation and, for the systems of the 21st century, the robustness evaluation, with the system designers, implementers and operators being the ideal audience.(Imprint: Nova)