Reviewerly/exHarmony huggingface dataset:
Challenges in Annotating a Treebank of Clinical Narratives in Brazilian Portuguese

Dependency parsing can enhance the performance of Named Entity Recognition (NER) models and can be leveraged to boost information extraction. NER tasks are essential to deal with clinical narratives, but models for Brazilian Portuguese dependency parsing are scarce, even less for clinical texts and its specificities. This paper reports on the development of a treebank of clinical narratives in Brazilian Portuguese and the drafting of guidelines. Based on a corpus of 1,000 clinical narratives manually annotated with semantic information, split into 12,711 sentences, we identified some characteristics of these texts that differ from traditional domains and have a deep impact on the annotation process, such as extensive use of acronyms and abbreviations, words not recognized by POS taggers, misspelling, special use of some symbols, different uses for numerals, heterogeneity of sentence sizes, and coordinated phrases without any punctuation. We developed a document to describe the annotation types and to explain how difficult cases should be treated to ensure consistency, including examples that could be found in this kind of texts. We created a Tag versus Frequency relation to justify some of the characteristics and challenges of the corpus. The corpus when completely annotated will be made available to the entire scientific community that performs research with clinical texts.
High-accuracy Object Detection Based on YOLOv3 Under Different Weather Conditions

To improve the accuracy and speed of image target detection under complex weather conditions, an image target detection method based on the YOLOv3 algorithm and image enhancement processing is proposed. Firstly, we make a data set specifically for image target detection by filtering the VOC 2007 data set. Then we select the yolov3 target detection model as the infrastructure network of image target detection. We improved the network structure for better accuracy. Then, the original data is enhanced by setting different atmospheric and channel conditions, and we use the improved YOLOv3 model to detect the image target and calculate the accuracy. Experimental results show that the system has good prediction accuracy for all 20 categories in the original data set. In addition, the image object detection method based on the combination of the YOLOv3 algorithm and image enhancement processing can effectively complete the task of image object detection under complex weather conditions.
Integration of Back-Propagation Neural Network to Classify of Cybercriminal Entities in Blockchain

Bitcoin is a decentralized, pseudonymous cryptocurrency that has become one among the most demanded digital assets to date. Because of its uncontrolled nature and users’ inherent anonymity, it has seen a significant surge in its use for illegal operations. As a result, numerous systems for characterizing diversified entities across the Bitcoin network must be developed. In this work, we offer a way for breaking Bitcoin anonymity using a revolutionary cascade machine learning model that only utilizes a few features taken straight from Bitcoin blockchain data. We gathered approximately 29 million samples from diverse sources and generated data for four different entities: exchanges, gambling, pools, and services. On a dataset balanced using SMOTE and weight of the entities, the back-propagation neural network (BPNN) model was trained and tested. Cross-validation accuracy has been utilized to evaluate the model’s accuracy. On the dataset balanced using the weight of the entities, the BPNN model classified the entities with 71.51%, while with SMOTE, the accuracy of classification is 71.22%.
Corpus construction for topic-based summarization of multi-party conversation

In this paper, we report corpus construction and topic-based summarization methods for multi-party conversation. We have already constructed reference summaries and a list of important utterances in each discussion. However, fine-grained summaries about topics in a discussion often are desired in many situations. Therefore, we construct topic-based summaries and propose an important utterance extraction method and two summarization processes using the extracted utterances; extractive and abstractive methods. For the important utterance extraction, we use SVMs with 12 types of features. We use mBART, which is a neural network-based model, as the abstractive method. In the experiment, the extractive method was superior in terms of “accuracy as a summary (relevance), “ while the readability of the abstractive method was superior.
Survey on Importance of Load Balancing for Cloud Computing

Cloud computing is very demanding technology worldwide to support industrial and academy related applications. Government department, universities and Enterprises move towards cloud computing, it is an extension of IT resources. Cloud Computing provide the facility to store the large number of data on the cloud and can access whenever it required from anywhere. Cloud Computing become very demanding technologyfor web applications, a huge number of users can access web application at a time so the concept of Load Balancer come into picture. In this survey paper we compare and contrast the various load balancing policy.
Improving Multiclass Classification of Cybersecurity Breaches in Railway Infrastructure using Imbalanced Learning

Machine learning approaches and algorithms are spreading in wide areas in research and technology. Cybersecurity breaches are the common anomalies for networked and distributed infrastructures which are monitored, registered, and described carefully. However, the description of each security breaches episode and its classification is still a difficult problem, especially in highly complex telecommunication infrastructure. Railway information infrastructure usually has a large scale and large diversity of possible security breaches. Today's situation shows the registering of the security breaches has a mature and stable character, but the problem of their automated classification is not solved completely. Many studies on security breaches multiclass classification show inadequate accuracy of classification. We investigated the origins of this problem and suggested the possible roots consist in disbalance the datasets used for machine learning multiclass classification. Thus, we proposed an approach to improve the accuracy of the classification and verified our approach on the really collected datasets with cybersecurity breaches in railway telecommunication infrastructure. We analyzed the results of applying three imbalanced learning methodologies, namely random oversampling, synthetic minority oversampling technique, and the last one with Tomek links. We have implemented three machine learning algorithms, namely Naïve Bayes, K-means, and support vector machine, on disbalances and balanced data to estimate imbalance learning methodologies with comparing results. The proposed approach demonstrated the increase of the accuracy for multiclass classification in the range from 30 to 41%, depending on the imbalanced learning technique.
Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation

Automated knowledge discovery from trending chemical literature is essential for more efficient biomedical research. How to extract detailed knowledge about chemical reactions from the core chemistry literature is a new emerging challenge that has not been well studied. In this paper, we study the new problem of fine-grained chemical entity typing, which poses interesting new challenges especially because of the complex name mentions frequently occurring in chemistry literature and graphic representation of entities. We introduce a new benchmark data set (CHEMET) to facilitate the study of the new task and propose a novel multi-modal representation learning framework to solve the problem of fine-grained chemical entity typing by leveraging external resources with chemical structures and using cross-modal attention to learn effective representation of text in the chemistry domain. Experiment results show that the proposed framework outperforms multiple state-of-the-art methods. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Decolonizing Racial Bioinformatics: Governing Education in Contagion and Dehiscence

In this chapter we are concerned with how presupposed ideas of life animate dead educational practices, often through viral racist practices. Notwithstanding the philosophical neglect that ‘life’ has received, today we grapple with new developments in bioinformatics that encourages a rethinking of what constitutes life. Rather than understanding life within humanist traditions (e.g., a contained subject position), we propose a speculative reading of bioinformatics as a particular moment of ‘excess contagion’. We argue that bioinformatics is a scientific and technological force that exceeds enclosures, but one that education will try to harness in order to widen its own limits, particularly through the optimization of human capital. If bioinformatics can simultaneously equalize and exacerbate unequal forms of life, we conclude paradoxically, that accelerating this bioinformatic moment might instantiate a ‘decoloniality of informatics’ through the proliferation of contagious, uncertain, errant, necrotic, and mutant life. Rather than reform education and its anti/racist declarations of vitalist life, we suggest an accelerated use of ‘contagious bioinformatics’ as a way to proliferate unknown becomings for new kinds of intra-connectivity, especially between human and inhuman networks of relationality.
An Active Contour Model with Local Variance Force Term and Its Efficient Minimization Solver for Multiphase Image Segmentation

In this paper, we propose a general active contour model with a local variance force (LVF) term that can be applied to multiphase image segmentation problems. With the LVF, the proposed model is very effective in the segmentation of images with noise. A well-targeted minimization algorithm called ICTM-LVF is then designed to solve this model efficiently. This minimization algorithm, developed from the iterative convolution-thresholding method (ICTM), enjoys the energy-decaying property under some conditions and has highly efficient performance in the segmentation. To overcome the initialization issue of active contour models, we generalize the inhomogeneous graph Laplacian initialization method to the multiphase case and then apply it to give the initial contour of the ICTM-LVF solver. Numerical experiments are conducted on synthetic images and real images to demonstrate the capability of our initialization method, and the effectiveness of the LVF for noise robustness in the multiphase image segmentation.
Asymptotics for Outlier Hypothesis Testing

We revisit the outlier hypothesis testing framework of Li et al. (TIT 2014) and derive fundamental limits for the optimal test under the generalized Neyman-Pearson criterion. In outlier hypothesis testing, one is given multiple observed sequences, where most sequences are generated i.i.d. from a nominal distribution. The task is to discern the set of outlying sequences that are generated according to anomalous distributions. The nominal and anomalous distributions are unknown. We consider the case of multiple outlying sequences where the number of outlying sequences is unknown and each outlying sequence can follow a different anomalous distribution. Under this setting, we study the tradeoff among the probabilities of misclassification error, false alarm and false reject. Specifically, we propose a threshold-based test that ensures exponential decay of misclassification error and false alarm probabilities. We study two constraints on the false reject probability, with one constraint being that it is a non-vanishing constant and the other being that it has an exponential decay rate. For both cases, we derive bounds on the false reject probability, as a function of the threshold, for each tuple of nominal and anomalous distributions.
IMPROVING STUDENTS SPELLING SKILLS THROUGH USING JOLLY PHONICS METHOD (AGE 5 YEARS)

This research describes a classroom action research (CAR) held in Bilingual Kindergarten, Gambir - DKI Jakarta, through the implementation of Jolly Phonics Method to improve the basic ability in spelling using the letter sounds. There are 10 students as a population in one class for K2 level (age 5 years). In this research article, the writer used qualitative method and experimental method to get the data. The major goal of Jolly Phonics method teaching, students have achievements are much greater in both reading and writing relating the letter sounds based on students spelling skills. This research was conducted in two cycles. Each cycle included four stages: planning, observing, improving, and practicing. The data were obtained by the result of pre-cycle, cycle I and cycle II. The finding result of the students of at pre-cycle was 63, then increase to 68 for the first cycle, the last increase up to 73 for the second cycle. The teaching of Jolly Phonics includes 5 teaching points. Those are learning the letter sounds, learning letter formation, blending, identifying sounds in words, and tricky words. The data that were obtained qualitatively and quantitatively show that Jolly Phonics Method could improve: (l) the students’ ability in remembering the letter that represent of each sound is taught with an action, (2) the students’ ability in pronouncing the letter sounds correctly, (3) the students’ ability in spelling the words correctly. The major goal and result of Jolly Phonics method that students have achievements are much greater in both reading and writing relating the letter sounds based on students spelling skills.
Cost and Efficiency Analysis of Steganography in the IEEE 802.11ah IoT Protocol

The widespread use of the Internet of Things (IoT) applications has enormously increased the danger level of data leakage and theft in IoT as data transmission occurs through a public channel.As a result, the security of the IoT has become a serious challenge in the field of information security.Steganography on the network is a critical tool for preventing the leakage of private information and enabling secure and encrypted communication.The primary purpose of steganography is to conceal sensitive information in any form of media such as audio, video, text, or photos, and securely transfer it through wireless networks.In this paper, we analyse the performance characteristics of one of the steganography techniques called Hidden Communication System for Corrupted Networks (HCCNETs) for hiding sensitive data.This performance analysis includes the efficiency and the cost of the system in Wireless Local Area Networks (WLANs), specifically in the IEEE 802.11ahIoT protocol.The analysis is mainly based on a two-dimensional Markov chain model in the presence of an error channel.Additionally, the model considers packet arrival rate, back-off timer freezing, back-off stages, and short retry limit to ensure compliance with IEEE 802.11ah requirements.It stresses the importance of taking these elements into consideration while modeling the efficiency and cost of the steganographic channel system.These parameters often result in a high precise channel access estimation, a more accurate and efficient accuracy measurements system, efficient channel utilisation, avoidance of throughput saturation overestimation, and ensuring that no packet is served endlessly.Evaluated results demonstrate that HCCNETs is an effective approach at low cost.
Evolutionary Algorithm for Parameter Optimization of Context-Steering Agents

Context steering is a local approach to control an agent’s movement in a dynamically changing scene. Recent works have formalized the context-steering approach by Fray and presented a multiobjective view of the context-steering problem. Combining a variety of different behaviors, which can be used multiple times in different configurations for different context maps, introduces a large number of parameters that need to be tuned to obtain well-performing agents. This work aims to use evolutionary algorithms to optimize context-steering agents for various environments. A special focus lies on the evolution of agents that perform robustly across multiple variations of the same environment. To this end, we develop a real-valued encoding for a context-steering agent along with three different fitness functions to represent different goals of the agent. Our experimental evaluation shows that an evolutionary optimization can produce agent configurations that perform well with respect to different tasks and show a high intratask robustness. The proposed approach based on evolutionary optimization enables the user to optimize context-steering agents such that they can explore environments while avoiding dynamic obstacles.
Algorithms for Linguistic Description of Categorical Data

The paper proposes a method that comprises five algorithms for producing composite linguistic summaries from categorical data. The generated composite summaries reflect Evidence, Contrast, or Emphasis relations between at least two constituent summaries. The constituent summaries are instances of the LDS classical protoforms created, in this case, with frequent L1 item sets and association rules obtained from applying an association rule mining algorithm. In order to verify the feasibility of implementing the method, we performed a use case with a dataset of 2128 cases of the Economic Chamber of the Provincial People’s Court of Havana. The results were consistent with expectations, obtaining 18 Evidence relations, 11 Contrast relations, and 16 Emphasis relations. Furthermore, we evaluated the interpretability of the composite summaries obtained in the use case. Specifically, we measured the accuracy of identifying the relation type implicit in the summary and their understandability. In both cases, the results were positive.
Beard and Hair Detection, Segmentation and Changing Color Using Mask R-CNN

Beard and hair detection and segmentation have a significant role in gender identification, age assessment, and facial recognition. Due to the variability of their forms, colors, and intensities and the impact of shadows and light objects. This paper has proposed an efficient state-of-the-art system for beard and hair detection and segmentation for changing color in challenging facial images. After segmentation, the color of hair and beard can be changed. We have used a modified version of a Mask R-CNN model for hair and beard detection and segmentation. We have collected and prepared a dataset of 1500 images equally divided into both hair and beard images. This dataset is online available on the NCAI1 website. Finally, we have retrained a modified version of Mask R-CNN through transfer learning on our dataset to detect and segment out hair and beard on any given image. Mask R-CNN has outperformed compared to earlier systems designed for the same task with an accuracy of 91.2%.
GRU-based Encoder-Decoder Attention Model for English to Bangla Translation on Novel Dataset

Neural machine translation (NMT) is being used widely for its better translation quality for many languages. Though Bangla is a widely used language, many experiments on different NMT approaches have not been carried out for the English-Bangla language pair. In this paper, we experimented with a specific NMT model to analyze the results on the language pair. We have chosen a model on which research for translating from English to Bangla has not been conducted yet. We used a GRU-based unidirectional RNN model with an encoder-decoder-attention for this research. The research is conducted on a small-size balanced dataset. We have created a dataset of 1,014 sentences by translating from the classic novel ‘Gulliver’s Travels’ and appended them with another balanced dataset. This model gives a huge BLEU score of 50.07. Qualitative and quantitative analysis on the resulting outcome and the BLEU score has been made in the research.
A Parameter-Adaptive Convolution Neural Network for Capturing the Context-Specific Information in Natural Language Understanding

Natural Language Understanding (NLU) aims to make sense of language by enabling computers to comprehend text in semantic level, which is a fundamental but challenging task in natural language processing. Recently, BERT has achieved state-of-the-art performances in NLU utilizing pretraining and fine-tuning techniques to capture the task-specific information without any task-specific structure. However, the existing models still cannot capture context-specific information, leading to poor performance when dealing with the pervasive ambiguity of language caused by the polysemous words. In this paper, we propose a Parameter-Adaptive Convolution Neural Network (PACNN) to capture the context-specific information which can deal with the polysemous word better. Instead of the convolution layers in existing models, the parameters of convolutional filters in PACNN, generated by a deconvolution (e.g. convolution transpose) neural network, are adaptable according to the input sentences, which can filter the local information via the global information and capture the context-specific meaning of the polysemous words. We empirically demonstrate the efficiency of the proposed PACNN by performing a series of experiments on the General Language Understanding Evaluation (GLUE) benchmark, a collection of popular datasets on different tasks, and the PACNN significantly outperforms all the baselines. Besides, our model can be appended to BERT to further improve its performances. As BERT and the proposed PACNN capture information from various aspects, the proposed BERT+PACNN achieves the best performances compared with BERT and other baselines. Furthermore, we visualize the task-specific information and context-specific information captured by BERT and the PACNN, separately, using Singular Value Decomposition (SVD) to demonstrate the efficiencies of the two models further.
Image Encryption Using Multi-Scroll Attractor and Chaotic Logistic Map

In the current scenario, data transmission over the network is a challenging task as there is a need for protecting sensitive data.Traditional encryption schemes are less sensitive and less complex thus prone to attacks during transmission.It has been observed that an encryption scheme using chaotic theory is more promising due to its non-linear and unpredictable behavior.Hence, proposed a novel hybrid image encryption scheme with multi-scroll attractors and quantum chaos logistic maps (MSA-QCLM).The image data is classified as inter-bits and intra-bits which are permutated separately using multi scroll attractor & quantum logistic maps to generate random keys.To increase the encryption efficiency, a hybrid chaotic technique was performed.Experimentation is performed in a Qiskit simulation tool for various image sets.The simulation results and theoretical analysis show that the proposed method is more efficient than its classical counterpart, and its security is verified by the statistical analysis, keys sensitivity, and keyspace analysis.The Number of changing pixel rate (NPCR) & the Unified averaged changed intensity (UACI) values were observed to be 99.6% & 33.4% respectively.Also, entropy oscillates from 7.9 to 7.901 for the different tested encrypted images.The proposed algorithm can resist brute force attacks well, owing to the values of information entropy near the theoretical value of 8.The proposed algorithm has also passed the NIST test (Frequency Monobit test, Run test and DFT test).