Reviewerly/exHarmony huggingface dataset:
Cell Population Data–Driven Acute Promyelocytic Leukemia Flagging Through Artificial Neural Network Predictive Modeling

A targeted and timely offered treatment can be a benefitting tool for patients with acute promyelocytic leukemia (APML). Current round of study made use of potential morphological and immature fraction–related parameters (cell population data) generated during complete blood cell count (CBC), through artificial neural network (ANN) predictive modeling for early flagging of APML cases. We collected classical CBC items along with cell population data (CPD) from hematology analyzer at diagnosis of 1067 study subjects with hematological neoplasms. For morphological assessment, peripheral blood films were examined. Statistical and machine learning tools including principal component analysis (PCA) helped in the evaluation of predictive capacity of routine and CPD items. Then selected CBC item–driven ANN predictive modeling was developed to smartly use the hidden trend by increasing the auguring accuracy of these parameters in differentiation of APML cases. We found a characteristic triad based on lower (53.73) platelet count (PLT) with decreased/normal (4.72) immature fraction of platelet (IPF) with addition of significantly higher value (65.5) of DNA/RNA content–related neutrophil (NE-SFL) parameter in patients with APML against other hematological neoplasm's groups. On PCA, APML showed exceptionally significant variance for PLT, IPF, and NE-SFL. Through training of ANN predictive modeling, our selected CBC items successfully classify the APML group from non-APML groups at highly significant (0.894) AUC value with lower (2.3 percent) false prediction rate. Practical results of using our ANN model were found acceptable with value of 95.7% and 97.7% for training and testing data sets, respectively. We proposed that PLT, IPF, and NE-SFL could potentially be used for early flagging of APML cases in the hematology-oncology unit. CBC item–driven ANN modeling is a novel approach that substantially strengthen the predictive potential of CBC items, allowing the clinicians to be confident by the typical trend raised by these studied parameters.
At a Glance: The Impact of Gaze Aggregation Views on Syntactic Tagging

Readers' eye movements used as part of the training signal have been shown to improve performance in a wide range of Natural Language Processing (NLP) tasks.Previous work uses gaze data either at the type level or at the token level and mostly from a single eyetracking corpus.In this paper, we analyze type vs token-level integration options with eye tracking data from two corpora to inform two syntactic sequence labeling problems: binary phrase chunking and part-of-speech tagging.We show that using globally-aggregated measures that capture the central tendency or variability of gaze data is more beneficial than proposed local views which retain individual participant information.While gaze data is informative for supervised POS tagging, which complements previous findings on unsupervised POS induction, almost no improvement is obtained for binary phrase chunking, except for a single specific setup.Hence, caution is warranted when using gaze data as signal for NLP, as no single view is robust over tasks, modeling choice and gaze corpus.
Dialect Text Normalization to Normative Standard Finnish

We compare different LSTMs and transformer models in terms of their effectiveness in normalizing dialectal Finnish into the normative standard Finnish.As dialect is the common way of communication for people online in Finnish, such a normalization is a necessary step to improve the accuracy of the existing Finnish NLP tools that are tailored for normative Finnish text.We work on a corpus consisting of dialectal data from 23 distinct Finnish dialect varieties.The best functioning BRNN approach lowers the initial word error rate of the corpus from 52.89 to 5.73.
Enhancing Conversational Dialogue Models with Grounded Knowledge

Leveraging external knowledge to enhance conversational models has become a popular research area in recent years. Compared to vanilla generative models, the knowledge-grounded models may produce more informative and engaging responses. Although various approaches have been proposed in the past, how to effectively incorporate knowledge remains an open research question. It is unclear how much external knowledge should be retrieved and what is the optimal way to enhance the conversational model, trading off between relevant information and noise. Therefore, in this paper, we aim to bridge the gap by first extensively evaluating various types of state-of-the-art knowledge-grounded conversational models, including recurrent neural network based, memory networks based, and Transformer based models. We demonstrate empirically that those conversational models can only be enhanced with the right amount of external knowledge. To effectively leverage information originated from external knowledge, we propose a novel Transformer with Expanded Decoder (Transformer-ED or TED for short), which can automatically tune the weights for different sources of evidence when generating responses. Our experiments show that our proposed model outperforms state-of-the-art models in terms of both quality and diversity.
Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings

Contextualized word embeddings (CWE) such as provided by ELMo (Peters et al., 2018), Flair NLP (Akbik et al., 2018), or BERT (Devlin et al., 2019) are a major recent innovation in NLP. CWEs provide semantic vector representations of words depending on their respective context. Their advantage over static word embeddings has been shown for a number of tasks, such as text classification, sequence tagging, or machine translation. Since vectors of the same word type can vary depending on the respective context, they implicitly provide a model for word sense disambiguation (WSD). We introduce a simple but effective approach to WSD using a nearest neighbor classification on CWEs. We compare the performance of different CWE models for the task and can report improvements above the current state of the art for two standard WSD benchmark datasets. We further show that the pre-trained BERT model is able to place polysemic words into distinct 'sense' regions of the embedding space, while ELMo and Flair NLP do not seem to possess this ability.
Sentiment and Emotion Based Text Representation for Fake Reviews Detection

Fake reviews are increasingly prevalent across the Internet.They can be unethical and harmful.They can affect businesses and mislead customers.As opinions on the Web are increasingly relied on, the detection of fake reviews has become more critical.In this study we explore the effectiveness of sentiment and emotions based representations for the task of building machine learning models for fake reviews detection.The experiment performed with three real-world datasets demonstrate that improved data representation can be achieved by combining sentiment and emotion extraction methods, as well as by performing sentiment and emotion analysis on a part-by-part basis by segmenting the reviews.
Classification of Leukemic B-Lymphoblast Cells from Blood Smear Microscopic Images with an Attention-Based Deep Learning Method and Advanced Augmentation Techniques

Acute Lymphoblastic Leukemia (ALL) is a blood cell cancer type characterized by an increased number of immature lymphocytes. It is one of the leading cancer inflicted causes of death among children and affected around 876.000 people globally in 2015. In this work, we describe methods used for the “Classification of Normal versus Malignant Cells in B-ALL White Blood Cancer Microscopic Images” ISBI challenge. Due to the morphological similarity between malignant and normal cells, the classification of these cell types is a very challenging task, even for human experts. Deep learning using Convolutional Neural Networks (CNN) provides state-of-the-art techniques for image classification, even with limited annotation data, based on techniques like transfer learning and data augmentation. Our solution to tackle this classification problem is based upon advanced augmentation techniques to counter overfitting the small dataset and transfer learning with adaptive learning rates. Additionally, we incorporated a basic attention mechanism based on a region proposal subnetwork to boost our results for this competition. The ResNet 18 network with an additional regression head achieved a weighted F1 score of 0.8284 on the final test set and 0.8746 on the preliminary test set.
Accurate detection of shock waves and shock interactions in two-dimensional shock-capturing solutions

This paper describes an innovative technique that is capable of automatically detecting shock-wave shapes and shock-interaction patterns by post-processing the results obtained from a shock-capturing calculation using any of the available, state-of-the-art, shock-detection techniques. This new technique makes use of the Hough transform, the least-squares fit and the fuzzy logic to post-process the data supplied by classical shock-detection techniques; it not only allows to significantly enhance the quality of the extracted shock-fronts, but, even more importantly, allows to recognize the different types of shock-shock and shock-wall interactions that may occur within the flowfield.
Smart Contract-Based Secured Business-to-Consumer Supply Chain Systems

Blockchain technology platform Ethereum (open-source platform) has rapidly evolved and provides interfaces that allow users to develop variety of Blockchain applications using its "Solidity" programming language. Due to its security and privacy, and smart contract execution capabilities, Ethereum became a favorite option for banking and other application domains. In this paper, we investigate the feasibility of Ethereum capabilities for online Supply Chain systems in Business-to-Consumer (B2C) business model. This work aims to research Ethereum platform capabilities and language, including smart contracts and their advantages over regular contracts. We develop illustrative application to demonstrate smart contracts in the B2C Supply Chain system. The application demonstrates the role of a smart contracts in executing order transactions, from placing an order to receiving the shipment. Furthermore, we add to the application a third component to track and update shipping information between suppliers and consumers.
diveXplore 4.0: The ITEC Deep Interactive Video Exploration System at VBS2020

Having participated in the three most recent iterations of the annual Video Browser Showdown (VBS2017–VBS2019) as well as in both newly established Lifelog Search Challenges (LSC2018–LSC2019), the actively developed Deep Interactive Video Exploration (diveXplore) system combines a variety of content-based video analysis and processing strategies for interactively exploring large video archives. The system provides a user with browseable self-organizing feature maps, color filtering, semantic concept search utilizing deep neural networks as well as hand-drawn sketch search. The most recent version improves upon its predecessors by unifying deep concepts for facilitating and speeding up search, while significantly refactoring the user interface for increasing the overall system performance.
Multimodal Representation Learning: Advances, Trends and Challenges

Representation learning is the base and crucial for consequential tasks, such as classification, regression, and recognition. The goal of representation learning is to automatically learning good features with deep models. Multimodal representation learning is a special representation learning, which automatically learns good features from multiple modalities, and these modalities are not independent, there are correlations and associations among modalities. Furthermore, multimodal data are usually heterogeneous. Due to the characteristics, multimodal representation learning poses many difficulties: how to combine multimodal data from heterogeneous sources; how to jointly learning features from multimodal data; how to effectively describe the correlations and associations, etc. These difficulties triggered great interest of researchers along with the upsurge of deep learning, many deep multimodal learning methods have been proposed by different researchers. In this paper, we present an overview of deep multimodal learning, especially the approaches proposed within the last decades. We provide potential readers with advances, trends and challenges, which can be very helpful to researchers in the field of machine, especially for the ones engaging in the study of multimodal deep machine learning.
Story embedding: Learning distributed representations of stories based on character networks

This study aims to learn representations of stories in narrative works (i.e., creative works that contain stories) using fixed-length vectors. Vector representations of stories enable us to compare narrative works regardless of their media or formats. To computationally represent stories, we focus on social networks among characters (character networks). We assume that the structural features of the character networks reflect the characteristics of stories. By extending substructure-based graph embedding models, we propose models to learn distributed representations of character networks in stories. The proposed models consist of three parts: (i) discovering substructures of character networks, (ii) embedding each substructure (Char2Vec), and (iii) learning vector representations of each character network (Story2Vec). We find substructures around each character in multiple scales based on proximity between characters. We suppose that a character's substructures signify its ‘social roles’. Subsequently, a Char2Vec model is designed to embed a social role based on co-occurred social roles. Since character networks are dynamic social networks that temporally evolve, we use temporal changes and adjacency of social roles to determine their co-occurrence. Finally, Story2Vec models predict occurrences of social roles in each story for embedding the story. To predict the occurrences, we apply two approaches: (i) considering temporal changes in social roles as with the Char2Vec model and (ii) focusing on the final social roles of each character. We call the embedding model with the first approach ‘flow-oriented Story2Vec.’ This approach can reflect the context and flow of stories if the dynamics of character networks is well understood. Second, based on the final states of social roles, we can emphasize the denouement of stories, which is an overview of the static structure of the character networks. We name this model as ‘denouement-oriented Story2Vec.’ In addition, we suggest ‘unified Story2Vec’ as a combination of these two models. We evaluated the quality of vector representations generated by the proposed embedding models using movies in the real world.
The Application of Multi-agent Robotic Systems for Earthquake Rescue

Earthquake is one of the most serious catastrophes that cause a fatal threat to people and animals among all of the disasters. In order to rescue the victims as soon as possible, this paper proposed to apply the multi-agent robotic systems to the search and rescue operation to improve the rescue efficiency. Firstly, the rescue operations and the sequence of the rescue process are analyzed according to statistical data. Secondly, the search theory is researched based on probability theory. Thirdly, the intelligent optimization algorithm is used to ensure that agents complete the tasks more efficiently. At last, the collision avoidance trajectories of agents are planned in ROS. The development and application of rescue robotic systems are very important to modern society, especially involved in natural or man-made disasters because of their unpredictability and the possibility of large human losses. This paper provides a good model to solve the emergency with the help of multi-agents.
Improved Grasshopper Algorithm Based on Gravity Search Operator and Pigeon Colony Landmark Operator

The grasshopper optimization algorithm (GOA) is a new meta-heuristic algorithm inspired by the behavior of grasshopper groups. Aiming at the shortcomings of poor development ability and low convergence accuracy of GOA, this paper introduces the gravity search operator into the optimization process of GOA to improve the grasshopper’s global exploration and avoid falling into local optimum in advance. At the same time, a pigeon search operator-landmark operator is introduced to improve and balance the algorithm’s exploration and development capabilities. In order to verify the validity of the improved algorithm, this paper will adopts the gravity search operator and a deterrent landmark operator hybrid grasshoppers algorithm (HGOA) with basic grasshopper algorithm (GOA), particle swarm optimization (PSO) algorithm, sine and cosine algorithm (SCA), moth-flame optimization (MFO) algorithm, salp swarm algorithm (SSA), and bat algorithm (BA) to optimize 28 test functions. And the analysis and comparison of the obtained statistical data results finally show that the proposed improved grasshopper algorithm has better optimization ability.
AnomiGAN: Generative Adversarial Networks for Anonymizing Private Medical Data

Typical personal medical data contains sensitive information about individuals. Storing or sharing the personal medical data is thus often risky. For example, a short DNA sequence can provide information that can identify not only an individual, but also his or her relatives. Nonetheless, most countries and researchers agree on the necessity of collecting personal medical data. This stems from the fact that medical data, including genomic data, are an indispensable resource for further research and development regarding disease prevention and treatment. To prevent personal medical data from being misused, techniques to reliably preserve sensitive information should be developed for real world applications. In this paper, we propose a framework called anonymized generative adversarial networks (AnomiGAN), to preserve the privacy of personal medical data, while also maintaining high prediction performance. We compared our method to state-of-the-art techniques and observed that our method preserves the same level of privacy as differential privacy (DP) and provides better prediction results. We also observed that there is a trade-off between privacy and prediction results that depends on the degree of preservation of the original data. Here, we provide a mathematical overview of our proposed model and demonstrate its validation using UCI machine learning repository datasets in order to highlight its utility in practice. The code is available at https://github.com/hobae/AnomiGAN/.
Boosting evolutionary optimization via fuzzy-classification-assisted selection

In evolutionary optimization, solution selection is an important operator since it will be normally used to decide the optimization direction via determining new solutions. Most selection methods are objective fitness-based approaches which will lead to a waste of fitness evaluations. This is because some evaluated but unpromising solutions are discarded without contributing useful search information. We are, thus, motivated to treat the solution selection as a classification procedure, where the selected solutions and discarded solutions belong to different classes. However, another problem is that the difference between ‘promising’ and ‘unpromising’ solutions becomes fuzzy when iterations go on. Therefore, we employ fuzzy classification to predict the categories of solutions by the fuzzy membership function. And then the predicted results are used to assist solution selection to reduce the number of fitness evaluations. Finally, we propose a fuzzy-classification-assisted selection (FCAS) strategy to boost evolutionary optimization. FCAS is experimentally integrated into two state-of-the-art algorithms and studied on three test suites. The results reveal the efficiency of FCAS for boosting evolutionary optimization.
A document skew detection method using fast Hough transform

The majority of document image analysis systems use a document skew detection algorithm to simplify all its further processing stages. A huge amount of such algorithms based on Hough transform (HT) analysis has already been proposed. Despite this, we managed to find only one work where the Fast Hough Transform (FHT) usage was suggested to solve the indicated problem. Unfortunately, no study of that method was provided. In this work, we propose and study a skew detection algorithm for the document images which relies on FHT analysis. To measure this algorithm quality we use the dataset from the problem oriented DISEC‘13 contest and its evaluation methodology. Obtained values for AED, T OP80, and CE criteria are equal to 0.086, 0.056, 68.80 respectively.
A New Computational Intelligence for Face Emotional Detection in Ubiquitous

Ubiquitous was increasingly used in industrial technology 4.0, especially those aimed at facilitating human's activities. This was due to the fact that ubiquitous technology was small and inexpensive, but had limited resources. Research on ubiquitous had been widely developed, especially in the use of sensors in ubiquitous. Sensor optimization of facial detections had also been done a lot. High computation for face detection and facial emotions caused overload on ubiquitous. This was caused by limited resources. These limitations caused a decrease in the level of face detection accuracy. This research developed new techniques for face detection, especially emotions by using ubiquitous. This facial emotion detection used a hybrid Computational Intelligence algorithm whose process was done on ubiquitous. Fuzzy C-Mean algorithm functioned as a color grouping in face detection. The results of this color grouping were histograms. After the histogram process was complete, numerical values were obtained for each photo of facial emotion. Each facial emotion had a histogram numeric value which was used as a data set in this study. Experimental testing on this ubiquitous device produced an accuracy value of 77.5%.
Design of Dynamic and Personalized Deception: A Research Framework and New Insights

Deceptive defense techniques (e.g., intrusion detection, firewalls, honeypots, honeynets) are commonly used to prevent cyberattacks.However, most current defense techniques are generic and static, and are often learned and exploited by attackers.It is important to advance from static to dynamic forms of defense that can actively adapt a defense strategy according to the actions taken by individual attackers during an active attack.Our novel research approach relies on cognitive models and experimental games: Cognitive models aim at replicating an attacker's behavior allowing the creation of personalized, dynamic deceptive defense strategies; experimental games help study human actions, calibrate cognitive models, and validate deceptive strategies.In this paper we offer the following contributions: (i) a general research framework for the design of dynamic, adaptive and personalized deception strategies for cyberdefense; (ii) a summary of major insights from experiments and cognitive models developed for security games of increased complexity; and (iii) a taxonomy of potential deception strategies derived from our research program so far.