Reviewerly/exHarmony huggingface dataset:
Classification of Idiomatic Sentences Using AWD-LSTM

Idioms are a mixture of terms with a figurative meaning distinct from the literal meanings of each word or expression. Automatic meaning detection of idioms represents a serious challenge in understanding the language. Because their meaning cannot be directly retrieved from the words, the development of computational models for human processing human languages is concerned with natural language processing (NLP). Idiomatic phrase identification is utmost important in many NLP applications like machine translation system, chatbot and information retrieval system (IR). Text classification is one of the fundamental tasks of NLP and is mostly attempted using supervised algorithms. This paper has perceived the identification of idioms as a text classification task. In this paper, we propose a classification model to classify the idioms and the literal sentences using ASGD weight-dropped LSTM (AWD-LSTM) model and universal language model fine-tuning (ULMFiT) for transfer learning to fine-tune the language model. The proposed model has been evaluated using precision, recall and F1-score metrics. The proposed model has been tested with the TroFi metaphor dataset and an in-house dataset and achieved 81.4 and 85.9% of F-Score, respectively.
Word-Based Human Edit Rate (WHER) as an Indicator of Post-editing Effort

Estimating post-editing effort is essential to identify translation difficulties and decide the payment for post-editors. Keystrokes, fixations, and production duration, as well as lexical and syntactic variations of the translation product, are frequently used as indicators of post-editing effort. This chapter introduces Word-based Human Edit Rate (WHER), a measure derived from HTER, as a new predictor to measure post-editors’ effort on the word-level. The original HTER metric calculates an edit distance between MT output and its post-edited version from the minimum number of assumed edit operations. The WHER metric matches these edit operations to the corresponding words in the TT segment and maps them via alignment links to ST words. WHER thus provides the minimum number of expected edit operations for each ST word given the MT output. The chapter describes an experiment in which 21 student translators were invited to post-edit audiovisual texts and their translation processes were recorded with eye-tracking and keystroke-logging devices. After correlating WHER operations with the other common effort indicators derived from the process and product, we find that WHER is a reliable predictor for word-level post-editing effort.
DTEC: Dual Training Error based Correction approach for recommender systems

The Dual Training Error based Correction approach (DTEC) is applied following the execution of a recommender system to improve its recommendation accuracy. These corrections are performed in user and item view point, so that finally a dual system efficiently combines both of them. DTEC is applicable to any model-based recommender system with positive training error. An open-source Matlab implementation with no external dependencies of this algorithm is available.
Usability testing of an android application of tractor distribution information system (GIS Tractor)

Abstract An android application for tractor system information, namely GIS Tractor, was launched recently. As a new application, it needs usability testing as a part of an assessment. Usability level can affect the ease of use of an application. This study was conducted to measure the acceptability of GIS Tractor based on USE questionnaire evaluation (Usefulness, Satisfaction, and Ease of Use). Online questionnaires are used as a research instrument constructed as five-point Likert rating scales to rate a set of statements. The results showed that GIS Tractor had met the five aspects of usability testing. The use of the GIS tractor android application can assist the user in locating the distribution of tractor within Maros Regency faster than the manual system.
Transformer-based unsupervised patient representation learning based on medical claims for risk stratification and analysis

The claims data, containing medical codes, services information, and incurred expenditure, can be a good resource for estimating an individual's health condition and medical risk level. In this study, we developed Transformer-based Multimodal AutoEncoder (TMAE), an unsupervised learning framework that can learn efficient patient representation by encoding meaningful information from the claims data. TMAE is motivated by the practical needs in healthcare to stratify patients into different risk levels for improving care delivery and management. Compared to previous approaches, TMAE is able to 1) model inpatient, outpatient, and medication claims collectively, 2) handle irregular time intervals between medical events, 3) alleviate the sparsity issue of the rare medical codes, and 4) incorporate medical expenditure information. We trained TMAE using a real-world pediatric claims dataset containing more than 600,000 patients and compared its performance with various approaches in two clustering tasks. Experimental results demonstrate that TMAE has superior performance compared to all baselines. Multiple downstream applications are also conducted to illustrate the effectiveness of our framework. The promising results confirm that the TMAE framework is scalable to large claims data and is able to generate efficient patient embeddings for risk stratification and analysis.
Semi-supervised LDA Based Method for Similarity Distance Metric Learning

In recent years, computer vision technology has drawn much attention of people and been applied into many fields of human's living. Data classification/identification is a key task in computer vision. The similarity distance metric learning based method is wildly used to compare the similar positive pairs from dissimilar negative pairs. However, there are more and more challenging computer vision task have been proposed. Traditional similarity distance metric learning methods are fail to metric the similarity of these task due to the drastic variation of feature caused by illumination, view angle, pose and background changes. Thus, the existing methods are unable to learn effective and complete patterns to describe the appearance change of individuals. To overcome this problem, we proposed a novel semi-supervised (Linear Discriminant Analysis) LDA based method for similarity distance metric learning. The proposed method first learn a metric projection with traditional LDA method. The then test data are identified with the potential positive pairs to fine-turning the metric model by forcing the identified data to be close to the center of positive training data pairs. Finally, the proposed method are compared to some classic metric learning algorithms to demonstrate its effectiveness and accuracy.
Passenger Flow Forecast of Catering Business based on Autoregressive Integrated Moving Average and Smoothing Index Prediction Model

As people’s living standards improve, more and more catering enterprises are trying to develop more reasonable business development by using machine learning methods for data mining. Through the prediction for restaurant patronage, the dining experience of customers can be improved, and at the same time, the restaurant industry can achieve more efficient operation. This paper proposes the use of ARIMA time series model and smoothed exponential prediction model algorithm for restaurant patronage prediction, and demonstrates the rationality and feasibility of the idea through experiments. It provides new ideas for applying machine learning techniques and achieving efficient operation in the restaurant industry.
Effects of Text Simplification on Reading Behavior of Older and Younger Users

Research shows that simplifying short textual passages to lower reading levels (e.g., lower than 10th grade reading level) can improve the viewing behavior of younger users. However, little work has been done to examine the viewing behavior of younger users for simplified text that are longer in length and are more difficult than specific reading levels such as 10th grade reading level. Similarly, little work has been done to examine whether and how older and younger users differ in viewing such long and complex textual content. In this study, we used eye-tracking to examine older and younger users’ viewing behavior for two relatively long and complex text passages with the same content but varying reading difficulty. Our results supported previous research that suggests age-related cognitive difficulties are likely to be mitigated by task experience. Our results also supported previous research that shows text simplification can improve users’ viewing behavior. Our results extended the previous research by showing that the positive effects of text simplification continue to hold even when the simplified text is relatively long and still considered to be hard to read.
Underwater Image Enhancement Algorithm Using Advanced Fusion

We present an effective implementation of enhancing the pictures and videos taken underwater, which are not clearly visible because of the underwater scattering and selective absorption and other obstacles faced underwater using an efficient and simple technique. This method does not use any extravagant hardware and is simply a single image approach which also does not necessarily need the complete knowledge of the underwater environment and scene structure. The backbone of this technique is the multi-scale fusion which blends two images that are directly derived from the color compensated original degraded image after it is passed through white balancing.
Towards a Federated Learning Approach for NLP Applications

Traditional machine learning involves the collection of training data to a centralized location. This collected data is prone to misuse and data breach. Federated learning is a promising solution for reducing the possibility of misusing sensitive user data in machine learning systems. In recent years, there has been an increase in the adoption of federated learning in healthcare applications. On the other hand, personal data such as text messages and emails also contain highly sensitive data, typically used in natural language processing (NLP) applications. In this paper, we investigate the adoption of federated learning approach in the domain of NLP requiring sensitive data. For this purpose, we have developed a federated learning infrastructure that performs training on remote devices without the need to share data. We demonstrate the usability of this infrastructure for NLP by focusing on sentiment analysis. The results show that the federated learning approach trained a model with comparable test accuracy to the centralized approach. Therefore, federated learning is a viable alternative for developing NLP models to preserve the privacy of data.
A Survey on Kernelized Fuzzy Clustering Tools for Data Processing

Although most of the well-known fuzzy clustering algorithms are somewhat sensitive to noise, there are some more profound possibilistic fuzzy clustering techniques based on kernel distance metrics that are not subject to this problem. The paper presents a survey on this type of fuzzy clustering methods. Meanwhile, introducing a particular distance measure based on the Bregman divergence to a fuzzy clustering tool made it possible to improve the algorithm’s performance compared to traditional Euclidean-based analogs. A bunch of experimental evaluation is performed for this set of methods.
Semantic Correspondence with Geometric Structure Analysis

This article studies the correspondence problem for semantically similar images, which is challenging due to the joint visual and geometric deformations. We introduce the Flip-aware Distance Ratio method (FDR) to solve this problem from the perspective of geometric structure analysis. First, a distance ratio constraint is introduced to enforce the geometric consistencies between images with large visual variations, whereas local geometric jitters are tolerated via a smoothness term. For challenging cases with symmetric structures, our proposed method exploits Curl to suppress the mismatches. Subsequently, image correspondence is formulated as a permutation problem, for which we propose a Gradient Guided Simulated Annealing (GGSA) algorithm to perform a robust discrete optimization. Experiments on simulated and real-world datasets, where both visual and geometric deformations are present, indicate that our method significantly improves the baselines for both visually and semantically similar images.
Application Research of the Current Situation of College English Online Teaching Model in the Big Data Era

The way of teaching English online in colleges and universities is an important way to promote students’ autonomous learning ability. With the continuous innovation of technology, the level of college English online teaching is also constantly improving. In the context of the era of big data, English teaching in colleges and universities has become more intelligent and informatized. This article mainly introduces BP neural network method and hill climbing algorithm. This paper uses big data to analyze the current situation of college English online teaching mode, and establishes a potential BP neural network mathematical model. The model is solved by the BP neural network method, the current situation analysis and application status of the college English online teaching model are analyzed, and the model is revised using historical data to improve the current situation analysis and application status evaluation of the college English online teaching model accuracy. The experimental results of this paper show that the BP neural network method improves the application research effect of college English online teaching mode by 33%. Finally, by comparing the results of online and offline tests, the teaching impact of college English online teaching mode is analyzed.
Domain Generating Algorithm based Malicious Domains Detection

Botnets often use Domain Generating Algorithms (DGAs) to facilitate covert server communication in carrying out different types of cyber-attacks. Attackers employ these algorithms to generate millions of sites for victim machines to connect to, thus evading defense using blacklists. DGAs enables attacks to be facilitated without the fear of command and control (C&C) servers being identified and permanently blocked. Utilizing the domain fluxing technique, attackers making use of DGAs can constantly change the domains used by their C&C servers from one to another in a very short time, whenever they are blocked. Therefore, automated detection of DGA generated domains can serve as an essential countermeasure to prevent malicious botnet communication promptly. In our research, we devise a comprehensive solution to detect malicious DGA generated domains used in malware communication. Two distinct feature extraction methods, the Bigram model and the Word2Vec model, are applied for text processing in combination with machine learning and deep learning techniques on a large and very diverse dataset for DGAs that exist currently, containing 84 different traditional and dictionary-based DGA families. Our results demonstrate exceptional success in both binary classification (classifying a given domain as benign or malicious), and multiclass classification (identifying the specific DGA variation or family that produced the domain).
Defogging Algorithm Based on the Combination of FCM Clustering Segmentation and Otsu Method for Image Structure Based on Lab Color Space

When a single image is de-fogged by dark channel prior, halo is easy to appear at the boundary of the image, and color distortion is easy to be caused in the bright area. To overcome the above defects, this paper proposes an image defogging algorithm based on Lab color space for image structure FCM clustering and segmentation combined with the improved Otsu algorithm. First, convert the foggy image from RGB color space to Lab color space, and decompose the image into structure and texture in Lab color space. Then, FCM clustering is used to calculate the transmittance on the image structure, and the atmospheric light value is optimized in combination with the improved Otsu algorithm. Finally, the structure of the fog-free image is restored through the inverse process of the atmospheric physical model, and the obtained structure is integrated with the texture of the image to obtain the final fog-free image. Experiments show that the algorithm can effectively de-fog, solve the problem of partial distortion in the sky in the dark channel prior de-fog algorithm, can obtain better color vision fidelity, and has stronger applicability.
Notes on note-making: Introduction

This special issue aims to explore what is educational in the seemingly humble gesture of making notes: not only how and why the practice of note-taking is educative in and of itself, but also what it says about education as such. The contributions to the issue each highlight different aspects of note-making and approach it differently, but all assume that note-making is an educational practice that merits philosophical study. Interestingly, they mostly focus on note-making as a non-digital practice (putting aside the use of laptops for note-making in class), perhaps because most were written prior to the great digitisation ushered in by educational institutions’ responses to the COVID-19 pandemic. Let us first address how we see the different aspects of note-making highlighted by the contributions to the issue.
Soft Computing: Three Decades Fuzzy Models and Applications

The paper attempts to give protection of soft computing in the investigations of the scientist form the Institutes of Informatics, Information Technologies and Information and Communications Technologies of the Bulgarian Academy of Sciences in recent three decades. There are discussed researches and results on multicriteria fuzzy decision making, fuzzy decision making software in economic clustering, fuzzy inference system in support of financial investments, genetic fuzzy system for asset management and fuzzy methods and applications. It is determined that in the coming periods soft computing will expand and enrich with new subsuming principal partners. Presented is a short list of 60 publications on Soft Computing.
An Analysis of Twitter Security and Privacy Using Memory Forensics

Twitter is a popular online social media network platform that has millions of users worldwide. Twitter activities have grown in popularity among different age groups. Various Twitter activities such as instant chats, wall comments and following others could create a number of footprints in different locations of the device. The footprints may be misused, which would compromise privacy and security of the users. In order to protect their personal data, it is important that the users to take every precautions when using the social media. The question is what measures are available for the users to take. In this research, through memory forensics we assess Twitter user’s privacy and security when is accessed via web browser on Windows 10 machine. We carry out a set of memory forensics analysis experiments in different modes. In each mode, we evaluate the retrieved forensics artifacts. Based on the results of our experiment, we recommend the best approach for the users.
Subsampling and Knowledge Distillation on Adversarial Examples: New Techniques for Deep Learning Based Side Channel Evaluations

This paper has four main goals. First, we show how we solved the CHES 2018 AES challenge in the contest using essentially a linear classifier combined with a SAT solver and a custom error correction method. This part of the paper has previously appeared in a preprint by the current authors (e-print report 2019/094) and later as a contribution to a preprint write-up of the solutions by the winning teams (e-print report 2019/860). Second, we develop a novel deep neural network architecture for side-channel analysis that completely breaks the AES challenge, allowing for fairly reliable key recovery with just a single trace on the unknown-device part of the CHES challenge (with an expected success rate of roughly 70% if about 100 CPU hours are allowed for the equation solving stage of the attack). This solution significantly improves upon all previously published solutions of the AES challenge, including our baseline linear solution. Third, we consider the question of leakage attribution for both the classifier we used in the challenge and for our deep neural network. Direct inspection of the weight vector of our machine learning model yields a lot of information on the implementation for our linear classifier. For the deep neural network, we test three other strategies (occlusion of traces; inspection of adversarial changes; knowledge distillation) and find that these can yield information on the leakage essentially equivalent to that gained by inspecting the weights of the simpler model. Fourth, we study the properties of adversarially generated side-channel traces for our model. Partly reproducing recent computer vision work by Ilyas et al. in our application domain, we find that a linear classifier that generalizes to an unseen device much better than our linear baseline can be trained using only adversarial examples (fresh random keys, adversarially perturbed traces) for our deep neural network. This gives a new way of extracting human-usable knowledge from a deep side channel model while also yielding insights on adversarial examples in an application domain where relatively few sources of spurious correlations between data and labels exist. The experiments described in this paper can be reproduced using code available at https://github.com/agohr/ches2018 .
Distributed Latent Dirichlet Allocation on Streams

Latent Dirichlet Allocation (LDA) has been widely used for topic modeling, with applications spanning various areas such as natural language processing and information retrieval. While LDA on small and static datasets has been extensively studied, several real-world challenges are posed in practical scenarios where datasets are often huge and are gathered in a streaming fashion. As the state-of-the-art LDA algorithm on streams, Streaming Variational Bayes (SVB) introduced Bayesian updating to provide a streaming procedure. However, the utility of SVB is limited in applications since it ignored three challenges of processing real-world streams: topic evolution , data turbulence , and real-time inference . In this article, we propose a novel distributed LDA algorithm—referred to as StreamFed-LDA— to deal with challenges on streams. For topic modeling of streaming data, the ability to capture evolving topics is essential for practical online inference. To achieve this goal, StreamFed-LDA is based on a specialized framework that supports lifelong (continual) learning of evolving topics. On the other hand, data turbulence is commonly present in streams due to real-life events. In that case, the design of StreamFed-LDA allows the model to learn new characteristics from the most recent data while maintaining the historical information. On massive streaming data, it is difficult and crucial to provide real-time inference results. To increase the throughput and reduce the latency, StreamFed-LDA introduces additional techniques that substantially reduce both computation and communication costs in distributed systems. Experiments on four real-world datasets show that the proposed framework achieves significantly better performance of online inference compared with the baselines. At the same time, StreamFed-LDA also reduces the latency by orders of magnitudes in real-world datasets.