Reviewerly/exHarmony huggingface dataset:
Detecting Semantic Similarity Of Documents Using Natural Language Processing

The similarity of documents in natural languages can be judged based on how similar the embeddings corresponding to their textual content are. Embeddings capture the lexical and semantic information of texts, and they can be obtained through bag-of-words approaches using the embeddings of constituent words or through pre-trained encoders. This paper examines various existing approaches to obtain embeddings from texts, which is then used to detect similarity between them. A novel model which builds upon the Universal Sentence Encoder is also developed to do the same. The explored models are tested on the SICK-dataset, and the correlation between the ground truth values given in the dataset and the predicted similarity is computed using the Pearson, Spearman and Kendall’s Tau correlation metrics. Experimental results demonstrate that the novel model outperforms the existing approaches. Finally, an application is developed using the novel model to detect semantic similarity between a set of documents.
Empirical evaluation of code smells in open-source software (OSS) using Best Worst Method (BWM) and TOPSIS approach

Purpose Code smells indicate deep software issues. They have been studied by researchers with different perspectives. The need to study code smells was felt from the perspective of software industry. The authors aim to evaluate the code smells on the basis of their scope of impact on widely used open-source software (OSS) projects. Design/methodology/approach The authors have proposed a methodology to identify and rank the smells in the source code of 16 versions of Apache Tomcat Software. Further, the authors have analyzed the categorized smells by calculating the weight of the smells using constant weights as well as Best Worst Method (BWM). Consequently, the authors have used Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to determine the rank of versions using constant weights as well as BWM. Findings Version 1 of Apache Tomcat has least smell, and version 8 is reported to contain the maximum code smells. Notable differences in both the cases during the trend analysis are reported by the study. The findings also show that increase is observed in the number of code smells with the release of newer versions. This increment is observed till version 8, followed by a subtle marked depreciation in the number of code smells in further releases. Originality/value The focus is to analyze smells and rank several versions of Apache Tomcat, one of the most widely used software for code smell study. This study will act as a significant one for the researchers as it prioritizes the versions and will help in narrowing down the options of the software used to study code smell.
Character Input in Augmented Reality: An Evaluation of Keyboard Position and Interaction Visualisation for Head-Mounted Displays

Character input in immersive environments is a non trivial task that has attracted much attention in recent years. This paper presents an evaluation of keyboard position, orientation and interaction together with the influence of visual interaction feedback in a controlled character input task with 27 participants in Augmented Reality (AR). It presents 5 different keyboard locations (3 bounded to the headset and 2 bounded to the non-dominant hand of the user) and 3 visual interaction feedback methods (finger raycast, fingertip glow and both combined). Objective (completion time, accuracy, Key per Minute (KPM)) and subjective (After Scenario Questionnaire (ASQ)) metrics are presented. Results showed that keyboard placement had an effect on accuracy, KPM metrics and subjective preference, with keyboard visualisation parallel and bounded to the headset position and orientation outperforming other keyboard locations.
Optimized hybrid decoupled visual servoing with supervised learning

This study proposes an optimized hybrid visual servoing approach to overcome the imperfections of classical two-dimensional, three-dimensional and hybrid visual servoing methods. These imperfections are mostly convergence issues, non-optimized trajectories, expensive calculations and singularities. The proposed method provides more efficient optimized trajectories with shorter camera path for the robot than image-based and classical hybrid visual servoing methods. Moreover, it is less likely to lose the object from the camera field of view, and it is more robust to camera calibration than the classical position-based and hybrid visual servoing methods. The drawbacks in two-dimensional visual servoing are mostly related to the camera retreat and rotational motions. To tackle these drawbacks, rotations and translations in Z-axis have been separately controlled from three-dimensional estimation of the visual features. The pseudo-inverse of the proposed interaction matrix is approximated by a neuro-fuzzy neural network called local linear model tree. Using local linear model tree, the controller avoids the singularities and ill-conditioning of the proposed interaction matrix and makes it robust to image noises and camera parameters. The proposed method has been compared with classical image-based, position-based and hybrid visual servoing methods, both in simulation and in the real world using a 7-degree-of-freedom arm robot.
Network Topologies for Composable Data Centers

Suitable composable data center networks (DCNs) are essential to support the disaggregation of compute components in highly efficient next generation data centers (DCs).However, designing such composable DCNs can be challenging.A composable DCN that adopts a full mesh backplane between disaggregated compute components within a rack and employs dedicated interfaces on each point-to-point link is wasteful and expensive.In this paper, we propose and describe two (i.e., electrical, and electricaloptical) variants of a network for composable DC (NetCoD).NetCoD adopts a targeted design to reduce the number of transceivers required when a mesh physical backplane is deployed between disaggregated compute components in the same rack.The targeted design leverages optical communication techniques and components to achieve this with minimal or no network performance degradation.We formulate a MILP model to evaluate the performance of both variants of NetCoD in rack-scale composable DCs that implement different forms of disaggregation.The electrical-optical variant of NetCoD achieves similar performance as a reference network while utilizing fewer transceivers per compute node.The targeted adoption of optical technologies by both variants of NetCoD achieves greater (4 -5 times greater) utilization of available network throughput than the reference network which implements a generic design.Under the various forms of disaggregation considered, both variant of NetCoD achieve near-optimal compute energy efficiency in the composable DC while satisfying both compute and network constraints.This is because marginal concession of optimal compute energy efficiency is often required to achieve overall optimal energy efficiency in composable DCs.
Constant optimization and feature standardization in multiobjective genetic programming

Abstract This paper extends the numerical tuning of tree constants in genetic programming (GP) to the multiobjective domain. Using ten real-world benchmark regression datasets and employing Bayesian comparison procedures, we first consider the effects of feature standardization (without constant tuning) and conclude that standardization generally produces lower test errors, but, contrary to other recently published work, we find much less clear trend for tree sizes. In addition, we consider the effects of constant tuning – with and without feature standardization – and observe that (1) constant tuning invariably improves test error, and (2) usually decreases tree size. Combined with standardization, constant tuning produces the best test error results; tree sizes, however, are increased. We also examine the effects of applying constant tuning only once at the end a conventional GP run which turns out to be surprisingly promising. Finally, we consider the merits of using numerical procedures to tune tree constants and observe that for around half the datasets evolutionary search alone is superior whereas for the remaining half, parameter tuning is superior. We identify a number of open research questions that arise from this work.
Med-Flair: medical named entity recognition for diseases and medications based on Flair embedding

Named Entity Recognition (NER) is a vital step in medical information extraction, especially Electronic Health Records (EHRs). Proper extraction of medical entities such as disease and medications can automate the process of EHR coding as well as considerably improve the filtering of EHR resulting in better extraction of medical information. NER systems are generally trained and evaluated on relatively small standard datasets. However, they are applied on real-world applications, they exposed to different collection of texts, varying in topic, entity distribution, and text type (e.g. abstract vs. full text). This mismatch between the internal structure and application can cause drop in performance and consequently, unreliability. In this paper, we propose Med-Flair, an NER tagger covering mainly multiple entity types, medications and diseases. Med-Flair is mainly based on the Flair NLP framework, in addition, it's integrated by adding Bidirectional A Long Short Term Memory network (BiLSTM) and Conditional Random Fields (CRF) for sequence tagger. To validate the performance of Med-Flair, it is tested on 4 benchmark datasets, two for medications entities and two for diseases entities. Med Flair successfully achieves high performance, as it achieves 92%, 88%, 92% and 95% of F1-score which are mostly highest compared to state of the art deep neural network architectures such as BioBERT, DTranNER, BERT and BioNerFlair.
Comparative Study of Univariate and Multivariate Long Short-Term Memory for Very Short-Term Forecasting of Global Horizontal Irradiance

Accurate global horizontal irradiance (GHI) forecasting is crucial for efficient management and forecasting of the output power of photovoltaic power plants. However, developing a reliable GHI forecasting model is challenging because GHI varies over time, and its variation is affected by changes in weather patterns. Recently, the long short-term memory (LSTM) deep learning network has become a powerful tool for modeling complex time series problems. This work aims to develop and compare univariate and several multivariate LSTM models that can predict GHI in Guntur, India on a very short-term basis. To build the multivariate time series models, we considered all possible combinations of temperature, humidity, and wind direction variables along with GHI as inputs and developed seven multivariate models, while in the univariate model, we considered only GHI variability. We collected the meteorological data for Guntur from 1 January 2016 to 31 December 2016 and built 12 datasets, each containing variability of GHI, temperature, humidity, and wind direction of a month. We then constructed the models, each of which measures up to 2 h ahead of forecasting of GHI. Finally, to measure the symmetry among the models, we evaluated the performances of the prediction models using root mean square error (RMSE) and mean absolute error (MAE). The results indicate that, compared to the univariate method, each multivariate LSTM performs better in the very short-term GHI prediction task. Moreover, among the multivariate LSTM models, the model that incorporates the temperature variable with GHI as input has outweighed others, achieving average RMSE values 0.74 W/m2–1.5 W/m2.
Multiview Clustering via Proximity Learning in Latent Representation Space

Most existing multiview clustering methods are based on the original feature space. However, the feature redundancy and noise in the original feature space limit their clustering performance. Aiming at addressing this problem, some multiview clustering methods learn the latent data representation linearly, while performance may decline if the relation between the latent data representation and the original data is nonlinear. The other methods which nonlinearly learn the latent data representation usually conduct the latent representation learning and clustering separately, resulting in that the latent data representation might be not well adapted to clustering. Furthermore, none of them model the intercluster relation and intracluster correlation of data points, which limits the quality of the learned latent data representation and therefore influences the clustering performance. To solve these problems, this article proposes a novel multiview clustering method via proximity learning in latent representation space, named multiview latent proximity learning (MLPL). For one thing, MLPL learns the latent data representation in a nonlinear manner which takes the intercluster relation and intracluster correlation into consideration simultaneously. For another, through conducting the latent representation learning and consensus proximity learning simultaneously, MLPL learns a consensus proximity matrix with k connected components to output the clustering result directly. Extensive experiments are conducted on seven real-world datasets to demonstrate the effectiveness and superiority of the MLPL method compared with the state-of-the-art multiview clustering methods.
Design of Multimedia Assisted Teaching System for Basketball Theory Course

Abstract With multimedia technology into the university classroom, changed the traditional way of teaching in modern teaching style, bring so many changes back to the classroom teaching, especially some more boring theory courses, chunks of text teaching was replaced by the multimedia way, become colorful, not only brought convenience to the teacher’s teaching, and for the students to improve the learning interest in basketball theory course, for example, this paper studies in which the design of the multimedia auxiliary teaching system.
Autonomic Security Management for IoT Smart Spaces

Embedded sensors and smart devices have turned the environments around us into smart spaces that could automatically evolve, depending on the needs of users, and adapt to the new conditions. While smart spaces are beneficial and desired in many aspects, they could be compromised and expose privacy, security, or render the whole environment a hostile space in which regular tasks cannot be accomplished anymore. In fact, ensuring the security of smart spaces is a very challenging task due to the heterogeneity of devices, vast attack surface, and device resource limitations. The key objective of this study is to minimize the manual work in enforcing the security of smart spaces by leveraging the autonomic computing paradigm in the management of IoT environments. More specifically, we strive to build an autonomic manager that can monitor the smart space continuously, analyze the context, plan and execute countermeasures to maintain the desired level of security, and reduce liability and risks of security breaches. We follow the microservice architecture pattern and propose a generic ontology named Secure Smart Space Ontology (SSSO) for describing dynamic contextual information in security-enhanced smart spaces. Based on SSSO, we build an autonomic security manager with four layers that continuously monitors the managed spaces, analyzes contextual information and events, and automatically plans and implements adaptive security policies. As the evaluation, focusing on a current BlackBerry customer problem, we deployed the proposed autonomic security manager to maintain the security of a smart conference room with 32 devices and 66 services. The high performance of the proposed solution was also evaluated on a large-scale deployment with over 1.8 million triples.
Guided Colorization Using Mono-Color Image Pairs

Compared to color images captured by conventional RGB cameras, monochrome (mono) images usually have higher signal-to-noise ratios (SNR) and richer textures due to the lack of color filter arrays in mono cameras. Therefore, using a mono-color stereo dual-camera system, we can integrate the lightness information of target monochrome images with the color information of guidance RGB images to accomplish image enhancement in a colorization manner. In this work, based on two assumptions, we introduce a novel probabilistic-concept guided colorization framework. First, adjacent contents with similar luminance are likely to have similar colors. By lightness matching, we can utilize colors of the matched pixels to estimate the target color value. Second, by matching multiple pixels from the guidance image, if more of these matched pixels have similar luminance values to the target one, we can estimate colors with more confidence. Based on the statistical distribution of multiple matching results, we retain the reliable color estimates as initial dense scribbles and then propagate them to the rest of the mono image. However, for a target pixel, the color information provided by its matching results is quite redundant. Hence, we introduce a patch sampling strategy to accelerate the colorization process. Based on the analysis of the posteriori probability distribution of the sampling results, we can use much fewer matches for color estimation and reliability assessment. To alleviate incorrect color propagation in the sparsely scribbled regions, we generate extra color seeds according to the existed scribbles to guide the propagation process. Experimental results show that, our algorithm can efficiently and effectively restore color images with higher SNR and richer details from the mono-color image pairs, and achieves good performance in solving the color bleeding problem.
A BERT Based Approach for Arabic POS Tagging

Large pre-trained language models, such as BERT, have recently achieved state-of-the-art performance in different natural language processing tasks. However, BERT based models in Arabic language are less abundant than in other languages. This paper aims to design a grammatical tagging system for texts in Arabic language using BERT. The main goal is to label an input sentence with the most likely sequence of tags at the output. We also build a large corpus by combining the available corpora such as the Arabic WordNet and the Quranic Arabic Corpus. The accuracy of the developed system reached 91.69%. Our source code and corpus are available at GitHub upon request.
Machine Learning Robustness, Fairness, and their Convergence

Responsible AI becomes critical where robustness and fairness must be satisfied together. Traditionally, the two topics have been studied by different communities for different applications. Robust training is designed for noisy or poisoned data where image data is typically considered. In comparison, fair training primarily deals with biased data where structured data is typically considered. Nevertheless, robust training and fair training are fundamentally similar in considering that both of them aim at fixing the inherent flaws of real-world data. In this tutorial, we first cover state-of-the-art robust training techniques where most of the research is on combating various label noises. In particular, we cover label noise modeling, robust training approaches, and real-world noisy data sets. Then, proceeding to the related fairness literature, we discuss pre-processing, in-processing, and post-processing unfairness mitigation techniques, depending on whether the mitigation occurs before, during, or after the model training. Finally, we cover the recent trend emerged to combine robust and fair training in two flavors: the former is to make the fair training more robust (i.e., robust fair training), and the latter is to consider robustness and fairness as two equals to incorporate them into a holistic framework. This tutorial is indeed timely and novel because the convergence of the two topics is increasingly common, but yet to be addressed in tutorials. The tutors have extensive experience publishing papers in top-tier machine learning and data mining venues and developing machine learning platforms.
Normative ascent with local gaussians for unsupervised lesion detection

Unsupervised abnormality detection is an appealing approach to identify patterns that are not present in training data without specific annotations for such patterns. In the medical imaging field, methods taking this approach have been proposed to detect lesions. The appeal of this approach stems from the fact that it does not require lesion-specific supervision and can potentially generalize to any sort of abnormal patterns. The principle is to train a generative model on images from healthy individuals to estimate the distribution of images of the normal anatomy, i.e., a normative distribution, and detect lesions as out-of-distribution regions. Restoration-based techniques that modify a given image by taking gradient ascent steps with respect to a posterior distribution composed of a normative distribution and a likelihood term recently yielded state-of-the-art results. However, these methods do not explicitly model ascent directions with respect to the normative distribution, i.e. normative ascent direction, which is essential for successful restoration. In this work, we introduce a novel approach for unsupervised lesion detection by modeling normative ascent directions. We present different modelling options based on the defined ascent directions with local Gaussians. We further extend the proposed method to efficiently utilize 3D information, which has not been explored in most existing works. We experimentally show that the proposed method provides higher accuracy in detection and produces more realistic restored images. The performance of the proposed method is evaluated against baselines on publicly available BRATS and ATLAS stroke lesion datasets; the detection accuracy of the proposed method surpasses the current state-of-the-art results.
Distributed and Parallel Computing System Using Single-Client Multi-Hash Multi-Server Multi-Thread

There are relatively new approaches of Parallel Distributed Processing. Distributed cloud uses cloud computing technology from different geographic locations to interconnect data and applications served. Distributed, in the sense of information technology (IT), something is exchanged between various systems that may also be in different places. The amount of data and exhausting time to process and monitor the predicted outcomes effectively and with as little time as possible has been significantly increased.This paper proposed a system designed to help users with the lowest processing time interactively solve composite work. The two most popular technologies, Distributed Parallel Processing (DPP) and Cloud Computing (CC) rely on the clarity of solving the user's problem in this method. The proposed system has been built depending on different types of (Clients, Hash-codes, Servers, and server logical processors) with a cloud system (webserver). The client-side produces and sends the Hash-codes to the cloud-side. The web server distributes them among the chosen servers in charge of executing the cracking operation. When using light load (single hash-code) with multi-servers and multi-processors, it has been verified getting the lowest time consumed (i.e., Kernel, User, and Total time) proposed system offers better efficiency. Although it is shown that using heavy load (multi hash-codes) with multi-servers and multi-processors, the proposed system provides better performance from the point of view of the parallel processing technique. Because of this hash-codes cracking effects by two critical parameters (minimum cracking time and economical usage of machine-resources), these instances were taken into account by the proposed system.