Reviewerly/exHarmony huggingface dataset:
Teaching Mathematics to Non-Mathematics Majors through Problem Solving and New Technologies

The role of mathematics in several scientific disciplines is undisputed; work and everyday life take great advantage of its application. Nevertheless, students often tend to not particularly like it and to consider it of little interest. It is also believed that only people with a certain attitude are capable of mastering the subject. In consideration of this, we aimed to help science students develop mathematical competences by designing a course specifically oriented to applications and problem solving. We administered our course to students attending the first year of a program in biotechnology, asking them to work with technologies instilling curiosity and interest, thus achieving a better proficiency as a consequence. Two questionnaires, along with access and proficiency data, allowed us to collect information about students’ attitudes, beliefs, and activity, which we analyzed by means of descriptive statistics. The promotion of the interaction among learners made them active users of the contents, thus allowing for the adaptation of their learning paths according to their personal necessities, as well as the development of teamwork skills and flexibility. Finally, students recognized the usefulness of the problem-solving approach and the role played by software.
An Empirical Study on Obsolete Issue Reports

Issue reports record valuable maintenance details. Developers write issue numbers into code comprehension and researchers mine knowledge from issue reports to assist various programming tasks. Although issue reports are useful, some of them can be obsolete, in that their corresponding commits are overwritten or rolled back, with the evolution of software. The obsolete issue reports can invalidate their references and descriptions, and can have far-reaching impacts on the approaches built on them. To explore their impacts, we conduct the first empirical study to analyze obsolete issue reports.To measure how an issue report becomes obsolete, we define an obsolete ratio of an issue report as its deleted lines over all its modified lines. To support our analysis, we build a tool, ICLINKER, that builds the links between an issue report and its commits, and calculates the obsolete ratio for each issue report. In our study, we analyze 70,180 commits and 46,257 issue reports that are collected from 5 Apache projects. We explore two research questions, which concern the distributions of obsolete issue reports and the obsolete references in code comments. Our findings to these research questions enrich the knowledge on obsolete issue reports, and some are even counterintuitive. For example, we find that obsolete issue reports are mixed with other issue reports. Even when recent issue reports are obsolete, some old issue reports keep up-to-date.
Measurement of clustering effectiveness for document collections

Abstract Clustering of the contents of a document corpus is used to create sub-corpora with the intention that they are expected to consist of documents that are related to each other. However, while clustering is used in a variety of ways in document applications such as information retrieval, and a range of methods have been applied to the task, there has been relatively little exploration of how well it works in practice. Indeed, given the high dimensionality of the data it is possible that clustering may not always produce meaningful outcomes. In this paper we use a well-known clustering method to explore a variety of techniques, existing and novel, to measure clustering effectiveness. Results with our new, extrinsic techniques based on relevance judgements or retrieved documents demonstrate that retrieval-based information can be used to assess the quality of clustering, and also show that clustering can succeed to some extent at gathering together similar material. Further, they show that intrinsic clustering techniques that have been shown to be informative in other domains do not work for information retrieval. Whether clustering is sufficiently effective to have a significant impact on practical retrieval is unclear, but as the results show our measurement techniques can effectively distinguish between clustering methods.
An Analysis of Integrating Machine Learning in Healthcare for Ensuring Confidentiality of the Electronic Records

The adoption of sustainable electronic healthcare infrastructure has revolutionized healthcare services and ensured that E-health technology caters efficiently and promptly to the needs of the stakeholders associated with healthcare.Despite the phenomenal advancement in the present healthcare services, the major obstacle that mars the success of E-health is the issue of ensuring the confidentiality and privacy of the patients' data.A thorough scan of several research studies reveals that healthcare data continues to be the most sought after entity by cyber invaders.Various approaches and methods have been practiced by researchers to secure healthcare digital services.However, there are very few from the Machine learning (ML) domain even though the technique has the proactive ability to detect suspicious accesses against Electronic Health Records (EHRs).The main aim of this work is to conduct a systematic analysis of the existing research studies that address healthcare data confidentiality issues through ML approaches.B.A. Kitchenham guidelines have been practiced as a manual to conduct this work.Seven well-known digital libraries namely IEEE Xplore, Science Direct, Springer Link, ACM Digital Library, Willey Online Library, PubMed (Medical and Bio-Science), and MDPI have been included to perform an exhaustive search for the existing pertinent studies.Results of this study depict that machine learning provides a more robust security mechanism for sustainable management of the EHR systems in a proactive fashion, yet the specified area has not been fully explored by the researchers.K-nearest neighbor algorithm and KNIEM implementation tools are mostly used to conduct experiments on EHR systems' log data.Accuracy and performance measure of practiced techniques are not sufficiently outlined in the primary studies.This research endeavour depicts that there is a need to analyze the dynamic digital healthcare environment more comprehensively.Greater accuracy and effective implementation of ML-based models are the need of the day for ensuring the confidentiality of EHRs in a proactive fashion.
Online Social Network Interactions (OSNI): A Novel Online Reputation Management Solution

The development and popularity of social media have expanded the concept of reputation in the online realm. Yet, domain experts sill lack solutions to manage online reputation. In this paper, we propose an Online Social Network Interactions (OSNI) solution that uses sentiment analysis techniques to assess, monitor, and visualize social media content for online reputation management purposes. To verify and validate the design and architecture of our solution, we present an online reputation management case study of three COVID-19 vaccines; Pfizer BioNTech, Oxford-AstraZeneca, and Johnson &#x0026; Johnson. The analysis of the collected data and the obtained results demonstrate how our OSNI solution contributes to the development of online reputation management.
Optimizing Data Usage for Low-Resource Speech Recognition

Automatic speech recognition has made huge progress recently. However, the current modeling strategy still suffers a large performance degradation when facing the low-resource languages with limited training data. In this paper, we propose a series of methods to optimize the data usage for low-resource speech recognition. Multilingual speech recognition helps a lot in low-resource scenarios. The correlation and similarity between languages are further exploited for multilingual pretraining in our work. We utilize the posterior of the target language extracted from a language classifier to perform data weighing on training samples, which assists the model in being more biased towards the target language during pretraining. Furthermore, dynamic curriculum learning for data allocation and length perturbation for data augmentation are also designed. All these three methods form the new strategy on optimized data usage for low-resource languages. We evaluate the proposed method using rich resource languages for pretraining (PT) and finetuning (FT) the model on the target language with limited data. Experimental results show that the proposed data usage method obtains a 15 to 25&#x0025; relative word error rate reduction for different target languages compared with the commonly adopted multilingual PT+FT method on CommonVoice dataset. The same improvement and conclusion are also observed on Babel dataset with conversational telephone speech, and <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>40&#x0025; relative character error rate reduction can be obtained for the target low-resource language.
Chinese Event Extraction via Graph Attention Network

Event extraction plays an important role in natural language processing (NLP) applications, including question answering and information retrieval. Most of the previous state-of-the-art methods were lack of ability in capturing features in long range. Recent methods applied dependency tree via dependency-bridge and attention-based graph. However, most of the automatic processing tools used in those methods show poor performance on Chinese texts due to mismatching between word segmentation and labels, which results in error propagation. In this article, we propose a novel character-level C hinese e vent e xtraction framework via graph a ttention network (CAEE). We build our model upon the sequence labeling model, but enhance it with word information by incorporating the word lexicon into the character representations. We further exploit the inter-dependencies between event triggers and argument by building a word-character-based graph network via syntactic shortcut arcs with dependency-parsing. The architecture of the graph minimizes error propagation, which is the result of the error detection of the word boundaries in the processing of Chinese texts. To demonstrate the effectiveness of our work, we build a large-scale real-world corpus consisting of announcements of Chinese financial news without golden entities. Experiments on the corpus show that our approach achieves competitive results compared with previous work in the field of Chinese texts.
Random Decision Forest approach for Mitigating SQL Injection Attacks

Structured Query Language (SQL) is extensively used for storing, manipulating and retrieving information in the relational database management system. Using SQL statements, attackers will try to gain unauthorized access to databases and launch attacks to modify/retrieve the stored data, such attacks are called as SQL injection attacks. Such SQL Injection (SQLi) attacks tops the list of web application security risks of all the times. Identifying and mitigating the potential SQL attack statements before their execution can prevent SQLi attacks. Various techniques are proposed in the literature to mitigate SQLi attacks. In this paper, a random decision forest approach is introduced to mitigate SQLi attacks. From the experimental results, we can infer that the proposed approach achieves a precision of 97% and an accuracy of 95%.
A Class of Directional Zero-Phase 2D Filters Designed Using Analytical Approach

This paper proposes an analytical design procedure for a class of 2D filters with a directional characteristic in the frequency plane. The method starts from two types of low-pass prototypes, namely a Gaussian filter and a wide-band filter. The two prototypes and the resulted 2D filters are zero-phase, a very useful property in image processing tasks. The prototypes are parametric and can be scaled along the frequency axis, thus resulting narrower or wider. A 1D to 2D frequency mapping, based on accurate approximations, is derived and applied to each prototype, obtaining the desired directional 2D frequency response with specified selectivity and orientation. The designed filters are adjustable, with a frequency response in factored matrix form depending on specifications. The Gaussian prototype yields very selective directional filters, used in detecting oriented straight lines in images, as show simulation results on several test images. Using oriented wide-band filters, other useful 2D filters result, namely square and parallelogram filters. The designed filters have accurate frequency responses, good directional selectivity and high linearity at relatively low order. An advantage of this analytical approach is that the filter transfer function results in parametric form and allows for easy tunability.
Three-dimensional point cloud denoising via a gravitational feature function

Point cloud noise is inevitable in the LiDAR scanning of objects and affects measurement accuracy and integrity. To minimize such noise, we propose a gravitational feature function-based point cloud denoising algorithm and a universal gravitation formula for a point cloud. First, we calculate the point cloud barycenter (i.e., the position of the average mass distribution) and the spherical neighborhood of points in terms of the distribution of the point cloud in three-dimensional space. Next, using the proposed formula, we calculate the gravitational forces between the barycenter and the spherical neighborhood of all points. We then combine all of the gravitational forces into a gravitational feature function and filter the noises in the point cloud using a gravitational feature-function threshold. This novel algorithm, to the best of our knowledge, effectively removes drift noises and takes into account the local and global structure of point clouds. Finally, we demonstrate the effectiveness of the algorithm through extensive experiments in which sparse, dense, and mixed noises are removed.
Secure and Efficient AKA Scheme and Uniform Handover Protocol for 5G Network Using Blockchain

The fifth generation (5G) cellular network provides users with high-quality services due to its high transmission rate and low latency. It will support Internet of Things (IoT) devices and enable new applications in health, banking, education, etc. Security is essential in this network because vulnerabilities may be exploited to disrupt these applications which may directly impact our life. Authentication and key agreement (AKA) and handover (HO) are usually the target of cyberattacks in any cellular network. On the other hand, blockchain is a peer-to-peer network that aims to maintain an immutable and secure ledger. This new technology will be used widely to secure many applications. This paper aims to develop an efficient and secure AKA scheme and uniform handover protocol for 5G network using blockchain. The home network (HN) is not involved in the AKA scheme and HO protocol to protect the HN from attacks, such as denial of service (DoS) attacks, and also lower the communication and computation overhead. Moreover, our HO protocol is uniform in the sense that it can be used for all HO scenarios. The protocol is also efficient because it requires exchanging a few amount of data. It can also achieve forward/backward secrecy. Furthermore, the blockchain is used to verify the public keys of the network nodes which is necessary to secure our AKA scheme and HO protocol. It also records the locations of the users which is necessary for the functionality of the network. Our evaluations demonstrate that the proposed HO protocol is secure, uniform, and can achieve the forward/backward secrecy. Furthermore, our AKA scheme requires lower computation and computation overhead comparing to the existing schemes, and preserves the energy of the limited-energy mobile devices.
Multi-gene Genetic Programming based Predictive Models for Full-reference Image Quality Assessment

Many objective quality metrics for assessing the visual quality of images have been developed during the last decade. A simple way to fine tune the efficiency of assessment is through permutation and combination of these metrics. The goal of this fusion approach is to take advantage of the metrics utilized and minimize the influence of their drawbacks. In this paper, a symbolic regression technique using an evolutionary algorithm known as multi-gene genetic programming (MGGP) is applied for predicting subject scores of images in datasets using a combination of objective scores of a set of image quality metrics (IQM). By learning from image datasets, the MGGP algorithm can determine appropriate image quality metrics, from 21 metrics utilized, whose objective scores employed as predictors in the symbolic regression model, by optimizing simultaneously two competing objectives of model &#x2018;goodness of fit&#x2019; to data and model &#x2018;complexity&#x2019;. Six large image databases (namely LIVE, CSIQ, TID2008, TID2013, IVC and MDID) that are available in public domain are used for learning and testing the predictive models, according the k-fold-cross-validation and the cross dataset strategies. The proposed approach is compared against state-of-the-art objective image quality assessment approaches. Results of comparison reveal that the proposed approach outperforms other state-of-the-art recently developed fusion approaches.
A Classification of Software Defect Prediction Models

Quality and security are major concerns in largescale software development. The early prediction of defective modules is becoming an important aspect in large-scale software systems to minimize resources spent (i.e., effort, time, etc.) to increase quality and security, and to reduce the overall cost of software production. This paper investigates different types of Software Defect Prediction Models and their evolution over time to assist researchers and practitioners in better understanding their maturity and help them in identifying the ones that best suite their needs since no model is able to perform well in any conditions. we have identified 11 different types of defect prediction models and we have investigated how those groups evolved over time. Among the 11 types of identified models, the Within-Project Defect Prediction Model is the most popular type of model from 1990 to 2019.
Roman-Urdu-Parl: Roman-Urdu and Urdu Parallel Corpus for Urdu Language Understanding

Availability of corpora is a basic requirement for conducting research in a particular language. Unfortunately, for a morphologically rich language like Urdu, despite being used by over a 100 million people around the globe, the dearth of corpora is a major reason for the lack of attention and advancement in research. To this end, we present the first-ever large-scale publicly available Roman-Urdu parallel corpus, Roman-Urdu-Parl, with 6.37 million sentence-pairs. It is a huge corpus collected from diverse sources, annotated using crowd-sourcing techniques, and also assured for quality. It has a total of 92.76 million Roman-Urdu words, 92.85 million Urdu words, Roman-Urdu vocabulary of 42.9 K words, and Urdu vocabulary of 43.8 K words. Roman-Urdu-Parl has been built to ensure that it not only captures the morphological and linguistic features of the language but also the heterogeneity and variations arising due to demographic conditions. We validate the authenticity and quality of our corpus by using it to address two natural language processing research problems, that is, on learning word embeddings and building a machine transliteration system. Our contribution of the corpus leads to exceptional results in both settings, for example, our machine transliteration system sets a new state-of-the-art with a Bilingual Evaluation Understudy (BLEU) score of 84.67. We believe that Roman-Urdu-Parl can serve as fuel for igniting and advancing works in many research areas related to the Urdu language.
On the Best Accuracy Arguments for Probabilism

Abstract In a recent paper, Pettigrew (2022) reports a generalization of the celebrated accuracy-dominance theorem due to Predd et al. (2009), but Pettigrew’s proof is incorrect. I will explain the mistakes and provide a correct proof.
Superpixel Guided Network for Three-Dimensional Stereo Matching

Superpixel provides local pixel coherence and respects object boundary, which is beneficial to stereo matching. Recently, superpixel cues are introduced into deep stereo networks. These methods develop a superpixel-based sampling scheme to downsample input color images and upsample output disparity maps. However, in this way, the image details are inevitably lost in the downsampling and the upsampling process introduces errors in the final disparity as well. Besides, this mechanism further limits the possibility of utilizing larger and multi-scale superpixels, which are important to alleviate the matching ambiguity. To address these problems, a superpixel-guided stereo matching method (LSG-Stereo) is proposed, which explicitly exploits the feature and disparity consistency within multi-scale superpixels to improve disparity estimation. To effectively incorporate superpixel cues into a stereo matching network, two novel modules are designed, including Superpixel Attention Spatial Pyramid Pooling (SA-SPP) and Superpixel-Guided Refinement (SGR). The SA-SPP module takes advantage of the content-aware superpixel pooling to construct an adaptive spatial pooling pyramid for better feature extraction. The SGR module explicitly utilizes the disparity consistency over multi-scale superpixels to further refine the disparity estimation in details and ill-posed regions. The proposed method is evaluated on the Scene Flow dataset, KITTI 2012, and KITTI 2015 stereo benchmarks with comprehensive experiments. Experimental results demonstrate that our method can significantly improve the accuracy of stereo matching, especially in details, occlusions, and texture-less regions.
Privacy-preserving kNN query processing algorithms via secure two-party computation over encrypted database in cloud computing

Abstract Since studies on privacy-preserving database outsourcing have been spotlighted in a cloud computing, databases need to be encrypted before being outsourced to the cloud. Therefore, a couple of privacy-preserving k NN query processing algorithms have been proposed over the encrypted database. However, the existing algorithms are either insecure or inefficient. Therefore, in this paper we propose a privacy-preserving k NN query processing algorithm via secure two-party computation on the encrypted database. Our algorithm preserves both data privacy and query privacy while hiding data access patterns. For this, we propose efficient and secure protocols based on Yao’s garbled circuit. To achieve a high degree of efficiency in query processing, we also propose a parallel k NN query processing algorithm using encrypted random value pool. Through our performance analysis, we verify that our proposed algorithms outperform the existing ones in terms of a query processing cost.