Reviewerly/exHarmony huggingface dataset:
Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement

Cross-Lingual Information Retrieval (CLIR) aims to rank the documents written in a language different from the user’s query. The intrinsic gap between different languages is an essential challenge for CLIR. In this paper, we introduce the multilingual knowledge graph (KG) to the CLIR task due to the sufficient information of entities in multiple languages. It is regarded as a “silver bullet” to simultaneously perform explicit alignment between queries and documents and also broaden the representations of queries. And we propose a model named CLIR with HIerarchical Knowledge Enhancement (HIKE) for our task. The proposed model encodes the textual information in queries, documents and the KG with multilingual BERT, and incorporates the KG information in the query-document matching process with a hierarchical information fusion mechanism. Particularly, HIKE first integrates the entities and their neighborhood in KG into query representations with a knowledge-level fusion, then combines the knowledge from both source and target languages to further mitigate the linguistic gap with a language-level fusion. Finally, experimental results demonstrate that HIKE achieves substantial improvements over state-of-the-art competitors.
A Multiscale CNN Framework for Wireless Technique Classification in Internet of Things

Wireless technique classification (WTC) is of crucial importance in Internet of Things for realizing efficient spectrum sharing and interference management. However, the existing deep-learning-based methods have low classification accuracy, especially at low signal-to-noise ratio levels. In this article, a multiscale convolutional neural network framework is proposed for WTC. A multiscale module is exploited to capture the higher abstraction features. Simulation results demonstrate that our proposed scheme can achieve a better classification performance and a higher convergence speed compared to the state-of-the-art schemes.
Building a Distributed XR Immersive Environment for data Visualization

We present a novel way to support business organizations’ decision-making in the context of analysis of network data sets. For such a purpose we built a mixed-reality, multi-user platform, which we named Aroaro and can be deployed on different virtual reality and augmented reality devices. In Aroaro, users can experience data visualization, either individually or as part of a team. With a set of lab tasks designed to lead lab subjects to analyze the network information while immersing into the rich virtual environment, our approach belongs to the nascent area of immersive analytics. We assume a business unit has already procured some network data set and been tasked with problems that need to reveal information about the network's connectivity properties. This paper is concerned with how a business unit achieves problem solving (decisionmaking) when relying on immersive analytics. Our approach combines a high-quality virtual environment such as Aroaro, its data visualization engine and the analytics afforded by concepts of network centrality in a network. We report on preliminary results of having lab subjects experience two visualization environments: a 3D visualization system on a 2D-flat screen and our mixed-reality, multi-user platform for network data visualization.
Effective Training Improves Disaster Response

Preparedness training and continuity‐of‐operations plans helped a Texas water utility's staff keep mission‐essential functions going and water flowing during 2021’s record‐breaking winter storm.
Automatizing Software Cognitive Complexity Reduction

Software plays a central role in our life nowadays. We use it almost anywhere, at any time, and for everything: to browse the Internet, to check our emails, and even to access critical services such as health monitoring and banking. Hence, its reliability and general quality is critical. As software increases in complexity, developers spend more time fixing bugs or making code work rather than designing or writing new code. Thus, improving software understandability and maintainability would translate into an economic relief over the total cost of a project. Different cognitive complexity measures have been proposed to quantify the understandability of a piece of code and, therefore, its maintainability. However, the cognitive complexity metric provided by SonarSource and integrated in SonarCloud and SonarQube is quickly spreading in the software industry due to the popularity of these well-known static code tools for evaluating software quality. Despite SonarQube suggests to keep method’s cognitive complexity no greater than 15, reducing method’s complexity is challenging for a human programmer and there are no approaches to assist developers on this task. We model the cognitive complexity reduction of a method as an optimization problem where the search space contains all sequences of Extract Method refactoring opportunities. We then propose a novel approach that searches for feasible code extractions allowing developers to apply them, all in an automated way. This will allow software developers to make informed decisions while reducing the complexity of their code. We evaluated our approach over 10 open-source software projects and was able to fix 78% of the 1,050 existing cognitive complexity issues reported by SonarQube. We finally discuss the limitations of the proposed approach and provide interesting findings and guidelines for developers.
BFClass: A Backdoor-free Text Classification Framework

Backdoor attack introduces artificial vulnerabilities into the model by poisoning a subset of the training data via injecting triggers and modifying labels.Various trigger design strategies have been explored to attack text classifiers, however, defending such attacks remains an open problem.In this work, we propose BF-Class, a novel efficient backdoor-free training framework for text classification.The backbone of BFClass is a pre-trained discriminator that predicts whether each token in the corrupted input was replaced by a masked language model.To identify triggers, we utilize this discriminator to locate the most suspicious token from each training sample and then distill a concise set by considering their association strengths with particular labels.To recognize the poisoned subset, we examine the training samples with these identified triggers as the most suspicious token, and check if removing the trigger will change the poisoned model's prediction.Extensive experiments demonstrate that BFClass can identify all the triggers, remove 95% poisoned training samples with very limited false alarms, and achieve almost the same performance as the models trained on the benign training data.
Big Data Analysis using BigQuery on Cloud Computing Platform

End the age of digitalization, data generated from numerous online and offline sources in every second. The Data are having a considerable amount of size and several properties termed as Bigdata. It is challenging to store, manage processes, analyze, visualize, and extract useful information from Bigdata using traditional approaches in local machines. To resolve this cloud computing platform is the solution. Cloud computing has high-level processing units, storage, and applications that do not depend on user devices' performance. Many users can access resources and demanded services remotely from the cloud on a pay-as-use basis. That is why users are not needed to buy and install costly resources locally. Some cloud services providers are Google, AWS, IBM, and Microsoft, and they have their Bigdata analyzing robust systems and products in a cost-efficient manner. There are many Cloud Service Providers (CSP's) having different services of Bigdata analyzing filed. However, we discuss in the paper about an excellent service BigQuery in the Data warehouse product of Google to analyze and represent numerous samples of datasets in real-time for making the right decisions within a short time.
Exploring Foreign Language Students’ Perceptions of the Guided Use of Machine Translation (GUMT) Model for Korean Writing

This study examines students’ perceptions of the Guided Use of Machine Translation (GUMT) model and their perceptions of GUMT’s impact on their foreign language (FL) writing. Adapted from O’Neill (2016, 2019b), GUMT model activities were developed and implemented in an upper-elementary Korean as a FL course at a large southwestern U.S. university. At the beginning of the semester, students received an instructional session on how to use machine translation (MT) effectively as the first step of GUMT. The session included 1) the potential strengths and weaknesses of two online Korean MT platforms, Google Translate and Papago; and 2) the combined use of other online resources (e.g., image and news searches) to enhance students’ awareness of pragmatic issues related to MT output. Throughout the semester, students applied the GUMT model to writing assignments and wrote reflections on their GUMT practice. Students also received continuous feedback from instructors on MT use. Analyses of pre- and post-surveys and students’ reflections indicate that the GUMT model played an important role in fostering MT use strategies and improving students’ confidence and self-perceptions of their fluency in FL writing.
Generation of a bright four-photon entangled state from a warm atomic ensemble via inherent polarization entanglement

We report the stable generation of a bright four-photon polarization-entangled Greenberger–Horne–Zeilinger (GHZ) state obtained by the spatial multiplexing of inherently polarization-entangled photons from a warm atomic ensemble of 87Rb. Owing to the total angular momentum conservation of the atomic state in the spontaneous four-wave mixing process of a cascade-type atomic system, the polarizations of the signal and idler photons in the two-photon decay are entangled. With a Rb vapor cell, we realize the generation of robust and bright four-photon GHZ states using two symmetrical spatial modes of inherently polarization-entangled photon pairs without applying an interferometric configuration. The four-photon average coincidence rate of the GHZ state with a fidelity of 0.88(2) is measured as 1.69(7) Hz under a weak pump power of 10 μW. The minimum Allan variance for the stability of the four-photon GHZ state is estimated as 0.92° for an average time of 10 h.
Design and Development of Mobile Healthcare Application for “Ayurvedic” based Clinical Documents

In the modern world, people need immediate remedies for their health problems. Without considering the future effects of the heavy dose medicines, people intake those medicines to get immediate relief. As a result, they forget the natural and side effects free home remedies. And there is no awareness among the current generation youngsters and kids. To overcome this problem, we are developing a mobile application in dart language using flutter framework [2]. It uses KMP Algorithm [4] to recommends the medicine based on their allergic conditions matching. By this our system recommends the natural homemade medicines for their temporary health issues. This will increase the acceptance of these homemade medicines among the people.
Effectual Single Image Dehazing with Color Correction Transform and Dark Channel Prior

Image acquisition during bad weather conditions like haze may adversely affect the quality and information processing. The image degradation affects object detection, which plays a prominent role in computer vision applications. Restoration of hazy images is necessary for exact identification and location of objects. This paper proposes a new prior based dehazing algorithm named Color Correction Transform Dark Channel Prior (CCTDCP). The proposed algorithm utilizes white balance color correction transform, dark channel prior and gamma correction for retaining the originality of the image. The experimental results on benchmark images exhibit superior performance of CCTDCP algorithm over the state-of-the-art methods.
Detection of Credit Card Fraud Using Isolation Forest Algorithm

Credit card payment is increasing day by day. This leads to expanding digitization of banking administrations and versatile financial applications. A large number of transactions are identified as frauds concerning all transactions. Many different types of data mining algorithms are used to find fraudulent transactions. But in the case of big data, the algorithm has some limitations. This article discusses the Isolation Forest algorithm for detecting fraud in the credit card system and also compares the result with the Local Outlier algorithm. The Isolation Forest performs much better than the LOF while taking a gander at bungle accuracy and recall for 2 models. It has also been recognized that the fraud detection rate is almost 27% compared to the LOF discovery rate of only 2%. Isolation Forest has a 99.774% greater precision than 99.65% LOF.
Capsule-Forensics Networks for Deepfake Detection

Abstract Several sophisticated convolutional neural network (CNN) architectures have been devised that have achieved impressive results in various domains. One downside of this success is the advent of attacks using deepfakes, a family of tools that enable anyone to use a personal computer to easily create fake videos of someone from a short video found online. Several detectors have been introduced to deal with such attacks. To achieve state-of-the-art performance, CNN-based detectors have usually been upgraded by increasing their depth and/or their width, adding more internal connections, or fusing several features or predicted probabilities from multiple CNNs. As a result, CNN-based detectors have become bigger, consume more memory and computation power, and require more training data. Moreover, there is concern about their generalizability to deal with unseen manipulation methods. In this chapter, we argue that our forensic-oriented capsule network overcomes these limitations and is more suitable than conventional CNNs to detect deepfakes. The superiority of our “Capsule-Forensics” network is due to the use of a pretrained feature extractor, statistical pooling layers, and a dynamic routing algorithm. This design enables the Capsule-Forensics network to outperform a CNN with a similar design and to be from 5 to 11 times smaller than a CNN with similar performance.
A comparison of in-service teachers’ conceptions of barriers to mobile technology-integrated instruction and technology-integrated instruction

Researchers have identified three orders of barriers to probe teachers’ conceptions of technology-integrated instruction, namely extrinsic obstacles, intrinsic obstacles and the lack of design thinking. However, few studies have investigated barriers to teachers’ mobile technology-integrated instruction and compared them with the barriers to their technology-integrated instruction. This study recruited 20 in-service teachers with experience in adopting mobile learning (in practice) to compare their conceptions of the two kinds of barriers. Firstly, we utilised the phenomenographic method and subsequently transformed qualitative interview data into quantitative data. The research results found an additional crucial barrier, namely classroom management. Thus, this study revealed four classifications of barriers to (mobile) technology-integrated instruction. Moreover, teachers with less experience in adopting mobile learning confronted more obstructions to mobile technology-integrated instruction than to technology-integrated instruction at the first-order level (i.e., extrinsic obstacles). On the contrary, teachers with more experience in adopting mobile learning encountered more barriers related to design thinking upon the educational integration of mobile technology over conventional technology. This study provides the field with new insights into understanding teachers’ conceptions of barriers to the adoption of emerging technology-integrated instruction and sheds light for researchers and policymakers who are working on teacher professional development. Implications for practice or policy: This research reveals a framework of the four orders of barriers for providing an understanding of teachers’ barriers to advanced technology-integrated instruction as the technology grows. More courses involving design thinking for mobile learning should be provided as part of teacher training. Future research should carefully explore the effective solutions for eliminating the barriers to mobile technology-integrated instruction.
The Digital Competence of Vocational Education Teachers and of Learners with and Without Cognitive Disabilities

Nowadays, digital competence is required for participation in working life, education, and social activities. Vocational education is the key to teaching and development of digital skills. Technology enhanced learning offers enormous potential for improving equal participation and reducing access barriers. In order to meet the demands for equal access to digital technologies, to a digitized labor market and to an inclusive education system, teachers and learners need to have the necessary expertise. A survey was conducted using the Digital Competency Profiler (DCP) to explore the digital competence of teachers and learners in vocational education. The items were adapted linguistically according to requirements for people with cognitive disabilities. The aim is to identify possible gaps in the development of digital competencies in three survey groups: teachers in vocational training, trainees in food occupations, and employees with disabilities of sheltered workshops. The digital technology usage habits of the test groups are analyzed and possible differences are determined. Based on an expert assessment of the DCP items, 13 relevant competencies for vocational education are defined. Overall, the participants consider their digital competence to be good. The competencies sending text messages, making phone calls and watching videos show the highest frequency and confidence in the total sample and the competencies creating documents, writing e-mails and managing online accounts the lowest. The index value social competency is particularly high in comparison to the epistemological competency. Needs for intervention are identified, such as the systematic qualification of teachers and learners as condition for digital learning in vocational education.
MPPTM: A Bio-Inspired Approach for Online Path Planning and High-Accuracy Tracking of UAVs

The path planning and tracking problem of the multi-robot system (MRS) has always been a research hotspot and applied in various fields. In this article, a novel multi-robot path planning and tracking model (MPPTM) is proposed, which can carry out online path planning and tracking problem for multiple mobile robots. It considers many issues during this process, such as collision avoidance, and robot failure. The proposed approach consists of three parts: a neural dynamic path planner, a hyperbolic tangent path optimizer, and an error-driven path tracker. Assisted by Ultra-wideband positioning system, the proposed MPPTM is a low-cost solution for online path planning and high-accurate tracking of MRS in practical environments. In the proposed MPPTM, the proposed path planner has good time performance, and the proposed path optimizer improves tracking accuracy. The effectiveness, feasibility, and better performance of the proposed model are demonstrated by real experiments and comparative simulations.
Effects of Similarity Score Functions in Attention Mechanisms on the Performance of Neural Question Answering Systems

Abstract Attention mechanisms have been incorporated into many neural network-based natural language processing (NLP) models. They enhance the ability of these models to learn and reason with long input texts. A critical part of such mechanisms is the computation of attention similarity scores between two elements of the texts using a similarity score function. Given that these models have different architectures, it is difficult to comparatively evaluate the effectiveness of different similarity score functions. In this paper, we proposed a baseline model that captures the common components of recurrent neural network-based Question Answering (QA) systems found in the literature. By isolating the attention function, this baseline model allows us to study the effects of different similarity score functions on the performance of such systems. Experimental results show that a trilinear function produced the best results among the commonly used functions. Based on these insights, a new T-trilinear similarity function is proposed which achieved the higher predictive EM and F1 scores than these existing functions. A heatmap visualization of the attention score matrix explains why this T-trilinear function is effective.
2D facial landmark localization method for multi-view face synthesis image using a two-pathway generative adversarial network approach

One of the key challenges in facial recognition is multi-view face synthesis from a single face image. The existing generative adversarial network (GAN) deep learning methods have been proven to be effective in performing facial recognition with a set of pre-processing, post-processing and feature representation techniques to bring a frontal view into the same position in-order to achieve high accuracy face identification. However, these methods still perform relatively weak in generating high quality frontal-face image samples under extreme face pose scenarios. The novel framework architecture of the two-pathway generative adversarial network (TP-GAN), has made commendable progress in the face synthesis model, making it possible to perceive global structure and local details in an unsupervised manner. More importantly, the TP-GAN solves the problems of photorealistic frontal view synthesis by relying on texture details of the landmark detection and synthesis functions, which limits its ability to achieve the desired performance in generating high-quality frontal face image samples under extreme pose. We propose, in this paper, a landmark feature-based method (LFM) for robust pose-invariant facial recognition, which aims to improve image resolution quality of the generated frontal faces under a variety of facial poses. We therefore augment the existing TP-GAN generative global pathway with a well-constructed 2D face landmark localization to cooperate with the local pathway structure in a landmark sharing manner to incorporate empirical face pose into the learning process, and improve the encoder-decoder global pathway structure for better representation of facial image features by establishing robust feature extractors that select meaningful features that ease the operational workflow toward achieving a balanced learning strategy, thus significantly improving the photorealistic face image resolution. We verify the effectiveness of our proposed method on both Multi-PIE and FEI datasets. The quantitative and qualitative experimental results show that our proposed method not only generates high quality perceptual images under extreme poses but also significantly improves upon the TP-GAN results.