Reviewerly/exHarmony huggingface dataset:
Understanding Translationese in Multi-view Embedding Spaces

Recent studies use a combination of lexical and syntactic features to show that footprints of the source language remain visible in translations, to the extent that it is possible to predict the original source language from the translation.In this paper, we focus on embedding-based semantic spaces, exploiting departures from isomorphism between spaces built from original target language and translations into this target language to predict relations between languages in an unsupervised way.We use different views of the data -words, parts of speech, semantic tags and synsets -to track translationese.Our analysis shows that (i) semantic distances between original target language and translations into this target language can be detected using the notion of isomorphism, (ii) language family ties with characteristics similar to linguistically motivated phylogenetic trees can be inferred from the distances and (iii) with delexicalised embeddings exhibiting source-language interference most significantly, other levels of abstraction display the same tendency, indicating the lexicalised results to be not "just" due to possible topic differences between original and translated texts.To the best of our knowledge, this is the first time departures from isomorphism between embedding spaces are used to track translationese.
Vehicle license plate recognition for fog‐haze environments

The technique of vehicle license plate recognition can recognize and count the vehicles automatically, and thus many applications regarding the vehicles are greatly facilitated. However, the recognitions of vehicle license plates are extremely difficult especially in some fog-haze environments because the fog and haze blur the boundaries and characters of license plates significantly, which makes the license plates hard to be detected or recognised. To this end, this paper proposes a vehicle License Plate Recognition method for Fog-Haze environments (LPRFH). In LPRFH, a dark channel prior algorithm based on the local estimation of atmospheric light value is applied to dehaze the blurred images preliminarily. Then, the images are further dehazed, and the license plate regions are detected through a Joint Further-dehazing and Region-extracting Model on basis of an object detection convolution neural network. Finally, the image super-resolution is accomplished with a convolution-enhanced super-resolution convolutional neural network, and hence the characters of license plates can be recognised successfully. Extensive experiments have been conducted, and the results indicate that LPRFH can recognise the license plates accurately even in some severe fog-haze environments.
A cloud-based approach to library management solution for college libraries

Purpose The study aims to focus on the present automation status of the college libraries with an objective to offer enhanced Web-based library service on an affordable virtualization on cloud computing model. Design/methodology/approach With Infrastructure as a Service (Infrastructure as a Service) delivery model, this study demonstrates how libraries of colleges/smaller institutes could be connected to cloud Library Management System infrastructure through internet or dedicated point-to-point WAN connectivity. The Software as a Service (SaaS) delivery model depicts how college libraries could form library consortium at its own private cloud environment with installation of the required LMS application, database, middleware and other prerequisites. Findings A cloud-based consortium approach for the college libraries will reduce the cost of purchasing hardware equipment and setting up of infrastructural facilities; relieve libraries of involving additional IT skilled manpower; foster collaborative approach with shared environment and minimise duplication in resource subscription. Originality/value To the best of the authors’ knowledge, the present study is the first of its kind in the light of shifting of infrastructure, software and hardware requirements of smaller libraries for cooperative sharing in both IaaS and SaaS cloud platform. The study delineates step by step how college libraries could effectively leverage the cooperative cloud architecture for enhanced library services to reach wider user community.
Interactive Optimization of Generative Image Modelling using Sequential Subspace Search and Content‐based Guidance

Abstract Generative image modeling techniques such as GAN demonstrate highly convincing image generation result. However, user interaction is often necessary to obtain desired results. Existing attempts add interactivity but require either tailored architectures or extra data. We present a human‐in‐the‐optimization method that allows users to directly explore and search the latent vector space of generative image modelling. Our system provides multiple candidates by sampling the latent vector space, and the user selects the best blending weights within the subspace using multiple sliders. In addition, the user can express their intention through image editing tools. The system samples latent vectors based on inputs and presents new candidates to the user iteratively. An advantage of our formulation is that one can apply our method to arbitrary pre‐trained model without developing specialized architecture or data. We demonstrate our method with various generative image modelling applications, and show superior performance in a comparative user study with prior art iGAN [ZKSE16].
Enabling Interactive Transcription in an Indigenous Community

We propose a novel transcription workflow which combines spoken term detection and humanin-the-loop, together with a pilot experiment.This work is grounded in an almost zero-resource scenario where only a few terms have so far been identified, involving two endangered languages.We show that in the early stages of transcription, when the available data is insufficient to train a robust ASR system, it is possible to take advantage of the transcription of a small number of isolated words in order to bootstrap the transcription of a speech collection.
Two-decade bibliometric overview of publications in the Journal of Knowledge Management

The purpose of this paper is to accomplish a bibliometric analysis, investigate the underlying knowledge structure, founding and development, and evolution of the Journal of Knowledge Management (JKM) through its articles published between 1997 and 2020.,A total of 1,346 research papers from JKM were selected and VantagePoint® software was used to generate bubble maps, auto-correlation maps, and matrix maps through techniques such as principal component analysis (PCA) and natural language processing (NLP). The analysis gives insights about the foundation of knowledge structure, its evolution and the development of JKM.,The systematic mapping of research illustrates topics emerging as new offshoots, global favourites, saturated and plateaued and reached academic maturity. The USA, the UK, Australia, Spain, Italy, China, Canada, Germany, and France have contributed the most to JKM. This paper provides a robust roadmap for future research investigation of JKM.,The authors humbly admit the possibility of overlooking some research papers while evaluating and filtering the database of JKM. The research outcome summarizes 23 years, subject to information retrieval from archival files.,This research is a detailed bibliometric analysis explaining paradigm shifts in the body of knowledge of JKM. The bibliometric outcomes can act as beacons for future researchers and academicians to revisit the current trends that shape the domain of knowledge management, particularly for the JKM audience with a focus on contemporary topics of research interest.,This is a unique endeavour to accomplish a systematic bibliometric analysis of the JKM for two decades, offering insights about its structural body of knowledge through an overview of the chronology of scholarly development in the field of knowledge management.
Secure Transactions Management Using Blockchain as a Service Software for the Internet of Things

The Internet of Things (IoT) has enabled communication anywhere between physical devices. Currently, concerns have been raised about suspicious transactions in IoT systems. Suspicious transactions may have a logical structure inconsistent with current information in the context of IoT. This article describes suspicious transactions in IoT systems and manages them using the blockchain as a service software plans. This study builds software-specific components for blockchain functions to implement in IoT networks. This study was conducted using Hyperledger Fabric as a blockchain service to test the software-defined blockchain components blockchain. The model was evaluated using average transaction throughput and latency. We observed that using blockchain as a software service system can provide excellent performance and security.
YOLOv3-Based Matching Approach for Roof Region Detection from Drone Images

Due to the large data volume, the UAV image stitching and matching suffers from high computational cost. The traditional feature extraction algorithms—such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Oriented FAST Rotated BRIEF (ORB)—require heavy computation to extract and describe features in high-resolution UAV images. To overcome this issue, You Only Look Once version 3 (YOLOv3) combined with the traditional feature point matching algorithms is utilized to extract descriptive features from the drone dataset of residential areas for roof detection. Unlike the traditional feature extraction algorithms, YOLOv3 performs the feature extraction solely on the proposed candidate regions instead of the entire image, thus the complexity of the image matching is reduced significantly. Then, all the extracted features are fed into Structural Similarity Index Measure (SSIM) to identify the corresponding roof region pair between consecutive image sequences. In addition, the candidate corresponding roof pair by our architecture serves as the coarse matching region pair and limits the search range of features matching to only the detected roof region. This further improves the feature matching consistency and reduces the chances of wrong feature matching. Analytical results show that the proposed method is 13× faster than the traditional image matching methods with comparable performance.
Tenant-Led Ciphertext Information Flow Control for Cloud Virtual Machines

When users upload their private data to the cloud, they lose control of the data stored in the cloud server.If the cloud system cannot provide an effective security mechanism to protect the data, the consequent data leakage issue will hinder the development of cloud computing.Conventional access control and encryption technologies cannot effectively control the propagation of tenant private data in the system.The mandatory one-way information flow control model is limited by the complexity of the cloud environment, and it is difficult to effectively protect private data stored in the cloud.To solve the above problems, this article proposes a tenant-led ciphertext information flow control method for cloud virtual machines.Through the design of a decentralized information flow control security policy, a secret-domain key management scheme, and a multi-ID-based threshold encryption scheme, the information flow control strategies of taint infection, secret-level reduction, and ability propagation are realized in a ciphertext form, which can effectively prevent malicious users inside and outside the system from illegally reading private data.The feasibility of this method is verified by a security proof and an experiment.
Pair Programming: An Empirical Investigation in an Agile Software Development Environment

Several experiments carried out on Pair Programming (PP) in a controlled environment by researchers and practitioners have been said to have a positive effect on software quality and time of delivery. Pair programming can be applied in all phases of software development. Although few empirical studies have shown the benefits of pair programming, not so much work has been done on maintainability of codes in a real agile environment. Therefore, in this work, we experimented using industry-based practitioners (working at an agile software development environment) to correct errors that were introduced deliberately into a set of python codes. Data was collected by recording the time to correct the mistakes. One hundred software practitioners were paired randomly and one hundred individual junior programmers to work on the same set of codes. Data obtained were analyzed, and we got very interesting results.
A novel intelligent underwater image enhancement method via color correction and contrast stretching

With the rapid development of artificial intelligence, image processing technology has been more and more widely used. Image enhancement is an important part of image processing, and has become a research hotspot of theory and application of image processing technology. This article proposes a new method for underwater image enhancement to solve the problems of color distortion, low contrast and blurring in underwater images. The compensation factor is used to compensate the badly damaged color channels, and the compensation factor is constructed by the mean differences between the damaged color channels and the well-preserved color channel. Then, multi-scale convolution MSRCR technology is used to denoising and correct color distortion, in conclusion, CLAHS and global contrast stretching are used to improve the local and global contrast of the images. Qualitative and quantitative evaluations prove that the proposed method can solve the color cast effect and improve the contrast of underwater images. The images processed by our method have natural color, high contrast and high clarity. Similarly, our method can also achieve good results in underwater low light and underwater images captured by different camera scenes.
Aplikasi Stemming Kata Bahasa Lampung Dialek Api Menggunakan Pendekatan Brute-Force dan Pemograman C#

Lampung is one of the areas on the island of Sumatra that has the regional language and script of Lampung. In this province there are two main regional dialects, namely the dialect of fapi and the dialect of nyo. Research efforts for the preservation of the Lampung language digitally have been conducted by researchers from various Universities. The research stemming from Lampung dialects of api is based on the findings of the fact that the dictionary-based Lampung dialects of the Lampung language dialect cannot translate the affix words. Stemming of Lampung language dialects of api is worked with a Brute-force approach. In the Lampung language there are inflexional verbs and derivational verbs. Inflexional verbs are verbs formed from bases that are also categorized verbs while derivational verbs are verbs formed from bases that are categorized in addition to verbs such as nouns, adjectives, adverbs, pronouns and numerals. The purpose of this research is to (1) conduct word stemming with a Brute-force approach, (2) produce an application as a Lampung language word Stemmer dialect of api using C # programming language and online database using Firebase. The methods used in this study consisted of (1) Researchers are looking for, identifying, recording, manually typing 2000 words following the basic words of the Lampung dialect of api, (2) creating a stemming algorithm with a Brute-force approach (3) testing applications that have made. As for the result obtained is the application is able to do word stemming for words that have been identified in 2000 words and if stemming can not be done then the facility is provided to update the database used in the application to be used for stemming because the stemming application is very supportive of the application dictionary-based translation engine. The urgency of Stemming application research is to address the affix word in the Lampung language machine translation machine translation application for further research
A novel digital watermarking scheme using dragonfly optimizer in transform domain

In today’s technologically-wise advanced environment, it is very significant for the entrepreneurs and business people to secure or protect the digital content. Their digital content can be protected legally using the copyright information such as copyrights embedded into the data. Bio-inspired computing algorithms will help find the optimal scale factor(s) by developing an appropriate fitness function and parameter selection. This article proposes a novel robust and blind watermarking method for digital images using a dragonfly optimizer. This article provides a solution for a false-positive problem using third party protection code. The proposed watermarking system outperforms the competing methods both on efficiency and efficacy measures such as experimental time, Bit Error Rate, Peak Signal to Noise Ratio, Normalized Correlation in the standard databases (Signal and Image Processing Institute (SIPI), Break Our Watermarking System (BOWS)) and also found to be robust to various image processing attacks.
Are Cryptocurrencies a Safe Haven for Equity Markets? An International Perspective from the COVID-19 Pandemic

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Continuous monitoring and identification of driver drowsiness alert system

Accident is the very hard word to listen in any one's life, and as per the statistics provided by road transport authorities most of the accidents are happening between the times of 02:00AM to 05:00AM because of the drowsiness of drivers. The drivers might have drove more than 12 to 15 h and they need at least 2 to 3 h sleep, but due to the targets what they have or because of less traffic in night times the drivers will drive vehicles continuously. The motivation behind this paper was to devise an approach to caution lazy drivers in the demonstration of driving. One of the reasons for auto crashes originates from sleepiness of the driver. Accordingly, this investigation endeavored to address the issue by making a trial so as to figure the level of tiredness. A need for this paper was the utilization of a Camera and module, which could figure the level of tiredness in drivers. The recurrence of head tilting and flickering of the eyes was utilized to decide if a driver felt tired. With an assessment on volunteers, the precision of face and eye identification was huge. So this proposed methodology will be a one of the approach to identify the drowsiness and alert the driver by which at least few of the accidents can be avoided.
BriNet: Towards Bridging the Intra-class and Inter-class Gaps in One-Shot Segmentation

Few-shot segmentation focuses on the generalization of models to segment unseen object instances with limited training samples. Although tremendous improvements have been achieved, existing methods are still constrained by two factors. (1) The information interaction between query and support images is not adequate, leaving intra-class gap. (2) The object categories at the training and inference stages have no overlap, leaving the inter-class gap. Thus, we propose a framework, BriNet, to bridge these gaps. First, more information interactions are encouraged between the extracted features of the query and support images, i.e., using an Information Exchange Module to emphasize the common objects. Furthermore, to precisely localize the query objects, we design a multi-path fine-grained strategy which is able to make better use of the support feature representations. Second, a new online refinement strategy is proposed to help the trained model adapt to unseen classes, achieved by switching the roles of the query and the support images at the inference stage. The effectiveness of our framework is demonstrated by experimental results, which outperforms other competitive methods and leads to a new state-of-the-art on both PASCAL VOC and MSCOCO dataset.
ANALISIS ANTUSIASME SISWA DALAM EVALUASI BELAJAR MENGGUNAKAN PLATFORM QUIZIZZ

One way to evaluate student learning outcomes is through question processing. However, nowadays evaluations are usually carried out using conventional methods such as a paper-based test. Even though often in the implementation of evaluations, students feel less enthusiastic about their work. This study aims to identify students' enthusiasm in solving questions through the Quizizz platform. The subjects of this study were 34 students of class XI Science 4 Senior High School 1 Tahunan. This research uses a descriptive quantitative method because shows a description of the students' enthusiasm for using the Quizizz platform as a tool for solving trigonometric questions. To analyze the enthusiasm of students in solving questions through the Quizizz platform, was carried out in three stages: 1) distributing a questionnaire with 25 question items via Google Form based on five indicators that measure enthusiasm (Response, attention, concentration, willingness, and self-involvement), 2) questionnaire data analyzed based on the answer score for each question item. Based on the research results, student enthusiasm in learning evaluation on trigonometric material through the Quizizz platform got a percentage of 62.38%. So that the Quizizz platform can be an alternative for teachers in evaluating student learning outcomes to create an atmosphere of evaluation that is fun and interesting for students to follow which will increase student enthusiasm in participating in learning evaluations.
Strongly regular graphs with parameters (81, 30, 9, 12) and a new partial geometry

Twelve new strongly regular graphs with parameters (81, 30, 9, 12) are found as graphs invariant under certain subgroups of the automorphism groups of the two previously known graphs that arise from 2-weight codes. One of these new graphs is geometric and yields a partial geometry with parameters pg(5, 5, 2) that is not isomorphic to the partial geometry discovered by J. H. van Lint and A. Schrijver [13] in 1981.
Opinion subset selection via submodular maximization

Current research on subset selection for opinion analysis assumes that their methods can retrieve the opinions expressed in documents from general text features. However, such relaxed conditions can hardly maintain the performance of the analysis in opinion mining, especially when given strict limitations on the subset size. In this paper, we propose a framework for opinion subset selection. This framework can select a small set of instances from original data to convey a subjective representation for opinion classification and regression. Compared with our framework, the conventional submodular based subset selection approach cannot capture the fine-grained opinion features expressed in the corpus. Specifically, we propose a monotone non-decreasing score function and a framework based on topic modeling and submodular maximization for filtering irrelevant information and selecting the subsets. Our work further introduces an opinion-sensitive algorithm for optimizing the proposed function for opinion subset construction. We perform extensive experiments and comparative analysis of different subset selection methods in this work. The experimental result shows that the proposed opinion subset selection framework can compress the original text training set and preserve the test set’s classification and regression metric performance at the same time.