Reviewerly/exHarmony huggingface dataset:
Improving a Software Modernisation Process by Differencing Migration Logs

Software written in legacy programming languages is notoriously ubiquitous and often comprises business-critical portions of codebases and portfolios. Some of these languages, like COBOL, mature, grow, and acquire modern tooling that makes maintenance activities more bearable. Others, like many fourth generation languages (4GLs), stagnate and become obsolete and unmaintained, which first urges and eventually forces migrating to other languages, if the software needs to be kept in production. In this paper, we dissect a software modernisation process endorsed by Raincode Labs, utilised in particular to migrate software from a 4GL called PACBASE, to pure COBOL. Having migrated upwards of 500 MLOC of production code to COBOL using this process, the company has ample experience with this process. Nevertheless, we identify some improvement points and explain the technical side of a possible solution, based on migration log differencing, that is currently being put to the test by Raincode migration engineers.
THE DESIGN AND IMPLEMENTATION OF STUDENT ATTENDANCE TRACKING SYSTEM USING QR CODE CARD

Students' attendance taking and tracking are important in order to monitor students' performance in class which is closely related to their attendance. Good attendance usually leads to good performance and vice versa. Therefore, any problems related to students' attendance should be identified as early as possible so that appropriate measures can be taken to address them. However, tracking students' attendance, especially if done manually, can be tedious and time-consuming, especially for classes with a large number of students. Higher education institutions in Malaysia are now encouraged to explore the potential use of Quick Response (QR) code and smartphone as a future attendance management system to track and record the student's attendance to the class and tutorial. In response, this study attempts to explain the design and implementation of a proposed system developed using Google Drive, Google Form, Google Sheet, Notepad, and QR Code Generator Add-ons in Google Docs. Students were required to scan the unique QR code assigned to them using their smartphone during or at the beginning of each lecture in order to confirm their attendance. This affordable QR code-based attendance system enables lecturers to speed up the process of taking attendance, especially in a large classroom and would save them valuable teaching time. The proposed system provides better security than the traditional methods that depend on the sign-in sheet or roll calling student names including eliminating chances of students signing up for others who may not be present. In addition, it also provides a faster and simpler way to take attendance, which could be easily adopted by lecturers to facilitate the process of taking, managing and keeping the record of students' attendance.
Deep Snapshot HDR Imaging Using Multi-exposure Color Filter Array

In this paper, we propose a deep snapshot high dynamic range (HDR) imaging framework that can effectively reconstruct an HDR image from the RAW data captured using a multi-exposure color filter array (ME-CFA), which consists of a mosaic pattern of RGB filters with different exposure levels. To effectively learn the HDR image reconstruction network, we introduce the idea of luminance normalization that simultaneously enables effective loss computation and input data normalization by considering relative local contrasts in the “normalized-by-luminance” HDR domain. This idea makes it possible to equally handle the errors in both bright and dark areas regardless of absolute luminance levels, which significantly improves the visual image quality in a tone-mapped domain. Experimental results using two public HDR image datasets demonstrate that our framework outperforms other snapshot methods and produces high-quality HDR images with fewer visual artifacts.
Dual Learning: Theoretical Study and an Algorithmic Extension

Dual learning has been successfully applied in many machine learning applications including machine translation, image-to-image transformation, etc. The high-level idea of dual learning is very intuitive: if we map an x from one domain to another and then map it back, we should recover the original x. Although its effectiveness has been empirically verified, the theoretical understanding of dual learning is still very limited. In this paper, we characterize sufficient conditions for dual learning to outperform vanilla translators. Based on our theoretical analysis, we further extend dual learning by introducing more related mappings and propose multi-step dual learning, in which we leverage feedback signals from additional domains to improve the qualities of the mappings. We show that multi-step dual learning has the potential to boost the performance of dual learning. Experiments on WMT 14 English $$\leftrightarrow$$ ↔ German, MultiUN English $$\leftrightarrow$$ ↔ French, and IWSLT’17 English $$\leftrightarrow$$ ↔ Chinese translations verify our theoretical findings on dual learning, and the results on the translations among English, French, and Spanish of MultiUN demonstrate the effectiveness of multi-step dual learning.
Sentiment Classification of reviews with RNNMS and GRU Architecture Approach Based on online customers rating

In neural language processing, the sentiment analysis of various types of reviews is a very common and important issue. In the current study, the main objective is to evaluate the reviews on food items to estimate a score of 1 to 5 for them using two finely adjusted recurrent neural networks. Accordingly, a new model based on the recursive neural network is proposed in the current study for multiple sentences. Compared to the baseline, the proposed model provided better results considering various measures selected for the study. Afterwards, the results were generalized to GRUs (i.e. gated recurrent units). Furthermore, two distinct strategies are also presented in the current study for working with the data which are significantly skewed. The skewness in the data is one of the most common problems seen in various types of reviews. The proposed models are ultimately verified in terms of accuracy.
Roman Urdu Headline News Text Classification Using RNN, LSTM and CNN

This paper presents the automated tool for the classification of text with respect to predefined categories. It has always been considered as a vital method to manage and process a huge number of documents in digital forms which are widespread and continuously increasing. Most of the research work in text classification has been done in Urdu, English and other languages. But limited research work has been carried out on roman data. Technically, the process of the text classification follows two steps: the first step consists of choosing the main features from all the available features of the text documents with the usage of feature extraction techniques. The second step applies classification algorithms on those chosen features. The data set is collected through scraping tools from the most popular news websites Awaji Awaze and Daily Jhoongar. Furthermore, the data set splits in training and testing 70%, 30%, respectively. In this paper, the deep learning models, such as RNN, LSTM, and CNN, are used for classification of roman Urdu headline news. The testing accuracy of RNN (81%), LSTM (82%), and CNN (79%), and the experimental results demonstrate that the performance of the LSTM method is state-of-art method compared to CNN and RNN.
Integerwise Functional Bootstrapping on TFHE

We propose a new technique for implementing an arbitrary 1-variable function during a bootstrapping procedure based on an integerwise variant of the fully homomorphic encryption scheme TFHE. The integerwise TFHE was implicit in the original TFHE paper (Asiacrypt’ 2016), and Bourse et al. provided an explicit form (CRYPTO’ 2018). However, the modified integerwise TFHE scheme can perform only the homomorphic evaluations of the integer addition and the sign function, and thus, the application of the scheme is restricted. Our scheme has diverse functionalities of the integerwise TFHE scheme. Based on our bootstrapping procedure, we propose several useful basic functions: homomorphic equality testing, multiplication by a binary number and a division algorithm. We also derive empirical results that show that our division algorithm is approximately $$3.4$$ x faster than the fastest division algorithm in the literature based on fully homomorphic encryption schemes, with a runtime less than 1 s for each 4-bit integer division task.
An Efficient Algorithm for Feature Selection Problem in Gene Expression Data: A Spider Monkey Optimization Approach

In this paper, we presented an adequate algorithm in gene expression data for feature selection. A Spider Monkey Optimization (SMO) based approach has implemented to get feature selection subset from gene expression data of high dimension. In consideration of high dimensionality data where lie large amount of superfluous features, we are using a well defined fast preprocessing heuristic based approach for diminishing features. Fitness function can be illustrated as it supports SMO scheme to knob the inconsistent targets i.e., to decrease feature cardinality and preserving unique efficiency (i.e., accuracy of classification). The bench mark datasets of microarray gene expression have used and demonstrated the results along in-depth provisional research using a classifier named k-NN.
WITHDRAWN: Development of Regional Logistics Economy Based on FPGA and Embedded System

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
Unified Density-Aware Image Dehazing and Object Detection in Real-World Hazy Scenes

It is an important yet challenging task to detect objects on hazy images in real-world applications. The major challenge comes from low visual quality and large haze density variations. In this work, we aim to jointly solve the image dehazing and the object detection tasks in real hazy scenarios by using haze density as prior knowledge. Our proposed Unified Dehazing and Detection (UDnD) framework consists of three parts: a residual-aware haze density classifier, a density-aware dehazing network, and a density-aware object detector. First, the classifier exploits the residuals of hazy images to accurately predict density levels, which provide rich domain knowledge for the subsequent two tasks. Then, we design respectively a High-Resolution Dehazing Network (HRDN) and a Faster R-CNN-based multi-domain object detector to leverage the extracted density information and tackle hazy object detection. Experiments demonstrate that UDnD performs favorably against other methods for object detection in real-world hazy scenes. Also, HRDN achieves better results than state-of-the-art dehazing methods in terms of PSNR and SSIM. Hence, HRDN can conduct haze removal effectively, based on which UDnD is able to provide high-quality detection results.
Faulty Point Unit: ABI Poisoning Attacks on Intel SGX

This paper analyzes a previously overlooked attack surface that allows unprivileged adversaries to impact supposedly secure floating-point computations in Intel SGX enclaves through the Application Binary Interface (ABI). In a comprehensive study across 7 widely used industry-standard and research enclave shielding runtimes, we show that control and state registers of the x87 Floating-Point Unit (FPU) and Intel Streaming SIMD Extensions (SSE) are not always properly sanitized on enclave entry. First, we abuse the adversary’s control over precision and rounding modes as a novel “ABI-level fault injection” primitive to silently corrupt enclaved floating-point operations, enabling a new class of stealthy, integrity-only attacks that disturb the result of SGX enclave computations. Our analysis reveals that this threat is especially relevant for applications that use the older x87 FPU, which is still being used under certain conditions for high-precision operations by modern compilers like gcc. We exemplify the potential impact of ABI-level quality-degradation attacks in a case study of an enclaved machine learning service and in a larger analysis on the SPEC benchmark programs. Second, we explore the impact on enclave confidentiality by showing that the adversary’s control over floating-point exception masks can be abused as an innovative controlled channel to detect FPU usage and to recover enclaved multiplication operands in certain scenarios. Our findings, affecting 5 out of the 7 studied runtimes, demonstrate the fallacy and challenges of implementing high-assurance trusted execution environments on contemporary x86 hardware. We responsibly disclosed our findings to the vendors and were assigned two CVEs, leading to patches in the Intel SGX-SDK, Microsoft OpenEnclave, the Rust compiler’s SGX target, and Go-TEE.
Variational Bayesian Sequence-to-Sequence Networks for Memory-Efficient Sign Language Translation

Memory-efficient continuous Sign Language Translation is a significant challenge for the development of assisted technologies with real-time applicability for the deaf. In this work, we introduce a paradigm of designing recurrent deep networks whereby the output of the recurrent layer is derived from appropriate arguments from nonparametric statistics. A novel variational Bayesian sequence-to-sequence network architecture is proposed that consists of a) a full Gaussian posterior distribution for data-driven memory compression and b) a nonparametric Indian Buffet Process prior for regularization applied on the Gated Recurrent Unit non-gate weights. We dub our approach Stick-Breaking Recurrent network and show that it can achieve a substantial weight compression without diminishing modeling performance.
Measuring the benefits of lying in MARA under egalitarian social welfare

When some resources are to be distributed among a set of agents following egalitarian social welfare, the goal is to maximize the utility of the agent whose utility turns out to be minimal. In this context, agents can have an incentive to lie about their actual preferences, so that more valuable resources are assigned to them. In this paper we analyze this situation, and we present a practical study where genetic algorithms are used to assess the benefits of lying under different situations.
Style at the Scale of the Canon. A Stylometric Analysis of 100 Romanian Novels Published between 1920 and 1940

The present study proposes an experimental exploration of the Romanian novel written between 1920 and 1940 through the use of stylometry, a method of distant reading employed for the statistical analysis of style. Drawing from the most recent advances in the field of computational stylistics, we select a formal standpoint from which we seek to investigate the relation between the Romanian novelistic canon and minor, tertiary novels published in the same. In our test cases, we will attempt to establish some of the more promising aspects of stylometric analysis, as well as single out the experiments that yield no relevant result. Because of the relative novelty of the method, the purpose of our investigations is to offer a kind of pilot experiment that can illustrate the benefits of using computational methods on Romanian literary corpora.
Subword Segmentation and a Single Bridge Language Affect Zero-Shot Neural Machine Translation.

Zero-shot neural machine translation is an attractive goal because of the high cost of obtaining data and building translation systems for new translation directions.However, previous papers have reported mixed success in zero-shot translation. It is hard to predict in which settings it will be effective, and what limits performance compared to a fully supervised system.In this paper, we investigate zero-shot performance of a multilingual EN {FR,CS,DE,FI} system trained on WMT data.We find that zero-shot performance is highly unstable and can vary by more than 6 BLEU between training runs, making it difficult to reliably track improvements.We observe a bias towards copying the source in zero-shot translation, and investigate how the choice of subword segmentation affects this bias. We find that language-specific subword segmentation results in less subword copying at training time, and leads to better zero-shot performance compared to jointly trained segmentation.A recent trend in multilingual models is to not train on parallel data between all language pairs, but have a single {\em bridge} language, e.g. English.We find that this negatively affects zero-shot translation and leads to a failure mode where the model ignores the language tag and instead produces English output in zero-shot directions. We show that this bias towards English can be effectively reduced with even a small amount of parallel data in some of the non-English pairs.
Umwelt Extended: Toward New Approaches in the Study of the Technologically Modified Body

ABSTRACT This study introduces the practice of body hacking, modification, and extension of the body through technological means, and discusses a possible approach to anthropological research on people with technologically extended sensory apparatus. Most DIY modifications promise to extend capacities of the body not just quantitatively by enhancing what already exists, but qualitatively by adding new senses or organs that enable new connections with the outer world. The question is how it is possible to approach in social science research the subjective bodily experience of someone with sensory abilities beyond the human norm. This article proposes to look at this phenomenon from the standpoint of von Uexküll’s concept of Umwelt, which focuses on the study of the subjective experiential world of nonhuman animals and puts emphasis on processes of meaning creation, and not only in a symbolic and discursive way. Following current criticism of representationalism in anthropology, it will be suggested here that the Umwelt concept has a great potential for conceptualization of the technologically modified body in sociocultural anthropology and related disciplines.
Digital Learning Experience in Museums: Cultural Readings in a Virtual Environment

This study revolves around primary and secondary school visual arts teachers’ cultural and critical readings on their VR museum experience through object and representation. As part of the in-service training and research project, the study was carried out in the provinces of Çanakkale, Erzincan, Kayseri, Diyarbakır, Giresun, Mersin, Denizli, which were selected from seven regions of Turkey within a seven-month period. 508 primary and secondary school visual arts teachers participated in the study. The study is a case study and teachers’ readings related to the object and representation in the virtual museum and their VR museum experiences formed the units of analysis in the study. The data of the study was collected through worksheets, pre-evaluation forms, participant diaries and focus group interviews with voluntary teachers and content analysis method was employed to analyse the data. In the study, teachers were motivated to incorporate social and personal contexts into their readings of the object-space-representation. The results denoted that the activity enhanced teachers’ learning experiences in terms of personal and professional contexts. Although teachers highlighted that VR museum experiences could not offer much more rich experiences than the actual museum visits, they believed that it could be effective for visual arts classes and be blended with various learning activities given that the virtual museum experience offers the possibility of exploring a museum without a barrier of distance and incite enthusiasm and learning interest through digital technologies (e.g., sound, motion, immersive experience).
Medical Knowledge-enriched Textual Entailment Framework

One of the cardinal tasks in achieving robust medical question answering systems is textual entailment.The existing approaches make use of an ensemble of pre-trained language models or data augmentation, often to clock higher numbers on the validation metrics.However, two major shortcomings impede higher success in identifying entailment: (1) understanding the focus/intent of the question and (2) ability to utilize the real-world background knowledge to capture the context beyond the sentence.In this paper, we present a novel Medical Knowledge-Enriched Textual Entailment framework that allows the model to acquire a semantic and global representation of the input medical text with the help of a relevant domain-specific knowledge graph.We evaluate our framework on the benchmark MEDIQA-RQE dataset and manifest that the use of knowledgeenriched dual-encoding mechanism help in achieving an absolute improvement of 8.27% over SOTA language models.We have made the source code available here.
Fast generation of W state via superadiabatic-based shortcut in circuit quantum electrodynamics*

We propose a scheme to fast prepare the three-qubit W state via superadiabatic-based shortcuts in a circuit quantum electrodynamics (circuit QED) system. We derive the effective Hamiltonian to suppress the unwanted transitions between different eigenstates by counterdiabatic driving, and obtain the W state with high-fidelity based on the superadiabatic passage. The numerical simulation results demonstrate that the proposed scheme can accelerate the evolution, and is more efficient than that with the adiabatic passage. In addition, the proposed scheme is robust to the decoherence caused by the resonator decay and qubit relaxation, and does not need additional parameters, which could be feasible in experiment.
Research on UAV Wing Structure Health Monitoring Technology Based on Finite Element Simulation Analysis

In order to solve the real-time health monitoring problem of the UAV wing structure, a wing monitoring method based on finite element simulation analysis and signal anomaly analysis is proposed. Firstly, CATIA software is used to perform full-scale three-dimensional modeling of the wing, and then FLUENT and ANSYS software are used for finite element simulation analysis of the wing and determine the fault monitoring nodes. On this basis, common faults are injected into the wing, and node data is collected at the same time, and Beacon Exception Analysis Method (BEAM) is used to perform fault detection on the data. The results prove that the method can accurately check the fault in real time and determine the location of the fault according to the node distribution and channel contribution rate, which provides an effective guarantee for improving the efficiency of UAV fault diagnosis and improving the flight safety of UAV.