Reviewerly/exHarmony huggingface dataset:
MRDQA: A Deep Multimodal Requirement Document Quality Analyzer

In the field of requirement document quality assessment, existing methods mainly focused on textual patterns of requirements. Actually, the cognitive process that experts read and qualitatively measure a requirement document is from outward appearance to inner essence. Inspired by this intuition, this paper proposed a Multimodal Requirement Document Quality Analyzer (MRDQA), a neural model which combines the textual content with the visual rendering of requirement documents for quality assessing. MRDQA can capture implicit quality indicators which do not exist in requirement text, such as tables, diagrams, and visual layout. We evaluated MRDQA on the requirement documents collected from ZTE and achieved 81.3% accuracy in classifying their quality into three levels (high, medium, and low). We have successfully applied MRDQA as a pre-filter in ZTE’s requirement review system. It identifies low and medium quality requirements, thereby allows review experts to focus only on high-quality requirements. With this mechanism, the workload can be greatly reduced and the requirement review process can be accelerated.
Real-Time Semantic Segmentation with Dual Encoder and Self-Attention Mechanism for Autonomous Driving

As the techniques of autonomous driving become increasingly valued and universal, real-time semantic segmentation has become very popular and challenging in the field of deep learning and computer vision in recent years. However, in order to apply the deep learning model to edge devices accompanying sensors on vehicles, we need to design a structure that has the best trade-off between accuracy and inference time. In previous works, several methods sacrificed accuracy to obtain a faster inference time, while others aimed to find the best accuracy under the condition of real time. Nevertheless, the accuracies of previous real-time semantic segmentation methods still have a large gap compared to general semantic segmentation methods. As a result, we propose a network architecture based on a dual encoder and a self-attention mechanism. Compared with preceding works, we achieved a 78.6% mIoU with a speed of 39.4 FPS with a 1024 × 2048 resolution on a Cityscapes test submission.
Tokenization of Assets: Security Tokens in Liechtenstein and Switzerland

Technological innovations invariably lead to legal questions being raised; the sphere of digitalization of physical documents and securities by use of blockchain and other distributed ledger technologies does not constitute an exception. While solutions for many questions can be found within the existing legal framework, some developments call for legislative measures. This paper examines the hurdles in connection with the issuance and transfer of securities by virtue of a mere digital transaction and the approaches of the Liechtenstein and Swiss legislator to overcome them. Both have recognized that entries in distributed ledgers may fulfill the same main functions as the possession of a physical document. While the focus of the selective legal adaptations in Switzerland is on the use of DLT and securities law, the Liechtenstein legislator strives towards a holistic legal and regulatory framework for the entire token economy by introduction of a new set of rules.
OpenAthens odyssey: challenges of implementing federated authentication for a multi-institutional user population

The Texas Medical Center (TMC) is home to one of the world's largest cohorts of faculty, students, researchers, and clinicians who rely on seamless and immediate access to digital biomedical and health resources. This group is served by the TMC Library, with a collection that includes over 380,000 ebooks and 59,000 ejournals. In 2018, the TMC Library implemented OpenAthens, a federated authentication system to replace a locally hosted instance of EZproxy.The TMC Library is unique in its multi-institutional user population, which presents distinct challenges in adopting a single sign-on authentication system. Our project involved OpenAthens technical support, information technology teams from six academic institutions, and over thirty publishers. Implementation included the creation of an OpenAthens parent account, an active user directory, a resource catalog, and installation of our OpenAthens credentials at each publisher site. Because the TMC Library serves multiple institutions, OpenAthens built a custom login page and a portal to support both single sign-on and a generic username and password option. This case report discusses the reasons why OpenAthens was chosen, the preparation methods for implementation, the various challenges encountered and resolved, and recommendations for other health sciences libraries considering this system.The OpenAthens system provides important benefits: granular usage reports, single sign-on access, and data to negotiate reduced pricing for online resources. With prior knowledge and preparation, health sciences libraries can successfully implement OpenAthens with customizations tailored to their specific resources and user population.
Efficient Quantum Gate Discovery with Optimal Control

Optimal control theory provides a framework for numerical discovery of device controls that implement quantum logic gates, but common objective functions used for optimization often assign arbitrarily high costs to otherwise useful controls. We propose a framework for designing objective functions that permit novel gate designs such as echo pulses or locally-equivalent gates. We use numerical simulations to demonstrate the efficacy of the new objective functions by designing microwave-only pulses that act as entangling gates for superconducting transmon architectures. We observe that the proposed objective functions lead to higher fidelity controls in fewer optimization iterations than obtainable by traditional objective functions.
Subword Recognition in Historical Arabic Documents using C-GRUs

The recent years have witnessed an increased tendency to digitize historical manuscripts that not only ensures the preservation of these collections but also allows researchers and end-users’ direct access to these images. Recognition of Arabic handwriting is challenging due to the highly cursive nature of the script and other challenges associated with historical documents (degradation etc.). This paper presents an end-to-end system to recognize Arabic handwritten sub words in historical documents. More specifically, we introduce a hybrid CNN-GRU model where the shallow convolutional network learns robust feature representations while the GRU layers carry out the sequence modelling and generate the transcription of the text. The proposed system is evaluated on two different datasets, IBN SINA and VML-HD reporting recognition rates of 96.10% and 98.60% respectively. A comparison with existing techniques evaluated on the same datasets validates the effectiveness of our proposed model in characterizing Arabic subwords.
Chinese Medical Entity Recognition Model Based on Character and Word Vector Fusion

The medical information carried in electronic medical records has high clinical research value, and medical named entity recognition is the key to extracting valuable information from large-scale medical texts. At present, most of the studies on Chinese medical named entity recognition are based on character vector model or word vector model. Owing to the complexity and specificity of Chinese text, the existing methods may fail to achieve good performance. In this study, we propose a Chinese medical named entity recognition method that fuses character and word vectors. The method expresses Chinese texts as character vectors and word vectors separately and fuses them in the model for features. The proposed model can effectively avoid the problems of missing character vector information and inaccurate word vector partitioning. On the CCKS 2019 dataset for the named entity recognition task of Chinese electronic medical records, the proposed model achieves good performance and can effectively improve the accuracy of Chinese medical named entity recognition compared with other baseline models.
Evaluating the Security of Machine Learning Based IoT Device Identification Systems Against Adversarial Examples

Machine learning (ML) has been extensively used in Internet of Things (IoT) applications, including traffic profiling, network security, and IoT device identification. However, machine learning models are vulnerable to adversarial examples/attacks leading to misclassification and system malfunction. Though these attacks have been studied in domains such as computer vision, a comprehensive exploration in the IoT context is lacking. This work takes the first step in evaluating the adversarial attacks in this setting and particularly focuses on IoT device identification. To this end, our empirical analyses considering various attack techniques, including Fast Gradient Sign Methods and Jacobian-based Saliency Map, on the real-world IoT device classification dataset demonstrate that the ML-based IoT device classification is vulnerable to these attacks in both white box and black box scenarios. Moreover, these attacks are highly imperceptible in IoT networks and remain stealthy as demonstrated by applying the Kolmogorov-Smirnov goodness-of-fit test and ability to evade one-class Support Vector Machine and Isolation Forest based network intrusion detection systems.
A Crazy Particle Swarm Optimization with Time Varying Acceleration Coefficients for Economic Load Dispatch

In power generating plants, the expenses on combustible fuel is extremely costly and the concept of ELD (Economic Load Dispatch) make possible to save the considerable portion of profits. Practically generators have economic dispatch problems in terms of non-convexity. These kinds of problem cannot be resolved by conventional optimization techniques because the complication escalates due to manifold constrained that require to be fulfilled in all operating conditions. Recently a Particle Swarm Optimization (PSO) algorithm stimulated by collective conduct of swarm can be applied effectively to translate the ELD problems. The classical PSO bears the difficulty of early convergence mainly when the space of search is asymmetrical. To overcome the trouble “Crazy PSO with TVAC (Time Varying Acceleration Coefficients)” is launched which improve the search ability of the PSO by rebooting the vector of velocity whenever diffusion or saturation locate inside and to employ a scheme of parameter automation to maintain correct equilibrium between global hunt and local hunt and also circumvent the congestion. This arrangement is developed crazy PSO with TVAC and also demonstrated on two different model experimental structures of three generation units and six generation units. The result acquired from proposed method is evaluate with classical PSO and Real coded genetic algorithm (RGA) and it is found to be superior. This method is mathematically simple, gives fast convergence and robustness to resolve the rigid optimization inconvenience.
Analysis and comparison of Gaussian noise denoising algorithms

Abstract Collecting and processing various images has become an irreversible trend. It is meaningful to conduct more in-depth research on image denoising algorithms. We proposed mean filtering, median filtering, wiener filtering and wavelet filtering to denoise the image with Gaussian noise separately. And the objective image quality assessments are used to evaluate the quality of the images after denoising. Among them, wavelet filtering and Wiener filtering have a better effect on weaken Gaussian noise. Mean filtering and median filtering can also weaken Gaussian noise to some extent but the effect is limited. At the same time, it is equally important to select the appropriate denoising block for the diverse mean and variance of Gaussian noise. In wavelet filtering, the number of layers to be decomposed and the choice of threshold will also affect the effect of image denoising.
Efficient Action Recognition with Introducing R(2+1)D Convolution to Improved Transformer

The mainstream methods in video action recognition includes 3D convolutional neural networks (CNNs) and two-stream networks. Recently, the Transformer architecture which is outstanding in sequence modeling has made a breakthrough in Natural Language Processing (NLP) tasks. In this paper, we proposed a simple but effective approach to promote the performance of the action recognition. Firstly, the improved Transformer is designed to ameliorate the dependencies and reduce the error of representation of features. Then we employ a R(2+1)D convolution to capture the low-level partial spatiotemporal features of patching clips split from the whole input video sequence. After that, we stack all the feature maps to build a latent time domain sequential data which will feed into the improved Transformer to obtain the global attention and model the high-level temporal information. Finally, a method similar to channel attention is used to update the weight of the latent time domain dimension of sequential data dynamically which better maps the features to class labels. Experimental results show that our method performs superior compared with the state-of-the-art works.
Smart Contract Innovation and Blockchain-Based Tokenization in Higher Education

Blockchain which includes smart contract and tokenization features is the latest technology in the world, especially Indonesia. Smart contracts and tokenization make it very easy for users and can maintain valid data security, but there are still many universities that have not implemented the system so they have to involve many parties and costs. The problem taken in this study is the payment process for transactions such as credit and data processing that is vulnerable to illegal data leakage. This study aims to develop a smart contract system and blockchain tokenization in universities in the payment transaction process. The method used in this research is literature review analysis and testing method. The implementation of smart contracts and tokenization can replace third parties as security guards of transaction data with all Blockchain users paying attention and ensuring the integrity of the entire process and activity. This of course can avoid problems that arise from the presence of third parties in the transaction process. So it can be concluded that the implementation of smart contracts and Blockchain tokenization in payment transactions is the right solution to be applied in the payment transaction process at universities.
Avoiding Covid-19 Using a 3D Digital Mock Up and Augmented Reality with Cobot in Digital Factory

The Fourth Industrial Revolution or Industry 4.0 is extremely relevant and important in manufacturing for several reasons. Failing to adopt the technology of the Fourth Industrial Revolution can cause organizations to fall behind, as their operations are not digitized enough in benchmarking competitors. A major industry as bicycle manufacturing needs to be transformed into a digital factory in order to keep up with the evolving technology. The objectives of this research are in designing a 3D digital mock up and Augmented Reality (AR) for bicycle frame production in the form of digital factory for increasing production capability by decreasing the break-even point whilst avoiding Covid-19 contraction using Tecnomatix Plant Simulation and Unity 3D programs. This research constructed three alternative digital factory layouts that are designed by using a 3D simulation program. The risk of Covid-19 contraction points and the three layouts of cost per unit are analyzed. The results show that the break-even point of the first to the third layouts with Automated Guided Vehicle (AGV) are 26,580, 26,322 and 25,354 units respectively. The result of the risk about Covid-19 contraction points from the first to the third layout with AGV are 21,272, 2,872 and 0 points respectively. This means the most appropriate layout for bicycle frame production is the third layout due to the best break-even point that can significantly avoid Covid-19 contraction. The benefit of this research is to integrate a 3D digital mock up, AR and Cobot including AGV to assist the resilience of operations with a dynamic industrial environment.
Hubungan antara Kebiasaan Membaca Dengan Hasil Belajar Bahasa Indonesia Siswa di SD Inpres 12/79 Macanang

The problem in this study is the lack of students' reading habits such as rarely visiting the library and when they have free time they do not use it to read so that it can affect their learning outcomes. This study is a quantitative study with a correlational design that aims to determine whether or not there is a relationship between reading habits and Indonesian language learning outcomes of students at SD Inpres 12/79 Macanang. Based on the results of inferential statistical analysis, the tcount value (4.166) is greater than the ttable value (1.66365) at a significant level of 5%, thus HO is rejected and H1 is accepted. Indonesian students at SD Inpres 12/79 Macanang.
The Impact of English Cultural Awareness on Indonesian Advanced EFL Learners’ Grammar Knowledge

The current research examined the impacts of English cultural awareness on Indonesian EFL students’ grammar knowledge. To achieve this objective, 40 advanced participants were chosen according to their performance on the Oxford Quick Placement Test (OQPT). Then, the participants were equally divided into two groups of 20; an experimental and a control group. A grammar pre-test was administered to assess their knowledge of grammar prior to applying the instruction. After conducting the pre-test, the researchers taught the grammar points to the experimental group through using English cultural materials. On the other side, the grammar points were taught to the control group by using a traditional method. The instruction was conducted in 9 sessions of 45 minutes, and in the last session, the post-test of grammar was given to both groups in order to measure the impacts of the instruction on the participants’ grammar improvement. The findings revealed that the culture group had better performance than the conventional group after the treatment.
Remote sensing image matching featured by the optimal entropy classification

The scale-invariant feature transform (SIFT) algorithm is widely used in remote sensing optical image matching because of its robustness and universality. However, owing to the large amount of data and complex and nonlinear change spectral information of multisource and multiscale optical remote sensing images, the matching performance of classic SIFT and related improved algorithms is not ideal. To mitigate this, we propose an SIFT matching algorithm for remote sensing optical images based on entropy classification optimization. After using the classic SIFT to extract features based on the entropy classification standard, the features are classified to reduce the number of invalid features in the matching process and high-precision transformation parameters. Then, the projection model is used to detect stable features to obtain correctly matched pairs, which addresses the problem of minimal matched pairs due to complex and nonlinear changes. We compared it with commonly used algorithms, such as the SIFT, uniformly robust SIFT, and scale normalization and size classification algorithms. The proposed algorithm was successfully applied to multisource and multiscale remote sensing images. Experimental results show that the proposed algorithm performs better than the comparison algorithms in terms of precision, distribution quality, and in matching highly differing images.
Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learning

Retrieve-based dialogue response selection aims to find a proper response from a candidate set given a multi-turn context. Pre-trained language models (PLMs) based methods have yielded significant improvements on this task. The sequence representation plays a key role in the learning of matching degree between the dialogue context and the response. However, we observe that different context-response pairs sharing the same context always have a greater similarity in the sequence representations calculated by PLMs, which makes it hard to distinguish positive responses from negative ones. Motivated by this, we propose a novel \textbf{F}ine-\textbf{G}rained \textbf{C}ontrastive (FGC) learning method for the response selection task based on PLMs. This FGC learning strategy helps PLMs to generate more distinguishable matching representations of each dialogue at fine grains, and further make better predictions on choosing positive responses. Empirical studies on two benchmark datasets demonstrate that the proposed FGC learning method can generally and significantly improve the model performance of existing PLM-based matching models.
Unified Mathematical Formulation of Monogenic Phase Congruency

Phase congruency is a technique that has been used for edge, corner and symmetry detection. Its implementation through the use of monogenic filters has improved its computational cost. For this purpose, different methods of implementation have been published, but they do not use a common notation, which makes it difficult to understand. Therefore, this paper presents a unified mathematical formulation that allows a general understanding of the Monogenic phase congruency concepts and establishes criteria for its use. A new protocol for parameter tuning is also described, allowing better practical results to be obtained with this technique. Some examples are presented allowing one to observe the changes produced in the parameter tuning, evidencing the validity of the proposed criteria.
Comparative analysis of climate and topography in Chaco and Oriental, Paraguay

The article reports the issues corresponding to the climate variability and possibility of droughts in spatially distinct regions of Paraguay, the Chaco and the Oriental (Paraná Basin). Based on the GMT scripting approach and high-resolution datasets from the TerraClimate and GEBCO, the study examined the extent of the annual climate and environmental variables over Paraguay in 2020: extreme temperatures, wind speed, precipitation, soil moisture, evapotranspiration, Palmer's Drought Severity Index (PDSI), vapour pressure deficit (VPD) and the topography of the country. The environmental and climate parameters demonstrated spatial variability in the Dry Chaco with notable differences in Gran Chaco and Chaco Boreal, wetlands of Pantanal, and tropical rainforests in east of the Paraguay. Besides climate analysis, technical snippets from the GMT scripts regarding the mapping techniques and data processing were explained and commented. Research results show that actual evapotranspiration reaches maximal values in the Paraná Basin (180 mm/yr), and has peaks along the Pilcomayo river (140-150 mm/yr) which well coincides with the soil moisture; the PDSI values are maximal in Chaco Boreal and Gran Chaco; the temperatures are the highest in the N and NW regions of Paraguay; the highest VPD (over 2.0) corresponds to the Chaco Boreal.