Reviewerly/exHarmony huggingface dataset:
An Approach Based on Multilevel Convolution for Sentence-Level Element Extraction of Legal Text

In the judicial field, with the increase of legal text data, the extraction of legal text elements plays a more and more important role. In this paper, we propose a sentence-level model of legal text element extraction based on the structure of multilabel text classification. Our proposed model contains an encoder and an improved decoder. The encoder applies multilevel convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) as feature extraction networks to extract local neighborhood and context information from legal text, and a decoder applies LSTM with multiattention and full connection layer with an improved initialization method to decode and generate label sequences. To our best knowledge, it is one of the first attempts to apply a multilabel classification algorithm for element extraction of legal text. In order to verify the effectiveness of our model, we conduct experiments not only on three real legal text datasets but also on a general multilabel text classification dataset.The experimental results demonstrate that our proposed model outperforms baseline models on legal text datasets, and our model is competitive to baseline models on the general text multilabel classification dataset, which indicates that our proposed model is useful for multilabel classification tasks of ordinary texts and legal texts with an uncertain number of characters in words and short lengths.
Enabling a Question-Answering System for COVID Using a Hybrid Approach Based on Wikipedia and Q/A Pairs

Chicaiza, Janneth Bouayad-Agha, NadjetThe research on COVID-19 disease has produced much information, but there are more questions than certainty. This proposal aims to contribute by providing reliable and updated answers to questions aimed at the general public. To achieve this goal, we design a question-answering architecture that leverages two information sources of different nature, controlled-official and open-collaborative. Thus, the system can answer several questions that the community may have about COVID. During the experimentation, we found that thanks to knowledge graphs, information retrieval, and NLP methods, the system can provide explainable answers; i.e., they obtain direct answers and can browse into enriched responses.
An Efficient Attention Based Image Adversarial Attack Algorithm with Differential Evolution on Realistic High-Resolution Image

Deep learning methods with convolutional neural network (CNN) have achieved significant success in image classification tasks. Meanwhile, adversarial image attack algorithms are also becoming more effective within low-resolution images. However, in high-resolution images, such algorithms are still lacking a way to balance between efficiency and success rate. In this paper, we proposed an efficient attention-based image adversarial attack algorithm with differential evolution on realistic high-resolution images that make changes negligible to human eye but can achieve great success in deceiving Deep Neural Networks (DNNs) such as LeNet and ResNet. This attention-based algorithm uses the theory of Region of Interest (ROI) in the image and reduce the search area accordingly to maximize the attack accuracy. This paper proposed two image perturbation methods: strike-slip attack and Hue-Saturation-Value (HSV) filter attack, which apply changes universally to a given area of pixels to minimize the visual difference between two adjacent pixels. Then, based on population-based metaheuristic search theory, this paper used differential evolution algorithm to find the optimal attack solution. Finally, this paper compared above two attack methods and evaluate their effectiveness when attacking images of different resolutions.
Solid and Effective Upper Limb Segmentation in Egocentric Vision

Upper limb segmentation in egocentric vision is a challenging and nearly unexplored task that extends the well-known hand localization problem and can be crucial for a realistic representation of users’ limbs in immersive and interactive environments, such as VR/MR applications designed for web browsers that are a general-purpose solution suitable for any device. Existing hand and arm segmentation approaches require a large amount of well-annotated data. Then different annotation techniques were designed, and several datasets were created. Such datasets are often limited to synthetic and semi-synthetic data that do not include the whole limb and differ significantly from real data, leading to poor performance in many realistic cases. To overcome the limitations of previous methods and the challenges inherent in both egocentric vision and segmentation, we trained several segmentation networks based on the state-of-the-art DeepLabv3+ model, collecting a large-scale comprehensive dataset. It consists of 46 thousand real-life and well-labeled RGB images with a great variety of skin colors, clothes, occlusions, and lighting conditions. In particular, we carefully selected the best data from existing datasets and added our EgoCam dataset, which includes new images with accurate labels. Finally, we extensively evaluated the trained networks in unconstrained real-world environments to find the best model configuration for this task, achieving promising and remarkable results in diverse scenarios. The code, the collected egocentric upper limb segmentation dataset, and a video demo of our work will be available on the project page1.
Deep Networks for Monitoring Waterway Traffic in the Mekong Delta

Our investigation aims at training deep networks for monitoring waterway traffic means on the rivers in the Mekong Delta. We collected the real videos of the waterway traffic, and then tagging the five most popular means in frames extracted from the videos, making an image dataset. We propose to train recent deep network models such as YOLO v4 (You only look once), RetinaNet and EfficientDet on this image dataset to detect the five most popular means in the videos. The numerical test results show that YOLO v4 gives highest accuracy than two other methods, including RetinaNet and EfficientDet. YOLO v4 achieves the performances on the testset with a precision of 91%, a recall of 98%, F1-score of 94% and mean average precision (mAP@0.50) of 97.51%.
Investigating Recurrent Neural Networks for Diacritizing Arabic Text and Correcting Soft Spelling Mistakes

Modern Arabic language processing research aims to find efficient solutions to important problems such as adding diacritics to undiacritized text and correcting spelling mistakes. Some previous machine learning solutions solve these problems as one-to-one sequence transcription problems, which imposes restrictions on the type of errors that can be corrected. This work investigates transformer and encoder-decoder recurrent neural network models taking these two problems as case studies. These models solve sequence to sequence transcription problems without restrictions on the input and output lengths. We investigated several alternatives and evaluated them on two benchmark datasets. We recommend an encoder-decoder model that provides excellent accuracy. The encoder part has an embedding layer and a long short-term memory (LSTM) layer. The decoder part has an embedding layer, an LSTM layer, Luong attention between the two parts, and a dense output layer with softmax activation.
Gaussian Process based Tracklets Association Method

In the field of target tracking, track breaks is a common problem because of the strong maneuverability, accuracy of measurement and detection, sampling interval, etc. In order to get a complete target, tracklets association is usually necessary. The related work has been done concerning tracklets association is usually under the assumption of the target motion model. In this paper, Gaussian process is proposed to solve tracklets association in non parametric learning. This method is capable of stitching the broken tracks and improve the correct association rate.
Few-Shot Relation Extraction on Ancient Chinese Documents

Traditional humanity scholars’ inefficient method of utilizing numerous unstructured data has hampered studies on ancient Chinese writings for several years. In this work, we aim to develop a relation extractor for ancient Chinese documents to automatically extract the relations by using unstructured data. To achieve this goal, we proposed a tiny ancient Chinese document relation classification (TinyACD-RC) dataset annotated by historians and contains 32 types of general relations in ShihChi (a famous Chinese history book). We also explored several methods and proposed a novel model that works well on sufficient and insufficient data scenarios, the proposed sentence encoder can simultaneously capture local and global features for a certain period. The paired attention network enhances and extracts relations between support and query instances. Experimental results show that our model achieved promising performance with scarce corpus. We also examined our model on the FewRel dataset and found that outperformed the state-of-the-art no pretraining-based models by 2.27%.
NPU-aware Parameter Generation in Quantization ONNX Model

In this paper, we address the design of the parameter generation method for the autonomous controlling of Neural Processing Unit (NPU) on the edge device. The design is based on quantization Open Neural Network Exchange (ONNX) model that can utilize deep learning models of various frameworks. In order to generate parameters of the quantization deep neural network (DNN) model, we parse model and generate NPU-aware parameters. When parsing model, we extract and arrange model information as parameter by traversing the quantization ONNX model. Furthermore, according to the requirements of the target NPU, the number of parameters can be reduced by offline calculation, and the floating-point arithmetic is converted into an integer arithmetic. As a result, the proposed design allows the target NPU to process various framework models by and generates NPU-aware parameters according to the target NPU. Furthermore, it can reduce the quantization parameters by average 35.4% without accuracy degradation.
Action-Transformer for Action Recognition in Short Videos

Action recognition methods are mostly based on a 3-Dimensional (3D) Convolution Network which have some limitations in practice, e.g. redundant parameters, big memory consumed and low performance. In this paper, a new convolution-free model called action-transformer is proposed to address the mentioned problems. The model proposed is mainly composed of three modules: spatial-temporal transformation module, hybrid feature attention module, and residual-transformer module. The spatial-temporal transformation module is designed to map the split short video into spatial and temporal features. The hybrid feature attention module is designed to extract the fine-grained features from the spatial and temporal features and produce the hybrid features. The residual-transformer module is designed with the combination of the attention, feed-forward network, and the residual mechanism to extract local and global features from the hybrid features. The model is tested on the HMDB51 and UCFIOI data set, and the result shows that the memory, the parameters used by the proposed model are less than those models mentioned in the literature, and it achieves better performance too.
Online Hate Detection Systems: Challenges and Action Points for Developers, Data Scientists, and Researchers

Automated online hate detection has garnered interest from various stakeholders to make online platforms safer. Despite this interest, there remain a plethora of unresolved issues that hinder advancement. We review fourteen state-of-the-art articles discussing these challenges, and present a meta-synthesis. Six themes are identified: (1) Dataset selection, (2) Detection of False Positives and Negatives, (3) Semantic Context of Hate Messages, (4) Privacy and Anonymity, (5) Ethical Considerations, and (6) Minimizing Bias. For each theme, we provide a set of action points to support researchers, data scientists, and developers to improve hate detection systems.
BUCEPHALUS: a BUsiness CEntric cybersecurity Platform for proActive anaLysis Using visual analyticS

Analyzing and mitigating the threats that cyber-attacks pose on the services of a critical infrastructure is not a trivial activity. Research solutions have been developed using data about the devices used for implementing the services, services dependencies, network topology, and the vulnerabilities that can be exploited to attack the network. However, most of the proposed solutions fail to consider these aspects in an integrated fashion, allowing the user to understand global dependencies and weaknesses. This paper contributes this issue with BUCEPHALUS, a Visual Analytics solution providing a) a visual overview of the existing relationships among business functions, devices, and vulnerabilities, and b) a what-if analysis scenario, in which the user is supported on making decisions on which vulnerabilities are more appropriate to fix. BUCEPHALUS has been developed and validated within a user-centered design process involving security professionals.
“Everything´s online, now what?!”: Knowledge Management in a Remote Working Environment

The requirements analysis process is a crucial step for any software development. In times of pandemic, with increasing social distance, the use of remote work tools and sharing information quickly and intelligently make the process even more challenging. This paper aims to present an experience report of a mature project that started before the pandemic and had to adapt to the new standard: online meetings, information sharing, team management tools and work environment simulators. In this report, we present several problems arising from remote knowledge management and we also present solutions that mitigate these problems. We also demonstrate that these solutions have shown that it is possible to build quality software, even in such difficult times.
The effects of using the agile methodology as an instructional format for software development courses

The objective of traditional software development courses focuses on competencies in the programming languages and technical tools. Project methodologies and software development are typically taught as theory-driven and separate courses in Information Systems undergraduate programs. Rather than teaching project methodologies as secondary to the learning phase of software development, these methodologies can be actively incorporated into the software development course, applying the theoretical concepts in the classroom with the same tools used in the industry for product development. This research evaluates the effects of instituting the project methodology Agile as an active learning, instructional tool for a low-code software development course using the Mendix platform to give students hands-on learning of Agile while increasing their expertise in software development. The use of Agile in an instructional pedagogical approach enhanced student learning and prepared students with skills directly applicable in the industry. Future research could be applied to measure the Agile methodology as an instructional format for technical courses other than software development.
The necessity of machine learning in clinical emergency medicine; a narrative review of the current literature

Clinical databases may be categorized by medical experts as massive records that consist of massive portions of facts. These facts can include medical health sufferers and their scientific conditions. Analyzing the quantitative and qualitative medical records further to produce relationships amongst a massive variety of samples requires the use of machine learning strategies. These strategies will unveil hidden scientific know-how in phrases of correlation and affiliation of reputedly impartial variables. This review intends to focus on the use of a predictive set of rules for the prediction of trauma sufferers on admission to the health center if the medical industry wants to are expecting the vital remedy for sufferers and furnished the vital measures for the trauma sufferers who are earlier than getting into the crucial situation. Managing large data sets is a challenge for most medical systems that involve Artificial Intelligence. This study offers an overview of machine learning applications in medical medication, and specifically the ability to respond to emergencies. The relevant, recently posted research on machine learning and scientific records with a focal went through various investigation procedures. The goal of this process was to address professionals and the challenge of such approaches in clinical medicine. The effects of this look may be utilized in the prediction of trauma patient’s reputations six hours after admission to the health center.
The Contribution of Spatial Augmented Reality to the Synergy of Special and Alternative Forms of Tourism in Greece

This research investigates whether the evolution of augmented reality contributes to the combination of specific and alternative forms of tourism in Greece. More specifically, in the first stage, information and communication technologies and the new emerging perspectives that appear over time are described. Then, talking about special and alternative forms of tourism, the authors talk about cultural, gastronomic, and educational tourism, elements of which intersect in the light of spatial augmented reality (SAR). The main purpose of the chapter is to prove that SAR excels in the synergy of the three forms of tourism mentioned. Also, a successful example of the development of SAR in the Greek tourist area is given, with the presentation of a case study of a museum in Greece. Finally, the benefits provided by the combination of special forms of tourism with SAR are presented, and it is concluded that all ages have the opportunity to actively participate in such projects using SAR, without the informal exclusion of older people.
Robust Feedback Motion Primitives for Exploration of Unknown Terrains

Unknown properties of a robot’s environment are one of the sources of uncertainty in autonomous navigation. This uncertainty has to be accounted for when modelling robot dynamics. For ground vehicles in particular, terrain structure is one of the main environmental factors that can strongly influence the dynamics. Therefore, to ensure the ability of a robot to safely and efficiently navigate new environments, robust motion planning and control systems are needed. This paper investigates a data-driven approach to planning and control based on construction of robust motion primitives (MPs) and corresponding feedback rules that ensure a bounded error along the planned trajectory. The approach is tested in an exploration scenario in which a robot systematically inspects an area consisting of several terrain types with the aim of recognizing changes in dynamical properties, learning new dynamics models when such changes are detected and recording that information for future use. The advantage of incorporating the collected data into motion planning in multi-terrain environments is illustrated via simulation.
What Does a Language-And-Vision Transformer See: The Impact of Semantic Information on Visual Representations

Neural networks have proven to be very successful in automatically capturing the composition of language and different structures across a range of multi-modal tasks. Thus, an important question to investigate is how neural networks learn and organise such structures. Numerous studies have examined the knowledge captured by language models (LSTMs, transformers) and vision architectures (CNNs, vision transformers) for respective uni-modal tasks. However, very few have explored what structures are acquired by multi-modal transformers where linguistic and visual features are combined. It is critical to understand the representations learned by each modality, their respective interplay, and the task’s effect on these representations in large-scale architectures. In this paper, we take a multi-modal transformer trained for image captioning and examine the structure of the self-attention patterns extracted from the visual stream. Our results indicate that the information about different relations between objects in the visual stream is hierarchical and varies from local to a global object-level understanding of the image. In particular, while visual representations in the first layers encode the knowledge of relations between semantically similar object detections, often constituting neighbouring objects, deeper layers expand their attention across more distant objects and learn global relations between them. We also show that globally attended objects in deeper layers can be linked with entities described in image descriptions, indicating a critical finding - the indirect effect of language on visual representations. In addition, we highlight how object-based input representations affect the structure of learned visual knowledge and guide the model towards more accurate image descriptions. A parallel question that we investigate is whether the insights from cognitive science echo the structure of representations that the current neural architecture learns. The proposed analysis of the inner workings of multi-modal transformers can be used to better understand and improve on such problems as pre-training of large-scale multi-modal architectures, multi-modal information fusion and probing of attention weights. In general, we contribute to the explainable multi-modal natural language processing and currently shallow understanding of how the input representations and the structure of the multi-modal transformer affect visual representations.
Agenda Extraction from Assembly Minutes by exploiting Relation between Questions and Answers

Obtaining agenda discussed in assembly minutes is useful for grasping their structure. In this paper, we propose a method to automatically extract a list of agenda items from given minutes. Different from the well-known topic detection, the agenda extraction is required to output the short phrases that directly express agenda items without duplication. We cast the problem as extracting the linguistic expressions directly from the minutes and clustering them to remove their duplication. We propose different clustering methods for this purpose focusing on the relationship between questions and answers in the minutes. To see the effectiveness of our methods, we also conduct the experimental evaluation by comparing the agenda items obtained by our methods with two kinds of human created agenda.
Experience of (for) science and higher education in times of the coronavirus crisis causing COVID-19 disease

The coronavirus causing COVID-19 disease, among many other effects, has re-evaluated the world of science and higher education. Experts representing the world of science have become indispensable for developing strategies to fight the pandemic, and their knowledge has become valuable not only for developing and producing an effective vaccine. Relying on modern and innovative facilities of higher education institutions, representatives of the business sector, state and local administration and science, must jointly participate in finding practical solutions to the problems of the state and society. Countries affected by the pandemic need such a system of research and higher education, in which science will serve social and economic development, university graduates will find absorbing work, and those in power will benefit from an independent expert base, regardless of which political option is at the helm of government. Given the new challenges faced by researchers and higher education in times of the coronavirus pandemic that causes COVID-19 disease and the changing economic and social conditions resulting from the pandemic, this paper examines the role and importance of the scientific world both during and after the pandemic.