Reviewerly/exHarmony huggingface dataset:
Comparing Word Embeddings through Visualisation

Asset management is a branch of facilities management that is responsible for the operation and maintenance of assets. The most common means of managing assets and their life-cycle is through requests and work orders. A request is used to report an occurrence that is detected either by a sensory device, a technician, or non-technical personnel; they are used to pointing out that something is wrong in a given asset, and needs appropriate attention. Depending on the problem, a request can give rise to a work order if the solution is not trivial. Work orders consist in technical reports that specify the asset that needs intervention and has the details about the work to be done or, in the case that the work is unknown from the start, the characteristics of the malfunctioning. Work orders contain a set of words, free text, that are not restricted from a fixed set of vocabulary, making it difficult to automatically analyse them. In this paper, we discuss the application of modern Natural Language Processing techniques to process the work order's description, while presenting a comparison between two Word Embedding models - Word2Vec and Fasttext- through semantic similarity tests between the encoded words, and a visualisation of the vector space through dimensionality reduction of the encoded vectors. The results show a better performance of the Fasttext approach, considering the semantics of the results.
An Overview of the Design and Development for Dynamic and Physical Bar Charts

This paper aims to present the design process of dynamic data physicalization for bar charts and their variants, simple bars, stacked bars, and clustered bars. The physical artifact has 12 bars that are automatically configured according to the dataset by electromechanical components controlled by an Arduino board. The models of the printed 3D parts, the electromechanical components used and their connection scheme, the design aspects to set up the dynamic physical visualization according to the data, how the users choose the dataset and the kind of physical chart, usage scenarios with different types of charts and physicalization parameters are presented in detail. Finally, some strengths and difficulties in creating dynamic physical visualizations are highlighted and future works are proposed.
A data driven approach to mineral chemistry unveils magmatic processes associated with long-lasting, low-intensity volcanic activity

The most frequent volcanic eruptions are of low-intensity and small magnitude. They produce abundant ash-sized (< 2 mm) clasts, which are too small to establish quantitative links between magmatic processes and eruptive dynamics using classic approaches. This inhibits our ability to study the past behaviour of frequently erupting volcanoes, essential to predict their future activity and mitigate their impact. The Palizzi unit (10-13th century, Vulcano, Italy) includes a prototype sequence of ash deposits resulting from prolonged Vulcanian eruptions punctuated by those of two larger sub-Plinian events. We apply Hierarchical Clustering to chemical analyses of clinopyroxene collected along the stratigraphy to decipher magma dynamics during this eruptive period. We identify periods of magma input and we link deep magmatic processes to eruptive dynamics, also showing that our approach can be used to connect magma and eruptive dynamics in any volcanic sequence. This is essential to track the processes occurring during frequent eruptions and to identify the build-up to larger explosive events.
A Small Object Detection Algorithm Based on Modulated Deformable Convolution and Large Kernel Convolution

Object detection is one of the most critical areas in computer vision, and it plays an essential role in a variety of practice scenarios. However, small object detection has always been a key and difficult problem in the field of object detection. Therefore, considering the balance between the effectiveness and efficiency of the small object detection algorithm, this study proposes an improved YOLOX detection algorithm (BGD-YOLOX) to improve the detection effect of small objects. We present the BigGhost module, which combines the Ghost model with a modulated deformable convolution to optimize the YOLOX for greater accuracy. At the same time, it can reduce the inference time by reducing the number of parameters and the amount of computation. The experimental results show that BGD-YOLOX has a higher average accuracy rate in terms of small target detection, with mAP0.5 up to 88.3% and mAP0.95 up to 56.7%, which surpasses the most advanced object detection algorithms such as EfficientDet, CenterNet, and YOLOv4.
UAV Payload Detection Using Deep Learning and Data Augmentation

In recent years, the technology behind Unmanned Aerial Vehicles (UAVs) has continually advanced. However, with these developments, malicious activities employing UAVs have also been on the rise. Within this study, Deep Learning (DL) algorithms are utilized to detect and classify UAVs transporting payload based on the sound they release. In order to exercise DL algorithms on a set of data, a sufficient amount of audio data is necessary to obtain a more reliable result. So UAV sound recordings have been collected alongside the use of data augmentation to secure a satisfactory sample size for testing purposes. Afterward, a feature-based classification was applied to the groups of audio identifying each UAV’s payload (or lack thereof). Lastly, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Convolutional Recurrent Neural Network(CRNN) are utilized in analyzing the final data-set. They are evaluated for their abilities to correctly categorize the unloaded, one payload, and two payload of UAV classes and noise class solely through audio. As a result, MFCC showed the best performance in CNN, RNN, and CRNN, which are 0.9493, 0.8133, and 0.9174 accuracies. Our contribution to this study is that a cost-efficient data collection method was applied by utilizing laptop microphones. Moreover, DL technology was used in UAV payload detection, whereas neural network was used in prior study. Also, the best feature for UAV payload detection with the three DL technologies was found. The limitation of the paper is that only two UAV models and one kind of payload were used to collect data. Diverse UAVs and payload are expected to be used to collect data in future works.
Real-world human gender classification from oral region using convolutional neural netwrok

Gender classification is an important biometric task. It has been widely studied in the literature. Face modality is the most studied aspect of human-gender classification. Moreover, the task has also been investigated in terms of different face components such as irises, ears, and the periocular region. In this paper, we aim to investigate gender classification based on the oral region. In the proposed approach, we adopt a convolutional neural network. For experimentation, we extracted the region of interest using the RetinaFace algorithm from the FFHQ faces dataset. We achieved acceptable results, surpassing those that use the mouth as a modality or facial sub-region in geometric approaches. The obtained results also proclaim the importance of the oral region as a facial part lost in the Covid-19 context when people wear facial mask. We suppose that the adaptation of existing facial data analysis solutions from the whole face is indispensable to keep-up their robustness.
Bounding System-Induced Biases in Recommender Systems with a Randomized Dataset

Debiased recommendation with a randomized dataset has shown very promising results in mitigating system-induced biases. However, it still lacks more theoretical insights or an ideal optimization objective function compared with the other more well-studied routes without a randomized dataset. To bridge this gap, we study the debiasing problem from a new perspective and propose to directly minimize the upper bound of an ideal objective function, which facilitates a better potential solution to system-induced biases. First, we formulate a new ideal optimization objective function with a randomized dataset. Second, according to the prior constraints that an adopted loss function may satisfy, we derive two different upper bounds of the objective function: a generalization error bound with triangle inequality and a generalization error bound with separability. Third, we show that most existing related methods can be regarded as the insufficient optimization of these two upper bounds. Fourth, we propose a novel method called debiasing approximate upper bound ( DUB ) with a randomized dataset, which achieves a more sufficient optimization of these upper bounds. Finally, we conduct extensive experiments on a public dataset and a real product dataset to verify the effectiveness of our DUB.
Mortality prediction among ICU inpatients based on MIMIC-III database results from the conditional medical generative adversarial network

Improved mortality prediction among intensive care unit (ICU) inpatients is a valuable and challenging task. Limited clinical data, especially with appropriate labels, are an important element restricting accurate predictions. Generative adversarial networks (GANs) are excellent generative models and have shown great potential for data simulation. However, there have been no relevant studies using GANs to predict mortality among ICU inpatients. In this study, we aim to evaluate the predictive performance of a variant of GAN called conditional medical GAN (c-med GAN) compared with some baseline models, including simplified acute physiology score II (SAPS II), support vector machine (SVM), and multilayer perceptron (MLP).Data from a publicly available intensive care database, the Medical Information Mart for Intensive Care III (MIMIC-III) database (v1.4), were included in this study. The area under the precision-recall curve (PR-AUC), area under the receiver operating characteristic curve (ROC-AUC), and F1 score were used to evaluate the predictive performance. In addition, the size of the dataset was artificially reduced, and the performance of the c-med GAN was compared in different size datasets.The results showed that c-med GAN achieves the best PR-AUC, ROC-AUC, and F1 score compared with SAPS II, SVM, and MLP when training in the full MIMIC-III dataset. When the size of the dataset was reduced, the prediction performances of both MLP and c-med GAN were affected. However, the c-med GAN still outperformed MLP on smaller datasets and had less degradation.The prediction of in-hospital mortality based on the c-med GAN for ICU patients showed better performance than the baseline models. Despite some inadequacies, this model may have a promising future in clinical applications which will be explored by further research.
Long-term robot manipulation task planning with scene graph and semantic knowledge

Purpose Autonomous robots must be able to understand long-term manipulation tasks described by humans and perform task analysis and planning based on the current environment in a variety of scenes, such as daily manipulation and industrial assembly. However, both classical task and motion planning algorithms and single data-driven learning planning methods have limitations in practicability, generalization and interpretability. The purpose of this work is to overcome the limitations of the above methods and achieve generalized and explicable long-term robot manipulation task planning. Design/methodology/approach The authors propose a planning method for long-term manipulation tasks that combines the advantages of existing methods and the prior cognition brought by the knowledge graph. This method integrates visual semantic understanding based on scene graph generation, regression planning based on deep learning and multi-level representation and updating based on a knowledge base. Findings The authors evaluated the capability of this method in a kitchen cooking task and tabletop arrangement task in simulation and real-world environments. Experimental results show that the proposed method has a significantly improved success rate compared with the baselines and has excellent generalization performance for new tasks. Originality/value The authors demonstrate that their method is scalable to long-term manipulation tasks with varying complexity and visibility. This advantage allows their method to perform better in new manipulation tasks. The planning method proposed in this work is meaningful for the present robot manipulation task and can be intuitive for similar high-level robot planning.
ESMAANI: A Static and Dynamic Arabic Sign Language Recognition System Based on Machine and Deep Learning Models

As the size of the population of sign language users increased, the importance of breaking the barrier between those who can use sign language and those who can not in the Arabic community increased. In this paper, We present ESMAANI, a computational solution that enables sign language recognition while utilizing machine learning and deep learning techniques. The proposed system aims to contribute to the study of the challenges and complexities associated with sign language recognition, specifically Arabic sign language. The proposed models present a non-intrusive computer vision approach to building a system specialized in Arabic sign language recognition translating the input sign gestures from a camera stream or video input into text output. Supporting static sign language input, which is common in fingerspelling and alphabet representation and dynamic sign language input which is employed for signing at the word level. The paper also presents a person and environment-independent dataset that's capable of generalizing to include further the various versions of ArSL the proposed static sign recognition system achieved an overall accuracy of 99.7%. And For the proposed dynamic sign recognition system achieved maximum recognition validation accuracy of 97% suggesting strong generalization.
S3QLRDF: distributed SPARQL query processing using Apache Spark—a comparative performance study

The proliferation of semantic data in the form of Resource Description Framework (RDF) triples demands an efficient, scalable, and distributed storage along with a highly available and fault-tolerant parallel processing strategy. There are three open issues with distributed RDF data management systems that are not well addressed altogether in existing work. First is the querying efficiency, second is that solutions are optimized for certain types of query patterns and don’t necessarily work well for all types, and third is concerned with reducing pre-processing cost. More precisely, the rapid growth of RDF data raises the need for an efficient partitioning strategy over distributed data management systems to improve SPARQL (SPARQL Protocol and RDF Query Language) query performance regardless of its pattern shape with minimized pre-processing time. In distributed RDF systems, both the data and the query processing are highly distributed. On the other hand, SPARQL workloads are dynamic and structurally diverse that can have different degrees of complexity. A complex SPARQL query over a large RDF graph in distributed systems requires combining a lot of distributed pieces of data through join operations. Therefore, designing an efficient data-partitioning schema and join strategy to minimize data transfer is the fundamental challenge in distributed RDF data management systems. In this context, we propose a new relational partitioning schema called Property Table Partitioning (PTP) for RDF data, that further partitions existing Property Table into multiple tables based on distinct properties (comprising of all subjects with non-null values for those distinct properties) in order to minimize input size and number of join operations of a query. This paper proposed a distributed RDF data management system called S3QLRDF, which is built on top of Spark and utilizes SQL to execute SPARQL queries over PTP schema. The experimental analysis with respect to preprocessing costs and query performance, using synthetic and real datasets shows that S3QLRDF outperforms state-of-the-art distributed RDF management systems.
OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue

Abstract This paper presents an ontology-aware pretrained language model (OPAL) for end-to-end task-oriented dialogue (TOD). Unlike chit-chat dialogue models, task-oriented dialogue models fulfill at least two task-specific modules: Dialogue state tracker (DST) and response generator (RG). The dialogue state consists of the domain-slot-value triples, which are regarded as the user’s constraints to search the domain-related databases. The large-scale task-oriented dialogue data with the annotated structured dialogue state usually are inaccessible. It prevents the development of the pretrained language model for the task-oriented dialogue. We propose a simple yet effective pretraining method to alleviate this problem, which consists of two pretraining phases. The first phase is to pretrain on large-scale contextual text data, where the structured information of the text is extracted by the information extracting tool. To bridge the gap between the pretraining method and downstream tasks, we design two pretraining tasks: ontology-like triple recovery and next-text generation, which simulates the DST and RG, respectively. The second phase is to fine-tune the pretrained model on the TOD data. The experimental results show that our proposed method achieves an exciting boost and obtains competitive performance even without any TOD data on CamRest676 and MultiWOZ benchmarks.
Performability Requirements in Making a Rescaling Decision for Streaming Applications

Maximising the benefits of auto-scaling is difficult due to challenges associated with precisely estimating resource usage in the face of significant variability in client workload patterns and trends. Most auto-scaling systems do not consider the impact of scaling time and often assume that application state sizes and offered load remain unchanged during the scaling interval. Long scaling durations, especially in rapidly varying workload environments, pose a challenge to auto-scaling, exacerbating the impact of suboptimal parallelism choices. In this paper, we empirically evaluate application state size and end-to-end checkpoint duration and identify their correlation with the duration of scaling procedures. We then develop predictive models to provide future autoscalers with further intelligence to inform scaling decisions.
Medical image blind super‐resolution based on improved degradation process

Abstract Clinical diagnosis has high requirements for the resolution of medical images, but most existing medical images super‐ resolution (SR) methods are performed under a known or specific degradation kernel. However, the difference between the actual degradations and their assumed degradation kernels results in a severe performance drop for the advanced SR methods in real applications. This paper proposes a medical image blind super‐resolution model (Med‐BSR) based on an improved degradation process to handle this issue. The model makes each of the degradation factors in medical image blind SR, such as blur, noise, and downsampling, more complex and practical. Specifically, the authors use the random select/combine strategy to randomly arrange and combine the type and order of each degradation factor, which significantly expands the degradation space. The authors also improved the loss function of the primary enhanced super‐resolution generative adversarial networks (ESRGAN) network. The extensive experimental results demonstrate that the authors’ designed model can accurately restore the natural degradation process, which can reconstruct high‐quality SR medical images. It also has a good generalization ability to realistic images simultaneously.
The transformation education concept in the digital ecosystem of a territorial production cluster

A new approach to the formation of distributed digital educational ecosystems in territorial production clusters is proposed. This can not only eliminate the shortcomings of the traditional education system, but also create a promising platform for a new modern educational technology. The basis for building such an ecosystem are modern achievements in the field of control systems, digitalization and artificial intelligence. Currently, the education market is seriously influenced by scientific results in the field of artificial intelligence and the rapid development of digital technologies. As practice has shown, it is necessary to take into account quite unpredictable natural disasters and pandemics, as well as the complex geopolitical and economic situation in the world. In these conditions, one of the few serious opportunities for universities to maintain and strengthen their positions in the education market, which will change rapidly in the near future, is the transformation of processes within the framework of new technological trends based on digital platforms of industrial cluster ecosystems. The digital twin can be used both for real-time decision-making and for forecasting and planning the process for the entire training period. The university and the enterprises of the territorial cluster that provide part of the educational services under this approach are integrated into a single mechanism for solving the tasks of flexible individual training in accordance with the needs of the cluster in highly qualified personnel. Within the framework of the proposed approach, it is possible to build a hybrid educational university environment integrated with the real objects of the territory’s economy, which in this case becomes the most important component of the digital educational ecosystem. The proposed concept makes it possible to predict and plan the training of the right specialists, since the entire scheme of the educational process in this case is closely connected with the enterprises of the real sector for the entire period of training. This becomes possible due to the fact that training takes place according to flexible, updated programs that reflect the constantly changing requirements of enterprises to the competencies of employees.
A Multi-Module Machine Learning Approach to Detect Tax Fraud

Tax fraud is one of the substantial issues affecting governments around the world.It is defined as the intentional alteration of information provided on a tax return to reduce someone's tax liability.This is done by either reducing sales or increasing purchases.According to recent studies, governments lose over $500 billion annually due to tax fraud.A loss of this magnitude motivates tax authorities worldwide to implement efficient fraud detection strategies.Most of the work done in tax fraud using machine learning is centered on supervised models.A significant drawback of this approach is that it requires tax returns that have been previously audited, which constitutes a small percentage of the data.Other strategies focus on using unsupervised models that utilize the whole data when they search for patterns, though ignore whether the tax returns are fraudulent or not.Therefore, unsupervised models are limited in their usefulness if they are used independently to detect tax fraud.The work done in this paper focuses on addressing such limitations by proposing a fraud detection framework that utilizes supervised and unsupervised models to exploit the entire set of tax returns.The framework consists of four modules: A supervised module, which utilizes a tree-based model to extract knowledge from the data; an unsupervised module, which calculates anomaly scores; a behavioral module, which assigns a compliance score for each taxpayer; and a prediction module, which utilizes the output of the previous modules to output a probability of fraud for each tax return.We demonstrate the effectiveness of our framework by testing it on existent tax returns provided by the Saudi tax authority.