Reviewerly/exHarmony huggingface dataset:
SAR: Single-Stage Anchor-Free Rotating Object Detection

As object detection is widely adopted in aerial images, scene texts and other fields, rotating object detection plays an important role and draws attention since it can provide highly accurate orientation and scale information. In this article, we propose a novel and simple baseline to effectively conduct rotating object detection. First, we design a brand-new representation for rotating objects by using a circle cut horizontal rectangle (CCH). The CCH ensures that the regression parameters will not exceed the defined domain and avoids vertex sorting, thus solving some problems in current common representations, including the boundary problem and order problem, and improving the robustness. Second, we design a lightweight head based on the CCH to add the rotating regression to classic benchmark in an almost cost-free manner and propose a single-stage anchor-free rotating (SAR) object detection convolutional neural network. Finally, we demonstrate the details of our method by applying it to data sets with different scenarios. The experiments confirm that our method achieves competitive accuracy and state-of-the-art speed in aerial image and scene text detection.
Real-Time Trajectory Planning Method Based On N-Order Curve Optimization

In recent years, many functionalities were developed for Automated Vehicles (AVs) and some of them with close-to-market prototypes. A required topic is the generation of continuous trajectories that reduces the amount of discrete and pre-coded instructions while leading the vehicle safely. Consequently, this work presents a novel real-time trajectory planning approach based on numerical optimization of n-order Bézier curves and lane-based information. The generation of a feasible trajectory considers the vehicle dimension while driving into a lane-corridor. The nonlinear optimization problem was solved with the Bound Optimization BY Quadratic Approximation method (BOBYQA), and it uses the passengers' comfort, safety, and vehicle dynamics as constraints of the problem. The solution is validated in a simulation environment using a bus with a length of 12 meters. Moreover, the validation considered the roundabouts due to its complexity, nevertheless, the solution is scalable to other scenarios.
When to Use OCR Post-correction for Named Entity Recognition?

In the last decades, a huge number of documents has been digitised, before undergoing optical character recognition (OCR) to extract their textual content. This step is crucial for indexing the documents and to make the resulting collections accessible. However, the fact that documents are indexed through their OCRed content is posing a number of problems, due to the varying performance of OCR methods over time. Indeed, OCR quality has a considerable impact on the indexing and therefore the accessibility of digital documents. Named entities are among the most adequate information to index documents, in particular in the case of digital libraries, for which log analysis studies have shown that around 80% of user queries include a named entity. Taking full advantage of the computational power of modern natural language processing (NLP) systems, named entity recognition (NER) can be operated over enormous OCR corpora efficiently. Despite progress in OCR, resulting text files still have misrecognised words (or noise for short) which are harming NER performance. In this paper, to handle this challenge, we apply a spelling correction method to noisy versions of a corpus with variable OCR error rates in order to quantitatively estimate the contribution of post-OCR correction to NER. Our main finding is that we can indeed consistently improve the performance of NER when the OCR quality is reasonable (error rates respectively between 2% and 10% for characters (CER) and between 10% and 25% for words (WER)). The noise correction algorithm we propose is both language-independent and with low complexity.
Portuguese word embeddings for the oil and gas industry: Development and evaluation

Over the last decades, oil and gas companies have been facing a continuous increase of data collected in unstructured textual format. New disruptive technologies, such as natural language processing and machine learning, present an unprecedented opportunity to extract a wealth of valuable information within these documents. Word embedding models are one of the most fundamental units of natural language processing, enabling machine learning algorithms to achieve great generalization capabilities by providing meaningful representations of words, being able to capture syntactic and semantic features based on their context. However, the oil and gas domain-specific vocabulary represents a challenge to those algorithms, in which words may assume a completely different meaning from a common understanding. The Brazilian pre-salt is an important exploratory frontier for the oil and gas industry, with increasing attractiveness for international investments in exploration and production projects, and most of its documentation is in Portuguese. Moreover, Portuguese is one of the largest languages in terms of number of native speakers. Nonetheless, despite the importance of the petroleum sector of Portuguese speaking countries, specialized public corpora in this domain are scarce. This work proposes PetroVec, a representative set of word embedding models for the specific domain of oil and gas in Portuguese. We gathered an extensive collection of domain-related documents from leading institutions to build a large specialized oil and gas corpus in Portuguese, comprising more than 85 million tokens. To provide an intrinsic evaluation, assessing how well the models can encode domain semantics from the text, we created a semantic relatedness test set, comprising 1,500 word pairs labeled by selected experts in geoscience and petroleum engineering from both academia and industry. In addition, we performed an extrinsic quantitative evaluation on a downstream task of named entity recognition in geoscience, plus a set of qualitative analyses, and conducted a comparative evaluation against a public general-domain embedding model. The obtained results suggest that our domain-specific models outperformed the general model on their ability to represent specialized terminology. To the best of our knowledge, this is the first attempt to generate and evaluate word embedding models for the oil and gas domain in Portuguese. Finally, all the resources developed by this work are made available for public use, including the pre-trained specialized models, corpora, and validation datasets.
The Orthogonal Wavelets in the Frequency Domain Used for the Images Filtering

At present, discrete wavelet transform (Mallat algorithm) is used for signal decomposition and reconstruction.Discrete wavelets are asymmetrical, not smooth functions and do not allow decomposition of signals with a multiplicity of less than two, which limits the number of decomposition levels.Continuous wavelet transform has a number of positive properties (symmetry, smoothness of the basis function) which are necessary for signal analysis and synthesis.The article proposes algorithms for calculating direct and inverse continuous wavelet transforms in the frequency domain, which allows decomposing, reconstructing and filtering the image with high speed and accuracy.It is established that application of fast Fourier transform reduces the conversion time by four orders of magnitude in compared to direct numerical integration.The results of applying algorithms to the images obtained with an optical microscope are presented.Orthogonal symmetric and anti-symmetric wavelets with rectangular amplitude frequency response are also presented.It is shown that these firstly designed wavelets allow one to reconstruct the signal even faster than the algorithms created using fast Fourier transform.Continuous wavelet transform has been found to allow multiscale analysis of signals with a multiplicity of less than two.In addition, the construction of orthogonal wavelets in the frequency domain with the maximum possible number of zero moments allows one to analyze the finer (high-frequency) structure of the signal, as well as to suppress its slowly changing components, which makes it possible to concentrate energy in a few significant coefficients, which is a prerequisite for compression.
A Novice-Reviewer Experiment to Address Scarcity of Qualified Reviewers in Large Conferences

Conference peer review constitutes a human-computation process whose importance cannot be overstated: not only it identifies the best submissions for acceptance, but, ultimately, it impacts the future of the whole research area by promoting some ideas and restraining others. A surge in the number of submissions received by leading AI conferences has challenged the sustainability of the review process by increasing the burden on the pool of qualified reviewers which is growing at a much slower rate. In this work, we consider the problem of reviewer recruiting with a focus on the scarcity of qualified reviewers in large conferences. Specifically, we design a procedure for (i) recruiting reviewers from the population not typically covered by major conferences and (ii) guiding them through the reviewing pipeline. In conjunction with the ICML 2020 --- a large, top-tier machine learning conference --- we recruit a small set of reviewers through our procedure and compare their performance with the general population of ICML reviewers. Our experiment reveals that a combination of the recruiting and guiding mechanisms allows for a principled enhancement of the reviewer pool and results in reviews of superior quality compared to the conventional pool of reviews as evaluated by senior members of the program committee (meta-reviewers).
DeepTrigger: A Watermarking Scheme of Deep Learning Models Based on Chaotic Automatic Data Annotation

With the rapid development of artificial intelligence, the intellectual property protection of deep learning models appeals widespread concerns of scientists and engineers. The black-box watermarking protection scheme has been favored by many scholars due to its many advantages. The trigger set containing data content and data annotation is the key of black-box watermarking technology. However, most of the trigger sets in literates were constructed by comprehensible features, such as Gaussian noise and badges on original data content. Then, the attacks based on machine learning can obtain the watermarking features and generate fake trigger set. Therefore, fraudulent ownership claim attacks may occur. In this paper, we turn our attention to data annotation and propose a black-box watermarking scheme based on chaotic automatic data annotation. Chaos has superior features, such as the sensitivity of initial value, aperiodic behavior and unpredictability of the chaotic sequence. We applies these chaotic features on data annotation so as to against the fraudulent ownership claim attacks. Firstly, this scheme applies chaotic automatic data annotation, which is time-saving and non-manual labeling. Secondly, chaotic sequences are unpredictable for long-terms, which can break the principle of empirical or statistical machine learning based attacks when chaotic labeling the trigger samples. Thirdly, the initial value and parameters in chaos offer a large range of key space, which can facilitate the commercialization of the intelligent models. The key formulation also guarantees the separation of the secret key and the trigger set. In addition, experiments and simulations show that the scheme is effective, secure and robust. It can resist fine-tuning attacks, compression attacks, fraudulent ownership claim attacks and overwriting attacks.
Masked Face Recognition Using Deep Metric Learning and FaceMaskNet-21

The coronavirus disease 2019 (COVID-19) has made it mandatory for people all over the world to wear facial masks to prevent the spread of the virus. As the mask hides most of the face, even humans find it difficult to identify a known face. Removing the mask can risk the health of people as it can lead to the transmission of the virus. The conventional face recognition systems used for security purposes have become incapable in the current situation because, the face mask covers most of the important facial features such as nose, mouth, etc. making it very difficult to recognize the person. The efficacy of a face recognition system is affected when trying to identify a masked face. We have proposed a system that uses the deep metric learning technique using our own FaceMaskNet-21 deep learning network to produce 128-d encodings that help in the face recognition process from static images, live video streams, as well as, video files. The HOG technique is used to enable faster recognition of faces with a mask. We achieved an accuracy of 88.92\% with an execution time of less than 10 milliseconds. The ability of the system to perform in real-time makes it suitable to recognize people in CCTV footage in malls, banks, ATMs, etc. Our system, due to its fast performance the system can be used in schools and colleges for attendance as well as in banks and other high-security zones to grant access to only authorized ones.
Dive Deeper into Box for Object Detection

Anchor free methods have defined the new frontier in state-of-the-art object detection researches where accurate bounding box estimation is the key to the success of these methods. However, even the bounding box has the highest confidence score, it is still far from perfect at localization. To this end, we propose a box reorganization method (DDBNet), which can dive deeper into the box for more accurate localization. At the first step, drifted boxes are filtered out because the contents in these boxes are inconsistent with target semantics. Next, the selected boxes are broken into boundaries, and the well-aligned boundaries are searched and grouped into a sort of optimal boxes toward tightening instances more precisely. Experimental results show that our method is effective which leads to state-of-the-art performance for object detection.
Deeply Recursive Low- and High-Frequency Fusing Networks for Single Image Super-Resolution

With the development of researches on single image super-resolution (SISR) based on convolutional neural networks (CNN), the quality of recovered images has been remarkably promoted. Since then, many deep learning-based models have been proposed, which have outperformed the traditional SISR algorithms. According to the results of extensive experiments, the feature representations of the model can be enhanced by increasing the depth and width of the network, which can ultimately improve the image reconstruction quality. However, a larger network generally consumes more computational and memory resources, making it difficult to train the network and increasing the prediction time. In view of the above problems, a novel deeply-recursive low- and high-frequency fusing network (DRFFN) for SISR tasks is proposed in this paper, which adopts the structure of parallel branches to extract the low- and high-frequency information of the image, respectively. The different complexities of the branches can reflect the frequency characteristic of the diverse image information. Moreover, an effective channel-wise attention mechanism based on variance (VCA) is designed to make the information distribution of each feature map more reasonably with different variances. Owing to model structure (i.e., cascading recursive learning of recursive units), DRFFN and DRFFN-L are very compact, where the weights are shared by all convolutional recursions. Comprehensive benchmark evaluations in standard benchmark datasets well demonstrate that DRFFN outperforms the most existing models and has achieved competitive, quantitative, and visual results.
Processing employees’ personal data during the Covid-19 pandemic

While needing to ensure the health and safety of their employees during the Covid-19 pandemic, employers face many burning data protection questions, including under what conditions they can process employees’ personal data (in particular health data) and whether gathering personal data concerning employees’ medical history, trips and contacts with infected persons, is allowed. This article focuses on issues that are problematic, based on the analysis of guidance issued by the European Data Protection Board, as well as national data protection authorities and practitioners from 20 countries in response to these concerns. The first section of the article analyses concepts of personal data and health data in the context of Covid-19. Then the article proceeds with exploring what possible legal bases employers can use to process employees’ personal data in general, and health data in particular, under the General Data Protection Regulation when applying different measures to combat Covid-19. In the latter part of the article two practical questions raised by employers – concerning the checking of employees’ body temperatures and informing them of possible infection – are discussed. The analysis indicates that national data protection authorities seem to look for a reasonable and pragmatic approach regarding compliance with the GDPR in light of the Covid-19 emergency. However, their guidance differs in several areas and the views in between nation states are not always aligned. A more specific, clear and uniform pan-European vision concerning the processing of employees’ data in times of emergency is needed to better protect employees and limit the spread of the virus.
An Adaptive Learning Mode of “Public Psychology” Based on Creative Thinking with Virtual Simulation Technology

Due to the outbreak of COVID-19, the teaching of psychology in college has become especially important, posting higher requirements on the teaching content and purpose of the course of psychology. Nowadays, the traditional course of public psychology in college only focuses on the prevention and correction of psychological problems among college students, but educators ignore the importance of teaching students to understand psychological problems through relevant courses, making it impossible to achieve the expected effect of mental health education for students. Colleges should apply the notion of positive mental health education in the course of public psychology designed for college students. Students can better adapt to the society only when they have sound mental quality. According to requirement of “the zone of proximal development” a conceptual framework of the course was established in this study based on adaptation theory in which students are encouraged to think independently, as required by and an autonomous learning situation was created for students with psychological problems. At the same time, the education and teaching programming software named Scratch was adopted and integrated in the virtual simulated experimental teaching system of psychology. The integrated teaching system included the management of psychological teaching software, the monitoring of psychological experimental process, the query and analysis of psychological experimental data and the management of academic experimental resources, forming an integrated teaching environment with adaptive learning functions. In the end, the ant colony algorithm was used to mine a learning mode that is commensurate with the learner’s knowledge level, meets the learner’s resource preferences and satisfies the learner’s needs for learning activities, and this learning mode was identified as the best application mode for the learning of public psychology. It was revealed in practice that the adaptive learning mode of psychology based on virtual simulation technology can improve students’ learning initiative for the course of public psychology and increase their independent innovation ability.
Education Transformation Using Block Chain Technology - A Student Centric Model

The Education sector is undergoing transformation-using technology. The virtual classrooms are replacing the traditional classrooms. The proposed model envisaged is student-centric, where the student has the choice to model his curriculum depending on the student interests and area to work and not follow the usual model, using credits from micro-credentials added per unit. The challenge is to use the features, benefits of Blockchain to introduce this new Education model technology to improve efficiency by reducing cost and improving accountability. The transformation in Education Framework can revolutionize the future Learning, teaching Industry to reduce cost and time. This revolution will also lead to improved chances for Learners to be employable. The Research paper proposes to use the Modified ADKAR Change Management Model to validate this Research study and will be a significant contribution to the research topic and the Theory known.
On the Heterogeneity of Existing Repositories of Movements Intended for the Evaluation of Fall Detection Systems

Due to the serious impact of falls on the autonomy and health of older people, the investigation of wearable alerting systems for the automatic detection of falls has gained considerable scientific interest in the field of body telemonitoring with wireless sensors. Because of the difficulties of systematically validating these systems in a real application scenario, Fall Detection Systems (FDSs) are typically evaluated by studying their response to datasets containing inertial sensor measurements captured during the execution of labelled nonfall and fall movements. In this context, during the last decade, numerous publicly accessible databases have been released aiming at offering a common benchmarking tool for the validation of the new proposals on FDSs. This work offers a comparative and updated analysis of these existing repositories. For this purpose, the samples contained in the datasets are characterized by different statistics that model diverse aspects of the mobility of the human body in the time interval where the greatest change in the acceleration module is identified. By using one-way analysis of variance (ANOVA) on the series of these features, the comparison shows the significant differences detected between the datasets, even when comparing activities that require a similar degree of physical effort. This heterogeneity, which may result from the great variability of the sensors, experimental users, and testbeds employed to generate the datasets, is relevant because it casts doubt on the validity of the conclusions of many studies on FDSs, since most of the proposals in the literature are only evaluated using a single database.
Twitter Bot Detection with Reduced Feature Set

Online social networks provide a novel channel to allow interaction between human beings. Its success has attracted interest in attacking and exploiting them through a wide range of unethical activities, such as malicious actions to manipulate users. One of the methods to carry out these abuses is the use of bots on Twitter. Recent examples of bots influencing public opinion in the election process demonstrate their potential harm to the democratic world. Such malicious behavior needs to be checked and its effects should be diminished. Recently, machine learning (ML) classifiers to distinguish between real and bot accounts have proven advances. Thus, in this work four ML algorithms were tested using a public dataset and a few expressive features based on simple user profile counters for the classification of bots on Twitter. We measured their performance compared to one state-of-the-art bot detection work. The classifier accuracy was considered homogeneous with a mean of 0.8549 and 0.1889 of standard deviation. Besides, all multiclass classifiers obtained AUCs greater than 0.9 indicating a practical benefit for bot detection on Twitter.