Reviewerly/exHarmony huggingface dataset:
Media Education in the ICT Era: Theoretical Structure for Innovative Teaching Styles

The era of information and communication technologies (ICTs) in which we live has transformed the foundations of education. This article starts from the premise that there is a convergence between technologies and media that makes ICTs adopt strategies and forms similar to traditional media, especially in their quest to create influence on citizens. For this reason, curricular objectives should include a critical analysis of this new reality in order to train new generations. We propose, based on the traditional parameters of media education, a new theoretical framework for their development that includes innovative teaching styles to achieve these goals. We used a critical pedagogy methodology with a qualitative and descriptive approach through the analysis of the content of theoretical studies and field work through which to establish an innovative pedagogical structure. The main result is that the influence that ICTs have on children and young people today is as strong as, or stronger than, that traditionally received by the classical media, and that there is a lack of adequate framework to address the problem. In this sense, and as a conclusion, we consider that they must create critical attitudes before the power of influence that ICT has from very early ages, which generate problems like consumerism, addiction, cyber-bullying, and ignorance of the reality. This requires new teaching styles in line with the current social context.
Face Detection and Recognition Based on Visual Attention Mechanism Guidance Model in Unrestricted Posture

Performance of face detection and recognition is affected and damaged because occlusion often leads to missed detection. To reduce the recognition accuracy caused by facial occlusion and enhance the accuracy of face detection, a visual attention mechanism guidance model is proposed in this paper, which uses the visual attention mechanism to guide the model highlight the visible area of the occluded face; the face detection problem is simplified into the high-level semantic feature detection problem through the improved analytical network, and the location and scale of the face are predicted by the activation map to avoid additional parameter settings. A large number of simulation experiment results show that our proposed method is superior to other comparison algorithms for the accuracy of occlusion face detection and recognition on the face database. In addition, our proposed method achieves a better balance between detection accuracy and speed, which can be used in the field of security surveillance.
Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images

Ultrasound (US) imaging can examine human bodies of various ages; however, in the process of obtaining a US image, speckle noise is generated. The speckle noise inhibits physicians from accurately examining lesions; thus, a speckle noise removal method is essential technology. To enhance speckle noise elimination, we propose a novel algorithm using the characteristics of speckle noise and filtering methods based on speckle reducing anisotropic diffusion (SRAD) filtering, discrete wavelet transform (DWT) using symmetry characteristics, weighted guided image filtering (WGIF), and gradient domain guided image filtering (GDGIF). The SRAD filter is exploited as a preprocessing filter because it can be directly applied to a medical US image containing speckle noise without a log-compression. The wavelet domain has the advantage of suppressing the additive noise. Therefore, a homomorphic transformation is utilized to convert the multiplicative noise into additive noise. After two-level DWT decomposition is applied, to suppress the residual noise of an SRAD filtered image, GDGIF and WGIF are exploited to reduce noise from seven high-frequency sub-band images and one low-frequency sub-band image, respectively. Finally, a noise-free image is attained through inverse DWT and an exponential transform. The proposed algorithm exhibits excellent speckle noise elimination and edge conservation as compared with conventional denoising methods.
Reducing the number of unnecessary biopsies of US-BI-RADS 4a lesions through a deep learning method for residents-in-training: a cross-sectional study

Objective The aim of the study is to explore the potential value of S-Detect for residents-in-training, a computer-assisted diagnosis system based on deep learning (DL) algorithm. Methods The study was designed as a cross-sectional study. Routine breast ultrasound examinations were conducted by an experienced radiologist. The ultrasonic images of the lesions were retrospectively assessed by five residents-in-training according to the Breast Imaging Report and Data System (BI-RADS) lexicon, and a dichotomic classification of the lesions was provided by S-Detect. The diagnostic performances of S-Detect and the five residents were measured and compared using the pathological results as the gold standard. The category 4a lesions assessed by the residents were downgraded to possibly benign as classified by S-Detect. The diagnostic performance of the integrated results was compared with the original results of the residents. Participants A total of 195 focal breast lesions were consecutively enrolled, including 82 malignant lesions and 113 benign lesions. Results S-Detect presented higher specificity (77.88%) and area under the curve (AUC) (0.82) than the residents (specificity: 19.47%–48.67%, AUC: 0.62–0.74). A total of 24, 31, 38, 32 and 42 identified as BI-RADS 4a lesions by residents 1, 2, 3, 4 and 5 were downgraded to possibly benign lesions by S-Detect, respectively. Among these downgraded lesions, 24, 28, 35, 30 and 40 lesions were proven to be pathologically benign, respectively. After combining the residents' results with the results of the software in category 4a lesions, the specificity and AUC of the five residents significantly improved (specificity: 46.02%–76.11%, AUC: 0.71–0.85, p&lt;0.001). The intraclass correlation coefficient of the five residents also increased after integration (from 0.480 to 0.643). Conclusions With the help of the DL software, the specificity, overall diagnostic performance and interobserver agreement of the residents greatly improved. The software can be used as adjunctive tool for residents-in-training, downgrading 4a lesions to possibly benign and reducing unnecessary biopsies.
Answer Again: Improving VQA with Cascaded-Answering Model

Visual Question Answering (VQA) is a very challenging task, which requires to understand visual images and natural language questions simultaneously. In the open-ended VQA task, most previous solutions focus on understanding the question and image contents, as well as their correlations. However, they mostly reason the answers in a one-stage way, which results in that the generated answers are significantly ignored. In this paper, we propose a novel approach, termed Cascaded-Answering Model (CAM), which extends the conventional one-stage VQA model to a two-stage model. Hence, the proposed model can fully explore the semantics embedded in the predicted answers. Specifically, CAM is composed of two cascaded answering modules: Candidate Answer Generation (CAG) module and Final Answer Prediction (FAP) module. In CAG module, we select multiple relevant candidates from the generated answers using a typical VQA approach with Co-Attention. While in FAP module, we integrate the information of question and image, together with the semantics explored from the selected candidate answers to predict the final answer. Experimental results demonstrate that the proposed model produces high-quality candidate answers and achieves the state-of-the-art performance on five large benchmark datasets, VQA-1.0, VQA-2.0, VQA-CP v2, TDIUC and COCO-QA.
A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction

Neural models have achieved great success on machine reading comprehension (MRC), many of which typically consist of two components: an evidence extractor and an answer predictor.The former seeks the most relevant information from a reference text, while the latter is to locate or generate answers from the extracted evidence.Despite the importance of evidence labels for training the evidence extractor, they are not cheaply accessible, particularly in many non-extractive MRC tasks such as YES/NO question answering and multi-choice MRC.To address this problem, we present a Self-Training method (STM), which supervises the evidence extractor with auto-generated evidence labels in an iterative process.At each iteration, a base MRC model is trained with golden answers and noisy evidence labels.The trained model will predict pseudo evidence labels as extra supervision in the next iteration.We evaluate STM on seven datasets over three MRC tasks.Experimental results demonstrate the improvement on existing MRC models, and we also analyze how and why such a self-training method works in MRC.
Hyperspectral field spectroscopy and SENTINEL-2 Multispectral data for minerals with high pollution potential content estimation and mapping

Mining in Tunisia generates a large amount of tailings charged with toxic minerals. As these tailings have a wide spread distribution, it is important to characterize and estimate their impact on soil contamination. This study examines the potential of field hyperspectral spectroscopy and SENTINEL-2 Multispectral data in estimating and mapping seven minerals content, including three toxic minerals (fluorite, barite and sphalerite), within soils around Hammam Zriba mine in Northen Tunisia. 69 soil and dike surface samples were collected, field Visible, Near InfraRed (VNIR) and Short-Wave InfraRed (SWIR) reflectance spectra were measured on these surfaces. The X-ray diffraction (XRD) method was used to identify the types of mineral and their associated contents on each collected soil samples. The mineral contents were predicted using the partial least squares regression (PLSR) method using i) field VNIR-SWIR spectra at raw spectral resolution, ii) field VNIR-SWIR spectra aggregated to the SENTINEL-2 spectral resolution and then iii) SENTINEL-2 spectra. This study shows 1) an accurate prediction of four of the seven minerals using field VNIR-SWIR spectroscopy, 2) a slight decrease of performances due to spectral resolution degradation (SENTINEL-2 simulated spectra) and 3) a significant decrease of performances due to spatial resolution degradation, except for fluorite. This work paves the way for large-scale mapping of minerals with high pollution potential using SENTINEL-2 data. In addition, the high frequency of SENTINEL-2 data may be used to monitor the spatial distribution of some minerals with high pollution potential in soils.
Language-aware Interlingua for Multilingual Neural Machine Translation

Multilingual neural machine translation (NMT) has led to impressive accuracy improvements in low-resource scenarios by sharing common linguistic information across languages.However, the traditional multilingual model fails to capture the diversity and specificity of different languages, resulting in inferior performance compared with individual models that are sufficiently trained.In this paper, we incorporate a language-aware interlingua into the Encoder-Decoder architecture.The interlingual network enables the model to learn a language-independent representation from the semantic spaces of different languages, while still allowing for language-specific specialization of a particular language-pair.Experiments show that our proposed method achieves remarkable improvements over state-of-the-art multilingual NMT baselines and produces comparable performance with strong individual models.
AMR Parsing with Latent Structural Information

Meaning Representations (AMRs) capture sentence-level semantics structural representations to broad-coverage natural sentences.We investigate parsing AMR with explicit dependency structures and interpretable latent structures.We generate the latent soft structure without additional annotations, and fuse both dependency and latent structure via an extended graph neural networks.The fused structural information helps our experiments results to achieve the best reported results on both AMR 2.0 (77.5% Smatch F1 on LDC2017T10) and AMR 1.0 (71.8% Smatch F1 on LDC2014T12).
Hierarchical Multi-task Learning for Organization Evaluation of Argumentative Student Essays

Organization evaluation is an important dimension of automated essay scoring. This paper focuses on discourse element (i.e., functions of sentences and paragraphs) based organization evaluation. Existing approaches mostly separate discourse element identification and organization evaluation. In contrast, we propose a neural hierarchical multi-task learning approach for jointly optimizing sentence and paragraph level discourse element identification and organization evaluation. We represent the organization as a grid to simulate the visual layout of an essay and integrate discourse elements at multiple linguistic levels. Experimental results show that the multi-task learning based organization evaluation can achieve significant improvements compared with existing work and pipeline baselines. Multiple level discourse element identification also benefits from multi-task learning through mutual enhancement.
A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm

Many hard optimization problems have been efficiently solved by two notable swarm intelligence algorithms, artificial bee colony (ABC) and firefly algorithm (FA).In this paper, a collaborative hybrid algorithm based on firefly and multi-strategy artificial bee colony, abbreviated as FA-MABC, is proposed for solving single-objective optimization problems.In the proposed algorithm, FA investigates the search space globally to locate favorable regions of convergence.A novel multi-strategy ABC is employed to perform local search.The proposed algorithm incorporates a diversity measure to help in the switch criteria.The FA-MABC is tested on 40 benchmark functions with diverse complexities.Comparative results with the basic FA, ABC and other recent state-of-the-art metaheuristic algorithms demonstrate the competitive performance of the FA-MABC.
Feature-Based Explanations Don't Help People Detect Misclassifications of Online Toxicity

We present an experimental assessment of the impact of feature attribution-style explanations on human performance in predicting the consensus toxicity of social media posts with advice from an unreliable machine learning model. By doing so we add to a small but growing body of literature inspecting the utility of interpretable machine learning in terms of human outcomes. We also evaluate interpretable machine learning for the first time in the important domain of online toxicity, where fully-automated methods have faced criticism as being inadequate as a measure of toxic behavior.We find that, contrary to expectations, explanations have no significant impact on accuracy or agreement with model predictions, through they do change the distribution of subject error somewhat while reducing the cognitive burden of the task for subjects. Our results contribute to the recognition of an intriguing expectation gap in the field of interpretable machine learning between the general excitement the field has engendered and the ambiguous results of recent experimental work, including this study.
Unsupervised Moving Object Detection in Complex Scenes Using Adversarial Regularizations

Moving object detection (MOD) is a fundamental step in many high-level vision-based applications, such as human activity analysis, visual object tracking, autonomous vehicles, surveillance, and security. Most of the existing MOD algorithms observe performance degradation in the presence of complex scenes containing camouflage objects, shadows, dynamic backgrounds, and varying illumination conditions, and captured by static cameras. To appropriately handle these challenges, we propose a Generative Adversarial Network (GAN) based on a moving object detection algorithm, called MOD_GAN. In the proposed algorithm, scene-specific GANs are trained in an unsupervised MOD setting, thereby enabling the algorithm to learn generating background sequences using input from uniformly distributed random noise samples. In addition to adversarial loss, during training, norm-based loss in the image space and discriminator feature-space is also minimized between the generated images and the training data. The additional losses enable the generator to learn subtle background details, resulting in a more realistic complex scene generation. During testing, a novel back-propagation based algorithm is used to generate images with statistics similar to the test images. More appropriate random noise samples are searched by directly minimizing the loss function between the test and generated images both in the image and discriminator feature-spaces. The network is not updated in this step; only the input noise samples are iteratively modified to minimize the loss function. Moreover, motion information is used to ensure that this loss is only computed on small-motion pixels. A novel dataset containing outdoor time-lapsed images from dawn to dusk with a full illumination variation cycle is also proposed to better compare the MOD algorithms in outdoor scenes. Accordingly, extensive experiments on five benchmark datasets and comparison with 30 existing methods demonstrate the strength of the proposed algorithm.
The Symposium on Search-Based Software Engineering: Past, Present and Future

Search-Based Software Engineering (SBSE) is the research field where Software Engineering (SE) problems are modelled as search problems to be solved by search-based techniques. The Symposium on Search Based Software Engineering (SSBSE) is the premier event on SBSE, which had its 11th edition in 2019. In order to better understand the characteristics and evolution of papers published at SSBSE, this work reports results from a mapping study targeting the proceedings of all SSBSE editions. Despite the existing mapping studies on SBSE, our contribution in this work is to provide information to researchers and practitioners willing to enter the SBSE field, being a source of information to strengthen the symposium, guide new studies, and motivate new collaboration among research groups. A systematic mapping study was conducted with a set of four research questions, in which 134 studies published in all editions of SSBSE, dated from 2009 to 2019, were evaluated. In a fifth question, 32 papers published in the challenge track were summarised. Throughout the years, 290 authors from 25 countries have contributed to the main track of the symposium, with the collaboration of at least two institutions in 46.3% of the papers. SSBSE papers have got substantial external visibility, as most citations are from different venues. The SE tasks addressed by SSBSE are mostly related to software testing, software debugging, software design, and maintenance. Evolutionary algorithms are present in 75% of the papers, being the most common search technique. The evaluation of the SBSE approaches usually includes industrial systems. SSBSE has helped increase the popularity of SBSE in the SE research community and has played an important role on making SBSE mature. There are still problems and challenges to be addressed in the SBSE field, which can be tackled by SSBSE authors in further studies.
Single- versus two-parameter Fisher information in quantum interferometry

In this paper we reconsider the single-parameter quantum Fisher information (QFI) and compare it with the two-parameter one. We find simple relations connecting the single-parameter QFI (both in the asymmetric and symmetric phase shift cases) to the two-parameter Fisher matrix coefficients. Following some clarifications about the role of an external phase [Phys. Rev. A 85, 011801(R) (2012)], the single-parameter QFI and its overoptimistic predictions have been disregarded in the literature. We show in this paper that both the single- and two-parameter QFI have physical meaning, and their predicted quantum Cram\'er-Rao bounds are often attainable with the appropriate experimental setup. Moreover, we give practical situations of interest in quantum metrology, where the phase sensitivities of a number of input states approach the quantum Cram\'er-Rao bound induced by the single-parameter QFI, outperforming the two-parameter QFI.
Fusing motion patterns and key visual information for semantic event recognition in basketball videos

Many semantic events in team sport activities e.g. basketball often involve both group activities and the outcome (score or not). Motion patterns can be an effective means to identify different activities. Global and local motions have their respective emphasis on different activities, which are difficult to capture from the optical flow due to the mixture of global and local motions. Hence it calls for a more effective way to separate the global and local motions. When it comes to the specific case for basketball game analysis, the successful score for each round can be reliably detected by the appearance variation around the basket. Based on the observations, we propose a scheme to fuse global and local motion patterns (MPs) and key visual information (KVI) for semantic event recognition in basketball videos. Firstly, an algorithm is proposed to estimate the global motions from the mixed motions based on the intrinsic property of camera adjustments. And the local motions could be obtained from the mixed and global motions. Secondly, a two-stream 3D CNN framework is utilized for group activity recognition over the separated global and local motion patterns. Thirdly, the basket is detected and its appearance features are extracted through a CNN structure. The features are utilized to predict the success or failure. Finally, the group activity recognition and success/failure prediction results are integrated using the kronecker product for event recognition. Experiments on NCAA dataset demonstrate that the proposed method obtains state-of-the-art performance.