Reviewerly/exHarmony huggingface dataset:
Architecture of a Data Portal for Publishing and Delivering Open Data for Atmospheric Measurement

Atmospheric data are collected by researchers every day. Campaigns such as GOAmazon 2014/2015 and the Amazon Tall Tower Observatory collect essential data on aerosols, gases, cloud properties, and meteorological parameters in the Brazilian Amazon basin. These data products provide insights and essential information for analyzing and predicting natural processes. However, in Brazil, it is estimated that more than 80% of the scientific data collected are not published due to the lack of web portals that collect and store these data. This makes it difficult, or even impossible, to access and integrate the data, which can result in the loss of significant amounts of information and significantly affect the understanding of the overall data. To address this problem, we propose a data portal architecture and open data deployment that enable Big Data processing, human interaction, and download-oriented approaches with tools that help users catalog, publish and visualize atmospheric data. Thus, we describe the architecture developed, based on the experience of the Atmospheric Radiation Measurement Data Center, which incorporates the principles of FAIR, the infrastructure and content management system for managing scientific data. The portal partial results were tested with environmental data from contaminated areas at the University of São Paulo. Overall, this data portal creates more shared knowledge about atmospheric processes by providing users with access to open environmental data.
A Machine Learning Based Approach to Analyze Food Reviews from Bengali Text

Nowadays, people share their opinion about restaurants and food items on different online platforms. Before visiting any restaurant people often read the reviews about the food items. Again, the food industry measures the satisfaction level of consumers through these reviews and tries to improve the food quality according to the demand. However, it’s a difficult task to manually go through the reviews. To make the task easier analyzing the reviews automatically would be a great achievement. Although there exists a good number of research works to analyze food reviews written in the English language. Unfortunately, very few focuses on food review analysis in Bangla language. Because of this unstructured research works in this field, we explore six different machine learning algorithms to classify food reviews from Bangla text into three categories i.e. good, neutral and bad. We collect almost 4000 food reviews from different online sites. Among them, 80% data is used for training and 20% is used for the testing purpose. To extract the feature two different feature extraction techniques Term Frequency – Inverse Document Frequency (TF-IDF) and CountVectorizer (CV) are used using unigram, bigram and tri-gram models. Our experimental results reveal that SGD classifier can reach up to 93% accuracy on bi-gram features that are extracted using TF-IDF technique.
Access Security Module of the Medical Data Management System

It is essential to pay attention to secure access to systems in today's online world. When designing access to medical data management systems, knowing who is accessing the data and ensuring access only to authorized users is necessary. At the same time, these services must be uniform for several services and technologies, which would make it easier for users of the services to access them. When designing similar services, it is necessary to decide all the risks of third-party verification and the benefits of your solution with a unique approach. As part of the medical system design with micro-services, it was necessary to resolve the authorization and the authentication of access to individual modules or data. Each service can have a different level of security, which needs to be constantly verified when accessing the service. In this work, we present the solution of access using web tokens and authentication using a third-party service - a Microsoft account, which becomes universal access to services in academic conditions and allows simplification of access to various systems.
An Efficient Deep Learning based Hybrid Model for Image Caption Generation

In the recent yeas, with the increase in the use of different social media platforms, image captioning approach play a major role in automatically describe the whole image into natural language sentence.Image captioning plays a significant role in computer-based society.Image captioning is the process of automatically generating the natural language textual description of the image using artificial intelligence techniques.Computer vision and natural language processing are the key aspect of the image processing system.Convolutional Neural Network (CNN) is a part of computer vision and used object detection and feature extraction and on the other side Natural Language Processing (NLP) techniques help in generating the textual caption of the image.Generating suitable image description by machine is challenging task as it is based upon object detection, location and their semantic relationships in a human understandable language such as English.In this paper our aim to develop an encoder-decoder based hybrid image captioning approach using VGG16, ResNet50 and YOLO.VGG16 and ResNet50 are the pre-trained feature extraction model which are trained on millions of images.YOLO is used for real time object detection.It first extracts the image features using VGG16, ResNet50 and YOLO and concatenate the result in to single file.At last LSTM and BiGRU are used for textual description of the image.Proposed model is evaluated by using BLEU, METEOR and RUGE score.
Indefinite Causal Order for Quantum Phase Estimation with Pauli Noise

This paper further explores the recent scheme of switched quantum channels with indefinite causal order applied to the reference metrological task of quantum phase estimation in the presence of noise. We especially extend the explorations, previously reported with depolarizing noise and thermal noise, to the class of Pauli noises, important to the qubit and not previously addressed. Nonstandard capabilities, not accessible with standard quantum phase estimation, are exhibited and analyzed, with significant properties that are specific to the Pauli noises, while other properties are found in common with the depolarizing noise or the thermal noise. The results show that the presence and the type of quantum noise are both crucial to the determination of the nonstandard capabilities from the switched channel with indefinite causal order, with a constructive action of noise reminiscent of stochastic resonance phenomena. The study contributes to a more comprehensive and systematic characterization of the roles and specificities of quantum noise in the operation of the novel devices of switched quantum channels with indefinite causal order.
A Lightweight Deep Learning Model for Real-time Detection and Recognition of Traffic Signs Images Based on YOLOv5

The rapid and accurate identification of various road traffic signs is an important research topic in automotive vision systems. Specifically, the correct identification of road signs is an urgent problem requiring effective solutions to facilitate automatic driving. This paper proposes a new approach, PP-LCNet-P2-CT, for the detection and recognition of urban road signs in an automotive vision system using an improved YOLOv5 deep learning model. The main improvement of the PP-LCNet-P2-CT model includes the following: (1) Replacing the YOLOv5 backbone network with the lightweight network PP-LCNet to improve the real-time performance of the detection network; (2) Adding a small target detection head to the detection head to meet the needs of target detection with different scales and mitigate the adverse effects caused by drastic target scale changes; and (3) Integrating the CBAM convolutional block attention model that focuses on target features and the transformer coding block that can capture different local information to ensure the accuracy of lightweight model target detection. The model was tested with the Tsinghua traffic sign dataset, TT100k. The results show that the mAP index of the PP-LCNet-P2-CT model is increased by 29.84% and the FPS is increased by 24.05%, while the number of model parameters is decreased by 32.78% and the GFLOPs decreased by 34.41% compared with the classic YOLOv5 algorithm. The PP-LCNet-P2-CT model allows complex deep learning to be used successfully for unmanned ground vehicles (UGVs) with ordinary computing speeds and high real-time requirements.
SmartMocap: Joint Estimation of Human and Camera Motion Using Uncalibrated RGB Cameras

Markerless human motion capture (mocap) from multiple RGB cameras is a widely studied problem. Existing methods either need calibrated cameras or calibrate them relative to a static camera, which acts as the reference frame for the mocap system. The calibration step has to be done a priori for every capture session, which is a tedious process, and re-calibration is required whenever cameras are intentionally or accidentally moved. In this letter, we propose a mocap method which uses multiple static and moving extrinsically uncalibrated RGB cameras. The key components of our method are as follows. First, since the cameras and the subject can move freely, we select the ground plane as a common reference to represent both the body and the camera motions unlike existing methods which represent bodies in the camera coordinate system. Second, we learn a probability distribution of short human motion sequences ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 1 sec) relative to the ground plane and leverage it to disambiguate between the camera and human motion. Third, we use this distribution as a motion prior in a novel multi-stage optimization approach to fit the SMPL human body model and the camera poses to the human body keypoints on the images. Finally, we show that our method can work on a variety of datasets ranging from aerial cameras to smartphones. It also gives more accurate results compared to the state-of-the-art on the task of monocular human mocap with a static camera.
Audio Adversarial Example Detection Using the Audio Style Transfer Learning Method

In this paper, we propose a method for defending against audio adversarial examples that operates by applying audio style transfer learning. The proposed method has the effect of maintaining the classification result produced by the target model and removing the adversarial noise by changing only the style while maintaining the content of the input audio sample. In an experimental evaluation using the Mozilla Common Voice dataset as the test data source and TensorFlow as the machine learning library, the proposed method improved the target model’s accuracy on the adversarial examples from 2.1% to 79.2% while maintaining its accuracy on the original samples at 81.4%.
Unsupervised Discovery and Manipulation of Continuous Disentangled Factors of Variation

Learning a disentangled representation of a distribution in a completely unsupervised way is a challenging task that has drawn attention recently. In particular, much focus has been put in separating factors of variation (i.e., attributes) within the latent code of a Generative Adversarial Network (GAN). Achieving that permits control of the presence or absence of those factors in the generated samples by simply editing a small portion of the latent code. Nevertheless, existing methods that perform very well in a noise-to-image setting often fail when dealing with a real data distribution, i.e., when the discovered attributes need to be applied to real images. However, some methods are able to extract and apply a style to a sample but struggle to maintain its content and identity, while others are not able to locally apply attributes and end up achieving only a global manipulation of the original image. In this article, we propose a completely (i.e., truly ) unsupervised method that is able to extract a disentangled set of attributes from a data distribution and apply them to new samples from the same distribution by preserving their content. This is achieved by using an image-to-image GAN that maps an image and a random set of continuous attributes to a new image that includes those attributes. Indeed, these attributes are initially unknown and they are discovered during training by maximizing the mutual information between the generated samples and the attributes’ vector. Finally, the obtained disentangled set of continuous attributes can be used to freely manipulate the input samples. We prove the effectiveness of our method over a series of datasets and show its application on various tasks, such as attribute editing, data augmentation, and style transfer.
On the resistance of new lightweight block ciphers against differential cryptanalysis

Many recently proposed lightweight block ciphers lack security evaluation against generic cryptanalytic attacks such as differential cryptanalysis. In this paper, we contribute towards security evaluation efforts by investigating four lightweight Feistel-based block ciphers including SLIM, LBC-IoT, SCENERY, and LCB. SLIM claims resistance to differential cryptanalysis since, using a heuristic technique, its designers could only find a 7-round differential trail. Despite having no analysis of security against attacks such as differential cryptanalysis, the designers of LBC-IoT and LCB claimed that their ciphers are secure. Meanwhile, the designers of SCENERY claim that the best 11-round differential trail for the cipher has a probability of 2−66. To substantiate these claims, we propose attacks on all four ciphers based on differential cryptanalysis. We presented practical key recovery attacks on SLIM which can retrieve the final round key for up to 14 rounds with a time complexity of 232. LBC-IoT was found to be weaker against differential cryptanalysis despite sharing many similarities with SLIM, whereby a key recovery attack of up to 19 rounds is possible with time complexity 231. For SCENERY, we found a differential trail of up to 12 rounds with probability 2−60, which was used as the distinguisher for a 13-round key recovery attack. We also discovered that LCB's design lacks nonlinearity, allowing us to easily derive deterministic differential trails regardless of the number of rounds. This flaw allowed us to perform a trivial distinguishing attack using a single known ciphertext. By using a different S-box to address this flaw, LCB is now more resilient to differential cryptanalysis than SLIM and LBC-IoT when using the same number of rounds. Our paper presents new independent cryptanalysis results for these ciphers.
ANSD-MA-MSE: Adaptive Neural Speaker Diarization Using Memory-Aware Multi-Speaker Embedding

In this paper, we propose a neural speaker diarization (NSD) network architecture consisting of three key components. First, a memory-aware multi-speaker embedding (MA-MSE) mechanism is proposed to facilitate a dynamical refinement of speaker embedding to reduce a potential data mismatch between the speaker embedding extraction and the NSD network. Next, a speaker selection procedure is introduced to handle situations where the detected number of speakers is different from the assumed speaker size in the NSD network. Finally, an adaptive procedure is proposed to improve the required prior information for the nonoverlap speech segments in a given utterance during each iteration. We call our proposed framework adaptive neural speaker diarization with memory-aware multi-speaker embedding (ANSD-MA-MSE). Our method improves diarization performance in realistic operating scenarios, such as adverse acoustic environments, domain mismatches, and a varying, rather than fixed, number of speakers. Having been tested on both the AMI corpus and the DIHARD-III evaluation sets, our proposed approach consistently outperforms other state-of-the-art techniques in diarization error rates, including the results reported by the best single-model system in the DIHARD-III challenge. Our code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Maokui-He/NSD-MA-MSE</uri> .
Effect of 'Bring Your Own Device‘ (BYOD) on Student Behavior, Well-Being, and Learning Economic Disciplines

Digital technologies are becoming an integral part of education. This study aims to quantify the effect of Bring Your Own Device (BYOD) on student behaviour, welfare and learning using a sample of Jordanian learners engaged in the study of economic disciplines. Data from the questionnaires were analyzed using Student’s t-test. According to the results of the study, participants in the educational process generally recognize the positive effect of BYOD initiatives on learning and support their implementation. Female students reported negative effects of BYOD on their behaviour more often than male students. Combining BYOD with a traditional learning approach was more effective than implementing the BYOD mode alone. Teachers with prior BYOD experience generally rated higher on the learning scale than those using BYOD for the first time. The present findings might be helpful for university teachers and administrators who want to implement a BYOD program; the learning discipline does not matter.
Dual Contrastive Learning Network for Graph Clustering

Graph representation is an important part of graph clustering. Recently, contrastive learning, which maximizes the mutual information between augmented graph views that share the same semantics, has become a popular and powerful paradigm for graph representation. However, in the process of patch contrasting, existing literature tends to learn all features into similar variables, i.e., representation collapse, leading to less discriminative graph representations. To tackle this problem, we propose a novel self-supervised learning method called dual contrastive learning network (DCLN), which aims to reduce the redundant information of learned latent variables in a dual manner. Specifically, the dual curriculum contrastive module (DCCM) is proposed, which approximates the node similarity matrix and feature similarity matrix to a high-order adjacency matrix and an identity matrix, respectively. By doing this, the informative information in high-order neighbors could be well collected and preserved while the irrelevant redundant features among representations could be eliminated, hence improving the discriminative capacity of the graph representation. Moreover, to alleviate the problem of sample imbalance during the contrastive process, we design a curriculum learning strategy, which enables the network to simultaneously learn reliable information from two levels. Extensive experiments on six benchmark datasets have demonstrated the effectiveness and superiority of the proposed algorithm compared with state-of-the-art methods.
Simple patch-wise transformations serve as a mechanism for slide-level augmentation for multiple instance learning applications

Data augmentation is a commonly applied method to increase the number of training samples for histopathology applications. However, data augmentation has yet to be explored for multiple instance learning applications in histopathology. Therefore, we developed a basic methodology to increase the number of bags for training in an application of attention-based multiple instance learning to classify whole slide histopathology images from Camelyon16. Our method applies common image transformations to histopathology patches at random to generate any number of bags for each slide. Across a ten-fold cross-validation with several magnitudes of augmentation, 10x augmentation (i.e. 10 augmented bags per slide plus the original bag) resulted in normal/tumor accuracies and AUC of 0.9463±0.0263, 0.7531±0.0755, and 0.8847±0.0241, respectively, which significantly exceeded baseline performance (i.e. no augmentation) of.0.9300±0.0363, 0.6918±0.1070, and 0.8827±0.0549, respectively. Additionally, our results suggest that greater levels of augmentation increase overall performance metrics.
The effects of sparsity induction methods on attention-based multiple instance learning applied to Camelyon16

Spectral decoupling, weight normalization, and L1 loss have been applied to varying degrees in studies concerning computational pathology. These methods induce sparsity and tend to improve overall model performance. However, no work has combined these methods to improve model performance. The work presented here combines and compares these three methods in an attention-based multiple instance learning model to classify whole slide histopathology images from Camelyon16. We observed that spectral decoupling improves accuracy and area under the curve (AUC), but that weight normalization and L1 loss do not. However, when either of the latter is combined with spectral decoupling, accuracy, and AUC further improve over just spectral decoupling. Finally, we demonstrate that varying the magnitude with which these three methods affect model training considerably affects the resulting testing accuracy and AUC.
Spatial uncertainty aggregation for false negatives detection in breast cancer metastases segmentation

Computational pathology, a developing area of primarily deep learning (DL) solutions aiming to aid pathologists at their daily tasks, has shown promising results in research settings. In recent years, uncertainty estimation has gained substantial recognition as having high potential to bring value to DL algorithms for medical applications. But it is not trivial how to incorporate it with a DL system to obtain a real positive impact. In this work we propose a framework to spatially aggregated epistemic uncertainty in order to detect false negatives produced by a segmentation algorithm of breast cancer metastases. We show a strong correlation between the false negative segmentation areas and the aggregated uncertainty values. Furthermore, the results include examples of reducing false negatives, where the uncertainty approach led to detection of some tumour metastases that had been missed.
Human-Machine Collaboration in the Teaching of Proof

This paper argues that interactive theorem provers (ITPs) could play an important role in fostering students' appreciation and understanding of proof and of mathematics in general.It shows that the ITP Lean has three features that mitigate existing difficulties in teaching and learning mathematical proof.One is that it requires students to identify a proof strategy at the start.The second is that it gives students instant feedback while still allowing them to explore with maximum autonomy.The third is that elementary formal logic finds a natural place in the activity of creating proofs.The challenge in using Lean is that students have to learn its command language, in addition to mathematics course content and elementary logic.