Reviewerly/exHarmony huggingface dataset:
Interpretable Deep Neural Network to Predict Estrogen Receptor Status from Haematoxylin-Eosin Images

The eligibility for hormone therapy to treat breast cancer largely depends on the tumor’s estrogen receptor (ER) status. Recent studies show that the ER status correlates with morphological features found in Haematoxylin-Eosin (HE) slides. Thus, HE analysis might be sufficient for patients for whom the classifier confidently predicts the ER status and thereby obviate the need for additional examination, such as immunohistochemical (IHC) staining. Several prior works are limited by either the use of engineered features, multi-stage models that use features unspecific to HE images or a lack of explainability. To address these limitations, this work proposes an end-to-end neural network ensemble that shows state-of-the-art performance. We demonstrate that the approach also translates to the prediction of the cancer grade. Moreover, subsets can be selected from the test data for which the model can detect a positive ER status with a precision of 94% while classifying 13% of the patients. To compensate for the reduced interpretability of the model that comes along with end-to-end training, this work applies Layer-wise Relevance Propagation (LRP) to determine the relevant parts of the images a posteriori, commonly visualized as a heatmap overlayed with the input image. We found that nuclear and stromal morphology and lymphocyte infiltration play an important role in the classification of the ER status. This demonstrates that interpretable machine learning can be a vital tool for validating and generating hypotheses about morphological biomarkers.
ViMs: a high-quality Vietnamese dataset for abstractive multi-document summarization

Automatic text summarization is important in this era due to the exponential growth of documents available on the Internet. In the Vietnamese language, VietnameseMDS is the only publicly available dataset for this task. Although the dataset has 199 clusters, there are only three documents in each cluster, which is small compared to typical datasets in English. This motivates us to construct ViMs—a big and high-quality Vietnamese dataset for abstractive multi-document summarization. To that end, we recruited 29 annotators and enhanced MDSWriter—an open-source annotation tool, to support the annotators in creating gold standard summaries. As a result, ViMs has 600 summaries corresponding to 300 clusters of 1,945 documents. We have verified the reliability of our dataset by using a variety of metrics including conventional Cohen’s $$\kappa $$ , relaxed Cohen’s $$\kappa $$ —a new metric that we propose to make it more suitable for abstractive summarization, and ROUGE scores. A relaxed $$\kappa $$ score of 0.55 indicate that ViMs could attain moderate agreement between annotators. Meanwhile, ROUGE scores are 0.729 of ROUGE-1, 0.507 of ROUGE-2 and 0.524 of ROUGE-SU4. We have further evaluated ViMs by using three different summarization systems: TextRank, CFVi and MUSEEC. Their performances are 0.628, 0.711 and 0.732 of ROUGE-1, respectively. These results show that the ViMs dataset is suitable for both training and evaluating multi-document summarization systems. We have made the dataset and evaluation results of this work publicly available for research community. It is noted that unlike previous work that only published the final summarization dataset, we also publish intermediate annotation results, which can be used in other NLP problems such as sentence classification.
A Method For Answer Selection Using DistilBERT And Important Words

Question Answering is a hot topic in artificial intelligence and has many real-world applications. This field aims at generating an answer to the user's question by analyzing a massive volume of text documents. Answer Selection is a significant part of a question answering system and attempts to extract the most relevant answers to the user's question from the candidate answers pool. Recently, researchers have attempted to resolve the answer selection task by using deep neural networks. They first employed the recurrent neural networks and then gradually migrated to convolutional neural networks. Nevertheless, the use of language models, which is implemented by deep neural networks, has recently been considered. In this research, the DistilBERT language model was employed as the language model. The outputs of the Question Analysis part and Expected Answer Extraction component are also applied with [CLS] token output as the final feature vector. This operation leads to improving the method performance. Several experiments are performed to evaluate the effectiveness of the proposed method, and the results are reported based on the MAP and MRR metrics. The results show that the MAP values of the proposed method improved by 0.6%, and the MRR metric is improved by 0.2%. The results of our research show that using a heavy language model does not guarantee a more reliable method for answer selection problem. It also shows that the use of particular words, such as Question Word and Expected Answer word, can improve the performance of the method.
Implementing Location-Based Cryptography on Mobile Application Design to Secure Data in Cloud Storage

Abstract This paper aims to identify security issues and existing solutions for cloud storage protection and to propose a mobile application design for securing data in cloud storage using the location-based cryptographic technique. In the proposed mobile application design, the user is required to perform the encryption before sending the data into the cloud storage. This paper provides a novel technique using location-based cryptography, secret keyword and hash function in order to further enhance the security of data in cloud storage that is currently only using either asymmetric or symmetric encryption algorithm. Asymmetric encryption’s has high security but due to the complexity in its computing, the performance rate is low while symmetric encryption’s performance is much faster as its implementation is easy. However, the attacker can still hack the data in cloud storage when either one of this encryption methods being used alone without any additional security layer. In this paper, location-based cryptography technique used consists of Advanced Encryption Standard (AES) algorithm and location information which are longitude and latitude coordinates. Those coordinates will be employed in the encryption and decryption process as additional security mechanism on top of existing cryptography method by generating supplementary encryption key called geo-lock key. Dynamic Toleration Distance (DTD) protocol will also be implemented with those coordinates to improve its practicality before it will be converted into the geo-lock key. AES algorithm will handle encryption and decryption process using the geo-lock key while secret keyword which defined by the user before starting the process will be used to authenticate the upload and downloading process. Both geo-lock key and secret keyword will be hashed using Secure Hash Algorithm 2 (SHA 2) and stored together with encrypted file in the cloud storage. The purpose of using location information is to ensure the encrypted file can be decrypted at intended location only while hash function is employed to protect the keys when they are stored in cloud storage. As a result, any unauthorized access to the file in cloud storage will not be allowed since each stored file was encrypted by the high performance of AES algorithm together with the decryption’s location restriction and the non-reversable hashed keys. The proposed mobile application design will serve an improvement in protecting stored data at cloud storage by using AES algorithm with location information as data encryption method and SHA 2 function for hashing the keys.
Prediction of Electricity Generation using Solar Radiation Forecasting Data

Solar energy is one of the most immerging sources of energy. Forecasting of electricity from any solar plant is very important due to nature of variability of solar energy on the surface of earth. For this, a new formula has been proposed for calculation of possible electricity generation from a PV plant on a particular day. The proposed formula has been checked by calculation of forecasted electricity from a 50kW grid connected PV plant and compares it with actual plant generated electricity. For this, solar radiation forecasting results from ten different univariate and multivariate models were used. From the results it has been observed that all the models were showing good results, among them random forest model showing best results.
A two authorities electronic vote scheme

In this paper we propose a new electronic multi-authority voting system based on blind signatures. We focus on the open problem of the efficiency of electronic voting systems. Most of the proposed systems rely on complex architectures or expensive proofs, in this work we aim to reduce the time-complexity of the voting process, both for the voter and the authorities involved. Our system is focused on simplicity and it is based on the assumption of two unrelated entities. This simplicity makes our approach scalable and flexible to multiple kinds of elections. We propose a method that limits the number of authorities to only 2 of them; we reduce the overall number of modular operations; and, propose a method which cut downs the interactions needed to cast a vote. The result is a voting protocol whose complexity scales linearly with the number of votes.
IIR Filtering on Graphs With Random Node-Asynchronous Updates

Graph filters play an important role in graph signal processing, in which the data is analyzed with respect to the underlying network (graph) structure. As an extension to classical signal processing, graph filters are generally constructed as a polynomial (FIR), or a rational (IIR) function of the underlying graph operator, which can be implemented via successive shifts on the graph. Although the graph shift is a localized operation, it requires all nodes to communicate synchronously, which can be a limitation for large scale networks. To overcome this limitation, this study proposes a node-asynchronous implementation of rational filters on arbitrary graphs. In the proposed algorithm nodes follow a randomized collect-compute-broadcast scheme: if a node is in the passive stage it collects the data sent by its incoming neighbors and stores only the most recent data. When a node gets into the active stage at a random time instance, it does the necessary filtering computations locally, and broadcasts a state vector to its outgoing neighbors. For the analysis of the algorithm, this study first considers a general case of randomized asynchronous state recursions and presents a sufficiency condition for its convergence. Based on this result, the proposed algorithm is proven to converge to the filter output in the mean-squared sense when the filter, the graph operator and the update rate of the nodes satisfy a certain condition. The proposed algorithm is simulated using rational and polynomial filters, and its convergence is demonstrated for various different cases, which also shows the robustness of the algorithm to random communication failures.
Learning Rubik's Cube through User Operation History

In this work, we propose a learning framework of the Rubik's Cube through a user operation history. The proposed system utilizes a spatial user interface by projection mapping of virtual Rubik's Cube onto a display cube. The user can play with the projected cube with predefined gestures via gesture recognition, and the system records the operation history automatically. As our case study shows, the user can explore the operation history on the tangible cube surface and retrieve previous operations with the gesture input. We confirm that the proposed framework is helpful in the learning and playing processes.
Mask-guided sample selection for semi-supervised instance segmentation

Image segmentation methods are usually trained with pixel-level annotations, which require significant human effort to collect. Weakly-supervised pipelines are the most common solution to address this constraint because they are trained with lower forms of supervision, such as bounding boxes or scribbles. Semi-supervised methods are another option, that leverage a large amount of unlabeled data and a limited number of strongly-labeled samples. In this second setup, samples to be strongly-annotated can be selected randomly or with an active learning mechanism that chooses the ones that will maximize the model performance. In this work, we propose a sample selection approach to decide which samples to annotate for semi-supervised instance segmentation. Our method consists in first predicting pseudo-masks for the unlabeled pool of samples, together with a score predicting the quality of each mask. This score is an estimate of the Intersection Over Union (IoU) of the segment with the ground truth mask. We study which samples should be annotated based on the quality score, leading to an improved performance for semi-supervised instance segmentation with low annotation budgets.
Interactive framework for the visual exploration of colonic data

Computerized Tomography (CT) and, more recently, Magnetic Resonance Imaging (MRI) have become the state-of-the art techniques for morpho-volumetric analysis of abdominal cavities. Due to its constant motility, the colon is an organ difficult to analyze. Unfortunately, CT’s radiative nature makes it only indicated for patients with important disorders. Lately, acquisition techniques that rely on the use of MRI have matured enough to enable the analysis of colon data. This allows gathering data of patients without preparation (i.e. administration of drugs or contrast agents), and incorporating data of patients with non life-threatening diseases and healthy subjects to databases. In this paper we present an end-to-end framework that comprises all the steps to extract colon content and morphology data coupled with a web-based visualization tool that facilitates the visual exploration of such data. We also introduce the set of tools for the extraction of morphological data, and a detailed description of a specifically-designed interactive tool that facilitates a visual comparison of numerical variables within a set of patients, as well as a detailed inspection of an individual. Our prototype was evaluated by domain experts, which showed that our visual approach may reduce the costly process of colon data analysis. As a result, physicians have been able to get new insights on the effects of diets, and also to obtain a better understanding on the motility of the colon.
Region-action LSTM for mouse interaction sequence based search satisfaction evaluation

Mouse interaction data contain a lot of interaction information between users and Search Engine Result Pages (SERPs), which can be useful for evaluating search satisfaction. Existing studies use aggregated features or anchor elements to capture the spatial information in mouse interaction data, which might lose valuable mouse cursor movement patterns for estimating search satisfaction. In this paper, we leverage regions together with actions to extract sequences from mouse interaction data. Using regions to capture the spatial information in mouse interaction data would reserve more details of the interaction processes between users and SERPs. To modeling mouse interaction sequences for search satisfaction evaluation, we propose a novel LSTM unit called Region-Action LSTM (RALSTM), which could capture the interactive relations between regions and actions without subjecting the network to higher training complexity. Simultaneously, we propose a data augmentation strategy Multi-Factor Perturbation (MFP) to increase the pattern variations on mouse interaction sequences. We evaluate the proposed approach on open datasets. The experimental results show that the proposed approach achieves significant performance improvement compared with the state-of-the-art search satisfaction evaluation approach.
Hand gesture recognition based on attentive feature fusion

Summary Video‐based hand gesture recognition plays an important role in human‐computer interaction (HCI). Recent advanced methods usually add 3D convolutional neural networks to capture the information from both spatial and temporal dimensions. However, these methods suffer the issue of requiring large‐scale training data and high computational complexity. To address this issue, we proposed an attentive feature fusion framework for efficient hand‐gesture recognition. In our proposed model, we utilize a shallow two‐stream CNNs to capture the low‐level features from the original video frame and its corresponding optical flow. Following, we designed an attentive feature fusion module to selectively combine useful information from the previous two streams based on the attention mechanism. Finally, we obtain a compact embedding of a video by concatenating features from several short segments. To evaluate the effectiveness of our proposed framework, we train and test our method on a large‐scale video‐based hand gesture recognition dataset, Jester. Experimental results demonstrate that our approach obtains very competitive performance on the Jester dataset with a classification accuracy of 95.77%.
Tuning electricity generation throughout the year with PV module technology

Currently, photovoltaic (PV) installations target a maximization of annual energy yield. However, as the grid penetration of PV is increasing, PV electricity generation will need to match better with local load profiles. Especially the seasonal variabilities remain challenging. While wind and PV tend to have complementary seasonal variability, wind turbine installation faces limitations especially in densely populated areas. In this paper, we discuss how this challenge may be addressed with climate- and consumption-specific PV module technology. In particular, we demonstrate how the temperature coefficient of a PV system can impact the energy yield throughout the year. In colder climates, higher temperature coefficients allow for a better energy balance, favoring production in colder seasons without a significant reduction of yearly energy yield. Simulations for locations at high latitude, and colder climates, indicate that higher temperature coefficients and improved low-light behavior not only enable a higher energy yield in cold seasons, but also negligible losses in the overall yearly energy yield compared to lower temperature coefficients and slightly better low-light behavior. Simulations show that these results can be obtained using commercial PV modules. More broadly, they indicate how PV module technology may be optimized depending on the location and climate.
Towards Unconstrained Facial Landmark Detection Robust to Diverse Cropping Manners

Facial landmark detection is one crucial step for face-based image/video analysis. Despite the fact that recently many facial landmark detection models have achieved remarkable performance, most state-of-the-art heatmap regression-based methods heavily rely on initialization of the face detector. However, there inevitably exists semantic gaps among different annotators or face detectors. An improper facial bounding box will tremendously drop off the performance of the facial landmark detection model. Facial landmark detection would be more practical if robust to face images cropped by diverse manners (see Figure 1, the col.1 shows face images cropped by a proper bounding box, col.2 and col.3 show face images cropped by an oversize and a small bounding boxes respectively). To this end, we present a “Unconstrained Facial Landmark Detection(UFLD)” mechanism, that aims at enhancing the robustness of facial landmark detection, to deal with the inconsistent cropping manner issue. UFLD consists of two aspects: a Transformation-Invariant Landmark Detector(TILD) and an Availability-Guided Solver(AGS). TILD gives the ability to detect consistent landmarks for face images cropped by diverse manners. And AGS can alleviate the by-effect of “landmarks outside the image” caused by improper cropping results or TILD, and further promote the performance. The proposed mechanism achieved above 6.5% improvement in standard normalized landmarks mean error reduction on face images cropped by diverse manners compared to baselines.
Graptor

Vectorization seeks to accelerate computation through data-level parallelism. Vectorization has been applied to graph processing, where the graph is traversed either in a push style or a pull style. As it is not well understood which style will perform better, there is a need for both vectorized push and pull style traversals. This paper is the first to present a general solution to vectorizing push style traversal. It more-over presents an enhanced vectorized pull style traversal.
I'm fine where I am, but I want to do more

Teaching is challenging in rural areas in developing countries because of large classroom sizes, limited basic infrastructure, and lack of teacher training. Teachers' impact on educational outcomes can be improved by teacher training programs but these programs are limited by poor infrastructure in rural areas. Although infrastructural challenges can be overcome by information technology today, it is unclear how such technology can support teacher training. Therefore to explore opportunities for teacher training in rural developing contexts with technology, we conducted interviews and observations with 22 primary school teachers in two regions of rural Côte d'Ivoire. We followed an aspirations-based approach that focuses on the user's aspirations (long term desires) to help understand teachers' intrinsic desire to grow in their professional life. We found that teacher aspirations conflict with their current role but their solidarity helps them find education role models who balance this conflict. We found that teachers face challenges in teacher training but they handle issues through their solidarity and by creating solutions. We then discuss ways to measure impact on aspirations, designing for conflicting aspirations and to leverage aspirations to create a social change in teacher training using technology.
LEAF: A Faster Secure Search Algorithm via Localization, Extraction, and Reconstruction

Secure search looks for and retrieves records from a (possibly cloud-hosted) encrypted database while ensuring the confidentiality of the queries. Researchers are paying increasing attention to secure search in recent years due to the growing concerns about database privacy. However, the low efficiency of (especially multiplicative) homomorphic operations in secure search has hindered its deployment in practice. To address this issue, Akavia et al. [CCS 2018, PETS 2019] proposed new protocols that bring down the number of multiplications in the search algorithm from O(n2) to O(n log2 n), and then to O(n log n), where n is the size of the database.