Reviewerly/exHarmony huggingface dataset:
Multi-label legal document classification: A deep learning-based approach with label-attention and domain-specific pre-training

Multi-label document classification has a broad range of applicability to various practical problems, such as news article topic tagging, sentiment analysis, medical code classification, etc. A variety of approaches (e.g., tree-based methods, neural networks and deep learning systems that are specifically based on pre-trained language models) have been developed for multi-label document classification problems and have achieved satisfying performance on different datasets. In the legal domain, however, one is often faced with several key challenges when working with multi-label classification tasks. One critical challenge is the lack of high-quality human labeled datasets, which prevents researchers and practitioners from achieving decent performance on respective tasks. Also, existing methods on multi-label classification typically focus on the majority classes, which results in an unsatisfying performance for other important classes that do not have sufficient training samples. In order to tackle the above challenges, in this paper, we first present POSTURE50K, a novel legal extreme multi-label classification dataset, which we will release to the research community. The dataset contains 50,000 legal opinions and their manually labeled legal procedural postures. Labels in this dataset follow a Zipfian distribution, leaving many of the classes with only a few samples. Furthermore, we propose a deep learning architecture that adopts domain-specific pre-training and a label-attention mechanism for multi-label document classification. We evaluate our proposed architecture on POSTURE50K and another legal multi-label dataset EUROLEX57K, and show that our approach achieves better performances than two baseline systems and another four recent state-of-the-art methods on both datasets.
Rotation adaptive correlation filter for moving object tracking in satellite videos

As a new method of Earth observation, video satellite can provide high-temporal resolution remote sensing images for object tracking. Object tracking in satellite videos is promising yet challenging in computer vision. Although many algorithms for satellite video object tracking have been proposed, none of them solve the problem of tracking rotating object. Due to the nadir view, the rotation of an object is very common in the satellite videos. This problem urgently needs to be addressed. In this paper, a rotation-adaptive correlation filter (RACF) tracking algorithm is proposed to address the problem caused by the rotation of object. The proposed algorithm provides the following improvements: (a) A method of estimating the object rotation angle to keep the feature map stable during the object rotation is proposed. This method can overcome the drawback of histogram of oriented gradient (HOG) based trackers, which cannot deal with the rotation of objects in satellite videos; and (b) making the algorithm capable of estimating the change in the bounding box size caused by object’s rotation. The experimental results demonstrate that our algorithm can track object with a 99.84% precision score and 92.96% success score in six videos from the Jilin-1 satellite constellation.
Escaping the McNamara Fallacy: Toward More Impactful Recommender Systems Research

Recommender systems are among today's most successful application areas of artificial intelligence. However, in the recommender systems research community, we have fallen prey to a McNamara fallacy to a worrying extent: In the majority of our research efforts, we rely almost exclusively on computational measures such as prediction accuracy, which are easier to make than applying other evaluation methods. However, it remains unclear whether small improvements in terms of such computational measures matter greatly and whether they lead us to better systems in practice. A paradigm shift in terms of our research culture and goals is therefore needed. We can no longer focus exclusively on abstract computational measures but must direct our attention to research questions that are more relevant and have more impact in the real world. In this work, we review the various ways of how recommender systems may create value; how they, positively or negatively, impact consumers, businesses, and the society; and how we can measure the resulting effects. Through our analyses, we identify a number of research gaps and propose ways of broadening and improving our methodology in a way that leads us to more impactful research in our field.
A secure authorized deduplication scheme for cloud data based on blockchain

Deduplication scheme based on convergent encryption (CE) is widely-used in cloud storage system to eliminate redundant data. However, the adversaries can obtain the data by the brute-force attack, if the data belongs to a predictable set for CE. In addition, previous works usually introduce the third-party auditors to execute the data integrity verification, suffering from data disclosure by the auditors. In this paper, we propose a secure authorized deduplication scheme based on blockchain, which can ensure the confidentiality and security of the users' data stored on cloud servers. In our scheme, the users can utilize the smart contract to create a tamper-proofing ledger, which can protect the data from illegal modification. Meanwhile, the users can execute the integrity audit protocols to check the users' data integrity by the smart contract. Moreover, a hierarchical role hash tree (HRHT) will be constructed to create the role key when the users upload their data to CSP, allowing the authorized users to access the data. Security analysis and performance evaluation demonstrate that our proposed scheme is resilient against the brute-force attack and the collusion attack, while it has limited computation overhead.
CCGraph

Software clone detection is an active research area, which is very important for software maintenance, bug detection, etc. The two pieces of cloned code reflect some similarities or equivalents in the syntax or structure of the code representations. There are many representations of code like AST, token, PDG, etc. The PDG (Program Dependency Graph) of source code can contain both syntactic and structural information. However, most existing PDG-based tools are quite time-consuming and miss many clones because they detect code clones with exact graph matching by using subgraph isomorphism. In this paper, we propose a novel PDG-based code clone detector, CCGraph, that uses graph kernels. Firstly, we normalize the structure of PDGs and design a two-stage filtering strategy by measuring the characteristic vectors of codes. Then we detect the code clones by using an approximate graph matching algorithm based on the reforming WL (Weisfeiler-Lehman) graph kernel. Experiment results show that CCGraph retains a high accuracy, has both better recall and F1-score values, and detects more semantic clones than other two related state-of-the-art tools. Besides, CCGraph is much more efficient than the existing PDG-based tools.
Automatic medical image interpretation: State of the art and future directions

Automatic Natural language interpretation of medical images is an emerging field of Artificial Intelligence (AI). The task combines two fields of AI; computer vision and natural language processing. This is a challenging task that goes beyond object detection, segmentation, and classification because it also requires the understanding of the relationship between different objects of an image and the actions performed by these objects as visual representations. Image interpretation is helpful in many tasks like helping visually impaired persons, information retrieval, early childhood learning, producing human like natural interaction between robots, and many more applications. Recently this work fascinated researchers to use the same approach by using more complex biomedical images. It has been applied from generating single sentence captions to multi sentence paragraph descriptions. Medical image captioning can assist and speed up the diagnosis process of medical professionals and generated report can be used for many further tasks. This is a comprehensive review of recent years’ research of medical image captioning published in different international conferences and journals. Their common parameters are extracted to compare their methods, performance, strengths, limitations, and our recommendations are discussed. Further publicly available datasets and evaluation measures used for deep-learning based captioning of medical images are also discussed.
Learning Topology From Synthetic Data for Unsupervised Depth Completion

We present a method for inferring dense depth maps from images and sparse depth measurements by leveraging synthetic data to learn the association of sparse point clouds with dense natural shapes, and using the image as evidence to validate the predicted depth map. Our learned prior for natural shapes uses only sparse depth as input, not images, so the method is not affected by the covariate shift when attempting to transfer learned models from synthetic data to real ones. This allows us to use abundant synthetic data with ground truth to learn the most difficult component of the reconstruction process, which is topology estimation, and use the image to refine the prediction based on photometric evidence. Our approach uses fewer parameters than previous methods, yet, achieves the state of the art on both indoor and outdoor benchmark datasets.
Teaching Physics for Computer Science Students in Higher Education During the COVID-19 Pandemic: A Fully Internet-Supported Course

The COVID-19 pandemic has modified and diversified the ways that students receive education. During confinements, complex courses integrating previous knowledge must be carefully designed and implemented to effectively replace the elements present in face-to-face learning to improve the students’ experience. This work assesses the implementation of a digital-learning physics course for computer science students in a skill-based education program in higher education. The assessment was useful for the institution to evaluate if the digital strategy implemented in the course fulfilled the original premises and objectives. The analyses performed provide useful knowledge of theoretical and operational actions applied in this methodology that could be adapted to similar courses for the younger generations in this university. COVID-19 confinement will continue in Mexico in 2021. This assessment resulted in a positive evaluation of the digital strategy being followed, which can be continued while the contingency lasts. Three teachers came together to design math, physics, and computational sciences content for various sections of a physics course. The analysis was developed and implemented according to an institutional digital delivery model for the COVID-19 pandemic. Elements related to attendance, digital access, performance distribution by gender, activity types, and the course learning sections were considered. The analysis was performed with some techniques found in the literature for small groups, complemented when necessary by standard statistical tests to discern meaningful trends. A primary goal was to assess skill-based learning in the course delivered digitally due to the COVID-19 confinement. Furthermore, additional issues concerning the learning dynamics were searched, reported, and analyzed. Finally, the outcomes of an institutional exit survey collecting students’ opinions supported certain observed behaviors. The analysis produced meaningful evidence that the course’s skill-based development was well supported by the digital delivery during the confinement. Furthermore, differences in the students’ performances in the various course content sections proved statistically significant and are discussed in this work.
Unsupervised neural networks for automatic Arabic text summarization using document clustering and topic modeling

Humans must easily handle the vast amounts of data being generated by the revolution of information technology. Thus, Automatic Text summarization has been applied to various domains in order to find the most relevant information and make critical decisions quickly. In the context of Arabic, text summarization techniques suffer from several problems. First, most existing methods do not consider the context or domain to which the document belongs. Second, the majority of the existing approaches are based on the traditional bag-of-words representation, which involves high dimensional and sparse data, and makes it difficult to capture relevant information. Third, research in Arabic Text summarization is fairly small and only recently compared to that on Anglo-Saxon and other languages due to the shortage of Arabic corpora, resources, and automatic processing tools. In this paper, we try to overcome these limitations by proposing a new approach using documents clustering, topic modeling, and unsupervised neural networks in order to build an efficient document representation model. First, a new document clustering technique using Extreme learning machine is performed on large text collection. Second, topic modeling is applied to documents collection in order to identify topics present in each cluster. Third, each document is represented in a topic space by a matrix where rows represent the document sentences and columns represent the cluster topics. The generated matrix is then trained using several unsupervised neural networks and ensemble learning algorithms in order to build an abstract representation of the document in the concept space. Important sentences are ranked and extracted according to a graph model with a redundancy elimination component. The proposed approach is evaluated on Essex Arabic Summaries Corpus and compared against other Arabic text summarization approaches using ROUGE measure. Experimental results showed that the models trained on topic representation learn better representations and improve significantly the summarization performance. In particular, ensemble learning models demonstrated an important improvement on Rouge recall and promising results on F-measure.
Cost Optimization for Big Data Workloads Based on Dynamic Scheduling and Cluster-Size Tuning

Analytical data processing has become the cornerstone of today's businesses success, and it is facilitated by Big Data platforms that offer virtually limitless scalability. However, minimizing the total cost of ownership (TCO) for the infrastructure can be challenging. We propose a novel method to build resilient clusters on cloud resources that are fine-tuned to the particular data processing task. The presented architecture follows the infrastructure-as-a-code paradigm so that the cluster can be dynamically configured and managed. It first identifies the optimal cluster size to perform a job in the required time. Then, by analyzing spot instance price history and using ARIMA models, it optimizes the schedule of the job execution to leverage the discounted prices of the cloud spot market. In particular, we evaluated savings opportunities when using Amazon EC2 spot instances comparing to on-demand resources. The performed experiments confirmed that the prediction module significantly improved the cost-effectiveness of the solution – up to 80% savings compared to the on-demand prices, and at the worst-case, 1% more cost than the absolute minimum. The production deployments of the architecture show that it is invaluable for minimizing the total cost of ownership of analytical data processing solutions.
Dual-path network with synergistic grouping loss and evidence driven risk stratification for whole slide cervical image analysis

Cervical cancer has been one of the most lethal cancers threatening women’s health. Nevertheless, the incidence of cervical cancer can be effectively minimized with preventive clinical management strategies, including vaccines and regular screening examinations. Screening cervical smears under microscope by cytologist is a widely used routine in regular examination, which consumes cytologists’ large amount of time and labour. Computerized cytology analysis appropriately caters to such an imperative need, which alleviates cytologists’ workload and reduce potential misdiagnosis rate. However, automatic analysis of cervical smear via digitalized whole slide images (WSIs) remains a challenging problem, due to the extreme huge image resolution, existence of tiny lesions, noisy dataset and intricate clinical definition of classes with fuzzy boundaries. In this paper, we design an efficient deep convolutional neural network (CNN) with dual-path (DP) encoder for lesion retrieval, which ensures the inference efficiency and the sensitivity on both tiny and large lesions. Incorporated with synergistic grouping loss (SGL), the network can be effectively trained on noisy dataset with fuzzy inter-class boundaries. Inspired by the clinical diagnostic criteria from the cytologists, a novel smear-level classifier, i.e., rule-based risk stratification (RRS), is proposed for accurate smear-level classification and risk stratification, which aligns reasonably with intricate cytological definition of the classes. Extensive experiments on the largest dataset including 19,303 WSIs from multiple medical centers validate the robustness of our method. With high sensitivity of 0.907 and specificity of 0.80 being achieved, our method manifests the potential to reduce the workload for cytologists in the routine practice.
SKNet: Detecting Rotated Ships as Keypoints in Optical Remote Sensing Images

Detecting rotated ships is difficult in optical remote sensing images due to the challenges of complex scenes. Existing advanced rotated ship detectors are typically anchor-based algorithms that require plenty of predefined anchors. However, the use of anchors brings three critical problems: 1) a large number of anchors bring a huge amount of calculation; 2) the attributes (e.g., size and aspect ratios) of anchors are designed via <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> heuristics; and 3) only a tiny fraction of anchors that overlap with ground-truth bounding boxes of ships tightly can be considered as positive samples, which causes an extreme imbalance between positive and negative samples. As a result, the detection accuracy will be influenced seriously when the design of anchors is not suitable. To address the above problems, this article proposes a novel anchor-free rotated ship detection framework, called SKNet, which detects rotated ships as keypoints in optical remote sensing images. In SKNet, a ship target is modeled as its center keypoint and morphological sizes, including the width, height, and rotation angle. Accordingly, we design two customized modules: orthogonal pooling and soft-rotate-nonmaximum suppression (NMS), where the former is to improve the prediction accuracy of the center keypoint and the morphological size, and the latter is to effectively remove redundant rotated ship detection results. Extensive experiments are conducted to demonstrate the effectiveness of SKNet on three optical remote sensing image data sets: HRSC2016, DOTA-ship, and HPDM-OSOD, which is collected by ourselves and published in this article. Empirical studies show that SKNet achieves state-of-the-art detection performance while being time-efficient. Overall, SKNet achieves the best speed–accuracy tradeoff.
UAS-Based Hyperspectral Environmental Monitoring of Acid Mine Drainage Affected Waters

The exposure of metal sulfides to air or water, either produced naturally or due to mining activities, can result in environmentally damaging acid mine drainage (AMD). This needs to be accurately monitored and remediated. In this study, we apply high-resolution unmanned aerial system (UAS)-based hyperspectral mapping tools to provide a useful, fast, and non-invasive method for the monitoring aspect. Specifically, we propose a machine learning framework to integrate visible to near-infrared (VNIR) hyperspectral data with physicochemical field data from water and sediments, together with laboratory analyses to precisely map the extent of acid mine drainage in the Tintillo River (Spain). This river collects the drainage from the western part of the Rio Tinto massive sulfide deposit and discharges large quantities of acidic water with significant amounts of dissolved metals (Fe, Al, Cu, Zn, amongst others) into the Odiel River. At the confluence of these rivers, different geochemical and mineralogical processes occur due to the interaction of very acidic water (pH 2.5–3.0) with neutral water (pH 7.0–8.0). This complexity makes the area an ideal test site for the application of hyperspectral mapping to characterize both rivers and better evaluate contaminated water bodies with remote sensing imagery. Our approach makes use of a supervised random forest (RF) regression for the extended mapping of water properties, using the samples collected in the field as ground-truth and training data. The resulting maps successfully estimate the concentration of dissolved metals and related physicochemical properties in water, and trace associated iron species (e.g., jarosite, goethite) within sediments. These results highlight the capabilities of UAS-based hyperspectral data to monitor water bodies in mining environments, by mapping their hydrogeochemical properties, using few field samples. Hence, we have demonstrated that our workflow allows the rapid discrimination and mapping of AMD contamination in water, providing an essential basis for monitoring and subsequent remediation.
A comprehensive study of spam detection in e-mails using bio-inspired optimization techniques

Electronic mail is a medium of communication used frequently for conveying a variety of information. It has become an integral part of people's lives owing to its ease of access and capability to reach out to a large number of recepients without much hassle. This boon, however, has turned into a bane due to exploitation by marketers for publicizing their products, and scammers for fooling people into falling for their schemes. Such e-mails are usually termed as spam e-mails. The menace of spam e-mails requires attention because in addition to consuming resources like bandwidth and storage, they are time-consuming as their removal may require manual effort. This paper proposes a classification method based on k-Nearest Neighbours integrated with several bio-inspired optimization techniques to classify e-mails as spam or legitimate. The study first evaluates the performance of three distance metrics namely Euclidean, Manhattan, and Chebyshev, when utilized in k-Nearest Neighbours classification. Further, five bio-inspired algorithms namely, Grey wolf optimization, Firefly optimization, Chicken swarm optimization, Grasshopper optimization, and Whale optimization, have been explored and their performance is compared based on different evaluation measures like accuracy, precision, recall, speed of convergence to global optimum solution, F1-measure and computational time.