Reviewerly/exHarmony huggingface dataset:
Novelty Detection: A Perspective from Natural Language Processing

Abstract The quest for new information is an inborn human trait and has always been quintessential for human survival and progress. Novelty drives curiosity, which in turn drives innovation. In Natural Language Processing (NLP), Novelty Detection refers to finding text that has some new information to offer with respect to whatever is earlier seen or known. With the exponential growth of information all across the Web, there is an accompanying menace of redundancy. A considerable portion of the Web contents are duplicates, and we need efficient mechanisms to retain new information and filter out redundant information. However, detecting redundancy at the semantic level and identifying novel text is not straightforward because the text may have less lexical overlap yet convey the same information. On top of that, non-novel/redundant information in a document may have assimilated from multiple source documents, not just one. The problem surmounts when the subject of the discourse is documents, and numerous prior documents need to be processed to ascertain the novelty/non-novelty of the current one in concern. In this work, we build upon our earlier investigations for document-level novelty detection and present a comprehensive account of our efforts toward the problem. We explore the role of pre-trained Textual Entailment (TE) models to deal with multiple source contexts and present the outcome of our current investigations. We argue that a multipremise entailment task is one close approximation toward identifying semantic-level non-novelty. Our recent approach either performs comparably or achieves significant improvement over the latest reported results on several datasets and across several related tasks (paraphrasing, plagiarism, rewrite). We critically analyze our performance with respect to the existing state of the art and show the superiority and promise of our approach for future investigations. We also present our enhanced dataset TAP-DLND 2.0 and several baselines to the community for further research on document-level novelty detection.
Transfer of quantum information via a dissipative protocol for data classification

We consider the equilibration dynamics of a two-level quantum system in contact with multiple structured reservoirs that carry quantum information. We develop a micromaser-like quantum master equation based on repeated interactions and show that some information is transferred from the quantum reservoir into the quantum system in the steady-state. In the results we obtained from the analytical methods we used, we have developed a clear expression of the information transferred to the two-level system in terms of information reservoir parameters and interaction coefficients of the probe qubit with the reservoirs. We propose an open quantum classifier model by developing a binary classification rule by use of these results. We also develop the analytical description of the classification process by quantum parameter estimation.
View knowledge transfer network for multi-view action recognition

As many data in practical applications occur or can be captured in multiple views form, multi-view action recognition has received much attention recently, due to utilizing certain complementary and heterogeneous information in various views to promote the downstream task. However, most existing methods assume that multi-view data is complete, which may not always be met in real-world applications.To this end, in this paper, a novel View Knowledge Transfer Network (VKTNet) is proposed to handle multi-view action recognition, even when some views are incomplete. Specifically, the view knowledge transferring is utilized using conditional generative adversarial network(cGAN) to reproduce each view's latent representation, conditioning on the other view's information. As such, the high-level semantic features are effectively extracted to bridge the semantic gap between two different views. In addition, in order to efficiently fuse the decision result achieved by each view, a Siamese Scaling Network(SSN) is proposed instead of simply using a classifier. Experimental results show that our model achieves the superiority performance, on three public datasets, against others when all the views are available. Meanwhile, the degradation of performance is avoided under the case that some views are missing.
Bayesian inference of local government audit outcomes

The scandals in publicly listed companies have highlighted the large losses that can result from financial statement fraud and weak corporate governance. Machine learning techniques have been applied to automatically detect financial statement fraud with great success. This work presents the first application of a Bayesian inference approach to the problem of predicting the audit outcomes of financial statements of local government entities using financial ratios. Bayesian logistic regression (BLR) with automatic relevance determination (BLR-ARD) is applied to predict audit outcomes. The benefit of using BLR-ARD, instead of BLR without ARD, is that it allows one to automatically determine which input features are the most relevant for the task at hand, which is a critical aspect to consider when designing decision support systems. This work presents the first implementation of BLR-ARD trained with Separable Shadow Hamiltonian Hybrid Monte Carlo, No-U-Turn sampler, Metropolis Adjusted Langevin Algorithm and Metropolis-Hasting algorithms. Unlike the Gibbs sampling procedure that is typically employed in sampling from ARD models, in this work we jointly sample the parameters and the hyperparameters by putting a log normal prior on the hyperparameters. The analysis also shows that the repairs and maintenance as a percentage of total assets ratio, current ratio, debt to total operating revenue, net operating surplus margin and capital cost to total operating expenditure ratio are the important features when predicting local government audit outcomes using financial ratios. These results could be of use for auditors as focusing on these ratios could potentially speed up the detection of fraudulent behaviour in municipal entities, and improve the speed and quality of the overall audit.
Low-Level Control of a Quadrotor using Twin Delayed Deep Deterministic Policy Gradient (TD3)

Unmanned Aerial Vehicles (UAVs) like Quadrotors are inherently under-actuated and unstable systems. The mechanical complexity and non-linearity of such systems make it a difficult task to control the flight of the mentioned systems. However, due to recent advancements in the fields of data science and machine learning, new algorithms for flight stabilization and trajectory control were developed using Deep Reinforcement Learning. This paper presents two low-level Quadrotor controllers based on the same algorithm. The first designed controller aims to stabilize the Quadrotor at a certain preset point given any random initial position. The second is to track any target position given in the 3D space. Twin Delayed Deep Deterministic Policy Gradient (TD3) is used to train the agents to achieve the required tasks. This method is an off-policy Actor-Critic based method. It was used as it does not require a system model and works on environments with continuous action and state spaces. The superb performance of the trained policies is demonstrated in a simulation to illustrate the effectiveness of the proposed controllers.
Application of Cloud Computing in the Optimization of College Calisthenics Teaching Mode

Informatization teaching has become an important method of physical education. Especially with the support of big data and cloud computing technology, physical education teaching pays more attention to the use of information technology to provide rich data information, focusing on the development of physical education under the rich virtual reality environment, targeted scene atmosphere layout, and abundant learning resources. The reform and practical research of college aerobics teaching mode has gradually become an important topic in college aerobics teaching and research, and it has gradually attracted wide attention from teachers and students in colleges and universities. This article explores the status quo of sports aerobics teaching under the conditions of informationization and explores the application methods and methods of information technology in aerobics teaching, practically applies microclass and MOOC resources to physical education, and strives to organize and develop sports teaching intelligence with the help of information technology. In order to improve the quality of physical education and training with the help of information technology, it can meet the needs of students’ independent learning and growth and optimize the efficiency of physical education.
When do businesses report cybercrime? Findings from a UK study

Although it is known that businesses report cybercrime to public authorities at a low rate, and this hinders prevention strategies, there is a lack of research on companies’ decisions to report cyber victimisation. This paper analyses the UK Cyber Security Breaches Survey to explore factors associated with cybercrime reporting by businesses. Results indicate that the type of cybercrime is relevant to the reporting decision, and that the likelihood of reporting increases when cybersecurity incidents generate negative impacts and when the company places high priority on cybersecurity. However, we find no association between having cybersecurity insurance and reporting. Finally, while having outsourced cybersecurity management is associated with reporting to anyone outside the organisation but not to public authorities, in-house cybersecurity teams seem more inclined to report to public authorities. Findings are discussed in relation to the role of the private cybersecurity sector and the criminal justice system in combatting cybercrime.
Iterative Contrast-Classify for Semi-supervised Temporal Action Segmentation

Temporal action segmentation classifies the action of each frame in (long) video sequences. Due to the high cost of frame-wise labeling, we propose the first semi-supervised method for temporal action segmentation. Our method hinges on unsupervised representation learning, which, for temporal action segmentation, poses unique challenges. Actions in untrimmed videos vary in length and have unknown labels and start/end times. Ordering of actions across videos may also vary. We propose a novel way to learn frame-wise representations from temporal convolutional networks (TCNs) by clustering input features with added time-proximity conditions and multi-resolution similarity. By merging representation learning with conventional supervised learning, we develop an "Iterative Contrast-Classify (ICC)'' semi-supervised learning scheme. With more labelled data, ICC progressively improves in performance; ICC semi-supervised learning, with 40% labelled videos, performs similarly to fully-supervised counterparts. Our ICC improves MoF by {+1.8, +5.6, +2.5}% on Breakfast, 50Salads, and GTEA respectively for 100% labelled videos.
Fast Motion-Deblurring of IR Images

Camera gimbal systems pervade various applications such as navigation, target tracking, security and surveillance. The need for higher steering rate (rotation angle per second) of gimbal often results in motion blur in the captured video frames. Motion deblurring in real-time is difficult with existing blind restoration methods which incur large execution times while attempting to retrieve latent images from blurry inputs using high-dimensional optimization. On the other hand, deep learning methods for motion deblurring, though fast, do not generalize satisfactorily with domain shifts. In this work, we address the problem of real-time motion deblurring in infrared (IR) images captured by a real gimbal-based system. We propose two blur-kernel estimation methods and reveal how <i>a priori</i> knowledge of the blur-kernel can be used in conjunction with non-blind deblurring methods to achieve real-time performance. We experimentally show that, in comparison to the state-of-the-art techniques in deblurring, our method is better-suited for practical gimbal-based imaging systems.
Optimal Multi-Tier Clustering of Permissioned Blockchain Systems for IoT

In this work we describe and analyze a novel multi-tier architecture for recording Internet of Things (IoT) data. Each tier in the architecture consists of vertically interconnected node clusters that may be operated by different service providers. Blocks of IoT data are accepted by a cluster tier by tier, until they reach the top tier which adds them to the global blockchain and makes them available to client applications. To allow for large geographical coverage and reduce the dependency on any individual node in the cluster, clusters run a multiple entry version of the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm. We describe an analytical model of the system and find the optimal allocation of clusters and nodes within clusters for a given number of tiers and system coverage area. Our results show that the total number of orderers for given system coverage and total load are main predictors of the block linking time, and indicate that minimizing block linking time is easier to achieve with a smaller number of tiers and large number of clusters with a smaller number of orderers per cluster. These observations can be used as guidelines for planning and dimensioning of multi-tier cluster architectures that create and maintain blockchain structures in a given coverage area.
Evolving Romanian Crossword Puzzles with Deep Learning and Heuristic Search

Crossword puzzles are a challenging game of skill in which humans have been competing for decades. Recently a heuristic-search-based Artificial Intelligence (AI) solver for Romanian crossword puzzles achieved competition-level scores. In this work in progress we tackle procedural content generation of crossword puzzles. Using genetic algorithms we evolve crossword puzzle instances on which the AI solver can achieve a high score. Since the solver takes a substantial time to solve each instance we first train a deep neural network to predict the solution score the solver would achieve. We then run the evolution of crossword puzzles with the network as the fast proxy fitness function. We show that doing so is an effective way of procedurally generating numerous crossword puzzles.
Coverage Path Planning for 3D Terrain with Constraints on Trajectory Curvature Based on Second-Order Cone Programming

In precision agriculture, the optimal construction of a complete coverage for an uneven field with parallel paths is often considered. As a rule, a coverage can be defined by some initial path relative to which the whole coverage is constructed. For various purposes, such as minimizing the number of turnarounds, some part of the field boundary can be selected as the starting path. The planned paths are used by agricultural machines to perform their job. Assume that a machine following the path has a so called Ackermann steering mechanism. In this case, the vehicle can only use paths whose curvature does not exceed the specified threshold value. For this reason, some paths may be infeasible, which leads to gaps areas in the field’s coverage. In this paper we consider the path planning method that uses some specified swaths’ overlap for exclusion of gaps areas (unprocessed areas). To approximate the paths, we use uniform cubic B-spline curves. Each elementary spline of this curve is defined by four control points, which are calculated by the algorithm. The target path always lies inside the convex hull of the four adjacent control points. Our method involves imposing conditions on an estimation of a path curvature, which can be represented as second-order cone constraints. In addition, the curvature estimation is included in the optimization criterion, which allows one to straighten the paths when constructing them. As a result, the task of constructing a neighboring path can be represented as a Second-Order Cone Programming problem (SOCP). The algorithm has been tested on a data taken in real fields.
From What to Why, the Growing Need for a Focus Shift Toward Explainability of AI in Digital Pathology

While it is impossible to deny the performance gains achieved through the incorporation of deep learning (DL) and other artificial intelligence (AI)-based techniques in pathology, minimal work has been done to answer the crucial question of why these algorithms predict what they predict. Tracing back classification decisions to specific input features allows for the quick identification of model bias as well as providing additional information toward understanding underlying biological mechanisms. In digital pathology, increasing the explainability of AI models would have the largest and most immediate impact for the image classification task. In this review, we detail some considerations that should be made in order to develop models with a focus on explainability.
GANBLR: A Tabular Data Generation Model

Generative Adversarial Network (GAN) models have shown to be effective in a wide range of machine learning applications, and tabular data generation process has not been an exception. Notably, some state-of-the-art models of tabular data generation, such as CTGAN, TableGan, MedGAN, etc. are based on GAN models. Even though these models have resulted in superiour performance in generating artificial data when trained on a range of datasets, there is a lot of room (and desire) for improvement. Not to mention that existing methods do have some weaknesses other than performance. E.g., the current methods focus only on the performance of the model, and limited emphasis is given to the interpretation of the model. Secondly, the current models operate on raw features only, and hence they fail to exploit any prior knowledge on explicit feature interactions that can be utilized during data generation process. To alleviate the two above-mentioned limitations, in this work, we propose a novel tabular data generation model– Generative Adversarial Network modelling inspired from Naive Bayes and Logistic Regression’s relationship (GANBLR), which can not only address the interpretation limitation in existing tabular GAN-based models but can provide capability to handle explicit feature interactions. By extensively evaluating on wide range of datasets, we demonstrate GANBLR’S superiour performance as well as better interpretable capability (explanation of feature importance in the synthetic generation process) as compared to existing state-of-the-art tabular data generation models.
Using Knowledge Graph Structures for Semantic Interoperability in Electronic Health Records Data Exchanges

Information sharing across medical institutions is restricted to information exchange between specific partners. The lifelong electronic health records (EHR) structure and content require standardization efforts. The existing standards such as openEHR, HL7, and CEN TC251 EN 13606 (Technical committee on Health Informatics of the European Committee for Standardization) aim to achieve data independence along with semantic interoperability. This study aims to discover knowledge representation to achieve semantic health data exchange. OpenEHR and CEN TC251 EN 13606 use archetype-based technology for semantic interoperability. The HL7 Clinical Document Architecture is on its way to adopting this through HL7 templates. Archetypes are the basis for knowledge-based systems as these are means to define clinical knowledge. The paper examines a set of formalisms for the suitability of describing, representing, and reasoning about archetypes. Each of the information exchange technologies such as XML, Web Ontology Language, Object Constraint Language, and Knowledge Interchange Format is evaluated as a part of the knowledge representation experiment. These examine the representation of Archetypes as described by Archetype Definition Language. The evaluation maintains a clear focus on the syntactic and semantic transformations among different EHR standards.
La Inteligencia Artificial en la educación: Big data, cajas negras y solucionismo tecnológico

The use of digital technology is constantly permeating and transforming all social systems, and education is not an exception. In the last decade, the development of Artificial Intelligence has given a new push to the hope of providing educational systems with ‘effective’ and more personalized solutions for teaching and learning. Educators, educational researchers, and policymakers, in general, lack the knowledge and expertise to understand the underlying logic of these new systems, and there is insufficient research based evidence to fully understand the consequences for learners’ development of both the extensive use of screens and the increasing reliance on algorithms in educational settings. This article, geared towards educators, academics in the field of Education, and policymakers, first introduces the concepts of ‘Big Data’, Artificial Intelligence, Machine Learning algorithms and how they are presented and deployed as ‘black boxes’, and the possible impact on education these new software solutions can have. Then, it focuses on the underlying educational discourses that historically have seen information and communication technologies as a panacea for solving educational problems, pointing out the need to analyse not only their advantages, but also their possible negative effects. It finishes with a short exploration of possible future scenarios and conclusions.
Computer-Aided Assessment of Melanocytic Lesions by Means of a Mitosis Algorithm

An increasing number of pathology laboratories are now fully digitised, using whole slide imaging (WSI) for routine diagnostics. WSI paves the road to use artificial intelligence (AI) that will play an increasing role in computer-aided diagnosis (CAD). In melanocytic skin lesions, the presence of a dermal mitosis may be an important clue for an intermediate or a malignant lesion and may indicate worse prognosis. In this study a mitosis algorithm primarily developed for breast carcinoma is applied to melanocytic skin lesions. This study aimed to assess whether the algorithm could be used in diagnosing melanocytic lesions, and to study the added value in diagnosing melanocytic lesions in a practical setting. WSI's of a set of hematoxylin and eosin (H&E) stained slides of 99 melanocytic lesions (35 nevi, 4 intermediate melanocytic lesions, and 60 malignant melanomas, including 10 nevoid melanomas), for which a consensus diagnosis was reached by three academic pathologists, were subjected to a mitosis algorithm based on AI. Two academic and six general pathologists specialized in dermatopathology examined the WSI cases two times, first without mitosis annotations and after a washout period of at least 2 months with mitosis annotations based on the algorithm. The algorithm indicated true mitosis in lesional cells, i.e., melanocytes, and non-lesional cells, i.e., mainly keratinocytes and inflammatory cells. A high number of false positive mitosis was indicated as well, comprising melanin pigment, sebaceous glands nuclei, and spindle cell nuclei such as stromal cells and neuroid differentiated melanocytes. All but one pathologist reported more often a dermal mitosis with the mitosis algorithm, which on a regular basis, was incorrectly attributed to mitoses from mainly inflammatory cells. The overall concordance of the pathologists with the consensus diagnosis for all cases excluding nevoid melanoma (n = 89) appeared to be comparable with and without the use of AI (89% vs. 90%). However, the concordance increased by using AI in nevoid melanoma cases (n = 10) (75% vs. 68%). This study showed that in general cases, pathologists perform similarly with the aid of a mitosis algorithm developed primarily for breast cancer. In nevoid melanoma cases, pathologists perform better with the algorithm. From this study, it can be learned that pathologists need to be aware of potential pitfalls using CAD on H&E slides, e.g., misinterpreting dermal mitoses in non-melanotic cells.