Reviewerly/exHarmony huggingface dataset:
Inappropriate arguments as a communication technology: types, peculiarities, and countering mechanisms

This article is dedicated to such type of communication technology as inappropriate arguments. Special attention is given to clarification of the key concepts of the topic &amp;ndash; &amp;ldquo;argumentation&amp;rdquo;, &amp;ldquo;appropriateness&amp;rdquo;, &amp;ldquo;inappropriateness&amp;rdquo;, and &amp;ldquo;context&amp;rdquo;. The original classification is provided to the types of inappropriate arguments. The author reveals the contextual peculiarities of using different types of inappropriate arguments; analyzes the specificity of using inappropriate arguments; as well as offers possible countering mechanisms. Particular examples of using incorrect arguments are confirmed by links to recorded live dialogues. The article employs the methods of intent analysis and comparative analysis. The Russian tradition of studying the theory and practice of argumentation features a number of research that touch upon the problem of inappropriate arguments. However, the study of arguments is just a part of the whole variety of communication technologies. The novelty of this work consists in the centralized, systematized presentation of the assemblage of variations of inappropriate arguments. The types of inappropriate arguments are illustrated on the specific examples from literature, cinematography, cultural -historical, social and political spheres of society. The acquired results can be valuable for the development of lecture courses on the theory and practice of argumentation, students majoring in philosophy, and audience interested in modern research dedicated to communication technologies.
Learning to Discover Explainable Clinical Features With Minimum Supervision

To compare supervised transfer learning to semisupervised learning for their ability to learn in-depth knowledge with limited data in the optical coherence tomography (OCT) domain.Transfer learning with EfficientNet-B4 and semisupervised learning with SimCLR are used in this work. The largest public OCT dataset, consisting of 108,312 images and four categories (choroidal neovascularization, diabetic macular edema, drusen, and normal) is used. In addition, two smaller datasets are constructed, containing 31,200 images for the limited version and 4000 for the mini version of the dataset. To illustrate the effectiveness of the developed models, local interpretable model-agnostic explanations and class activation maps are used as explainability techniques.The proposed transfer learning approach using the EfficientNet-B4 model trained on the limited dataset achieves an accuracy of 0.976 (95% confidence interval [CI], 0.963, 0.983), sensitivity of 0.973 and specificity of 0.991. The semisupervised based solution with SimCLR using 10% labeled data and the limited dataset performs with an accuracy of 0.946 (95% CI, 0.932, 0.960), sensitivity of 0.941, and specificity of 0.983.Semisupervised learning has a huge potential for datasets that contain both labeled and unlabeled inputs, generally, with a significantly smaller number of labeled samples. The semisupervised based solution provided with merely 10% labeled data achieves very similar performance to the supervised transfer learning that uses 100% labeled samples.Semisupervised learning enables building performant models while requiring less expertise effort and time by using to good advantage the abundant amount of available unlabeled data along with the labeled samples.
Sailor Integral System using Elastic Cloud Compute Module

The main objective of the work is to locate the live coordinates of the sailor during their journey using Global Positioning System (GPS). The work results in a quality product that ensures the safety of the sailor from crossing the borders or nearing the border of the contiguous countries or illegal borders. So, the system alerts the sailors from being captured by the naval of other countries. This is a serious issue that happens day to day. GPS gives information about the current latitude and longitude values, which is then sent to the microcontroller unit. The microcontroller unit compares the current location with the predefined value. If the current latitude and longitudinal values exceeds predefined values, the system alerts the fishermen that they are about to reach the nautical border. If the boat enters the zone nearer to the restricted zone, then the buzzer will turn on. The system provides precise location details, such as where the point they have started their journey and the point they are right now. The application user can bookmark location at the sea where there is high yield of fish breeds for fishing and pearl cultivation. The system can provide prior information whether the journey is safe to travel. If in case of any bad weather the storm trails can be indicated on the map.
On Minimum Parking Space Required by Automatic Parallel Parking

Automatic parking system has been widely equipped on manual driving vehicles for decades. Due to the limited environment sensing ability and not-accurate-enough positioning system, however, it usually requires a very large redundant space for a car to accomplish one successful parking maneuver. There would be two differences in the coming era of automatic driving: (1) sufficiently accurate perception and positioning systems can be expected to be available, and (2) the number of parking trials could be large when parking without passengers on the vehicle. Under such circumstances, it is quite important for the vehicle to know exactly whether the external and internal spaces are large enough for parking and how the minimum number of trials will be required. This paper presents closed-form solutions of minimum internal and external space required for automatic parallel parking and the corresponding trials if the parking is based on circular arcs. On the other hand, it is also very useful for the vehicle to know the required minimum space if only one trial is allowed, for which the solutions are presented both for circular arcs and continuous-curvature curves based parking, respectively. To the best of our knowledge, there is no so far systematic analysis and research on the issue addressed in this paper.
False Scattering Center Extraction Based on Template Matching Method

When electromagnetic (EM) coupling exists, false peaks occur periodically in high-resolution range profile (HRRP). These false peaks restrict the improvement of the success rate of the target recognition based on HRRPs. Therefore, identifying these false peaks and removing them is crucial, but has not been thoroughly investigated in the open literature. This letter first studies the EM coupling mechanism in a multiobject system according to the iterative physical optics theory. Then, templates to match the false peaks are constructed based on the established EM coupling theory. Next, cross correlation is performed to recognize and remove the false peaks. To verify the presented method, a metallic multitriangle model is used. The simulation results show that the proposed method can remove the false peaks and find the accurate locations of the metallic triangles. Finally, the practicability of the method is proved through experimental measurement results.
Instance-Based Neural Dependency Parsing

Abstract Interpretable rationales for model predictions are crucial in practical applications. We develop neural models that possess an interpretable inference process for dependency parsing. Our models adopt instance-based inference, where dependency edges are extracted and labeled by comparing them to edges in a training set. The training edges are explicitly used for the predictions; thus, it is easy to grasp the contribution of each edge to the predictions. Our experiments show that our instance-based models achieve competitive accuracy with standard neural models and have the reasonable plausibility of instance-based explanations.
Authorship Attribution for Kannada Text Using Profile Based Approach

The study of authorship analysis has a long history in settling authorial disagreements about historic and graceful work. Authorship identification has reached its popularity by extracting knowledge from the huge amount of data present online. Predicting the authorship of an anonymous text is mainly based on the author's writing style. Analyzing the author's writing style is a challenging task in the local languages. Kannada is one of the popular south Indian languages and spoken roughly by 52 million people. According to the survey we carried out, till now authorship attribution in Kannada digital text has not been done yet. In this paper we are proposing a new model which uses machine learning algorithms to predict the authorship of an anonymous text for Kannada documents based on the profile based approach and we obtained 88% accuracy.
Arabic text clustering technique to improve information retrieval

Arabic language has its own characteristics which are different than other languages. It is concatenative language, so there are many problems in processing of Arabic language. The work in this research project represents an attempt to solve the problems facing Arabic documents users by proposing a new approach where the retrieved documents are classified into limited number of groups (clusters) that may help the users in finding out the relevant documents efficiently and effectively. First the data was collected and pre-processed, and then a complete IR system has been implemented that included all IR processing, which in turn has been improved by clustering techniques for the user point of view where the retrieved documents were grouped into k clusters. Three clustering techniques (k-means as a type of flat clustering, Ward’s and Average agglomerative as a types of Hierarchal clustering) were applied. Two types of stemmer (Heavy and Light stemmer) were applied in pre-processing phase. The results obtained from applying the proposed approach on one thousand Arabic text documents (Collected from “Alsabaah” Newspaper), showed that the users can be conducted in a way the returns relevant documents with better accuracy and performance. Different techniques and algorithms were implemented using C# and Python programming languages.
Sustainable-Security Assessment Through a Multi Perspective Benchmarking Framework

The current cyber-attack environment has put even the most protected systems at risk as the hackers are now modifying technologies to exploit even the tiniest of weaknesses and infiltrate networks. In this situation, it's critical to design and construct software that is both secure and long-lasting. While security is the most well-defined aspect of health information software systems, it is equally significant to prioritise sustainability because any health information software system will be more effective if it provides both security and sustainability to the customers at the same time. In this league, it is crucial to determine those characteristics in the systems that can help in the accurate assessment of the sustainable-security of the health information software during the development stage. This research work employed the Fuzzy Analytic Network Process (Fuzzy ANP) to estimate the impact of the overall sustainable-security of health information software systems and their characteristics in order to achieve a high level of sustainable-security. Furthermore, the study validates the efficacy of the Fuzzy ANP procedure by testing it on five different versions of a health information software system through Fuzzy Technique for Order of Preference by Similarity to Ideal Solutions (Fuzzy TOPSIS). Despite the sensitivity of the health information software systems, this study employed multiple versions of health information software system. When compared with the existing procedures for testing the sustainable-security of health information software systems, the outcomes were conclusive and significantly more effective. Besides saving time and resources, the mechanism suggested in this research work aims to establish an outline that software practitioners can follow to enhance the sustainable-security of health information software systems.
Property Similarity Line Segment Detector

Most line segment detection methods suffer from over-segmentation. Therefore, we propose a line segment detector based on property similarity. It is composed of three steps. First, after the original line segments are generated, the competitive grouping method is used to group line segments with many same aligned endpoints, and a minimum bounding rectangle is adopted to enclose every group. In the second step, the line segments in the same group are verified whether they have similar positions and gradients. Finally, the center lines of the rectangles are the final line segments. We show that our method can significantly detect complete line segments and much less prone to over-segmentation while maintaining real-time performance. The experimental results illustrate that our method achieves a maximum F-score of approximately 27&#x0025;, which about 5 &#x0025; more than other leading methods when analyzing the images from the York urban line segments dataset.
Memory Attentive Cognitive Diagnosis for Student Performance Prediction

As the core of intelligent education, cognitive diagnosis aims to capture the proficiency of students on specific knowledge concepts. The Neural Cognitive Diagnosis (Neural CD) exploits an elegant method to simulate the interactions of student exercising process with deep neural networks. However, Neural CD still treats the student factor as static, which doesn’t change after the exercising process, against the common sense that a student will gain better proficiency after practices. Furthermore, the Neural CD focuses on the current exercise despite leveraging multi fully connected layers to model the complex interactions, ignoring the relationship between exercises. In this paper, we propose the Memory Attentive Cognitive Diagnosis (MACD) for student performance prediction. Specifically, MACD introduces memory-augmented neural networks to express the student factor, which can be updated with the process of solving exercises. Moreover, MACD replaces the multi fully connected layers with multi-heads attention layers to consider the relationship between the current exercise and the past exercises. We conduct experiments on several real-world datasets and the experimental results show that our model outperforms the state-of-the-art approaches.
“Mini-Benchmarking” Approach to Optimize Evolutionary Methods of Neural Architecture Search

Due to the rapid development of Artificial Neural Networks (ANN) models, the number of hyperparameters constantly grows. With such a number of parameters, it’s necessary to use automatic tools for building or adapting new models for new problems. It leads to the expansion of Neural Architecture Search (NAS) methods usage, which performs hyperparameters optimisation in a vast space of model hyperparameters, so-called hyperparameters tuning. Since modern NAS techniques are widely used to optimise models in different areas or combine many models from previous experiences, it requires a lot of computational power to perform specific hyperparameters optimisation routines. Despite the highly parallel nature of many NAS methods, they still need a lot of computational time to converge and reuse information from the generations of previously synthesised models. Therefore it creates demands for parallel implementations to be available in different cluster configurations and utilise as many nodes as possible with high scalability. However, simple approaches when the NAS solving is performed without considering results from the previous launches lead to inefficient cluster utilisation. In this article, we introduce a new approach of optimisation NAS processes, limiting the search space to reduce the number of search parameters and dimensions of the search space, using information from the previous NAS launches that allow decreasing demands of computational power and improve cluster utilisation as well.
A Deep Neural Network Machine Vision Application for Preventing Wildlife-Human Conflicts

Most wildlife-human conflicts can be prevented if humans, who could potentially be affected, can be alerted about the presence of wildlife nearby so that they can take avoidance measures. The alerts must be accurate and timely so that such measures can be taken. We propose a Deep Neural Network consisting of two stages, that we call ‘WildlifeNet’, to automatically detect the presence of specific wildlife. WildlifeNet is optimized for low power and low memory so that it can be embedded in edge devices such as surveillance cameras or low cost special-purpose cameras. The first stage in WildlifeNet is an object detection system using the MobileNet model in TensorFlow that detects animals in an image. This is followed by our custom Convolutional Neural Network classification system that identifies specific animal species from the animals detected in the first stage. WildlifeNet uses images from surveillance cameras or low cost cameras placed near typical animal paths to detect the presence of wildlife. The components surrounding WildlifeNet in the machine vision system presented in this paper can quickly alert those living near the specific location where detections occur via their mobile phones. The custom Convolutional Neural Network model in WildlifeNet’s second stage was trained using a large number of coyote images from the Caltech wildlife image dataset to demonstrate its usefulness in detecting specific wildlife. We observed a consistently high accuracy of coyote detection with a potential towards even higher accuracies with user feedback. Therefore, this system is a viable candidate for consideration as an effective, fast, low-cost technology to assist in preventing wildlife-human conflicts.
Features of Software Development for Data Mining of Storage System State

This article proposes the architecture of a diagnostic data mining software system for data storage systems. Analysis of logs consists of several stages: extraction of metrics from big data storage diagnostic information using artificial neural networks, analysis of time series anomalies for diagnostic data metrics using machine learning methods, generalization of the results of the analysis of anomalies and patterns of change and existence of storage performance metrics based on the characteristics of the subject area using a subject ontology, the inference of the recommendation to the administrator for correcting the storage system state through the composition of the results of the analysis of anomalies and subject ontology. In the article, an effective solution (software or algorithm) is found for the implementation of each stage. In the future, it is planned to develop this system and evaluate its effectiveness during experiments.
Evaluating Gender-Neutral Training Data for Automated Image Captioning

Amassing large-scale datasets used to train machine learning algorithms often includes crowd-sourcing or web scraping. The data resulting from these approaches can carry undesired societal biases that are reflected in the predictions of the learning system. Recently, researchers have proposed mitigation strategies targeting either the learning algorithms or the data used for training. In this paper, we evaluate a simple data augmentation strategy for the task of automated image captioning, namely substituting gendered terms for gender-neutral equivalents. We evaluated this approach on multiple recent image captioning models using both objective and subjective analysis. We found that human raters did not find a difference in the quality of the image captions and, in some cases, the model was able to generate more accurate captions with additional detail when trained with gender-neutral data.
Optimizing Loss Function for Uni-modal and Multi-modal Medical Registration

Recent learning-based methods render fast registration by leveraging deep networks to directly learn the spatial transformation fields between the source and target images. However, manually designing and tuning loss functions for multiple types of medical data need intensive labor and extensive experience, and automatic design of loss functions remains under-investigated. In this paper, we introduce a unified formulation of the loss function and raise automated techniques to search hyperparameters of losses to obtain the optimal loss function. Specifically, we take into consideration of the multifaceted properties of image pairs and propose a unified loss to constraint similarity from different aspects. Then, we propose a bilevel self-tuning training strategy, allowing the efficient search of hyperparameters of the loss function. Based on the adaptive degrees, the proposed unified loss would be applicable to the registration of arbitrary modalities and multiple tasks. Moreover, this training strategy also reduces computational and human burdens. We conduct uni-modal and multi-modal registration experiments on seven 3D MRI datasets, the networks trained with the searched loss functions deliver accuracy on par or even superior to those with the handcrafted losses. Extensive results demonstrate our advantages over state-of-the-art registration techniques in terms of accuracy with efficiency.
Exploring Ranked Local Selectors for Stable Explanations of ML Models

While complex machine learning methods can achieve great performance, human-interpretable details of their internal reasoning is to a large extent unavailable. Interpretable machine learning can remedy the lack of access to model reasoning but remains an elusive feat to fully achieve. Here we propose ranked selectors as a method for post-hoc explainability of classification outcomes from arbitrary classification models, with an initial emphasis on tabular data of moderate dimensions. The method is based on constructing a set of selectors, or rules, delimiting a local class consistent domain with maximal cover around the item of interest. The extended adjacent feature space is probed to achieve a ranking of the selectors. The method supports the use of an explicit foil class Q, allowing the formulation of contrastive queries in the form “Why inference P instead of alternative inference Q?”. The answer is given as a short list of disjoint rules, a format previously shown to be amenable to human interpretation. We demonstrate the proposed method in open datasets, and elaborate on its stability aspects relative to other comparable methods.
Cryptographic Techniques for Data Processing

This tutorial focuses on two principal lines of research aimed to provide secure data processing cloud-based data outsourcing — encryption and secret-sharing-based approaches that have been developed over the last two decades. The tutorial compares these techniques based on efficiency and information leakage. We discuss that existing cryptographic techniques are not sufficient alone to achieve the goal of efficient as well as secure data processing. To overcome the limitation of efficient and secure data processing, a new line of work that combines software and hardware mechanisms is required. We discuss an orthogonal approach designed around the concept of data partitioning, i.e., splitting the data processing into cryptographically secure and non-secure parts. Finally, we will discuss some open questions in designing secure cryptographic techniques that can process large-sized data efficiently.
Adaptive Rotation Forests: Decision Tree Ensembles for Sequential Learning

We have developed an ensemble-based approach for online machine learning: adaptive rotation forest and AD-WIN adaptive rotation forest. We focused on rotation forest, an offline supervised ensemble algorithm with a particularly high prediction accuracy while all the features are continuous. Our objective was to develop a high-performance online ensemble method that uses a process similar to that of rotation forest in an online environment. Our experiments demonstrated that the proposed approach simplifies the tree structure used for the base learners, reduces memory consumption, and improves prediction accuracy for some data streams.