Reviewerly/exHarmony huggingface dataset:
Towards a Reliable and Context-Based System Architecture for Autonomous Vehicles.

Full vehicle autonomy excludes a takeover by passengers in case a safety-critical application fails. Therefore, the system responsible for operating the autonomous vehicle has to detect and handle failures autonomously. Moreover, this system has to ensure the safety of the passengers, as well as the safety of other road users at any given time. Especially in the initial phase of autonomous vehicles, building up consumer confidence is essential. Therefore, in this regard, handling all failures by simply performing an emergency stop is not desirable. In this paper, we introduce an approach enabling a dynamic and safe reconfiguration of the autonomous driving system to handle occurring hardware and software failures. Since the requirements concerning safe reconfiguration actions are significantly affected by the current context the car is experiencing, the developed reconfiguration approach is sensitive to context changes. Our approach defines three interconnected layers, which are distinguished by their level of awareness. The top layer, referred to as the context layer, is responsible for observing the context. These context observations, in turn, imply a set of requirements, which constitute the input for the reconfiguration layer. The latter layer is required to determine reconfiguration actions, which are then executed by the architecture layer.
Relevance of Using Interpretability Indexes for the Design of Schedulers in Cloud Computing Systems

At the dawn of the fourth industrial revolution, artificial intelligence is impregnating our society by an overwhelming number of applications with rising complexity. This circumstance has triggered a new debate on explainable artificial intelligence in terms of transparency and confidence. Therefore, the relevance of interpretability is remarkable since this concept provides us with transparence and understandability in avoidance of black-box systems. On the other hand, cloud computing is a new paradigm of distributed computation based on the externalization of computing needs offered as services, whose performance has a significant economic and environmental impact and is strongly influenced by the scheduler, which is, likely, the most critical part in charge of allocation computational resources. In this regard, fuzzy rule-based systems are knowledge-based systems that are increasingly arising as an alternative for the development of cloud scheduling systems, mainly due to their intrinsic features such as adaptability to environments, dynamism and capability to cope with uncertainty. Bearing in mind the above-mentioned ideas, this paper presents a study-case in which the interpretability in fuzzy rule-based schedulers for cloud computing, obtained through automatic learning processed, has been analyzed. Results show how an inherent feature, like interpretability, vanishes due to the use of learning processes for both total execution time and power consumption optimization, which put the focus on the relevance of facing interpretability in this kind of systems.
Clustering Diagnostic Codes: Exploratory Machine Learning Approach for Preventive Care of Chronic Diseases

High prevalence of chronic diseases along with poor health condition and the rising diagnosis and treatment costs necessitates concentration on prevention, early detection and disease management. In this paper correlation among the chronic diseases is examined with the help of diagnostic codes using unsupervised Machine Learning (ML) approaches. ML approaches pave the way to accomplish this objective. Healthcare data is categorized into clinical, Medi-claim, drugs and emergency information. In this work, Medi-claim data is used for exploring five types of chronic disorders such as Diabetes, Heart, Kidney, Liver and Cancer. Medi-claim data is acceptable because of its legitimacy, volume and demography qualities. Hierarchical Condition Category (HCC) and International Classification of Diseases (ICD) based coding of med-claim data are perfect with guaranteed informational index, this nature of ICD and HCC code urged us to work with Medi-claim records. The categorization of chronic and non-chronic diseases is built up through HCC codes utilizing different clustering techniques such as partitional, hierarchical and Fuzzy-K means clustering. The model is evaluated using various metrics such as Homogeneity, Completeness, V-measure, Adjusted Rand index, Adjusted Mutual Information. Among all the clustering techniques used K means and K means random have shown promising results. A compelling end on clustering of chronic diseases is made, remembering the clinical significance.
Human Position Tracking and Detection Using Geometric Active Contours

Human position tracking and detection of human being is used as an initial step in biometric recognition. Continuous track a person will allow identifying people at any time. Its system consists of two parts person detection and another one is tracking. Active contours are a catch-all name for methods that found the curve that best separates objects in an image. This is known as segmentation. Segmentation is extraordinarily related to tracking. In 3D - it discusses about active surfaces. One way to divide segmentation methods - single object vs. multiple objects. Another way to divide segmentation methods: Edge-based - the object to be segmented should accept its boundary visible in the image, as some sort of outstanding edge. Region-based - the region of the object in the image should have a different actuarial in some sort of feature space, estimate to its surroundings. These functional are integrating with information on the shape of the area being extracted.
Algebraic geometry codes and some applications

This article surveys the development of the theory of algebraic geometry codes since their discovery in the late 70's. We summarize the major results on various problems such as: asymptotic parameters, improved estimates on the minimum distance, and decoding algorithms. In addition, we present various modern applications of these codes such as public-key cryptography, algebraic complexity theory, multiparty computation or distributed storage.
Supported Decision-Making by Explainable Predictions of Ship Trajectories

Machine Learning and Deep Learning models make accurate predictions based on a specifically trained task. For instance, models that classify ship vessel types based on their trajectory and other features. This can support human experts while they try to obtain information on the ships, e.g., to control illegal fishing. Besides the support in predicting a certain ship type, there is a need to explain the decision-making behind the classification. For example, which features contributed the most to the classification of the ship type. This paper introduces existing explanation approaches to the task of ship classification. The underlying model is based on a Residual Neural Network. The model was trained on an AIS data set. Further, we illustrate the explainability approaches by means of an explanatory case study and conduct a first experiment with a human expert.
Editor’s Note. Towards Blockchain Intelligence

I n 2008, Satoshi Nakamoto introduced the Bitcoin [1], the first cryptocurrency that had a significant impact in the financial scene.This currency relied on some technological innovations, including a cryptographic mining system and a distributed database with interesting properties, deemed "the Blockchain".
Context-Aware XGBoost for Glottal Closure Instant Detection in Speech Signal

In this paper, we continue to investigate the use of classifiers for the automatic detection of glottal closure instants (GCIs) in the speech signal. We introduce context to extreme gradient boosting (XGBoost) and show that the context-aware XGBoost outperforms its context-free version. The proposed context-aware XGBoost is also shown to outperform traditionally used GCI detection algorithms on publicly available databases.
An Info-Leak Resistant Kernel Randomization for Virtualized Systems

Given the significance that the cloud paradigm has in modern society, it is extremely important to provide security to users at all levels, especially at the most fundamental ones since these are the most sensitive and potentially harmful in the event of an attack. However, the cloud computing paradigm brings new challenges in which security mechanisms are weakened or deactivated to improve profitability and exploitation of the available resources. Kernel randomization is an important security mechanism that is currently present in all main operating systems. Function-Granular Kernel Randomization is a new step that aims to be the future of the kernel randomization, because it provides much more security than current kernel randomization approaches. Unfortunately, function-granular kernel randomization also impacts significantly on the performance and potential benefits of memory deduplication. Both function-granular kernel randomization and memory deduplication are desired and beneficial; the first for the strong protection it gives, and the second for the reduction of costs in terms of memory consumption. In this paper, we analyse the impact of function-granular kernel randomization on memory deduplication revealing why it cannot offer maximum security and shareability of memory simultaneously. We also discuss the reasons why having a full position independent kernel code counter-intuitively does not solve the problem introducing a challenge to kernel randomization designers. To solve these problems, we propose a function-granular kernel randomization modification for cloud systems that enables full function-granular kernel randomization while reduces memory deduplication cancellations to almost zero. The proposed approach forces guest kernels of the same tenant to have the same random memory layout of memory regions with high impact on deduplication, ensuring a high rate of deduplicated pages while the kernel randomization is fully enabled. Our approach enables cloud providers to have both, high levels of security and an efficient use of resources.
Analysis of Health Care Data Using Natural Language Processing

One in each of the applications of NLP is text classification. In this paper, a multi-label classification model of tagging the plain text is developed where the data is obtained from the Automatic Speech Recognition (ASR) model. However, the data collection process is utilized here by using automatic speech recognition systems (ASR). The main goal of the project is to set up an efficient model which guesses the tags for plain text received which achieves 82% accuracy. By studying all these, it has been confirmed that supervised learning algorithms like Naive Bayes, Support Vector Machine (SVM)) showed better performance for the task of Text Classification.
Generation of Spin 1 System

In this paper, we shall establish the connection between the group theory and quantum mechanics by showing how the group theory helps us to construct the spin operators.We look to the group generators SU(3).From these generators, new spin 1 operators will be constructed.These operators ,x y S S --We shall discuss the notion of spin squeezing and correlations for pure spin 1 system using our spin operators ,x y
3. The Anti-Ecological University: Competitive Higher Education as Ecological Catastrophe

&lt;?page nr="45"?&gt;Abstract The article aims at critiquing the current competitive framing which is shaping higher education policies and discourse at an international level. The argument initially takes an historical and genealogical approach to trace the roots of this framing to ideological misinterpretations of evolutionary theory and ecology, and to the influence of eugenics in the framing of educational policy. The article then uses a systems theory lens to articulate the negative impact of competitive dynamics in global education on an ecosystemic level, focusing on a “deecologization” of the two dimensions of time and space in the lives of university students and staff. Finally, the article concludes suggesting the necessity of an imaginative reecologization of thought as a precondition for any sustainable change in Higher Education philosophy and policy.
Teachers’ Perceptions about the Impact of Moodle in the Educational Field Considering Data Science

Today, Learning Management Systems (LMS) such as Moodle facilitate the teaching-learning process, promote the organization of creative activities from anywhere and allow the active participation of the students before, during and after the face-to-face sessions. The objective of this quantitative research is to analyze the teachers’ perceptions about the impact of Moodle in the educational field considering data science and machine learning. The independent variable is the use of Moodle during the organization of new school activities and the dependent variables are the performance of the activities inside and outside the classroom and the participation and communication during the educational process. The participants are 70 teachers from the National Autonomous University of Mexico (UNAM). The results of machine learning (linear regression) indicate that Moodle positively influences the participation and communication during the educational process. Likewise, this LMS positively influences the performance of the activities inside and outside the classroom. In particular, Moodle allows improving the educational field through the realization of the online exams and discussion forums, diffusion of the tasks and consultation of the contents at any time. Data science identifies 3 predictive models on the impact of Moodle in the educational field. In fact, the decision tree technique establishes the conditions on the use of this LMS considering the characteristics of the teachers (sex and maximum degree of study). The implications of this research allow affirming that teachers have the opportunity to create, organize and carry out various creative and active activities through this LMS. Finally, teachers can use Moodle to update the activities of the courses and build new educational spaces that allow the active role of the students during the learning process.
Descriptive Analysis of Emoticons/Emoji and Persuasive Digital Language Use in WhatsApp Messages

WhatsApp messaging has emerged as one of the major ways that people mostly use for communication purposes in conjunction with fast changes in Internet and computer technologies.Linguistic structures with emoticons/emoji enrich the quality of communication in the environment, which has created a digital language.One of the significant purposes of using such a digital language is obviously to persuade the receiver and send feedback to the sender of the message.On the one hand, persuasive communication is almost indispensable for people who use WhatsApp.On the other hand, persuasion is a real and intentional cognitive process based on preferring certain linguistic style, decision making and providing motivational feedback.In this regard, this work aims at clarifying and exploring persuasive messages with emoticons/emoji based on digital language styles with non-standard writing.The study particularly investigates persuasive digital language used in the What-sApp messages in terms of the following aspects: 1) languages which are used by the participants in social media platforms, 2) purposes of the participants in using social media platforms, 3) choices of persuasive language by the participants in social media platforms, 4) words that the participants initiate to start persuasive conversations in WhatsApp messages and 5) types of sentences the participants mostly produce in WhatsApp messages.It has been observed that Wh-adverbs (WRB) and Wh-pronouns (WP) are often used in initiation of persuasive language patterns.Female participants more frequently choose emoticons, photos, abbreviations, question/question marks, fiend's names and pictures of the events than male participants do.The participants mostly preferred to use interrogatives during production of persuasive language patterns in WhatsApp messages.
Diseño e implementación de un sistema de visión por computadora para el reconocimiento de objetos en movimiento

In this work we developed a computer vision system that allow the geometric figures recognition in real time using a web camera, also the identification of the objects size using a known object. To make this possible we used several techniques of the digital image processing as gray scale filters because that’s the first step to start the image processing, threshold to make the image transformation in binary form where 1 is the white color and 0 the black color, edge detection to detect the edges of the figures therefore an important parameter is the noise cancelation of the obtained signals, that’s the reason of the using of digital filters because those allow the oscillation cancelation in the signals. The possible application can be the microorganism detection in controlled environments accord of its morphology also the change of its area in real time.
Safety and Security Are Two Sides of the Same Coin

Abstract Whether safety and security overlap depends on how one defines each of these qualities. Definitions are man-made and the definer can include or exclude anything they want. The question really is what the definitions imply for the solution of the problems being defined and which definitions lead to the most effective achievement of the property or properties being defined. This chapter starts by proposing an inclusive definition that combines safety and security and then discusses the practical implications of this definition for solving our safety and security problems. These implications include (1) recognizing that safety and security are not equivalent to reliability, (2) broadening the focus in security from information security and keeping intruders out, and (3) creating new integrated analysis methods based on system theory.
Extended Relativistic Invariance, Quantization of the Kinetic Momentum

The aim of this research is a better understanding of the quantization in physics.The true origin of the quantization is the existence of the quantized kinetic momentum of electrons, neutrinos, protons and neutrons with the /2 value.It is a consequence of the extended relativistic invariance of the wave of fundamental particles with spin 1/2.This logical link is due to properties of the quantum waves of fermions, which are functions of space-time with value into the Cl * 3 = End(C 2 ) and End(Cl 3 ) Lie groups.Space-time is a manifold forming the auto-adjoint part of Cl * 3 .The Lagrangian densities are the real parts of the waves.The equivalence between the invariant form and the Dirac form of the wave equation takes the form of Lagrange's equations.The momentum-energy tensor linked by Noether's theorem to the invariance under space-time translations has components which are directly linked to the electromagnetic tensor.The invariance under Cl * 3 of the kinetic momentum tensor gives eight vectors.One of these vectors has a time component with value /2.Resulting aspects of the standard model of quantum physics and of the relativistic theory of gravitation are discussed.
A Subword Level Language Model for Bangla Language

Language models are at the core of natural language processing. The ability to represent natural language gives rise to its applications in numerous NLP tasks including text classification, summarization, and translation. Research in this area is very limited in Bangla due to the scarcity of resources, except for some count-based models and very recent neural language models being proposed, which are all based on words and limited in practical tasks due to their high perplexity. This paper attempts to approach this issue of perplexity and proposes a subword level neural language model with the AWD-LSTM architecture and various other techniques suitable for training in Bangla language. The model is trained on a corpus of Bangla newspaper articles of an appreciable size consisting of more than 28.5 million word tokens. The performance in comparison with various other models depicts the significant reduction in perplexity the proposed model provides, reaching as low as 39.84, in just 20 epochs.
Extracting Parallel Sentences from Nonparallel Corpora Using Parallel Hierarchical Attention Network

Collecting parallel sentences from nonparallel data is a long-standing natural language processing research problem. In particular, parallel training sentences are very important for the quality of machine translation systems. While many existing methods have shown encouraging results, they cannot learn various alignment weights in parallel sentences. To address this issue, we propose a novel parallel hierarchical attention neural network which encodes monolingual sentences versus bilingual sentences and construct a classifier to extract parallel sentences. In particular, our attention mechanism structure can learn different alignment weights of words in parallel sentences. Experimental results show that our model can obtain state-of-the-art performance on the English-French, English-German, and English-Chinese dataset of BUCC 2017 shared task about parallel sentences’ extraction.
Annotation-Free Word Spotting with Bag-of-Features HMMs

The annotation-free word spotting method that is proposed in this paper makes document images searchable without requiring any labeled training data. Thus, our method supports the exploration of a document collection directly without demanding any manual efforts from the users for the preparation of a training dataset. Our method works in the query-by-example scenario where the user selects an exemplary occurrence of the query word. Afterwards, the entire collection of document images is searched according to visual similarity to the query. The proposed method requires only minimal assumptions about the visual appearance of text. This is achieved by processing document images as a whole without requiring a given segmentation of the images on word level or on line level. Therefore, the method is also segmentation-free. Word size variabilities can be handled by representing the sequential structure of text with a statistical sequence model. In order to make the computationally costly application of the sequence model feasible in practice, regions are retrieved according to approximate similarity with an efficient model decoding algorithm. Re-ranking these regions according to the visual similarity obtained with the sequence model leads to highly accurate word spotting results. The method is evaluated on five benchmark datasets. In the segmentation-free query-by-example scenario where no annotated training data is available, the method outperforms all other methods that have been evaluated on any of these five benchmarks.
Sentiment Analysis of Pulwama Attack Using Twitter Data

Microblogging sites like Twitter have become significant sources of real-time information during a disaster. Millions of tweets are Microblogging sites like Twitter have become significant sources of real-time information during a disaster. Millions of tweets are posted during disasters. This algorithm is applied to twitter tweets to extract the sentiments of the public on such types of manmade disasters. In order to use microblogging sites effectively during disaster events, it is needed to summarize the large amounts of real-time non-situation information posted on twitter. In this study, non-situational tweets were analyzed which were posted during the recent disaster event of the Pulmawa attack. The proposed methodology is to develop a Gradient Boosting classifier using machine learning techniques to achieve better performance compared to support vector machine and random forest classifier to categorize various types of non-situation tweets collected during disaster into a set of different classes. Well-known sentiments were used for mining that exhibits eight basic emotions, that is, joy, Trust, fear, surprise, sadness, disgust, anger, and anticipation.