Reviewerly/exHarmony huggingface dataset:
Probing Causal Common Sense in Dialogue Response Generation.

Communication is a cooperative effort that requires reaching mutual understanding among the participants. Humans use commonsense reasoning implicitly to produce natural and logically-coherent responses. As a step towards fluid human-AI communication, we study if response generation (RG) models can emulate human reasoning process and use common sense to help produce better-quality responses. We aim to tackle two research questions: how to formalize conversational common sense and how to examine RG models capability to use common sense? We first propose a task, CEDAR: Causal common sEnse in DiAlogue Response generation, that concretizes common sense as textual explanations for what might lead to the response and evaluates RG models behavior by comparing the modeling loss given a valid explanation with an invalid one. Then we introduce a process that automatically generates such explanations and ask humans to verify them. Finally, we design two probing settings for RG models targeting two reasoning capabilities using verified explanations. We find that RG models have a hard time determining the logical validity of explanations but can identify grammatical naturalness of the explanation easily.
Wearable Devices Components for High Jump Sport: An Overview

Wearable devices like FitBit, Garmin and Amazfit smartwatch have become popular in recent years. Athlete like swimmers and runners mostly use smartwatch to track speed of the swim or run using embedded global positioning system (GPS) sensor in the wearable devices. Unfortunately, high jump sports still on the old style in tracking the movement of the jump by using vision system. Thus, this paper presents an overview on related work, existing methods and developments on the area of wearable sensor for high jump sports. The analysis of high jump sports related works in terms of main injury and parameters to prevent the injury are being analysed. The used of biosensors in recent wearable devices has been also reviewed in term of the types and issues of sensor that need to implements in the wearable device. Other than that, the processing methods that contains Internet of Thing (IoT) gateway device, network transmission and real-time monitoring system (RtMS) are proposed to develop a complete wearable device. Finally, the main injury parameters of the high jump sports and sensors related to the parameter has been reviewed and an overall idea of processing method RtMS has been summarised.
Country-level Arabic Dialect Identification Using Small Datasets with Integrated Machine Learning Techniques and Deep Learning Models

Arabic is characterised by a considerable number of varieties including spoken dialects. In this paper, we presented our models developed to participate in the NADI subtask 1.2 that requires building a system to distinguish between 21 country-level dialects. We investigated several classical machine learning approaches and deep learning models using small datasets. We examined an integration technique between two machine learning approaches. Additionally, we created dictionaries automatically based on Pointwise Mutual Information and labelled datasets, which enriched the feature space when training models. A semi-supervised learning approach was also examined and compared to other methods that exploit large unlabelled datasets, such as building pre-trained word embeddings. Our winning model was the Support Vector Machine with dictionary-based features and Pointwise Mutual Information values, achieving an 18.94% macros-average F1-score.
Correction to: A biometric cryptosystem scheme based on random projection and neural network

A correction to this paper has been published: https://doi.org/10.1007/s00500-021-05804-3
Position-based cryptography: Single-qubit protocol secure against multi-qubit attacks.

While it is known that unconditionally secure position-based cryptography is impossible both in the classical and the quantum setting, it has been shown that some quantum protocols for position verification are secure against attackers which share a quantum state of bounded dimension. In this work, we consider the security of two protocols for quantum position verification that combine a single qubit with classical strings of total length $2n$: The qubit routing protocol, where the classical information prescribes the qubit's destination, and a variant of the BB84-protocol for position verification, where the classical information prescribes in which basis the qubit should be measured. We show that either protocol is secure for a randomly chosen function if each of the attackers holds at most $n/2 - 5$ qubits. With this, we show for the first time that there exists a quantum position verification protocol where the ratio between the quantum resources an honest prover needs and the quantum resources the attackers need to break the protocol is unbounded. The verifiers need only increase the amount of classical resources to force the attackers to use more quantum resources. Concrete efficient functions for both protocols are also given -- at the expense of a weaker but still unbounded ratio of quantum resources for successful attackers. Finally, we show that both protocols are robust with respect to noise, making them appealing for applications.
Quantum protocols at presence of nonabelian superselection rules in the framework of algebraic model

In this paper, we study the influence of nonabelian superselection rules on the transfer of quantum information with the help of qubits on the base of an algebraic model and formulate quantum protocols. We pay the main attention to the superselection structure of the algebra of observables [Formula: see text] defined by the Cuntz algebra [Formula: see text] (a field algebra) that contains [Formula: see text] as a pointwise fixed subalgebra with respect to the action of the gauge group [Formula: see text]. We prove that it is possible to code information only with the help of states such that projectors on them belong to the algebra of observables. These projectors commute with the elements of the representation of the group [Formula: see text], and therefore allow the recipient to restore the obtained information.
YouTube Analytics Using Google Data Studio

In this paper we demonstrate the power of Big Data Analytics using Google Analytics as a platform. First the YouTube channel is opened, the start recording of the channel analytics is done here automatically by Google. This data is exported from YouTube in the form of Google sheets and then is fed to Google Analytics. After Google Analytics analyses this data, it is now integrated with Google data studio. Google data studio makes use of artificial intelligence insights techniques to generate artificial intelligence and prediction-based report graphs which can be analysed by the end user. This is the complete work flow of the project. In the future scope, not only YouTube, but any Google product or Google service data can be fed to Google Analytics and integrated in Google data studio for artificial intelligence based Big Data Analytics.
DEVELOPMENT OF MACHINE LEARNING METHOD WITH BIOMETRIC PROTECTION WITH NEW FILTRATION METHODS

Biometric images were processed and filtered by a newly developed Ateb-Gabor wavelet filter. Identification of biometric images was performed by machine learning methods. The Gabor filter based on Ateb functions is effective for filtering because it contains generalizations of trigonometric functions. Developed wavelet transform of Ateb-Gabor function. It is shown that the function depends on seven parameters, each of which makes significant changes in the results of filtering biometric images. A study of the wavelet Ateb-Gabor function was performed. The graphical dependences of the Gabor filter wavelet and the Ateb-Gabor filter wavelet are constructed. The introduction of wavelet transforms reduces the complexity of Ateb-Gabor filter calculations by simplifying function calculations and reducing filtering time. The complexity of the algorithms for calculating the Gabor filter wavelet and the Ateb-Gabor filter wavelet is evaluated. Ateb-Gabor filtering allows you to change the intensity of the entire image, and to change certain ranges, and thus change certain areas of the image. It is this property that biometric images should have, in which the minions should be contrasting and clear. Ateb functions have the ability to change two rational parameters, which, in turn, will allow more flexible control of filtering. The properties of the Ateb function are investigated, as well as the possibility of changing the amplitude of the function, the oscillation frequency to the numerical values ​​of the Ateb-Gabor filter. By using the parameters of the Ateb function, you can get a much wider range of shapes and sizes, which expands the number of possible filtering options. You can also implement once filtering, taking into account the direction of the minutes and reliably determine the sharpness of the edges, rather than filtering batocrates. The reliability results were tested on the basis of NIST Special Database 302, and good filtration results were shown. This was confirmed by a comparison experiment between the Wavelet-Gabor filtering and the Ateb-Gabor wavelet function based on the measurement of the PSNR signal-to-noise ratio.
Professional education: current condition and new approaches

The article deals with the problems of professional education at the current stage of national economy’s development. The authors attract attention to the difference between the approaches to improving secondary and higher professional education. Government agencies of the highest level focus on improving the system of higher education and labour force training, but training of mid-level specialists has recently fallen out of their sight. Lately a great number of colleges have become part of higher educational institutions. However, during the latest reorganization of the Ministry of Education and Science, which resulted in foundation of the Ministry of Education, secondary professional education fell under the jurisdiction of the latter. The authors suggest uniting secondary and higher professional education into one single system and consider that as higher education. Line engineers will be trained at colleges (even on the basis of basic general education) but probably the term of training will be a little longer due to the fact that colleges are expected to be part of the university structure. Such educational programs could be implemented in regional branches of head universities to provide local industries, small and medium businesses with skilled workforce. The practice of students’ training proved that the division of Bachelors’ and Masters’ competences by the type of their future job is senseless as students do not determine their preferences for future occupation during the period of their studies. The authors suggest developing generalized competence models of graduates. The models could be used to formulate the requirements to the graduates with different level of education. Graduates with professional education will receive diploma together with a supplement containing the list of competences obtained. New Federal State Educational Systems of all levels of higher education read that professional competences are established by an educational institution itself. Therefore, the authors suggest considering issuing university diplomas instead of those of state educational standard.
Mask RCNN for Human Motion and Actions Recognition

Human actions recognition (HAR) and understanding become very popular topics in the field of computer vision and signal processing. The purpose of human activities recognition is to automatically examine and characterize actions from a video sequence. The main goal of a HAR system is to identify simple actions of everyday life (like walking, running, jumping ...) from videos. Each of these actions, performed by one person or many persons within a specific period of time, must be represented by a simple movement model. In recent years, a large number of applications of HAR have been proposed in literature such as video surveillance, human-computer interaction and video indexing. In this line, we present our method for HAR using Mask Region Based CNN, MRCNN. This technique will help us to make the accent on the body of individual and this step facilitates the recognition of the current action. With the mask RCNN we used key frame extraction and background extraction. This framework was tested on two datasets KTH and WEIZMANN datasets and experimental results showed the performance of the proposed technique.
Quality of Service Evaluation with DSR (Dynamic Source Routing) Protocol in the Classroom Ad Hoc Network of the New Generation of Digital Open Universities (DOUNG)

Many learner devices with multimedia capacities such as computers, laptops and telephone cells derive from the nowadays technology evolution. The wide range of these electronic devices brings a real opportunity to build an ad hoc network in the DOUNG [, ] classroom with real time option of following courses. In addition of previous works that define significant Quality of Service (QoS) parameters for following courses alive in the DOUNG model, this paper aims to contribute by using the learner devices to extend the DOUNG network architecture. The idea is to use an ad hoc network in the classroom after a Wi-Fi antenna instead of duplicating this antenna when the signal is low. Thus, with an access mode that brings the network resource consumption to reach a critical threshold, we measure some QoS parameters by simulation in this new architecture using SCTP (Stream Control Transport Protocol) at the transport layer and DSR (Dynamic Source Routing) at the network layer. Curves are produced and the results are discussed.
Concept learning based on human interaction and explainable AI

In this article, we explore the role and usefulness of parts-based spatial concept learning about complex scenes. Specifically, we consider the process of teaching a spatially attributed graph how to utilize parts-detectors and relative positions as attributes in order to learn concepts and to produce human oriented explanations. First, we endow the graph with parts detectors and relative positions to determine the possible range of attributes that will limit the types of concepts that are learned. Next, we enable the graph to learn concepts in the context of recognizing structured objects in imagery and the spatial relations between these objects. As the graph is learning concepts, we allow human operators to give feedback on attribute knowledge, creating a system that can augment expert knowledge for any similar task. Effectively, we show how to perform online concept learning of a spatially attributed graph. This route was chosen due to the vast representational capabilities of attributed graphs, and the low-data requirement of online learning. Finally, we explore how well this method lends itself to human augmentation, leveraging human expertise to perform otherwise difficult tasks for a machine. Our experiments shed light on the usefulness of spatially attributed graphs utilizing online concept learning, and shows the way forward for more explainable image reasoning machines.
FEATURES OF THE ORGANIZATION OF THE EDUCATIONAL PROCESS IN THE CONTEXT OF THE CONSTRUCTIVIST CONCEPT OF LEARNING

The essence of the theory of constructivism as a component of the modern philosophy of education and the basics of the organization of the educational process in the context of the above concept are discussed. Analyzes the direction of the organization of the learning process is based on constructivism: the organization of meaningful knowledge; the development of conceptual understanding; engaging students in authentic, professionally oriented activities; the urge to reflect on their actions; provision of educational nature of the learning activities; creation of a community teaching; the development of internal motivation of learning of students; support for their individual identity. Attention is focused on the importance of the activity of a higher school teacher as a facilitator to support students in the educational process
COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List

Classical information retrieval systems such as BM25 rely on exact lexical match and carry out search efficiently with inverted list index. Recent neural IR models shifts towards soft semantic matching all query document terms, but they lose the computation efficiency of exact match systems. This paper presents COIL, a contextualized exact match retrieval architecture that brings semantic lexical matching. COIL scoring is based on overlapping query document tokens' contextualized representations. The new architecture stores contextualized token representations in inverted lists, bringing together the efficiency of exact match and the representation power of deep language models. Our experimental results show COIL outperforms classical lexical retrievers and state-of-the-art deep LM retrievers with similar or smaller latency.
Application of MOOC and rain classroom in the teaching of clinical microbiological examination technology

Clinical microbiological examination technology is an important professional course for medical laboratory students. Through the teaching of “Internet + education” of "MOOCs" in classroom and rain class, it stimulates students’ interest in learning and improves their self-learning ability. The effectiveness of students’ learning has increased the interaction between teachers and students. The rain class has also inspired the formative evaluation and ultimately improved the quality of teaching.
A machine learning approach to improving quality of atmospheric turbulence simulation

The availability of large quantities of labeled data is key to the successful application of artificial intelligence and machine-learning (AIML) algorithms. However, live field data is expensive to collect and the atmospheric conditions that affect image quality are impossible to control. The capability to generate realistic, synthetic training data under operational conditions represents a significant opportunity to operationalize AIML algorithms at a significant cost-savings compared to live field collections. This work seeks to improve upon prior NVESD efforts to simulate the degrading effects of atmospheric turbulence on long-range, ground-to-ground imagery. Specifically, we implement a novel Generative Adversarial Network (GAN) architecture – which we named Noise-GAN – that is capable of detecting and then emulating spatially-varying signals directly from image data. We present results from a proof-of-concept study in which we demonstrate the capability of the Noise-GAN learn the spatial statistics of blur and distortion from turbulence-degraded imagery.
The journey to collaborative AI at the tactical edge (CATE)

Warfighters at the tactical edge do not have “Big Compute” capability and are bandwidth constrained. This significantly affects capability, workload and utility of Intelligence, Surveillance and Reconnaissance (ISR) at the edge. Artificial Intelligence (AI) has the potential to improve quality of information and reduce workload if we optimize AI/ML (Machine Learning) techniques for the constrained tactical environment. For several years, we focused on sensor and data interoperability. Leveraging contested urban environment experiments, and our work with Canada, we cracked the “data interoperability nut” across the systems provided by Canada, the United Kingdom, Australia, and New Zealand. The increased data interoperability made it clear that the link between sensor interoperability and data reasoning is a missing component of ISR at the edge. Data overload for the tactical operators was almost immediate and overwhelming. The success in interoperability highlighted failures in data fusion, filtering, and presentation. Increased data flow does not equal increased situational awareness. CATE is a collaborative effort to develop a prototype AI/ML architecture and framework that enables simple, rapid integration of collaborative, multi-agent AI technology into the Processing, Exploitation and Dissemination (PED) chain at the edge, sensor, and tactical PED level. CATE imagines a soldier on patrol in a “smart city”. In the background, collaborative AI agents scan city cameras, review patterns of life, providing an AI enabled over watch. The AI agents determine there is a threat and alert the soldier, who never had to look down at a screen and take his eyes off his immediate surroundings.
DOWN SYNDROME DETECTION USING MODIFIED ANT COLONY OPTIMIZATION ALGORITHM

Nowadays, the systems related to healthcare are restructured with innovative skills to offer humans more intellectual and proficient healthcare facilities.Various intelligent healthcare systems are exhibited with the help of machine learning and artificial intelligent tools to offer intellectual and expert services.In human body genetic codes are stored in the genes.All of our inherited traits are associated with these genes and are grouped as structures generally called chromosomes.In typical cases, each cell consists of 23 pairs of chromosomes, out of which each parent contributes half.But if a person has a partial or full copy of chromosome 21, the situation is called Down syndrome.It results in intellectual disability, reading impairment, developmental delay, and other medical abnormalities.This paper introduces an intelligent prediction and classification system for healthcare, feature selection based on density with Ant Colony Optimization (ACO) algorithm for Down syndrome (DS).
Equivalence of Segmental and Neural Transducer Modeling: A Proof of Concept

With the advent of direct models in automatic speech recognition (ASR), the formerly prevalent frame-wise acoustic modeling based on hidden Markov models (HMM) diversified into a number of modeling architectures like encoder-decoder attention models, transducer models and segmental models (direct HMM). While transducer models stay with a frame-level model definition, segmental models are defined on the level of label segments directly. While (soft-)attention-based models avoid explicit alignment, transducer and segmental approach internally do model alignment, either by segment hypotheses or, more implicitly, by emitting so-called blank symbols. In this work, we prove that the widely used class of RNN-Transducer models and segmental models (direct HMM) are equivalent and therefore show equal modeling power. It is shown that blank probabilities translate into segment length probabilities and vice versa. In addition, we provide initial experiments investigating decoding and beam-pruning, comparing time-synchronous and label-/segment-synchronous search strategies and their properties using the same underlying model.