Reviewerly/exHarmony huggingface dataset:
Temporal Feature Aggregation for Text Classification Based on Ensembled Deep-Learning Models

In this paper, we focus on the text classification task, which is a most import task in the area of Natural Language Processing (NLP). We propose an innovative convolutional neural network (CNN) model to perform temporal feature aggregation (TFA) effectively, which has a highly representative capacity to extract sequential features from vectorized numerical embeddings. First, we feed embedded vectors into a bi-directional LSTM (Bi-LSTM) model to capture the contextual information of each word. Afterwards, we propose to use the state-of-the-art deep-learning models as key components of the architecture, i.e., the Xception model and the WaveNet model, to extract temporal features from deep convolutional layers concurrently. To facilitate an effective feature fusion, we concatenate the outputs of two component models before forwarding to a drop-out layer to alleviate over-fitting and subsequently a fully-connected dense layer to perform the final classification of input texts. Experiments demonstrate that the proposed method achieves performance comparable to the state-of-the-art models while at a significantly lower computational complexity. Our approach obtains the cross-validation score of 95.83% for the Quora Insincere Question Classification (QIQC) dataset, and the cross-validation score of 83.10% for the Spooky Author Identification (SAI) dataset, respectively, which are among the best published results. The proposed method can be readily generalized to signal processing tasks, e.g., environmental sound classification (ESC) and machine fault analysis (MFA).
Enhanced Regularizers for Attributional Robustness

Deep neural networks are the default choice of learning models for computer vision tasks. Extensive work has been carried out in recent years on explaining deep models for vision tasks such as classification. However, recent work has shown that it is possible for these models to produce substantially different attribution maps even when two very similar images are given to the network, raising serious questions about trustworthiness. To address this issue, we propose a robust attribution training strategy to improve attributional robustness of deep neural networks. Our method carefully analyzes the requirements for attributional robustness and introduces two new regularizers that preserve a model's attribution map during attacks. Our method surpasses state-of-the-art attributional robustness methods by a margin of approximately 3% to 9% in terms of attribution robustness measures on several datasets including MNIST, FMNIST, Flower and GTSRB.
INTERNATIONAL SOCIETY FOR ENGINEERING PEDAGOGY: HISTORY AND DEVELOPMENT TRENDS

The paper analyzes the history of the establishing and development of the International Society for Engineering Pedagogy IGIP – one of the fi rst international organizations specializing in the theory and practice of training TVET teachers (TVET stands for technical and vocational education and training). Engineering pedagogy is presented as a part of TVET pedagogy, which considers the training of professional personnel in various specialties and areas. Engineering pedagogy dates back to the 1950s. It was centered around the International Society for Engineering Pedagogy IGIP established in 1972 in Klagenfurt (Austria) under the leadership of Adolf Melecinek. Russia joined this movement in 1995. In a number of engineering universities, IGIP Centers were established and have been successfully operating since then. TVET teachers improve their professional and pedagogical qualifi cations and, if their level of training and professional experience is confi rmed to the IGIP standards, teachers are awarded the title of “European Lecturer at an Engineering University”. The development trends of the International Society for Engineering Pedagogy in Russia are primarily associated with its integration into the system of continuous vocational (TVET) teacher training in the context of the digital transformation of society. It is necessary to systematically update the tasks set for IGIP. A new international standard (third generation) of the minimum pedagogical training required for university teachers has been developed. Cooperation with other partner organizations is being established. The IGIP publication system was revised (a new iGEP journal was established – International Journal of Engineering Pedagogy). Annual IGIP Symposiums contribute to integration processes in professional education and promote academic mobility. The centers for engineering pedagogy are accredited according to the international IGIP standard. An important task of the International Society for Engineering Pedagogy is not only the modernization of the scientifi c and methodological foundations of university engineering pedagogy in accordance with the urgent tasks of professional education, but also the improvement of the methodology of engineering and technical
In-line quality control utilizing close-range photogrammetry and a CAD-model

Abstract This paper presents an overview of a measurement system that is designed for in-line shape inspection of metal sheet components in real-time utilizing pre-calibrated close-range photogrammetry and a CAD-model. The system is currently designed to measure parts thrown out on a conveyor belt that moves at about 1 m s −1 at a frequency of 0.5 Hz or less without fixturing. Detected features on the components in the camera images are used to align the CAD-model, describing the nominal shape, to the images from which required deviations are computed using photogrammetry. The measurement volume of the current system is 500 × 800 × 200 mm 3 and absolute measurements are performed with an accuracy in the order of 0.1 mm. The in-line functionalities of the system have been verified at several real production sites in Sweden. In this paper, the basic components of the system are described together with a few results from real tests.
The application of virtual and augmented reality technologies in the real estate industry in bahrain

Modern technologies continue to evolve in the Real Estate sector in attempts to increase profit and efficiency. Virtual Reality (VR) and Augmented Reality (AR) technologies are no exception, streamlining the way business is done. Today, more than ever, developers claim that the technology can provide an efficient means of visualizing, marketing and selling real estate project. VR and AR technologies fill the imagination gap in the real estate market, enabling investors to visualize the result of an on-plan project and to imagine how the space will look like after being built, refurbished or emptied. Through a set of semi-structured interviews, this research investigates the role of real estate developers as clients for these technologies and explores the contribution of technology providers in revolutionizing the real estate sector. Ten semi-structured in-depth interviews were conducted with seven developers and three suppliers to study the potentials and challenges of employing the technology in the real estate sector in Bahrain. The results showed the effectiveness of the technologies with younger clients and investors and the challenges that faces its adoption with the older generation. The research showcases the usefulness of employing AR and VR technologies in enabling better visualization and facilitating higher sales in the real estate sector. The study showed that even though VR and AR technologies provide people with a simulated lifelike experience, it cannot be identified as a replacement of the brochures, 2D and 3D drawings, and physical models. There seems to be still a gap between the developer, the consultant and the VR specialist concerning taste, sale potentials and the costs associated with the design. The technology still suffers from many shortcomings including its unjustifiable high cost for small projects, its heavy size, and its tendency to crash or jam often. In addition, the technology is still Internet dependent which requires a strong connection at the display venue.
Implementation of Image Security using Elliptic Curve Cryptography RSA Algorithm and Least Significant Bit Algorithm

Abstract Data exchange is a very important thing in today’s technological development. Two important factors must be considered in the process of data exchange, namely speed and security. Rapid technological development can result in insecure data because it can be stolen or manipulated by people who are not privileged. Thus, we need a way or method to be able to overcome these problems, such as cryptography and steganography. The Rivest-Shamir-Adleman or RSA algorithm is an asymmetric cryptographic algorithm that uses 2 prime numbers to generate a public key and a private key that is used in the encryption process for the data. The ECC concept is used to determine the seed or initial value in the generation of prime numbers in the RSA Algorithm by using point addition and multiplication in an elliptic curve. The Least Significant Bit Algorithm is a Steganography Algorithm that inserts bits from the message at the last bit in a cover, replacing the last bit. The parameter used is Running Time in steganography. The results of the research show that image files can be hidden in another image file and returned to normal with a combination of cryptography and steganography with an average Running Time for the insertion process of 3090.167 ms and the extraction process of 2917.167 ms.
Holistic Education by breaking the binaries beyond the boundaries

The educational system currently prevailing across the globe is of divergent genre. Education has become the prime focus among all the intelligentsia and educationalists. The efforts of the regime in nurturing the system, the cartel of government and private Institutions are the issues that keep bouncing back. And now the Pandemic crisis has been an extreme threat that signifies the importance on social distancing. So, with the shut off of the knowledge sharing, with direct contact has ignited the quest for seemingly establishing a virtual environment to feed the thirst for intellectual wisdom. Irrespective of infrastructure, accessibility to the external virtual environment has been a strong fervent desire of the current days of each one. It is beyond boundaries through online webinars, virtual classrooms, Learning Management Systems, video lecture uploaded anywhere and everywhere. This has provided a hustle free environment for all students except for the economically conducive regional folks to reach the remote Education. Various methodologies pertaining to remote education and the simplified approaches are discussed in this paper. And the concern to reach the unreached has been a visionary of discussion in this research through simple messaging service through mobile phones.
Focused Inference and System P

We bring in the concept of focused inference into the field of qualitative nonmonotonic reasoning by applying focused inference to System P. The idea behind drawing focused inferences is to concentrate on knowledge which seems to be relevant for answering a query while completely disregarding the remaining knowledge even at the risk of missing some meaningful information. Focused inference is motivated by mimicking snap decisions of human reasoners and aims on rapidly drawing still reasonable inferences from large sets of knowledge. In this paper, we define a series of query-dependent, syntactically-driven focused inference relations, elaborate on their formal properties, and show that the series converges against System P. We take advantage of this result in form of an anytime algorithm for drawing inferences which is accompanied by a thorough complexity analysis.
Intent Identification in Unattended Customer Queries Using an Unsupervised Approach

Customer’s satisfaction is crucial for companies worldwide. An integrated strategy composes omnichannel communication systems, in which chabot is widely used. This system is supervised, and the key point is that the required training data are originally unlabelled. Labelling data manually is unfeasible mainly nowadays due to the considerable volume. Moreover, customer behaviour is often hidden in the data even for experts. This work proposes a methodology to find unknown entities and intents automatically using unsupervised learning. This is based on natural language processing (NLP) for text data preparation and on machine learning (ML) for clustering model identification. Several combinations for preprocessing, vectorisation, dimensionality reduction and clustering techniques, were investigated. The case study refers to a Brazilian electric energy company, with a data set of failed customer queries, that is, not met by the company for any reason. They correspond to about 30% (4,044 queries) of the original data set. The best identified intent model employed stemming for preprocessing, word frequency analysis for vectorisation, latent Dirichlet allocation (LDA) for dimensionality reduction, and mini-batch [Formula: see text]-means for clustering. This system was able to allocate 62% of the failed queries in one of the seven found intents. For instance, this new labelled data can be used for the training of NLP-based chatbots contributing to a greater generalisation capacity, and ultimately, to increase customer satisfaction.
Visual Question-Answering System Using Integrated Models of Image Captioning and BERT

Visual question and answering (VQA) is a task that involves taking input as an image and a natural question about it to generate output of an answer to that question. This is a multidisciplinary problem: it includes problems faced in computer vision and natural language processing. This chapter uses a combination of network architectures of question answering (BERT) and image captioning (BUTD, show-and-tell model, CaptionBot, and show, attend, and tell model) models for VQA tasks. The chapter also highlights the comparison between these four VQA models.
Improved Particle Filter Based on Kernel Density Estimation

Particle filter (PF) algorithm can be used for nonlinear and non-gaussian system state estimation, the core lies in how to accurately describe the posterior density function with limited particles. In recent years, there have been many improved particle filter algorithms, but the effect is not good in the strong nonlinear/non-Gaussian environment. The improved method proposed in this paper can overcome this difficulty. This method uses kernel density estimation to fit the priori PDF, then evenly sample new particles in a reasonable area and calculate their priori weights. Then these particles' weights are updated according to the likelihood function, and then normalize the weights, and the state is estimated by using the particles and their normalized weights. This method is called KDE-PF. Simulation results show that this method is better than the general particle filter algorithm.
The Use of Dart Board Games as Strategy to Improve Students' Speaking Skill

This study aims to improve students' speaking abilities. The method used in research is a quantitative method with True Experimental as a research design using the Pre-Test Post-test the researcher will only use one class chosen randomly from total of population. The study was conducted at Muhammadiyah 6 junior high School tieng. The sampling technique used was Cluster Random Sampling, in this study the authors used a class from class 7 for sampling, the data were analyzed statistically using t-test. The results of this study indicate; the average value of grade VII students is 68.57 Statistical calculations show that the results of t0 (t-observation)> tt (t-table). Where t-observation (t0) obtained is 10 while the value of t-table (tt) is 1,711 with a significant 5% (0.05) which is t0 greater than tt, thus alternative hypotheses (Ha) are accepted and null hypothesis (H0) rejected. Based on the results listed it can be concluded that the dart board method develops children's ability to improve students' speaking abilities.
Modern educational institution and higher education in the DIY format: the possibility of harmonizing relations

This article analyzes the paradigmatic shifts in the modern education system as a whole. Modern reality is diagnosed as polyphonic/ polysemantic, in which the subject is constantly “completing” himself. In this regard, the need for social and philosophical analysis of a new problem field is actualized: adaptation of the modern education system to the challenges of modern social reality. The purpose of this article is to explore the possibility of applying the DIY (do it yourself) educational form to the modern higher education system.
Deep Neural Networks for Computer Vision

Computer vision problems are related to the understanding of digital images, video, or similar inputs such as 3D point clouds, solving problems such as image classification, object detection, segmentation, 3D scene understanding, object tracking in videos, and many more. Neural approaches to computer vision were originally modeled after the visual cortex of mammals, but soon became a science of their own, with many architectures already developed and new ones appearing up to this day. In this chapter, we discuss the most popular architectures for computer vision, concentrating mainly on ideas rather than specific models. We also discuss the first step towards synthetic data for computer vision: data augmentation.
Wild animal species detection using deep convolution neural network

Monitoring animals in forest one to one is very tedious job so technology have evolved which include installing cameras in animal living areas and acquire videos and pictures. But the pictures acquired from installed camera are always not good most of the time pictures are blurred and without animals, so it’s hard to detect animals, resulting in doubt and error. For solving this obstacle, we proposed a database of installed camera network captured images with multilevel graph of different animal species stored in database in spatiotemporal domain and pictures with animal are captured from forest are compared with the database, identifying whether a graph matches the animal or anything else. We created a model for detecting animals based on oneself training by the method explained in the paper. The technology is used for classification using machine learning (ML) and Artificial intelligent (AI) algorithms, hold up vector machine, k-mean closest neighbor, together with group tree. From the experimental results it is shown that, the proposed system accurately classifies wild animals up to 91% accuracy.
Fully Observable Nondeterministic HTN Planning -- Formalisation and Complexity Results

Much progress has been made in advancing the state of the art of HTN planning theory in recent years. However, scarce studies have been made with regards to the theory and complexity of HTN problems on nondeterministic domains. In this paper we provide a novel formalisation for fully observable nondeterministic HTN planning. We propose and study different solution criteria which differ in when nondeterministic action outcomes are considered: at plan generation or at plan execution. We integrate our solution criteria with notions of weak and strong plans canonical in nondeterministic planning and identify similarities and differences with plans in other fields of AI planning. We also provide completeness results for a majority of HTN problem subclasses and show the significant result that problems are not made any harder under nondeterminism for certain solution criteria by using compilation techniques to deterministic HTN planning. This supports and justifies the practicality and scalability of extending HTN problems over nondeterministic domains to deal with real world scenarios.
Topsis Method To Determine Recipients Of Covid 19 Social Assistance Funds

Pandemic and social restrictions are inseparable things, with the existence of these restrictions, several sectors of life have changed, including the economic sector, the number of unemployed due to layoffs has increased because a number of companies have difficulty covering operational costs. The central government finally took a policy to provide social assistance to residents who met the criteria of being poor and the determination was left to the local government. The method used to assist decision making to determine suitable beneficiaries is to use TOPSIS. With the TOPSIS method, it is hoped that the assessment will be more precise because it is based on predetermined criteria and weight values ??so that more accurate results will be obtained. In this research, the results obtained from ranking to determine the priority for citizens to get Covid 19 social assistance and the results of residents who do not earn because they do not work while having dependents of more than 5 people who are more than 30 years old get the top priority
Tri-op redactable blockchains with block modification, removal, and insertion

In distributed computations and cryptography, it is desirable to record events on a public ledger, such that later alterations are computationally infeasible.An implementation of this idea is called blockchain, which is a distributed protocol that allows the creation of an immutable ledger.While such an idea is very appealing, the ledger may be contaminated with incorrect, illegal, or even dangerous data, and everyone running the blockchain protocol has no option but to store and propagate the unwanted data.The ledger is bloated over time, and it is not possible to remove redundant information.Finally, missing data cannot be inserted later.Redactable blockchains were invented to allow the ledger to be mutated in a controlled manner.To date, redactable blockchains support at most two types of redactions: block modification and removal.The next logical step is to support block insertions.However, we show that this seemingly innocuous enhancement renders all previous constructs insecure.We put forward a model for blockchains supporting all three redaction operations and construct a blockchain that is provably secure under this formal definition.