Reviewerly/exHarmony huggingface dataset:
Towards a Systematic Approach for Chatbot Development in Digital Work Environments

Chatbots allow to interact with machines in a natural language dialog. Especially in digital work environments, they offer potential for a more intuitive human-computer interaction and remove the need for artificial user interfaces, which users have to learn to operate. However, chatbots typically have to be configured with knowledge about the use case at hand, potential dialog paths, and user interactions, which means to explicitly define user input and chatbot response. The interplay between human and machine defines the perceived user experience. In this chapter, we describe our approach to develop dialogs for chatbot interaction. We use and adapt the Interaction Room Method for this purpose and introduce necessary elements to define required components, especially utterances, intents, and entities.
Automatic signboard detection and localization in densely populated developing cities

Most city establishments of developing cities are digitally unlabeled because of the lack of automatic annotation systems. Hence location and trajectory services such as Google Maps, Uber etc remain underutilized in such cities. Accurate signboard detection in natural scene images is the foremost task for error-free information retrieval from such city streets. Yet, developing accurate signboard localization system is still an unresolved challenge because of its diverse appearances that include textual images and perplexing backgrounds. We present a novel object detection approach that can detect signboards automatically and is suitable for such cities. We use Faster R-CNN based localization by incorporating two specialized pretraining methods and a run time efficient hyperparameter value selection algorithm. We have taken an incremental approach in reaching our final proposed method through detailed evaluation and comparison with baselines using our constructed SVSO (Street View Signboard Objects) signboard dataset containing signboard natural scene images of six developing countries. We demonstrate state-of-the-art performance of our proposed method on both SVSO dataset and Open Image Dataset. Our proposed method can detect signboards accurately (even if the images contain multiple signboards with diverse shapes and colors in a noisy background) achieving 0.90 mAP (mean average precision) score on SVSO independent test set. Our implementation is available at: https://cutt.ly/dhr1D2K.
Pathfinder: A Fog Assisted Vision-Based System for Optimal Path Selection of Service Robots

Service delivery application involving robots relies on the success of the navigation process. To ensure maximum performance, the navigation process should generate optimal path avoiding collisions with dynamic obstacles. Selecting such a path by analyzing the dynamic environment condition while putting minimal overhead on robots is a challenging problem in service robot navigation. In this work, we develop a Fog assisted service robot navigation system that puts most of the computing tasks to a central Fog server. The server employs a vision-based monitoring system to locate the robots and obstacles. The optimal path is generated by analyzing the available information and the robots are instructed accordingly. We implement a test-bed system to evaluate the performance of the proposed system. The results depict that the proposed fog-based system can achieve as low as 23.81% of average distance covered, 22.05% of average service delivery time, and 22.72% of average energy consumption compared to the without fog systems. Contribution-A fog-based system for service robot navigation is proposed that uses computer vision to generate optimal obstacle free path.
Imperfect Premise Matching Controller Design for Interval Type-2 Fuzzy Systems under Network Environments

The interval type-2 fuzzy sets can describe nonlinear plants with uncertain parameters. It exists in nonlinearity. The parameter uncertainties extensively exist in the nonlinear practical Networked Control Systems (NCSs), and... | Find, read and cite all the research you need on Tech Science Press
NOV-RSI: A Novel Optimization Algorithm for Mining Rare Significance Itemsets

Rare itemsets mining is an important task for potential applications such as the detection of computer attacks, fraudulent transactions in financial institutions, bioinformatics and medicine. In the traditional data mining on transaction databases, such items have no weight (equal weight, as equal to 1). However, in real world application, each item often has a different weight (the importance/significance of each item). Therefore, we need to mine weighted frequent/rare itemsets on transaction databases. In this paper, we propose an algorithm for mining rare significance itemsets based on NOT satisfy the downward closure property. We propose an efficient algorithm called NOV-RSI. The experimental results show that the proposed algorithm performs faster than other existing algorithms on both real-life datasets of UCI and synthetic datasets generated by IBM Almaden.
Current State of Open Science in Balkan Universities

Open Science has been a relevant topic since the first introduction of open access repositories. Even though it has been 17 years, Current Research Information Systems (CRISs) are still being adopted and implemented to transfer innovation and research to industry and society. European Open Science cloud aims to solve problems with interoperability and fragmentation of CRISs and to achieve common standards for Open Science implementation in EU. Several reports show the current state of transition to Open Science in European Countries such as European University Association (EUA) report. However, the current state of research assessment in Balkan universities is not included in most of the surveys. The paper aims to fill this gap by investigating current situation with Open Science adoption in Bulgarian and Albanian Universities. The structure of the paper is very similar to EUA report in order to facilitate a comparison of these universities to other universities in Europe. It presents the spread of open access science; the state of progress to make research publications and data open, as well as challenges Bulgarian and Albanian university academics face to have an open approach to research assessment. In conclusion, it is shown that Bulgarian and Albanian researchers fully support Open Science and Open Access practices but most of the time they lack incentives and/or Open Access mandates on their universities. Both Bulgarian and Albanian institutions are still at the early stages of transition towards Open Access but very keen to support Open Science as the future of Research.
Experience of Using Neural Network Neocognitron in Solving the Inverse Problem of Geophysics

The paper considered the possibility of using the neocognitron neural network for the recognition of geological physical models of the environment (GPME). An algorithm for choosing a filter for the method for finding boundaries on the brightness difference and a modified neural network model for recognizing GPME of various shapes for modifying a neural network are proposed. The developed software is demonstrated. A series of experiments to restore the structure of the GPME was carried out using this software. Recommendations are given to improve the performance of the neural network. This work was supported by the RFBR grant No. 19-07-00170.
RETRACTED: Application of medical embedded system and clinical nursing effect of neonatal intestinal bacteria

Neonatal sepsis is a significant reason for dismalness and mortality in the Neonatal Intensive Care Units (NICU) the world. Since the two studies with the difficult topic under consideration, this is distributed, as samples, how to examine the digestive gland fed, and minimize major changes. Although this attempts to summarize the trend that has come from restrictions and change, attempted murder, research has been done on the human child. Since the two studies with the most difficult topic to consider are the most modified, but to examine this digestive gland, the model presented, the way of feeding, and minimize major changes and the data are processed based on proposed Artificial Neural Network (ANN) method. Despite these restrictions and changes, endeavoring research attempts to summarize the trend that the human child has come from since it has been made. Before this, medicine introduced the reader to the different types of embedded systems, their different applications, and the use of different types of embedded systems in embedded systems. Peripheral equipment and tools should be used in the description of embedded systems and are discussed here. Its focus is on a vulnerable medical device embedded in the new era, which contains some of the medical equipment already built.
Sign recognition with an electronic glove driven by a neural network programmed on an 8-bit microcontroller

This paper compares the general functionality of two algorithms used to recognize ten selected signs from the Peruvian Sign Language captured at their final spatial posture through an electronic glove. The first algorithm uses thresholds that separate accepted values from rejected ones, therefore being able to identify predefined signs. The second algorithm consists in a neural network trained with backpropagation. Both algorithms were programmed on an 8-bit microcontroller. After analyzing the measured performance data, we conclude that the neural network achieves higher recognition accuracy at the expense of more memory usage and a small increment in execution time.
Risk-Aware Multi-Agent Path Planning for Target Detection: A Multi-Agent Reinforcement Learning Approach

In this paper, we consider the problem when a multi-agent team is tasked with detecting a mobile target under a given time period. In particular, the movement of the target and the multi-agent team are restricted to a set of graph networks. The target travels along a road network while the agents travel along their individual air track networks. The objective is to derive path planning policies for the multi-agent team that minimize the risk of not detecting the target within a given time period. To solve the problem, we propose a multi-agent reinforcement learning approach. The key idea of the proposed approach is to first build an environment model that serves as a "white box'' to describe the target and agent dynamic models, and then build local actor-critic networks to train local path planning policies based on samples obtained via cueing the white box. In the proposed approach, we also design realistic reward metrics that reflect the team's probability in detecting the target when the team has not detected the target prior. The performance of the proposed approach is demonstrated by comparing the learned policies with a set of random policies in two simulation studies. The comparison shows that the proposed approach can derive individual path planning policies that dramatically outperform random policies.
On Graph Learning with Neural Networks

Graphs are ideal for modeling natural systems where relations may be intrinsic among data objects. With massive data available, learning graph models from data has become potentially feasible as well as necessary. Yet from the traditional machine learning perspective, learning structural topology of an unknown graphical model remains challenging. In particular, it is computationally intractable to learn graph topologies beyond a tree structure. Nevertheless, deep learning with neural networks, showing great potentials in visual imagery and other application domains, offers an alternative venue for effective machine learning on graphs. In this review, we discuss graph (structure) learning with deep neural networks. In particular, we examine graph neural networks (GNNs) from the task-based and the architecture-based perspectives, respectively.
Text Classification Method with Combination of Fuzzy Relation and Feature Distribution Variance

To accurately express the fuzzy relation between word features and texts, and fuzzy relation between word features and categories respectively. A text classification method is proposed based on Fuzzy Relation and Feature Distribution Variance (FRFDV). This method firstly performs feature reduction and category feature word extraction according to the distribution of features in inter-category and intra-category. Then the method defines the word feature set, test text set and category set as fuzzy sets. Next, each text and category are represented respectively by defining the membership function of the word feature set to the test text set and the category set. When using word feature sets to represent categories, pay attention to the membership degree of features to categories and their distribution between categories; when using feature sets to represent test texts, give categorical feature words and non-categorical feature words with different weights. Finally, the fuzzy set correlation formula is used to calculate the correlation between the text and each category, and the category with the largest correlation is the category of the text. Comparing with the XGBOOST [Fang, 2020, Gong and Wang, 2018] algorithm and SVM algorithm, it is proved that the text classification method based on FRFDV is feasible. The accuracy of the results is higher by 2 % and 4 % respectively.
A Latent Variables Augmentation Method Based on Adversarial Training for Image Categorization with Insufficient Training Samples

In the past few years, people have made great progress in image categorization based on convolutional neural networks (CNN) with the large-scale training set. However, in real-world applications, we may only have insufficient training samples for training CNN. To solve this problem, in this paper, we propose a latent variable augmentation method based on adversarial training (Lagat) to increase the categorization accuracy of CNN with insufficient training samples. In the proposed Lagat model, we propose a uniform loss function, where we take the following two tasks into account. Firstly, we consider the task that we augment latent variables (LVs) from a set of class-specific adaptive distributions and we propose constraints to adjust these LVs distributions. Secondly, we use these sampled LVs to train a predictive image classifier. Moreover, we present an alternative two-play minimization game to optimize this uniform loss function. In addition, the experiment results also demonstrate that the proposed Lagat method delivers higher accuracy than the existing state-of-the-art methods. The source code of the Lagat algorithm is available in Github <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> https://github.com/itapty-ily/Lagat.
Modeling Score Distributions and Continuous Covariates: A Bayesian Approach

Computer Vision practitioners must thoroughly understand their model's performance, but conditional evaluation is complex and error-prone. In biometric verification, model performance over continuous covariates - known, real-number attributes of images that affect performance - is particularly challenging to study. We develop a generative model of the match and non-match score distributions over continuous covariates and perform inference with modern Bayesian methods. We use mixture models to capture arbitrary distributions and local basis functions to capture non-linear, multivariate trends. Three experiments demonstrate the accuracy and effectiveness of our approach. First, we study the relationship between age and face verification performance and find previous methods may overstate performance and confidence. Second, we study preprocessing for CNNs and find a highly non-linear, multivariate surface of model performance. Our method is accurate and data efficient when evaluated against previous synthetic methods. Third, we demonstrate the novel application of our method to pedestrian tracking and calculate variable thresholds and expected performance while controlling for multiple covariates.
Material philosophy and the adaptability of materials

This reflection on Erik Rietveld’s The Affordances of Art for Making Technologies addresses, first, what it is to engage in material philosophy and make material propositions and then, second, what different materialities, or rather different ways of handling materialities, allow material philosophers to say. It notes that solidity is not an intrinsic property of any stuff. If only they are handled with care, and loved enough, even wood, concrete and metal turn out to be adaptable.
Blocking Phishing e-mail by extracting header information of e-mails

In-spite of availability of other communications means which includes electronic messages, social networking applications, mobile applications and e-mail, e-mail is widely used means of communication. These e-mail users receives several unsolicited e-mails which most of the times carries phishing attacks, these are Phishing e-mails. These e-mails carry a mechanism using which the users are unknowingly providing their secret information to Phishers such as passwords of internet banking, PIN, credit or debit card number or CVV number using which Phishers access the important information related to the bank accounts of these users.This paper has described development of filter based on origin-based information of e-mail called as Origin based Filter which blocks these phishing e-mails at MTA or MDA level by extracting header part information of these e-mails using Blacklist approach. The OBF has been tested using standard dataset `Public Phishing Corpus' and achieved 100 percent accuracy of this OBF.
Issues and Challenges for Content-Based Video Search Engines A Survey

With the vast growth and progress of multimedia on the Internet, particularly videos, which caused a growing demand for video retrieval, organizing, and automate systems, as many users mandate content-based retrieval systems over text-based retrieval systems. The process of extracting and selecting features, plays a very important role in content-based video retrieval regardless the significance of video characteristics. The reduction of time and space costs of the retrieval process is dependent on effective features selection. In this paper, we will discuss content-based video indexing and retrieval, in the application of video search engines CBVSE, which is the problem of searching for digital videos over the Internet. Addressing diverse approaches regarding video indexing and retrieval with brief outline and classification. Furthermore, we will propose a framework for a CBVSE that will offer a less complex solution for content-based video retrieval for video search engines over the web using reduced features vector with better accuracy to decrease the complexity trade-off. These features include temporal boundaries patterns combination and key-object concept features, as well as classifying the index list with a multi key-object concepts arrangement built on the statistical redundancy of the detected key-object concepts for each video shot in the sequence.
Augmented Reality in Visual Learning

Augmented reality (AR) is one of the upcoming technologies in the area of information technology. It is gaining wide momentum and is being deployed in various fields of advertising, marketing, manufacturing, health care, tourism, etc. With the increase in the usage of smart phones, Internet and cloud connectivity, mobile technology and augmented reality can be integrated to provide a richer learning experience in the field of higher education and research. Complex concepts, practical hands-on and laboratory experiments can be captured within customized AR application to enhance learning and provide in-depth understanding to students. This paper elaborates customized software design and development for mobile-based AR application to capture details specific to Indian medicinal plants. This application deployed on smart phones overlays digital information when a smart phone camera points to specific botanical plants. This application relies on Vuforia’s Image Target recognition that is a type of marker-based algorithm.
Interpreting the Basis Path Set in Neural Networks

The $${\cal G}$$ -SGD algorithm significantly outperforms the conventional SGD algorithm in ReLU neural networks by adopting the basis path set. However, how the inner mechanism of basis paths works remains mysterious, and the $${\cal G}$$ -SGD algorithm that helps to find a basis path set is heuristic. This paper employs graph theory to investigate structure properties of basis paths in a more general and complicated neural network with unbalanced layers and edge-skipping. The hierarchical Algorithm HBPS is proposed to find a basis path set, by decomposing the complicated network into several independent and parallel substructures. The paper theoretically extends the study of basis paths and provides one methodology to find the basis path set in a more general neural network.
Geochemistry of gold-bearing metamorphic rocks of the Natitingou area, Atacora structural unit, Northwestern Bénin (West Africa): Implications for Au genesis

The Natitingou area within the Atacora Structural Unit (ASU) of the Pan-African Dahomeyide orogenic belt, northwestern Bénin, is characterized by the monocyclic metasedimentary formations, amphibolites and orthogneisses which belong to the Internal zone of the belt. Representative samples of these different gold-bearing rocks have been analyzed geochemically to determine their petrogenesis, mode of emplacement, tectonic setting and role in the genesis of gold mineralization in the area. The orthogneisses are subalkaline, peraluminous and emplaced in a syn-collisional arc setting while the amphibolites are metaluminous, tholeiitic with an E-MORB affinity and emplaced in a rifted lithospheric setting. The metasedimentary rocks, comprising quartzites and mica schists are cross-cut by auriferous quartz veins. The metasedimentary rocks are recycled sediments varying from wackes to sublitharenite in composition and deposited in a passive margin setting. The quartz veins similar to the metasedimentary rocks are enriched in LREE, depleted in HREE and showed a negative Eu-anomaly (0.31–0.72). Their chondrite-normalised REE patterns deciphered them as Upper Continental Crustal materials which were probably derived from the reworking of their host metasedimentary rocks. Gold concentration in quartzites, amphibolites, mica schists, quartz veins and orthogneiss were 0.004–9.587, 0.26–1.76, 0.0005–0.464, 0.023–0.147 and 0.0−0.0005 ppm, respectively. Devolatilization process of the country-rocks, is the most probable mechanism for the ore-forming fluid, which could have been generated during the Pan-African Dahomeyide event. Remobilisation of gold and associated metal (As, W, Cu, Mo, Sb, and Sn) after the Pan-African Dahomeyide orogeny is suggested for enrichment of gold along shear and fault zones of the Natitingou area.
GQM-based Tree Model for Automatic Recommendation of Design Pattern Category

Software Design Patterns (DP) are formal approaches that propose generic reusable solutions to different design problems. Building DP automatic recommendation system is one of the most challenging topics in the field of software industry to improve the final software quality. Proposing a DP for a design problem requires good base knowledge about each DP and its functionality. In this paper, we propose an approach that automatically recommends the appropriate design pattern category. The proposed approach is a Goal Question Metric (GQM) based tree model of questions. The software engineer answers these questions based on the user requirements, and finally the approach recommends the category of the suitable DP category based on our designed tree model. The GQM is responsible for weight calculation process at each node based on the questions' answers. The software engineer is responsible for delivering the user requirements to our system, via answering the proposed model. The precision and accuracy obtained by our system is 80\% while the recall is 100\%.