Reviewerly/exHarmony huggingface dataset:
Multi-round Dialogue Intention Recognition Method for a Chatbot Baed on Deep Learning

With the continuous development of human-computer dialogue system, more and more dialogue robot products come into people’s lives. However, when human beings use short sentences and omit words, and in the process of identification often face problems such as more text noise, sparse characteristics, polysemy, backward and backward dialogue information. In order to solve the above problem, a deep learning based chatbot multi-round dialogue intention recognition method, according to the fit of deep learning algorithm and chatbot multi-round dialogue intention recognition model, by transforming the problem into a mathematical model, and obtain the final dialogue intention through the calculation of the model. First, the chat dialogue text was preprocessed, and the BERT model was established based on the processing results, the BERT model fused the deep learning model in the B E R T model, established a joint model, and data vectorized the short text of the human-computer dialogue. Finally, the multi-round dialogue intention identification similarity is calculated through the robot, realizing the dialogue intention recognition, and experiments show that the highest accuracy of the recognition method can reach 0.9912, the highest recall rate can reach 0.9914, and the highest f price is 0.9914, which can prove the superiority of the design method.
TF-GeneNAS: An evolution-based training-free approach to Neural Architecture Search

Neural Architecture Search (NAS) has received much attraction from the research community in recent years. However, due to the massive amount of computational resources required, it is still infeasible to employ NAS into research and production in small labs and companies. Recent methods in NAS that aim to speed up the evaluation process are often limit themselves in other aspects, such as the search space that NAS operates on. In this work, we propose TF-GeneNAS, an evolution-based training-free NAS approach with a dynamic search space and search strategy based on Gene Expression Programming. We conduct experiments on three tasks in both Computer Vision and Natural Language Processing domains to demonstrates the effectiveness of our method. With only 3 CPU days of searching needed, TF-GeneNAS can find network architectures with better performance than previous evolution-based methods, which can require days of GPU resources, thus significantly lower the cost of searching. We also perform further studies to show the impact of our training-free estimation strategy on the NAS process. We hope that our promising results can encourage further research into more efficient evolution-based NAS methods.
Prediction of Phishing Websites Using AI Techniques

The increase of internet usage in recent times has been a noticeable change in this generation. Users from all over the world use social sites to interact across the world. Countless websites are present today. With countless networks and sites, some people or companies tend to create new ways to lure out the random users using the web, such as phishing. In phishing, the normal users are swindled to use the fraudulent websites. The aim is to identify the phishing websites with great accuracy and compare different methods by which phishing websites can be tracked in an easier and more accurate way. Comparative studies of various algorithms are tested with the help of 10,000 datasets, each tested with 18 different parameters to increase the accuracy score of each algorithm. The paper shows the methods used for phishing detection are more accurate than other practices done so far using certain appropriate parameters and more useful.
A comparative study by separating and combining swarm-based algorithms into modules

The focus of this paper is to determine how the mechanisms of each algorithm contribute to search performance. swarm-based algorithms are composed of several mechanisms. This paper regards these independent mechanisms as modules. The modules constituting each algorithm are classified into three categories: (1) the common module: a collection of mechanisms that all swarm-based algorithms have in common; (2) the main module: the unique mechanisms of each algorithm that updates the solution by interacting with other solutions; and (3) the sub module: the unique mechanisms of each algorithm that do not directly improve the quality of the solution. Testing the algorithms combined into these modules reveals the contribution of each module to performance. The experiments are conducted for the three algorithms PSO, ABC, and CS to test the usefulness of this concept. The results of these experiments revealed the following implications: (a) the main module of ABC alone can have good performance; (b) the main module of CS does not perform well as a stand-alone; (c) the combination algorithm with the main module of PSO and the sub module of ABC can improve applicability.
A Wavelet-based Progressive Super-Resolution Model

Deep-learning based Super-Resolution (SR) methods have achieved remarkable performance in many aspects. However, most existing methods are constructed for a single specific scale, and few are multi-scale, lightweight, and have a short running time. To this end, we propose a wavelet-based progressive super-resolution method (WPSR). WPSR could process low and high-frequency information separately by reconstructing different wavelet coefficients in order and could generate multiple SR images at multiple scales sequentially. Experiments demonstrate that our WPSR could outperform existing state-of-the-art methods at multiple scales, especially for ×8 and ×16 scale factors.
Attributes Based Visible-Infrared Person Re-identification

Visible-infrared person re-identification (VI-ReID) is a challenging cross-modality pedestrian retrieval problem. Although there is a huge gap between visible and infrared modality, the attributes of person are usually not changed across modalities, such as person’s gender. Therefore, this paper proposes to use attribute labels as an auxiliary information to increase cross-modality similarity. In particular, we design the identity-based attention module to filter attribute noise. Then we propose the attributes-guided attention module to drive the model to focus on identity-related regions. In addition, we re-weight the attribute predictions considering the correlations among the attributes. Finally, we use the attention-align mechanism to align the attribute branch with the identity branch to ensure identity consistency. Extensive experiments demonstrate that proposed method achieves competitive performance compared with the state-of-the-art methods under various settings.
Itinerary Card Anti-cheating Beyond OCR

Since the COVID-19 pandemic, the itinerary card has become so pertinent to our lives that we need to show our itinerary card whether we take public transport or enter public places. The traditional way to manually check and record the information on the itinerary card is inefficient. It easily leads to congestion at the entrance, especially in high-traffic areas. In addition, some people even falsify their itinerary codes to evade mandatory testing or quarantine for COVID-19. Therefore, an efficient itinerary checking method is needed to alleviate the crowded problem, reduce cross-infection, and intelligently detect itinerary cheating. To address these issues, we propose a deep-learning-based method combined with OCR techniques. This method consists of five parts, including ROI locating, color classification, OCR, information pooling, and anti-cheating. The proposed scheme can extract the itinerary information on the itinerary card and check it. It also provides a certain anti-cheating function. Experimental results show that the proposed scheme can efficiently check the information on the itinerary card with high accuracy.
Quantitative Weak Linearisation

Weak linearisation was defined years ago through a static characterization of the intuitive notion of virtual redex, based on (legal) paths computed from the (syntactical) term tree. Weak-linear terms impose a linearity condition only on functions that are applied (consumed by reduction) and functions that are not applied (therefore persist in the term along any reduction) can be non-linear. This class of terms was shown to be strongly normalising with deciding typability in polynomial time. We revisit this notion through non-idempotent intersection types (also called quantitative types). By using an effective characterisation of minimal typings, based on the notion of tightness, we are able to distinguish between “consumed” and “persistent” term constructors, which allows us to define an expansion relation, between general $$\lambda $$ -terms and weak-linear $$\lambda $$ -terms, whilst preserving normal forms by reduction.
A Domain Specific Multi-Document Reading Comprehension Method for Artificial Intelligence Application

With the development of artificial intelligence, information retrieval and information extraction and knowledge services from large-scale texts are currently one of the most urgent needs of people. Machine reading comprehension technology is one of the key technologies that can be applied to knowledge mining. At present, multi-document reading comprehension has received a lot of attention, and its application scenarios are also very extensive. The main goal of this article is to find the answer to the question from a large number of smartphone manuals based on the questions raised by the user about the operation of the smartphone. This paper designs a pipeline structure with three modules: retrieval, extraction, and sorting. At the same time, it designs auxiliary tasks for the extraction model to improve the extraction ability, and uses a new answer scoring method to select answers. The final experiment proves that our method can effectively improve the answer's quality.
Problems in decision-making by young people to participate in entrepreneurial education programs

Purpose:The main purpose of this work is to analyze the attitudes, intentions and actions of young people in relation to educational programs on entrepreneurship.Design/methodology/approach: The survey of respondents was conducted through popular social networks in the region by filling out a questionnaire on the Internet.The sample group consisted of 179 people aged 17-35 years.Findings: Young people who refused to take part in an educational program on entrepreneurship, as a rule, have no ideas or projects for business and are characterized by zero entrepreneurial activity.Respondents from this group, in comparison with others, have a poor understanding of the goals and objectives of startups, and are also less likely than others to choose an intensive training program.All this may indicate that the real reason for the refusal of young people to participate in paid training programs may be a lack of motivation and a clear understanding by respondents of their vision of themselves as an entrepreneur.Research limitations/implications: The limitations of this study are related to the fact that the survey was of a regional nature for the target group of 17-35 years.In this regard, its results cannot be generalized.The results we have obtained provide a reasonable basis for further research on barriers to youth entrepreneurship.Practical implications: The research results are qualitatively, analyzed feedback from young people on the problems of low entrepreneurial activity which can be used to develop government measures to stimulate the private sector.In addition, the results provide a reasonable basis for further research on the role and relationship of financial and educational barriers to youth entrepreneurship.Social implications: The results of the research allow us to revise the measures of youth policy aimed at increasing entrepreneurial activity through the formation of sustainable behavioral entrepreneurial intentions.Originality/value: This study is an independent and comprehensive analysis of the low activity of young people in educational programs in the field of entrepreneurship.
On AGM Belief Revision for Computational Tree Logic

Computation Tree Logic is a temporal logic proposed as a tool for formal design and verification of concurrent systems in which execution flows may have many possible branches. A specification of a concurrent system may thus be constructed using a relational model for this logic, and the formal verification of the system's properties may be carried out through model checking. While CTL has obtained a great deal of success as a specification tool, there was a lack of systematic processes for reasoning about the revision and evolution of such specifications. Recently, some literature has proposed applying Belief Change techniques to the problem of revising CTL theories, with implications for revision of formal specifications of systems. This work expands this idea by proposing a Temporal Preference Logic, a multimodal logic with which we can reason about Belief Change in CTL. We show general results regarding the expressivity and decidability of some AGM Belief Revision operators for CTL.
COVI-PROC

As confusing as the world is right now, there are not that many options but to live through it. With safety belts fastened and survival kits prepped, we’re going to keep on rolling’ into 2022. In the upcoming years it is going to be mandatory to wear masks to protect each and everyone. People who don’t follow the safety protocols are a big danger to themselves and to the society. To address this issue we have come with a device called “CovidProc”, which are software systems which can be implemented using various hardware products like cameras, speakers, etc. to check if the people who aren’t following safety protocols. So, we plan to do our device with the help of AIML algorithms.
Non-Invasive Methods of Detecting Human Using Computer Vision by Incorporating Machine-Learning Techniques in Open Environment at Diverse Viewpoints

Over the period detecting human has become the most prominent area of research. To identify human different researchers have adopted various methods to create a framework that would be feasible for autonomous systems. Such systems if deployed in open environment has to face enormous challenges where human intervention is negligible. This paper depicts various arenas where computer vision with machine learning is used. With usage this paper portrays the deep aspect of the approaches used to identify human under two major categories of classifying distant objects and other classifying at different viewpoints. Because of these categorizations there are enormous challenges faced by researchers and how their endeavors still do not end in order to achieve the accuracy.
A Real-time Path Planner based on Improved Tentacle Algorithm for Autonomous Vehicles

In this paper, a real-time local path planner for obstacle avoidance of autonomous vehicles is proposed. Based on the tentacle algorithm, a novel tentacle curve that can ensure the continuity of the derivative of the path curvature is used to generate an alternate path set. After collision detection, we use the analytic hierarchy process (AHP) to calculate the weights to choose the best path. Finally, to verify the efficiency of the proposed planner, an obstacle avoidance case is designed in the simulation. The simulation results show that the proposed planner can obviously avoid the obstacle well and has better smoothness than the tentacle approach that uses the clothoid curve. The total time of local planning within a single period is less than 20 ms, which still has great real-time performance.
Fast Cost-aware Lazy-Theta over Euclidean distance functions for 3D planning of aerial robots in building-like environments

This paper presents a fast cost-aware any-angle path planning algorithm for aerial robots in 3D building-like environments. The approach integrates Euclidean Distance Fields (EDF) and Lazy Theta* algorithm to compute safe and smooth paths. We show how to consider the analytical proper-ties of EDFs for polygonal obstacles to get an approximation of the cost along the line of sight segments of the planner, reducing the computational requirements. Numerous tests in a realistic building-like environment are performed to evaluate the proposed algorithm with respect to other heuristic search algorithms considering the distance cost by using an EDF. The results show that the proposed algorithm considerably reduces the computation time in indoor and outdoor environments enabling fast, safe and smooth paths.
Are Graph Neural Network Explainers Robust to Graph Noises?

With the rapid deployment of graph neural networks (GNNs) based techniques in a wide range of applications such as link prediction, community detection, and node classification, the explainability of GNNs become an indispensable component for predictive and trustworthy decision making. To achieve this goal, some recent works focus on designing explainable GNN models such as GNNExplainer, PGExplainer, and Gem. These GNN explainers have shown remarkable performance in explaining the predictive results from GNNs. Despite their success, the robustness of these explainers is less explored in terms of vulnerabilities of GNN explainers. Graph perturbations such as adversarial attacks can lead to inaccurate explanations and consequently cause catastrophes. Thus, in this paper, we take the first step and strive to explore the robustness of GNN explainers. To be specific, we first define two adversarial attack scenarios—aggressive adversary and conservative adversary to contaminate graph structures. We then investigate the impacts of the poisoned graphs on the explainability of three prevalent GNN explainers with three standard evaluation metrics: $$Fidelity^+$$ , $$Fidelity^-$$ , and Sparsity. We conduct experiments on synthetic and real-world datasets and focus on two popular graph mining tasks: node classification and graph classification. Our empirical results suggest that GNN explainers are generally not robust to the adversarial attacks caused by graph structural noises.
Research on Cross Target Center Location Algorithm Based on Edge Refinement Fitting Method

The cross target has the characteristics of high identification, easy identification and easy sampling. Cross targets are usually used as cooperative targets for pose measurement, so it is of great significance to extract the central positioning of cross targets with high accuracy. In order to improve the accuracy of cross target center location, this paper proposes a center location algorithm based on edge refinement fitting method. Firstly, the image is extracted and segmented coarsely, and the improved edge thinning algorithm is used to produce a single pixel wide edge, and then the sub-pixel level location of the cross-shaped center is realized by using the fitting method. In order to verify the accuracy and anti-interference of the fitting method based on edge refinement in this paper, three algorithms are respectively used for positioning experiment and comparative analysis. They are Gray gravity center method, Hough transform method, and edge refinement based fitting method. Visual C++ programming generated the simulation cross target with the size of 800 × 600 pixels, respectively, using gray gravity center method, based on hough transformation method, and the fitting method based on edge refinement to conduct positioning experiments. The experimental results show that, the edge refinement fitting method has higher positioning accuracy and better anti-noise performance, compared with the traditional gray-scale barycenter method and hough transformation method.
A novel Hybrid technique for solving Data Aggregation problem based on Extended Weighted K-means algorithm and Key Performance Indicators

Data clustering is a common method for analyzing data in a wide range of applications, which includes data mining, picture analysis, pattern recognition, and so on. Each feature has a distinct importance in a non-supervised data collection. The clustering outcome will be improved if the feature is set with a correct weight that completely incorporates the lever of influence on the cluster effect. A feature evaluate function is introduced to produce a collection of vectors based on featured weights by reducing the function, which is a multiple objective issue. To solve the issue and determine the feature’s weight, a quick and elitist multi-objective clustering algorithm is applied. This paper proposes an Extended weight clustering technique based on the key performance indicator. The goal is to determine the patient’s chances of heart attack and prioritize the data without redundancy and to evaluate them in the weighted environment and to analyze the performance of the same with the help of key performance indicators.
Comparison of the Performance of Solar Panels Designed in Three Different Ways under the Same Conditions

Renewable energy sources such as solar energy have great potential to alleviate some of the negative environmental problems, including climate change, caused by intensive fossil fuel use. Solar energy will play an important part in future energy systems because of its rapid installation technology and lowering costs. In order to use solar radiation intensity in a wide range in the most efficient way, the plants must either be at an optimum angle in the area where they are installed or they should be designed with moving systems. In this study, performance and efficiency comparisons of solar tracking systems and fixed-designed systems based on real-field conditions have been made. In this way, price performance ratios can be determined clearly and it will be easier to decide on the types of power plants to be built. While solar tracking systems move according to the most efficient position that they can get from the sun through radiation sensors, they are designed with elements such as PLCs using multi-year solar data that operate with an open loop and based on meteorological data. In this study, three different power plant types designed as 1-axis, 2-axis, and fixed angle were controlled as open loop with PLC software and production differences between power plants were evaluated for 1 year. According to this research we have done, it has been concluded that 2-axis tracking systems produce approximately 32% more energy annually than a fixed-angle system, with an annual efficiency rate of 23% higher than a 1-axis system.
Legal aspects and data protection in relation to the CRIS system

When collecting, storing and providing research information, it should be taken into account whether and how the research information will be published. It is important under what framework conditions the data will be used further, what rights the researchers involved have to the research information, whether the research group or the project will leave and finally also whether and when the research information will be deleted again. With this in mind, our paper will consider two important aspects: From a data protection point of view, however, the processing of large amounts of data is one of the greatest conceivable challenges. The legal aspects depend on the legislation of the country where the CRIS system operates, the level at which CRIS operates and the type of data collected. Our paper will map legal aspects of processing and protection of research information in Slovakia and Germany.