Reviewerly/exHarmony huggingface dataset:
Towards the Joint Use of Symbolic and Connectionist Approaches for Explainable Artificial Intelligence

Artificial Intelligence (AI) applications are increasingly present in the professional and private worlds. This is due to the success of technologies such as deep learning and automatic decision-making, allowing the development of increasingly robust and autonomous AI applications. Most of them analyze historical data and learn models based on the experience recorded in this data to make decisions or predictions. However, automatic decision-making based on AI now raises new challenges in terms of human understanding of processes resulting from learning and of explanations of the decisions made (crucial issue when ethical or legal considerations are involved). To meet these needs, the field of Explainable Artificial Intelligence (XAI) has recently developed. Indeed, according to the literature, the notion of intelligence can be considered under four abilities: (a) to perceive rich, complex and subtle information, (b) to learn in a particular environment or context; (c) to abstract, to create new meanings and (d) to reason, for planning and decision-making. These four skills are implemented by XAI with the goal of building explanatory models, to try and overcome shortcomings of pure statistical learning by providing justifications, understandable by a human, for decisions made. In the last few years, several contributions have been proposed to this fascinating new research field. In this chapter, we will focus on the joint use of symbolic and connectionist artificial intelligence with the aim of improving explainability.
Expert System for Identifying Weeds on Oil Palm Plantations Using a Web Based Forward Chaining and Dempster Shafer Method

an expert system for identifying weeds on oil palm plantations using a web-based forward chaining and dempster shafer method. Palm oil is one of the plants that has its own charm in the community because the commodity of palm oil plays an important role in the Indonesian economy, therefore demand for palm oil continues to increase. Along with the increasing demand for palm oil in the world market, oil palm plantations have experienced many disturbances, one of which is weeds which are very detrimental to oil palm farmers, so oil palm farmers are trying to control it. Therefore, the purpose of this research is to build a system application that can be used by farmers to provide information related to weeds that attack oil palm and their control solutions.
Reconstruction of the Evolution of Palaeolithic Settlements within an Urban Agglomeration

With the use of available archival materials, the density and types of Palaeolithic settlements of the chosen urban agglomeration have been reconstructed. Considering the character of this study and availability of archival documents, the city of Brno, presently the second biggest city in the Czech Republic in terms of density of population, was selected. The discovered sites with evidence of Palaeolithic settlement were located using absolute coordinates and visualised using the ArcGis software. The research showed that even the present urban agglomeration holds relevant information on the original settlements and that it is possible to predict other potential locations with Palaeolithic finds.
Real Time Web Server Aggregator to Collect Fresh Information Based on Multi-Services

It is well-known that Internet information has grown substantially where millions of news spread online in a blink of an eye. This causes what so called information overloading that makes browsing important online information daunting and annoying task. Thus, this paper seeks to design a complete real-time webserver helps in gathering and distributing essential information based on user’s preferences. A one-stop webserver prototype is developed to serve as an automatic information collector from heterogenous sources and as well it’s evaluated. Evaluation process has shown the server enables users to browse different information from multiple sources on one site easily.
Application of Augmented Reality and Meta-quantum Analysis Technology in the Art Design of Cartoon Characters

This paper designs and implements a cartoon character design system based on augmented reality and meta-quantum analysis, so that children can create a three-dimensional cartoon scene from their two-dimensional hand-drawn drawings and perform interactive operations in the augmented reality environment. In order to enhance the realistic visual experience in the virtual scene, meta-quantum analysis and behavior control are performed on the characters in the 3D virtual animation design. Create a three-dimensional virtual animation character model in POS ER software, create a three-dimensional virtual animation character skeleton in 3D software and skin the bones to achieve animation character modeling. The skeletal animation technology is used to simulate the movement behavior of the character in 3D software, adjust the movement and drive the bones.
Expert System for Diagnosis of Pepper Plant Diseases Using Certainty Factor and Naïve Bayes Methods

Development expert system for diagnosis of pepper plant diseases using certainty factor and naïve bayes methods. Pepper is one type of plant that has long been traded on the European market. So that increasing the quality and quantity of pepper production is the main demand. However, diseases in pepper plants are also familiar to be found so that they can be detrimental to farmers and besides that, agricultural workers who are experts in the field of pepper plant diseases are still limited. Therefore, to overcome this problem, an expert system application is designed where this system can provide information about diseases that attack pepper plants, then provide suggestions or solutions to deal with these diseases. The purpose of this research is to build and design an expert system that is useful for determining pepper plant diseases and to apply certainty factor and nave Bayes methods in providing answers to the results of the consultation. The results of this study are expected to make it easier for users, especially farmers or farm workers in overcoming diseases in pepper plants.
An Efficient Accountable Oblivious Transfer With Access Control Scheme in the Public Cloud

In an oblivious transfer with access control (AC-OT) scheme, the database provider (DBP) can define different access control policies for each data record, and users are allowed to hide their choices from the DBP when accessing data. An accountable AC-OT (AAC-OT) scheme is an enhanced version of AC-OT that allows the DBP to revoke the access rights of malicious users. However, existing AAC-OT schemes have defects in their security model definition, malicious user revocation mechanism, and user-side performance. Therefore, the authors proposed an improved AAC-OT scheme that applies to the public cloud environment. In the definition of the security model, the definitions of access authorization and revocation are considered. By modifying the user tracing mechanism, the DBP can independently revoke the access rights of fraudulent users. In addition, the number of bilinear pairing operations performed by users in the transfer phase is kept constant by optimizing the generation of the underlying zero-knowledge proofs.
Vision Anomaly Detection Using Self-Gated Rectified Linear Unit

In the area of image processing and computer vision, visual anomaly detection is a critical and difficult task. For anomaly detection in surface image data, a customized neural network incorporating self-gated rectified linear unit (SGReLU) was designed, and the SGReLU-based model excelled other activation function-based models with a top-20 average test accuracy of 99.84%. The computational time needed for the operation is 10533 s for 20 epochs and the top-20 average test loss is 0.0125 using SGReLU, both of them were comparatively less than other activation functions.
Migration Prediction Approach for Predict the Overloaded and Under Loaded Workload in Cloud Environment

The resource necessity of any application substance differs dynamically dependent on its plan and other computational conditions like processor, memory, etc. A migration prediction approach is proposed to foresee the overburden and under stacked hosts, in light of the previous history of execution time taken for different workloads during VM migration. The rough set theory is incorporated in this proposed model to analyze the execution time taken for different workloads. The rough set theory is a popular prediction technique to predict execution time for different workloads during VM. The migration delay is minimized based on the past execution time of each processor for different categories of jobs. The execution time for each processor is calculated and maintained inside the prediction table. The quantity of future migrations is calculated based totally at the feasible allocations that can be made, in order that the migration delay is minimized based at the beyond execution time of each processor for extraordinary categories of jobs. Finally, optimized resource utilization is executed to give the exceptional answer amongst all possible solutions and it reduces makespan fee of jobs.
Integrating Democratic Cybersecurity

The expansion of pervasive and ubiquitous computing, especially with the advancement of the Internet of Things and the Smart City concept, extend the novel means of criminality and its investigation. We argue that current forms of investigation and discovery are not sufficient to limit injuries onto persons and communities. Nonetheless, cybersecurity approaches within criminal justice, criminology, and workforce development – together – offer models that significantly benefit efforts to address public cybersecurity harms, yet they have been largely overlooked. This paper draws on an interdisciplinary lens to address cybersecurity, including criminal justice and workforce development integration and employing empowerment theory. Applying empowerment theory, this presentation demonstrates the effects from integrating cybersecurity and forensic practices into traditional law enforcement. The effects are positive as public safety will be needed to provide public safety and security in our hybrid technical world. Thus, this paper illustrates how we must, in essence, “democratize” cybersecurity through its distributed availability. We present means to achieve this and results from efforts to promote this integration through several coordinated, yet differently targeted programs at one research university.
The European copyright system as a suitable incentive for AI-based journalism?

This Chapter analyses the aims of the current European copyright system and how these aims fit with AI-based journalism. It assesses the need to revise the current legal status and outlines some paths for the future of AI and news. Special attention is given to problems of too extensive or mistargeted protection well as to reconsidering what kind incentives are needed for producing high quality media content by utilisation of AI-based solutions.
A Brief Survey on Privacy-Preserving Methods for Graph-Structured Data

As one of the main manifestations of big data, graph-structured data widely exists in various fields such as social networks, smart cities, medical health, and finance, and is characterized by high dimension, nonlinear, scale-free, small world, etc. Extensive graph-structured data provides sufficient data resources for scientific research and commercial applications. However, graph data mining not only reveals the intrinsic value of data, but also brings the risk of privacy disclosure. Therefore, how to protect graph-structured data privacy is of great significance. In this paper, we survey the very recent research development on privacy preserving methods for graph-structured data. We introduce the common sensitive information and the related privacy risks in graph data, and elaborate the background knowledge for privacy inference attack. Then, the privacy inference attack methods and privacy preservation methods for graph-structured data are summarized. Finally, the shortcomings of the current research about graph-structured data privacy preservation and the possible research directions are discussed.
Multidimensional Scaling of Distributional Data

Multidimensional scaling (MDS) is a technique that visualizes dissimilarities between pairs of objects as distances between points in a low dimensional space. Standard MDS assumes that a single value of the dissimilarity is given for each pair of objects. To handle distributions of dissimilarities, histogram MDS has been proposed in Groenen and Winsberg (2007). They build on histograms of the empirical distributions of the dissimilarity of each of the pairs of objects. They also make use of so-called symbolic MDS for interval dissimilarities. The third ingredient of histogram MDS is the use of three-way MDS for several nested intervals that together describe the histograms. This chapter provides an overview of models for distributional dissimilarities by histogram MDS. Instead of representing an object by a single point in a low dimensional space, histogram MDS depicts objects as (concentric) circles or rectangles. The various models and their ingredients are illustrated by empirical examples.
Research on Multi-label of Gastritis Pathological Images Based on Weakly Supervised Deep Learning

Computer-aided diagnosis technology based on artificial intelligence has been widely used in the medical field, especially in pathological image diagnosis (Whole Slide Image, WSI). This paper mainly introduces the attention mechanism-based multiple instance networks (Attention-MIL) to detect three clinical indicators of “activity”, “atrophy” and “intestinal metaplasia” in gastritis. The model uses multiple parallel attention branches to automatically identify multiple tissue regions with high diagnostic value, and aggregate them with their corresponding attention scores to form WSI-level features. According to the gastritis pathology report, we assign three labels to each WSI. In the independent test analysis, comprehensive evaluation has fully proved the effectiveness and feasibility of this method in the multi-label prediction task of gastritis. Besides, the Attention-MIL model can visualize highly concerned lesion areas, which to a certain extent provides interpretability for clinical research. The method in this paper reveals the great potential of using weakly supervised learning to achieve assisted diagnosis of gastritis.
IDEAL: An Open-Source Identifier Name Appraisal Tool

The International Conference on Software Maintenance and Evolution is the premier international forum for researchers and practitioners from academia, industry, and government to present, discuss, and debate the most recent ideas, experiences, and challenges in software maintenance and evolution.
Identifying and classifying mechanical fingerprints of cells

Deep learning models and microfluidics experiments sort cells by mechanical properties, dynamics
Dynamic behavior of the quantum correlations of two cavity fields in the double Jaynes–Cummings model

Abstract The influence of the atom’s spatial degrees of freedom and the purity of the initial state on the dynamics of Bell non-locality, entanglement, geometric measure of quantum discord (GMQD) and quantum discord in a double Jaynes–Cummings model are investigated. We study the above four quantum correlations for an initial Werner-like state between the electronic states of the two atoms and between the two cavities respectively and find that all these quantum correlations are sensitive to the wave packet width and the purity of the initial state. For Bell non-locality, we find the phenomenon of sudden death and sudden birth, whereas the other three quantum correlations decay to zero asymptotically. We find that both the wave packet width and the purity of the initial state affect the GMQD and quantum discord in a similar way and we also notice that GMQD and quantum discord always exist, except where the purity of the initial state is zero, while Bell non-locality and entanglement can occur only when the purity meets certain conditions.
The Quantile Methods to Analyze Distributional Data

This chapter presents the quantile approach for the analysis of distributional data. This consists in representing each distributional observation by a given set of quantiles, thereby transforming the distributional data array to a standard numerical data array. The number m of quantiles used controls the granularity, therefore, a set of m+1 numerical vectors, called the quantile vectors, represents each object. The methods developed then explore the monotonicity property of quantile vectors. Visualization of each object is made by parallel monotone line graphs. Principal Component Analysis is applied to the quantile vectors based on the rank order correlation coefficients. Hierarchical conceptual clustering is performed based on the quantile vectors and on the concept sizes of the p-dimensional hyper-rectangles spanned by the quantile vectors. An application illustrates the proposed approaches.
Spatial data analytics for optimum data declustering in shale systems

Virtually all sparsely sampled, spatial data sets are sampled in a biased manner. Preferential sampling can result in biased statistics, for example, high mean porosity, resulting in overestimation of the oil in place. Cell-based declustering (CBD) for data weighting is commonly applied to mitigate bias due to spatial sample clustering, preferential sampling over regions with low or high feature value trends. CBD results are sensitive to cell-size selection and the current practice for choice of cell size is irrational, subjective and overly conservative. Hence, it is crucial to adopt a new workflow utilizing spatial data analytics, with spatial statistics to predict cell size rationally and objectively. The proposed SSADC workflow uses nearest neighbors and Ripley’s K to characterize the multiscale intersample distance, and based on a proposed criteria, a rational choice for cell size. The proposed spatial data analytics approach for cell size selection results in improved performance for sparsely sampled, spatial data declustering.
Pemulihan Trauma Pasca Bencana pada Tokoh Anak dalam Buku Cerita Bergambar (E Hon)

This study aimed to know the form of trauma recovery for child survivors after the 2011 T?hoku Tsunami, in the picture book (e hon) entitled Minna de Utau be! by Mari Mitsuoka. The method used in this research is descriptive analysis. Theories used in this research are Sigmund Freud's theory of Literature Psychology and Resilience theory by Masten (2014). The analysis results showed that child characters in e hon are described as having characteristics of mild trauma that can recover quickly through appropriate trauma recovery support. Those types of recovery support were affected by protective factors that interconnect and complement each other, such as the characteristics of child survivors, family support, and psychological support activities in the school and community environment. The effectiveness of appropriate recovery aid enhances children's resilience in the trauma recovery process, so they can recover and thrive after experiencing a natural disaster.
Factores que intervienen en el desarrollo de habilidades socioemocionales en la infancia, un estudio de caso

The solidity of social-emotional skills has become a useful tool related to success in the lives of individuals. Some statements claim that a high level of IQ does not ensure assertiveness when acting or making good decisions, but that there must be a balance between it and socio-emotional intelligence. What concerns social-emotional education cannot be simplified as a look inside, but represents a greater complexity, although it leads the subject to self-centeredness; individual concreteness, to discover their inner strength, promote personal growth and the construction of their own identity, its purpose is not individualism, but it is a collective consciousness, where competences such as self-knowledge, self-regulation, autonomy, empathy and collaboration come into play. The present research was carried out in the pandemic situation caused by the COVID-19 virus, it pursues the objectives of a more clearly understanding of socio-emotional-skills development process, in addition to identify the factors that prevent their acquisition, as well as explaining the benefits that individuals can achieve by promoting these abilities. This is channeled by an interpretative paradigm to analyze the phenomena in which the participating subjects are involved; how certain factors interfere with the development of social-emotional skills. This is linked to a qualitative approach, thus focusing on the experiences of the sample in question. The process takes the methodology of the case study due to the small number of students who manifest socio-emotional limitations. Data collection was carried out through interviews, observation guides, file research and anecdotal recording. These techniques and instruments made it possible to systematize the information obtained and then schematize them. It was found that there are different factors that influence the development of the emotional intelligence, such as parents, parenting style, teaching intervention, psychological, psychiatric or neurological issues, as well as the disposition and motivation of students. It is concluded that socio-emotional skills are not innate, but it is necessary to activate them, they are influenced, limited or propitiated, by the particular life experience, in the same way they are not acquired at the same pace. Another relevant point is the teaching preparation necessary to achieve a socio-emotional solidity, in order to lead students to emerge the best version of themselves.
SurfaceLens: near-surface spatial interaction with science museum exhibits

Smartphone applications have become an essential tool for museums because of their positive impact on visitor experience. However, smartphones can potentially distract visitors from the exhibit, crippling the hands-on experience. In this study, we present SurfaceLens which is a smartphone-based peripheral device enabling near-surface spatial interaction with exhibits. SurfaceLens detects proximity, location on the surface, and orientation of the device while a visitor moves it along the surface of exhibits like using a magnifying glass. It provides an information space and audio-haptic feedback reflecting its location and orientation so that the visitor can feel like seeing-through the exhibit. We compared this near-surface spatial interaction with on-screen touch interaction to investigate their effect on visitor experience and behaviour. The results indicate that SurfaceLens provided a more intuitive and transparent understanding of the content when used as an observation tool. Moreover, narrowing down the distance between visitors and exhibits promotes exploratory behaviours.
Study on the Adaptive Test Modelling Based on Knowledge Graph in the Domain of Substation

Abstract Traditional tests in the substation training fail to fit the ability of the operation and maintenance personnel and precisely describe their weakness. In this paper, an adaptive test model based on the knowledge graph in the domain of substation is constructed. With the help of the knowledge point correlations in the knowledge graph, the model updates the knowledge level of the testee in real time according to the response of each question, and automatically selects the next question that fits the individual. In cases where the test question covers different knowledge amount, the model constructs the knowledge state space or the adjacency/reachability matrix to infer the knowledge mastery, which improves the test efficiency and quality by reasonably reducing the number of questions during the test. Calculation results demonstrate that the model can accurately capture the weakness the substation personnel and has broad application prospects on evaluations in the vocational education.