Reviewerly/exHarmony huggingface dataset:
Explaining Software Fault Predictions to Spreadsheet Users

A variety of automated software fault prediction techniques was proposed in recent years, in particular for the important class of spreadsheet programs. Software fault prediction techniques commonly create ranked lists of "suspicious'' program statements for developers to inspect. Existing research suggests that solely providing such lists may not always be effective. It was found that developers often seek for explanations for the outcomes provided by a debugging tool and such explanations may be key for developers to trust the tool. Research on how to explain the outcomes of fault prediction techniques, which are often based on complex machine learning models, is scarce, and little is known regarding how such explanations are perceived by developers. With this work, we aim to narrow this research gap and study the perception of different forms of explanations by spreadsheet users in the context of a machine learning based fault prediction tool. A between-subjects user study (N=120) revealed significant differences between the explored explanation styles. We found that well-designed natural language explanations can indeed help users better understand why certain spreadsheet cells were marked by the debugging tool and such explanations can be effective to increase the users' trust compared to a black box system.
Neural Network Heuristic Functions for Classical Planning: Bootstrapping and Comparison to Other Methods

How can we train neural network (NN) heuristic functions for classical planning, using only states as the NN input? Prior work addressed this question by (a) per-instance imitation learning and/or (b) per-domain learning. The former limits the approach to instances small enough for training data generation, the latter to domains where the necessary knowledge generalizes across instances. Here we explore three methods for (a) that make training data generation scalable through bootstrapping and approximate value iteration. In particular, we introduce a new bootstrapping variant that estimates search effort instead of goal distance, which as we show converges to the perfect heuristic under idealized circumstances. We empirically compare these methods to (a) and (b), aligning three different NN heuristic function learning architectures for cross-comparison in an experiment of unprecedented breadth in this context. Key lessons are that our methods and imitation learning are highly complementary; that per-instance learning often yields stronger heuristics than per-domain learning; and the LAMA planner is still dominant but our methods outperform it in one benchmark domain.
Efficient and non-interactive ciphertext range query based on differential privacy

Differentially private range query schemes satisfy differential privacy by adding or deleting records during the process of creating index which suffers from the weakness of data loss in query results due to the negative noise. Recently, Sahin et al. (2018) proposed a differentially private index with overflow arrays in ICDE 2018, which ensures the integrity of query results. However, this scheme suffers from two drawbacks: 1) some private information (e.g., query requests or frequency) may be leaked because of querying over plaintext index; 2) the overflow arrays bring extra storage overhead. To this end, we present a non-interactive ciphertext range query based on differential privacy and comparable encryption. Our scheme can protect the query privacy since the query is performed over ciphertext based on comparable encryption. Besides, the experiment results show that our proposed scheme can save the storage overhead.
Panel-Method-Based Path Planning for eVTOL in Urban Environment

In this study the panel method used in fluid dynamics, is applied to the path planning problem of electric vertical take-off and landing (eVTOL) vehicles in urban environment. For an urban air mobility scenario the path planning problem of these vehicles includes the simultaneous operation of large number of vehicles around complex shaped buildings and architectural structures. Using the panel method, flow around complex shaped obstacles is modeled and calculated stream-lines of the fluid flow are used as trajectories for the eVTOL traffic. Since, streamlines do not cross each other or obstacles, using streamlines as vehicle trajectories guarantees collision free paths for vehicles. Moreover, source and sink elements are used to avoid collision of vehicles with each other in case of emergency, and attract the vehicles to target points. The application of panel method provides a solution to the whole path planning problem for multiple vehicles in a single step. Assuming complete knowledge of the city map, the path of each eVTOL vehicle in an air taxi fleet can be determined simultaneously.
Discussion on the Teaching Reform of the Course "Application of Computer in Materials Science"

The development of computer technology has brought revolutionary changes to the material industry.As an indispensable part of the curriculum system of materials science and engineering, the application of computer in materials is an effective docking and integration between materials and computer applications.In order to improve the teaching effect and cultivate students' comprehensive ability of analyzing and solving problems, a good teaching model takes the brunt.In view of this course, this paper puts forward several suggestions on how to reform the teaching content, teaching method and examination and evaluation system.
Curiosity: A game-based early mathematics case

This research aimed to generate a learning trajectory in an introduction to early mathematics, precisely to measure learning using educational games and Realistic Mathematics Education (RME) and to describe young children’s curiosity in learning early mathematics. Children need to have an understanding to take a measurement and use a learning trajectory to learn how to differentiate measurable and countable quantities and to tell the longer and the greater in number. The design of this research involves three phases, namely, preliminary design, experimentation, and retrospective analysis. A Hypothetical Learning Trajectory (HLT) using educational games was developed by collecting data from documentation, observations, and interviews. The results obtained from the implementation in the classroom showed that the educational games were able to help young children get to know early mathematics, particularly on measurements of lengths and volumes. There is an association with several factors in the selection of realistic problems. Communications during the game could stimulate an introduction to measurement. The use of objects in students’ vicinity also contributed to their experiments relating to measurement. The results of this research inform policymaking for teachers in designing learning in the classroom in introducing early mathematics based on educational games and Realistic Mathematics Education (RME).
Fluorescence Microscopy Images Segmentation Based on Prototypical Networks with a Few Annotations

The objects in fluorescence microscopy images (FMIs) are rather densely populated, and often have complex structures as well as large color variations. These issues result in time-consuming manual annotations for training a segmentation model. Few-shot segmentation (FSS) models reduce the requirements of annotations by using prototypical networks to generalize the object regions given only a few annotation masks. These methods, however, often fail to generalize to FMIs due to the domain shift from the images of source class to those of target class. To tackle these challenges, we adapt the prototypical networks from the few-shot learning scheme as our base model architecture to learn a compact feature representation of the object, with only a few annotated masks. Then, to solve the domain shift problem, we propose a resampling strategy to regularize the model training. Specifically, we construct the training set using not only the images form the source class, but also the support set from the target class by resampling the latter for regularizing the training. To distinguish our FMIs segmentation method from the conventional FSS models, we define it as a training regularization strategy based on the prototypical networks (TRPN). Experimental results on three public datasets, i.e.the ER, MITO and NUC, have confirmed the advantages of our method, which provides robust and accurate segmentation performance on FMIs only with a few annotation masks. Source code and data of this work are made publicly available at: https://github.com/cbmi-group/TRPN-model.
Development of Financial Planner Application Software Based on Waterfall Model

Financial planning is necessary, especially for those who started to have savings. It can be achieved through thorough planning, organizing, directing, and controlling financial activities. To overcome these problems, we developed and designed the financial planner app. It can help all our users to control their money flow according to their budgets every month. In developing this project, Business Model Canvas is used to assess the key points and Waterfall Model is used to define the process. All requirements of the financial planner app are modeled using Use Case Diagram. To cope with the reduplication of coding for the app development, we used the Object-Oriented approach and made a Class Diagram to portray the relation between objects involved. Besides that, we also use Sequence Diagram to develop the flow process of our system. We also developed a prototype of the User Interface to give a picture of how the app's visuals. Finally, we used the Black Box Method for testing the “Financially” app and the results are given in the form of tables representing every page. We hope that this “Financially” app will be helpful and the impacts on our users align with the purpose of the creation of the app.
Visualization of thermochromic ink &amp; disappearing ink writings through various techniques on different paper surfaces

Invisible ink are becoming a popular tool for committing various economical and financial frauds. Temperature dependent erasable ink and pH indicator disappearing ink both are widely used for this purpose. In this research paper we have explained different techniques to decipher these invisible writings. Examination through Photoshop software and U.V. Cabinet are very cost-effective, non destructive and easily available technique to decipher these writings. Chemical examination through iodine fuming, NaOH or KOH are also very effective and reliable method but they are destructive in nature. Visualization of invisible writing is best done by viewing the samples under the spot light and U.V. light available in Video Spectral Comparator -8000.
Kelayakan Buku Ajar Mata Kuliah Belajar dan Pembelajaran Berbantuan Modular Object Oriented Dynamic Learning Environment (MOODLE))

The purpose of this research is to determine the feasibility of textbooks based on the Modular Object Oriented Dynamic Learning Environment (MOODLE) at the virtual personal server (VPS) address: http://103.247.11.240. The research method used is descriptive quantitative research methods. The data collection instruments used questionnaires and observations. Furthermore, descriptive data analysis was carried out. Success is measured using a content activity design standard. Feasibility is reviewed from technical standards, content standards and visual content standards. The technical standard scored 82.5% in the very feasible category. Content and content standards scored 82% in the very feasible category and visual design standards scored 77.32% with the feasible category.
Research on AI-Assisted Teaching Mode for English Teaching from the Embodied Cognitive Perspective

Based on the construction of virtual platform with artificial intelligence technology, this article studies situational teaching mode and its application in English teaching from the embodied cognitive perspective, which includes the teaching program design of the virtual studio, the creation of the external environment of the virtual language, the observation of the situational teaching process and the evaluation of the contextual simulation effect. The purpose of this research is to explore the cognitive path of English teaching in virtual situations, and to explore the reform path of English teaching under virtual reality conditions in terms of teaching concept, teaching method, cognitive efficiency and mode innovation.
Precise Code Clone Detection with Architecture of Abstract Syntax Trees

In the field of code clone detection, there are token-based similarity and abstract syntax tree-based detection methods. The former consumes less resources and is faster to detect, while the latter consumes more space and is less efficient. And there are few tools that scale to large-scale databases. To address the challenges, an approach is proposed that can detect code clones using the similarity of tokens and architecture of abstract syntax trees. Architecture of the syntax trees preserves the precision of detecting clone pairs, at the same time, the method also preserves the speed of matching code similarity. In the approach, it first parses the tokens of the code fragments and gets the features of the syntax trees. It can eliminate the unqualified parts of them based on the architecture when matching the candidates quickly, and then detects the similarity in detail. Finally, the results are output according to the input threshold range. The experiments confirm that the method substantially improves the precision of code clone detection while keeping the recall rate unabated.
Modulation signal recognition based on lightweight complex residual attention neural network

To address the problem of poor recognition performance of real-valued deep learning models in modulation recognition applications and the practical need for lightweight in real applications, this paper proposes a lightweight complex residual attention neural network model. By constructing a complex convolution module, the complex convolution operation of the convolution layer is implemented to extract features in the complex domain of the modulated signal, and a residual attention mechanism is introduced to further select key features for recognition and classification. The experimental results show that compared with the real-valued deep learning model, the proposed model extracts more spatial variability in signal classification features and is easier to recognize, and increases the recognition rate by 10% when the signal-to-noise ratio is greater than OdB. In addition, the resource consumption of the model is reduced. Compared with the basic model, the model parameters are reduced by 99%, and the floating-point operation is reduced by 56%.
Analyzing the Need for Video Summarization for Online Classes Conducted During Covid-19 Lockdown

The evolution of wearable cameras has revolutionized the exponential growth of the industry due to the production of video content. This has ultimately generated the need for storage management, video management, video summarization, methods to reduce the cost of resources, etc. The present paper compares and evaluates the different techniques available for video summarization nowadays and to visualize the need of applying these methods to the content of online classes delivered during the COVID-19 pandemic. This pandemic has affected the lives of children as they all have got stuck totally onto the screen for their education and learning through their respective organizations. But this trend has raised many challenges among the parents, educational lists, and health experts. These challenges mainly include the availability of online gadgets with internet connectivity, especially in rural areas, and the health issues generated because of the overexposure to online gadgets by students all over the world. As per the educational experts, including UNESCO and UNICEF, this continuous exposure to online gadgets has alarmed the different potential dangers to their health including obesity, stubbornness, heart diseases, vision loss, etc. This paper mainly focuses on the need for summarizing the online class videos not only to reduce the burden of the overhead cost of Internet and resources, but also to reduce the harmful effects produced on the health of these students due to the continuous exposure of the unnatural light and waves generated through these gadgets.
On the Use of Width-Based Search for Multi Agent Privacy-Preserving Planning (Extended Abstract)

The aim of decentralised multi-agent (DMA) planning is to coordinate a set of agents to jointly achieve a goal while preserving their privacy. Blind search algorithms, such as width-based search, have recently proved to be very effective in the context of centralised automated planning, especially when combined with goal-oriented techniques. In this paper, we discuss a recent line of research in which the usage of width-based search has been extensively studied in the context of DMA planning, addressing the challenges related to the agents' privacy and performance.
An Improved AdaBoost-based Ensemble Learning Method for Data-Driven Dynamic Security Assessment of Power Systems

In modern power systems, power system dynamic security assessment is a critical task against the risk of blackout. This paper aims to develop reliable recognition models for system real-time dynamic security assessment, where ensemble learning models consisting of extreme learning machine, stochastic configuration networks and random vector functional link have been constructed. The principle for decision making is carried out based on the optimized tradeoff between credibility and accuracy. Moreover, the AdaBoost.RA strategy is afterwards introduced into the modelling process, which allows these critical (instances to be assigned with larger weights and verifies that this proposed methodology could provide more convincing models. This results in a more reliable recognition system for dynamic security assessment.
Topic Tracking Algorithm Based on Topic Structure Characteristics

Topic tracking task is used for public opinion monitoring, and its key technology is text classification algorithm. However, existing text classification algorithms need large-scale train corpus during training, while topic tracking task only provides a small amount of train corpus, resulting in that it has poor performance. We analyze story description contents in train corpus, and find that the report description contents of the same topic have the topic structure characteristics of high similarity. We use topic information to represent highly similar topic structure characteristics to make up for the lack of train corpus in text classification algorithm, and fuse topic structure characteristics and text classification algorithm to make the topic tracking algorithm consider topic structure characteristics, and propose Topic Tracking Algorithm based on Topic Structure characteristics (TTATS). To verify its performance, we carry out quantitative and qualitative experiments. The experimental results of multiple dimensions show that it has preferable topic tracking performance.
An Anti-Air-Weapon-Oriented Knowledge Graph Construction Method

As the information exp loding in internet, the weapon equipment data derived from internet has various deficiencies such as heterogeneity, redundancy and unprofessional. For alleviating these deficiencies, this paper has proposed a knowledge graph construction method used for anti-air weapon equipment. In the method, we first eliminating the redundant data in the information obtained from internet and then extract the entity-attribute-relationship properties, at the end, we have constructed a triadic data representation structure of RDF (Resource-Description-Framework) and create an Anti-Air-Weapon-Oriented Knowledge Graph using Neo4j. This method is able to provide retrieval and recommendation of associative knowledge in the anti-air-weapon field.
GESTURE-BASED HEAD-MOUNTED AUGMENTED REALITY GAME DEVELOPMENT USING LEAP MOTION AND USABILITY EVALUATION

The environment of augmented reality created by head-mounted displays can provide players with an immersive gaming experience and proper gestures can enable the integration of real-world objects with the virtual environment.This study explores the usability of gesture operations applied to games and tries to find new applications using current mature gesture sensors.This study develops a head-mounted augmented reality game based on gesture recognition, and evaluates their usability and impact on the user's interaction with the virtual interface.It uses the Unity engine and open-source glasses designed by Leap Motion to develop an adventure game, and it contains elements of a first-person shooter game.There is no need for any joystick or touch screen to control the game, as the game relies only on the player's hands to operate through a controller-less interface.The results show that the system usability score is 79.4,which indicates a good usability level.This study may be of importance in clarifying problems that may be encountered in the development of interactive-gesture head-mounted display augmented reality games.Finally, this study offers suggestions for the challenges and future of head-mounted augmented reality games.
Layer 3: Applications

The application layer represents the attributes, taxonomy, and portfolio of actual computer programs that provide digital enablement for delivering the capabilities, workflow, and services via the service and process layers to the BOM.
DIJE: Dense Image Jacobian Estimation and its Application to Visual Servo Control

In order for robots to move in the real world, they must first be able to understand how to move their own bodies, as there are uncertainties in robot structures, camera parameters, or the tool that it holds. We propose a Dense Image Jacobian Estimator (DIJE) as a method to estimate the image Jacobian online for every pixel in the image. The estimator is based on a Kalman Filter simplified to run on every pixel, and does not need a priori knowledge of the robot kinematics. The proposed method is applied to a markerless visual servoing controller and verified on a musculoskeletal robot.
Positioning pictures of the world order within the framework of transhumanism by heuristic methods

Accurate positioning of the current state of worldview perception allows you to more effectively and pragmatically carry out your activities. In connection with the active development of various technologies in the world, new views on the current world order have been formed, designated as transhumanism. In accordance with this, the purpose of the article is to identify the position of a new picture of the world in relation to its other analogues. As a result, the paper presents an overview of the basic concepts of world order that have existed up to the present time. By means of the heuristic method of pairwise comparisons, the numerical and graphical positioning of transhumanism is implemented in comparison with earlier ideas about the world. Scientific novelty is represented by the obtained comparison of world order, where their advantages in the formation and adoption of new value human attitudes were identified.
Application of the Certainty Factor and Forward Chaining Methods to a Cat Disease Expert System

An expert system is a computer-based system that uses knowledge, facts, and reasoning techniques to solve problems that are usually only solved by an expert in their field. Cats are the most popular animals in the world and have many fans. Sometimes cat owners do handle without going to the vet, so there are many wrong ways of handling. This research aims to implement a web-based expert system to diagnose diseases in cats using forward chaining and certainty factor (CF) methods. The knowledge base of this system uses sixteen types of symptoms, six types of diseases.