Reviewerly/exHarmony huggingface dataset:
Integrated Artificial Intelligence Systems

From Shakey the Robot to self‐driving cars, from the personal computer to personal assistants on our phones, the Defense Advanced Research Projects Agency (DARPA) has led the development of integrated artificial intelligence (AI) systems for more than half a century. From the earliest days of AI, it was apparent that a robust, generally intelligent system should include a complete set of capabilities: perception, memory, reasoning, learning, planning, and action; and when DARPA initiated AI research in the 1960s, ambitious projects such as Shakey the Robot went after the complete package. As DARPA realized the challenges, they backed away from the ultimate goal of integrated AI and tried to make progress on the individual problems of image understanding, speech and language understanding, knowledge representation and reasoning, planning and decision aids, machine learning, and robotic manipulation. Yet, even as researchers struggled to make progress in these subdisciplines, DARPA periodically resurrected the challenge of integrated intelligent systems and pushed the community to try again. In the 1980s, DARPA's Strategic Computing Initiative took on challenges of integrated AI projects such as the Autonomous Land Vehicle and the Pilot's Associate. These did not succeed, but instead set the stage for the several decades of more siloed research that followed, until it was time to try again. In the 2000s, DARPA took on the integrated AI problem again with its Grand Challenges, which led to the first self‐driving cars, and projects such as the Personalized Assistant that Learns, which produced Apple's Siri. These efforts created complex, richly‐integrated systems that represented quantum leaps ahead in machine intelligence. The integration of sophisticated capabilities in a fundamental way is the key to general intelligence. This is the story of DARPA's persistent long‐term support for this essential premise of AI.
A Novel Neural Network using the Genetic Algorithm and Structure of the Support Vector Machine

Recently, deep learning has been studied as one of the most effective methods in the machine-learning field, and lots of results have been reported. However, the most effective way to construct neural networks has not yet been determined. Besides, the interpretation of an obtained network by a user is difficult. To solve this problem, we have proposed a neural network with a support vector machine (SVM) called “SVM-NN”. In this proposed method, support vectors in the SVM determine the number of neurons in the neural network and their weights and biases. Then, the hyperplane of the neural network is expected to behave similarly to that of the SVM before training. This method has an advantage in that users can understand the mechanism of the network based on the support vectors. However, there are several problems to apply SVM-NN to real problems. In this study, we proposed the SVM-NN with the genetic algorithm. To confirm the effectiveness of proposed methods, the computer simulations are carried out taking benchmark problems as examples.
The Effect of Augmented Reality on the Memorization in History and Humanities Education

With the advent of the 5G era, the development of AR in education has great potential, and this technology can bring more diverse forms to education. Although there are many cases in which AR has been used to develop “science” education, there is relatively fewer applications of AR in the field of humanities education. We still don’t know whether AR can always provide positive guidance to students in the history and humanities education, and how does this technology affect students’ learning and memory effects? We developed an AR-assisted book for historical story education. The book combines virtual information with real-world information from both visual and auditory dimensions. In this paper, we used a control experiment to compare the difference of memory effects between AR-assisted book and traditional 2D book. We set up test questions to compare the memorization results of the two experimental groups, and analyzed the strengths and weaknesses of the two books. This paper aims to lay the foundation for how to stimulate students’ interest in learning and improve their memories ability of learning, and to provide advice on how to apply augmented reality technology in the history and humanities educational environment.
Optimized Fuzzy Rule-Based System to Measure Uncertainty in Human Decision Making System

An evaluation system to measure the performance of the students is uncertain. Normally, analytical techniques could not hold good result and evaluation may be skewed on certain human attributes. Moreover, examinations and periodic evaluation and other assessment techniques could not measure student performance in totality. Therefore, fuzzy rules which measure uncertainty may be applied in such cases to measure student’s performance in a range of particular attributes. So, researchers adapted soft computing techniques to measure uncertainty in human knowledge and their overall assessments. The aim of this paper is to use hybrid system by fusion of concepts of fuzzy with genetic algorithm to evaluate the performance module. Such a module can be helpful for evaluation imprecision in the knowledge adopting and decision-making system. The number of rules generated by fuzzy rule-based system increase exponentially. As a result, it increases the time and space complexity. This approach works in three phases’ knowledge base, coding and optimization. The analysis of this method with other traditional system showed that the optimized rule-based system is dynamic, adaptive and more responsive than that of only fuzzy rule-based system.
Training Multi-Layer Perceptron Using Population-Based Yin-Yang-Pair Optimization

This work proposes the Population-Based Yin-Yang-pair Optimization (PYYPO) algorithm and its implementation for successfully training an multi-layer perceptron (MLP) neural network. The proposed PYYPO trainer is then used on five healthcare-related classification datasets: breast cancer, diabetes, liver disorders, Parkinson disease, and vertebral column. The results of the proposed trainer are compared with four other algorithms: Particle swarm optimization, genetic algorithm, cuckoo search, and moth flame optimization algorithms. Lastly, it is shown in the findings that the proposed trainer achieves better scores on most datasets when compared to other algorithms on one performance metrics, namely classification accuracy.
Augmented Reality Project Poster

This work presents a process to create an Augmented Reality (AR) smartphone application which aims to enhance the way that contents of posters are delivered to viewers. Background and literature reviews provide context for the development of this project. The project consists of two parts: the poster and the mobile application. The app is created in Unity using the Vuforia engine. User will be able to download and install the app. This will allow any viewer to interact with the poster via AR technology.
A Fuzzy Logic Based Approach for Prediction of Squamous Cell Carcinoma

Squamous cell carcinoma (SCC) is a type of skin malignancy. It is a fast growing skin disease which rapidly ravel to other parts of the body, if occurs in any particular body part. SCC is a genuinely moderate developing skin disease. Expert system tools such as fuzzy systems, neural networks and genetic algorithms are very useful in prediction of these diseases. In this paper we accordingly developed an approach by applying fuzzy logic technique on various parameters on the input dataset in order to predict the existence of squamous cell carcinoma among the patients. The fuzzy system accepts five information parameters for the data source and generates one yield parameter as an output. The output produced by this fuzzy logic system is analyzed to find the existence of squamous cell carcinoma and its stage. It is worth to mention that the simulation outcomes of the proposed system are better in terms of prediction accuracy of SCC as compared with other prediction techniques.
A CLARIN Contractual Framework for Sharing Personal Data for Scientific Research

The development and use of language resources often involve the processing of personal data.
History and philosophy of cyber security education

The study of cyber security education is less than three decades old. Its philosophy is far from settled. This chapter provides a sketch of several historical aspects of education for professionals at the tertiary level and discusses the overall political/policy settings that have been in play. The underlying philosophy has involved a large-scale relegation of cyber security education and training to non-traditional education sources newly created for the purpose. This has led to a compartmentalisation through nano-credentials that may not pay adequate regard to over-arching issues and more comprehensive education, even though the field of cyber security itself was mapped out rather presciently by a ground-breaking US Task Force report first published in 1970. Taking Australia as a case study, the chapter questions the adequacy of financial and policy commitment to the challenges in that country against accelerating and more focused activity in some other countries, most notably the United States and the United Kingdom.
Learning Channel-Wise Ordered Aggregations in Deep Neural Networks

One of the most common techniques for approaching image classification problems are Deep Neural Networks. These systems are capable of classifying images with different levels of detail at different levels of detail, with an accuracy that sometimes can surpass even manual classification by humans. Most common architectures for Deep Neural Networks are based on convolutional layers, which perform at the same time a convolution on each input channel and a linear aggregation on the convoluted channels. In this work, we develop a new method for augmenting the information of a layer inside a Deep Neural Network using channel-wise ordered aggregations. We develop a new layer that can be placed at different points inside a Deep Neural Network. This layer takes the feature maps of the previous layer and adds new feature maps by applying several channel-wise ordered aggregations based on learned weighting vectors. We perform several experiments introducing this layer in a VGG neural network and study the impact of the new layer, obtaining better accuracy scores over a sample dataset based on ImageNet. We also study the convergence and evolution of the weighting vectors of the new layers over the learning process, which gives a better understanding of the way the system is exploiting the additional information to gain new knowledge.
Evaluation of Floating-Point Arithmetic Protocols Based on Shamir Secret Sharing

The development of privacy preserving collaborative applications can be simplified by starting with a collection of protocols that support secure computation with all basic data types and secure and efficient protocol composition. For some tasks, secure computation with real numbers can be efficiently achieved using fixed-point arithmetic. However, many other tasks need the dynamic range and accuracy of floating-point numbers. In this paper, we examine a recently proposed family of floating-point arithmetic protocols, to provide a more comprehensive analysis and evaluation of their construction, complexity, and performance, as well as refined variants and generalizations. The analysis is extended to more general issues: the relations between complexity metrics, protocol performance, and execution environment, as well as their importance for protocol evaluation and optimization.
Optimization of fractal dimension and shape analysis as discriminators of erythrocyte abnormalities. A new approach to a reproducible diagnostic tool

Manual microscopic analysis is the gold standard for analyzing blood smear. Microscopic analysis of blood smear by a hematologist is subjected to many challenges such as inter-observer variations, operator experience, and conditions of observation. This study aims to examine several parameters extracting from the features of blood smear images. These parameters were used to develop a predictive function, which can be used to automate the microscopic analysis of blood cells instead of manual observation. Fractal dimension, roundness, and aspect ratio were estimated for two types of abnormal erythrocytes: echinocyte and sickle cell. Standard conditions and the choosing of the optimum parameters through the imaging preprocessing were done in order to ensure that the chosen parameters reflect the morphological characteristics of examined erythrocytes. Statistical discriminant analysis was used to build the predictive function for erythrocytes morphological change by a linear combination of the measured parameters. The measured fractal dimensions were 1.825 ±0.008, 1.502 ±0.019 and 1.620 ±0.018 for control, echinocyte, and sickle cell, respectively. The roundness values were 0.94 ±0.05, 0.83 ±0.04 and 0.56 ±0.02 for control, echinocyte, and sickle cell, respectively. The aspect ratio values were 1.005 ±0.151, 1.046 ±0.089 and 1.742 ±0.162 for control, echinocyte, and sickle cell, respectively. The differences between the image analysis parameters for echinocyte and sickle, when compared to control, were statistically significant. The constructed discriminant function using measured parameters was effectively differentiating between examined erythrocytes. The results demonstrated that the selected image analysis parameters extracted from microscopic images with conjunction with statistical discriminant analysis could be used as powerful tools in the classification of erythrocytes according to their morphological characteristics. The findings of this study, in addition to the previous attempts in this filed, could help in the enhancement of a fully automated microscopic system for blood smear analysis.
Using BERT for the Detection of Architectural Tactics in Code

Quality-driven design decisions are often addressed by using architectural tactics that are re-usable solution options for certain quality concerns. However, it is not suficient to only make good design decisions but also to review the realization of design decisions in code. As manual creation of traceability links for design decisions into code is costly, some approaches perform structural analyses to recover traceability links. However, architectural tactics are high-level solutions described in terms of roles and interactions and there is a wide range of possibilities to implement each. Therefore, structural analyses only yield limited results. Transfer-learning approaches using language models like BERT are a recent trend in the field of natural language processing. These approaches yield state-of-the-art results for tasks like text classification. We intent to experiment with BERT and present an approach to detect architectural tactics in code by fine-tuning BERT. A 10-fold cross-validation shows promising results with an average F1-Score of 90%, which is on a par with state-of-the-art approaches. We additionally apply our approach to a case study, where the results of our approach show promising potential but fall behind the state-of-the-art. Therefore, we discuss our approach and look at potential reasons and downsides as well as potential improvements.
Development of a Virtual Reality Simulation for Remote Controlled Container Crane Operator Training

Any large scale cargo port requires the use of container cranes for loading of intermodal containers as well as unloading them. The operator of such a large crane must be appropriately trained to prevent any accidents at the cargo port. This should be achieved by training crane operators on all the protocols of crane operation with significant hours of practice. The aim of this project is to develop a virtual reality (VR) simulation for operator training of container cranes which are remotely controlled, situated at the cargo ports. The simulation consists of a virtual control room equipped with joysticks and display screens, container crane, container ships, trucks and a cargo port environment. We used an actual super post-Panamax quay crane as a reference model for this simulation. The project has been built using Unity3d for the programming and 3DSMAX for the designing of models. The virtual joysticks in the simulation can be interacted by using the Oculus Rift VR setup thus allowing to maneuver the crane and the container load in all three dimensions. This paper mainly discusses the structure and implementation of the virtual reality simulation for crane operator training, along with a proposal for further improvements.
Emotional Design and Human Factors Design as Tool for Understanding Efficiency in Information Design Process at Medical Documents

This work will focus on improving the design that currently has medical documents, specifically results of blood chemistry used in Mexico, which are not designed to be understood by the different users who really interact with this product. It is important to propose a taxonomy of the different users that interact with the documents that correspond to the results of clinical analysis, in order to develop products that are focused on the cognitive requirements of each of them.
Assessment of the empirical matched field processing algorithm for autonomous tracking of aftershock sequences

&amp;lt;p&amp;gt;Autonomous algorithms can improve the processing of aftershock sequences, for example by reducing the analyst workload. We present a system for automatic detection and location of aftershocks in a specific region following a large earthquake. The system seeks to identify all signals generated by seismic events in the target region, while passing over signals generated by sources in all other regions. For a given station, we can generate a sensitive empirical matched field (EMF) detector for the target region using only an empirical template from the mainshock signal. These EMF detectors perform much better on seismic arrays than on 3-component stations. For each selected station in the network, a multivariate detector combines the EMF detector with an optimized continuous AR-AIC detector to generate a target-optimized detection list. For arrays, an additional continuous calibrated f-k process reliably screens out likely signals from other sources. A region-specific phase association algorithm takes the screened detection lists from each station and generates a preliminary aftershock bulletin. We have processed aftershock sequences from four major earthquakes: the Tohoku event in 2011 (Japan), the Illapel event in 2015 (Chile), the Papua New Guinea event in 2018 and the &amp;lt;span&amp;gt;Gorkha event in 2015 (Nepal).&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We evaluate the results in detail by comparing the automatically generated origins and corresponding phase arrival times with matching events and associated arrivals in the analyst reviewed (REB) and automatic (SEL3) bulletins issued by the CTBTO Preparatory Commission. Between 40% and 65% of all events in the REB are found to closely match the locations and origin times of the events found by our EMF-based procedure. The resulting discrepancies are assessed with respect to signal-to-noise ratio, number of defining stations, and epicentral distance. Furthermore, the REB events not detected by the EMF method are analyzed and a few phase misidentifications (i.e., P vs. pP) are assessed to better understand the limitations of the autonomous procedure. In general, we find that our EMF solutions are closer to the matching REB events than the corresponding SEL3 events. The analyst is helped both by the improved location estimates and a lower number of qualitatively incorrect event hypotheses. A key factor in the performance is the number of contributing seismic arrays. Aftershock sequences in the southern hemisphere performed the worst given the poorer array coverage.&amp;lt;/p&amp;gt;
Development of the Fibonacci-Octal Error Detection Code for Telecommunication Systems

Today telecommunication systems (TS) use a large number of different codes, among which error detection codes (EDC) are distinguished, which are relatively simple to implement. Some of the error control codes (ECC), for example, cyclic codes, which have high noise immunity, require complex methods of encoding and decoding code combinations. As a result, the implementation of these methods in information transfer systems leads to an increase in their cost and a decrease in reliability and speed. In addition, most encoding and decoding devices, as a rule, do not possess the property of self-control. Therefore, it is difficult to implement end-to-end (E2E) control of information on their basis, covering both its processing and transmission using the same code. Such control makes it possible to reduce hardware costs and increase the speed of existing telecommunication systems. However, the existing codes designed to transmit information do not possess the property of E2E control. In this paper, for the E2E monitoring of telecommunication systems, the Fibonacci code is used in a minimal form, which has a simple structure and a fairly high error detection ability. An important task necessary for telecommunication systems using a Fibonacci code is the ability to convert it to binary code for communication with binary digital systems and vice versa the ability to convert binary code to a Fibonacci code. It is these tasks that are solved in this paper. To convert data, we propose and develop the Fibonacci-octal code, which consists of a sequence of 4-bit codes - modules containing 8 Fibonacci numbers. An important feature of the Fibonacci-octal code is those code combinations (Fibonacci numbers) of the modules are relatively easy to convert to the numbers of 3-bit binary modules and vice versa. This simplifies the conversion of Fibonacci numbers to binary numbers and vice versa and makes it faster and more reliable.
A Hybrid Protocol Using Fuzzy Logic and Rough Set Theory for Target Coverage

Background: Target coverage is considered a significant problem in the area of wireless sensor networks, which usually aims at monitoring a given set of targets with the required confidence level so that network lifetime can be enhanced while considering the constraints of the resources. Objective: To maximize the lifetime of the sensor network and minimize the overhead involved in the scheduling approach, such that the pre specified set of targets is monitored for longer duration with the required confidence level. Methods: The paper uses a fuzzy logic system based on Mamdani inference in which the node status to remain in the active state is determined on the basis of coverage probability, trust values and node contribution. The rule set for determining the set cover is optimized by using the rough set theory, which aims to determine the node validity for the trust calculation. Results: The results show that the proposed approach improved the network performance in terms of processing time, throughput and energy conservation by a factor of 50%, 74% and 74%, respectively, as compared to the existing approaches. Conclusion: The paper proposes a scheduling strategy of the nodes for target coverage as an enhancement to the Energy Efficient Coverage Protocol (EECP) on the basis of rough set theory. The rule set for determining the set cover is optimized by using the rough set theory so that the network performance is improved in terms of the processing time, throughput and energy consumption.