Reviewerly/exHarmony huggingface dataset:
Digital Image Exchange using a No-key(s) Protocol with Phase-only Encryption

The following topics are dealt with: learning (artificial intelligence); feature extraction; neural nets; pattern classification; convolutional neural nets; support vector machines; diseases; security of data; statistical analysis; medical computing.
IMPORTANCE OF RESEARCH AND DRAFTING IN THE SCIENTIFIC PRODUCTION OF THE HEALTH AREA

Research increases human knowledge and enriches science, generating progress, quality of life, health, innovation, hence the importance of promoting it at all educational levels. Higher education institutions must implement the necessary strategies to improve communication skills, especially to improve the way of writing in order to share with the public the scientific findings that are inherent in the area of health in particular and of all science in general. The objective is to promote research to increase the production of scientific articles, with the socialization of knowledge in the area of health. Several factors influence this process. Scientific writing must complement the strengthening and interest of research, the publication of the results of the studies carried out must be shared with the entire society to benefit it and increase scientific production.
Developing inventory information system using mobile computing with quick response (2d-barcode) and geotagging

Abstract Inventory management is the main task of an organization to reach its objective of maintaining the number of stocks precisely and minimizing waste. Web-based inventory management in a university environment is expected to help the employees in keeping up with innovations on the status of tools and equipment. It is only fitting that inventory data management is also capable of accommodating distance. The use of the internet by using mobile computing which automatically can do tagging of inventories including information about the coordinate will broaden the potential of database using mobile computing technology with wireless data communication aided with 2D-barcode decoding automatization makes it possible to synchronize (record and track) on-line to the central database using some computers that work simultaneously and directly from the field. This inventory system can synchronize data directly from the field and the information can simultaneously be accessed by the users. Mobile computing (android Smartphone, computer tablet) is used as a mobile station that is capable of decoding the tags of inventories and buildings (in the 2D-barcode) and associating automatically with the database wireless networks and the internet to eliminate the distance factor and overhead-process (pre-processing of data from the field to the data storing station).
Octree-Based Hierarchical 3D Pathfinding Optimization of Three-Dimensional Pathfinding

Despite the fact that the problem of pathfinding exists for quite a while, most of the related work is focused on methods that are applicable only in 2D environment. However, there is a demand for methods that can provide a solution for pathfinding tasks in 3D working space. These tasks mostly come from area of robotics and game development, where it is often required to get a solution in real-time.
Face Image Inpainting Algorithm Based on Generative Adversarial Network

Traditional image inpainting algorithms extract effective information from single source images, but the lack of high-level context information often leads to poor restoration results. In this paper, we propose face image inpainting algorithm based on generative adversarial network, which repairs the missing area by high-level context information from multi-source correlated images. The context loss and the perception loss are introduced to find the closest potential distribution space of the image to be repaired. The pixel consistency and content similarity can be guaranteed through the game between the generator and discriminator. Finally, the best mapping vector is used to generate the repaired content and complete the image repair task. Experiments show that this method can successfully predict up to 80% of the semantic missing of facial images on LFW face data set, generate complete and realistic images, and improve the image inpainting effect.
Using social media in writing among Primary School pupils

The advancement of technology has brought transformation in education. Utilisation of social media to promote learning are increasing exponentially at secondary and tertiary education levels. However, the researcher was still doubting using social media on current primary school pupils. Thus, this study aimed to explore the use of social media among primary school pupils and how they perceive towards the use of English in writing on social media. Questionnaires were distributed among 37 primary 4 pupils in a Chinese vernacular school in a Southern state in Malaysia. The findings showed that YouTube is the most prominent tool used among the pupils and they have good competency in using the social media which they always use. Pupils use social media for academically and non-academically purposes. However, slightly more than half of the pupils do not have positive perceptions towards using English in writing on social media. Social media could be exploited in learning writing activities to increase the awareness of pupils towards a better writing performance.
The Representativeness Representative

Abstract This case, involving a young male, focuses on one of the metaheuristics first described in the cognitive science literature: representativeness. Essentially, it is about pattern matching—one is more inclined to believe a patient is a member of a particular class the more features the patient manifests of that class. If these features are not present, one is less inclined to believe the patient belongs to that class—thus, prototypical presentations of disease may result in representativeness restraint as evidenced in this case.
Development of population document authentication system using a .p12 certificate

Abstract Government policies related to electronic transactions provide many positive responses. Throughthis policy, government services relating to authentication such as population documents can be done electronically while still paying attention to their validity. In the current service process, the document archive is less organized both in village and sub-district offices and the completion of documents is hampered for days because the officials are not available. To overcome this, a system that can overcome these problems is needed. In this system, the validity of the document was still observed by digital signing using an electronic certificate (.p12). Then, the development of this system was conducted using the waterfall model. There were three tests carried out namely the black-box test, white-box, usability testing using the system usability scale, and the user response test. The development of this system has passed the black-box test and white-box test, the overall results were in line with expectations. As for the usability scale system, the results of the category are acceptable. Then the user response test was conducted on five user roles and obtained the results with an average percentage that is in very good range.
Pool Detection with Concurrent Algorithms

Nowadays, high resolution aerial or satellite photos can be taken, which can be appropriate for further analyses. Photos can be taken using a satellite, aircraft, a robot drone or even a multicopter. However, generally speaking, every solution results in similarly high-resolution images. Analyses can be conducted on the resulting aerial photos in the light of various criteria, which conditions can be determined by the customer. One such task can be the enumeration of pools built in areas with detached houses, which can constitute the basis for specific data. One of the most obvious automated procedures for the detection of objects is to use neural networks, which utilizing properly chosen teaching samples is subsequently able to decide about individual image parts whether they contain the desired object or not. In spite of a large number of teaching samples, a neural network may give a false-positive or a false-negative result in the image passed over for analysis. These mistakes can occur from the fact that the teaching samples and the photos of the areas examined were not taken in the same season, in the same time of day or that they do not show pools having the same maintenance level. Instead of the further teaching of the network, the mentioned mistakes can be eliminated using classical image analysis methods in a way that the results of individual procedures are fusioned. Such methods may be color coding analysis or Fuzzy-logic analysis based on some property in the image. Fuzzy-logic can be used to detect visual objects if there is a definite, distinctive property, like color range or color depth.
Design and verification of improved CMERE against power analysis attacks

The CMERE algorithm was designed to resist DPA attacks on modular exponentiation. It was implemented and tested at the algorithmic level for different key sizes of the RSA cryptosystems. The strength of CMERE lied on the facts that it could be implemented both on left-to-right and right-to-left binary methods for modular exponentiation without any changes in the original algorithm. Also, the execution of modular exponentiation was completely bit independent making it a very strong countermeasure against simple and differential power analysis attacks. In this paper, we have verified the CMERE algorithm at hardware level using VHDL. During formal verification with VHDL on FPGA, the algorithm was modified for practical implementation. However, the overall strength of the improved CMERE algorithm remains the same as the original algorithm.
A Calibration Framework for Photosensor-based Eye-Tracking System

The majority of eye-tracking systems require user-specific calibration to achieve suitable accuracy. Traditional calibration is performed by presenting targets at fixed locations that form a certain coverage of the device screen. If simple regression methods are used to learn a gaze map from the recorded data, the risk of overfitting is minimal. This is not the case if a gaze map is formed using neural networks, as is often employed in photosensor oculography (PSOG), which raises the question of careful design of calibration procedure. This paper evaluates different calibration data parsing approaches and the collection time-performance trade-off effect of grid density to build a calibration framework for PSOG with the use of video-based simulation framework.
Learning Sets of Bayesian Networks

This paper considers the problem of learning a generalized credal network (a set of Bayesian networks) from a dataset. It is based on using the BDEu score and computes all the networks with score above a predetermined factor of the optimal one. To avoid the problem of determining the equivalent sample size (ESS), the approach also considers the possibility of an undetermined ESS. Even if the final result is a set of Bayesian networks, the paper also studies the problem of selecting a single network with some alternative procedures. Finally, some preliminary experiments are carried out with three small networks.
Facilitating Experiential Knowledge Sharing through Situated Conversations

This paper proposes a system that facilitates knowledge sharing among people in similar situations by providing audio of past conversations. Our system records all voices of conversations among the users in the specific fields such as tourist spots, museums, digital fabrication studio, etc. and then timely provides users in a similar situation with fragments of the accumulated conversations. For segmenting and retrieving past conversation from vast amounts of captured data, we focus on non-verbal contextual information, i.e., location, attention targets, and hand operations of the conversation participants. All voices of conversation are recorded, without any selection or classification. The delivery of the voices to a user is determined not based on the content of the conversation but on the similarity of situations between the conversation participants and the user. To demonstrate the concept of the proposed system, we performed a series of experiments to observe changes in user behavior due to past conversations related to the situation at the digital fabrication workshop. Since we have not achieved a satisfactory implementation to sense user's situation, we used Wizard of Oz (WOZ) method. That is, the experimenter visually judges the change in the situation of the user and inputs it to the system, and the system automatically provides the users with voices of past conversation corresponding to the situation. Experimental results show that most of the conversations presented when the situation perfectly matches is related to the user's situation, and some of them prompts the user to change their behavior effectively. Interestingly, we could observe that conversations that were done in the same area but not related to the current task also had the effect of expanding the user's knowledge. We also observed a case that although a conversation highly related to the user's situation was timely presented but the user could not utilize the knowledge to solve the problem of the current task. It shows the limitation of our system, i.e., even if a knowledgeable conversation is timely provided, it is useless unless it fits with the user's knowledge level.
Towards the right standards: The intersection of open science, responsible research and innovation, and standards

The introduction of standards in research and development leading to new products or innovative processes can be thought of as a particularly technical approach to framing scientific enterprises. At the other end of the spectrum, open science or responsible research and innovation may be initially thought of as concepts with no underlying technical approaches to support them. In reality as currently practiced, the development and use of standards engages significant non-technical aspects, needing to take into account research cultures or desired societal outcomes. Similarly, open science, and responsible research and innovation can operate using very practical and technical approaches. This essay focuses at the intersections of these concepts to try to contribute to larger discussions in both the research and governance communities as to how researchers should conduct their research, and what respective responsibilities of researchers, their institutes, and their supporters are.
Using Interactive Inventions

Interactive inventions are systems and processes that can be used by multiple actors at the same time. Many interactive inventions are the product of emerging technologies such as the Internet of Things that allow billions of everyday devices to communicate with each other via the Internet. Other interactive inventions are prevalent in the emerging fields of personalized medicine and FinTech (new financial technologies). Unfortunately, the law concerning how to determine liability when a patent directed to an interactive invention is infringed is dissonant across classes of inventions. Specifically, what it means to “use” an interactive system is different from what it means to “use” an interactive method. Why does the law treat these acts differently? Should it?This Article attempts to answer these important questions. Specifically, this Article contends that the current framework for what it means to “use” an interactive system is problematic. Interactive inventions are different than traditional method and system inventions. Unfortunately, courts have too often applied, especially in recent cases involving interactive systems, an outmoded framework to interactive inventions. When interactive inventions are analyzed under the proper framework, it allows for the application of sound legal principles to solve multi-actor liability problems. Specifically, this Article argues that a direct infringer “uses” an interactive system when it controls the system and obtains a benefit from it. This approach is also more consistent with the recently broadened standard for infringement of interactive methods.
VRNFC-Stego: Data Hiding Technique based on VR Images and NFC-Enabled Smartphones

The integration of Near Field Communication (NFC) in smartphones offered many applications in the market and in many other fields such as electronic ticketing, access control frameworks, etc.). On the other hand, virtual reality is another technology that has been on the rise recently and its applications span over many fields such as entertainment, education, military, aerospace and leisure fields. This paper presents a novel data hiding technique (VRNFC-Stego) in which this technique creates a highly secured connection based on NFC-Enabled android smartphones via VR images. The proposed technique solved one of the most challenging data hiding open problems that is the embedding capacity. The experimental results showed that our VRNFC-Stego is a powerfully secured and user friendly system which enables highly capacity data to be hidden along with high imperceptibility.
Security Assessment of Partially Encrypted Visual Data: Using Iris Recognition as Generic Measure

Security assessment of partially encrypted visual data is known to be difficult. We show that a set of known image quality measures turn out not to be good predictors for encryption strength, especially if a different extent of recognisability is present in the data. Iris recognition applied to encrypted sample data is proposed to assess the protection strength of the employed encryption scheme. When choosing the settings as identified in this work, iris recognition performance turns out to be a viable predictor for security of encrypted data, in more consistent manner compared to image quality measures.
Comparative study of classification method on diagnosis of plasmodium phase

Abstract Malaria is a plague in humans induced by the Plasmodium parasitoid transferred through a single bite of female Anopheles mosquitoes. Once reaching human blood passage, this parasitoid bears asexual proliferation which is classified toward three phases (trophozoite, schizont, and gametocyte). To discover the phases, the paramedic will analyse the blood specimen of the subject within the microscope. However, the aforementioned approach has the potential to misdiagnosis. Many CAD-based investigations have been carried out to reduce the diagnosis errors that have formed. This research sought to develop a CAD-based design that can help paramedics in diagnosing the parasitoid Plasmodium, including trying to get a classification algorithm that is able to analyze the Plasmodium phase precisely. Based on the experiment outcome, Naïve Bayes was mostly useful applied as a classifier that attained the accuracy of 97.29%, the sensitivity, and specificity of 97.30% toward the P.vivax case. In the case of P.falciparum, it emerged in the accuracy, sensitivity, and specificity of 98.36%, 98.40%, and 98.40% apiece. Meantime, Perceptron was a useless algorithm applied as a classifier that realized the accuracy up to 81.08%, sensitivity and specificity of 93.80% on each. In the case of P.vivax, the accuracy, sensitivity, and specificity realized were 80.33%, 92.50%, and 92.50% respectively.
A Method to Predict the Trends of Operating Performance of Capacitive Voltage Transformers

In order to predict the trend of the operating performance of capacitive voltage transformers (CVTs), an innovative method, known as dual regression analysis algorithm, is proposed. Measured environmental parameters and CVT performance parameters are divided into groups chronologically. Each group of data set establishes one first-round regression equation between the environmental parameters and the performance parameters of a certain CVT. By extracting the coefficients of the first-round regression equations in different time windows and forming a set of vectors containing the environmental parameter coefficients, the second-round regression equation between the environmental parameter coefficients and the chronological time windows can be established, and thereby obtaining the CVT performance parameters, which are used to evaluate the operating performance and to predict the trend of the CVT operating performance. Real data are used to explain and verify the theory and the procedure.
Decision-making Risks of Operational Tests from a Statistical Point of View

Abstract The operational test, along with the evaluation based on it, is the most important decision-making basis for the Department of Defence in acquiring weapons and equipment. However, there has been relatively little attention to the credibility of its conclusions. In order to evaluate the decision-making risks quantitatively, the relationship between the two types of errors in the operational tests is studied. By analysing the interaction between the factors affecting decision risk errors, it is pointed out that it is of great practical significance to ensure that the tests have sufficient statistical power, which is usually overlooked before in operational tests.
Weight Your Words: The Effect of Different Weighting Schemes on Wordification Performance

Relational data models are usually used by researchers and companies when a single-table model is not enough to describe their system. Then, when it comes to classification, there are mainly two options: apply the corresponding relational version of classification algorithms or use a propositionalization technique to transform the relational database into a single-table representation before classification. In this work, we evaluate a fast and simple propositionalization algorithm called Wordification. This technique uses the table name, attribute name and value to create a feature. Each feature is treated as a word and the instances of the database are represented by a Bag-Of-Words (BOW) model. Then, a weighting scheme is used to weight the features for each instance. The original implementation of Wordification only explored the TF-IDF, the term-frequency and the binary weighting schemes. However, works in the text classification and data mining fields show that the proper choice of weighting schemes can boost classification. Therefore, we empirically experimented different term weighting approaches with Wordification. Our results show that the right combination of weighting scheme and classification algorithm can significantly improve classification performance.
Research on the method of multi-robots autonomous collaborative map construction in unknown environment

Aiming at the problem of map construction in unknown environment, a multi-robots autonomous collaborative map construction method is proposed, including autonomous search method and map construction method. Firstly, in order to avoid repeated search of the same area by different robots, an optimal multi-robots deployment algorithm based on the Voronoi diagram of the center of mass is proposed. During the movement, the robots use the dynamic window method for local path planning to avoid obstacles. Secondly, in order to grasp the global map information of the unknown environment, a map fusion algorithm based on Hough peak matching is used to fuse the sub-maps of multi-robots. Finally, the proposed collaborative map construction method is simulated in ROS simulation environment, and the effectiveness and practicability of the proposed method has been proved.