Reviewerly/exHarmony huggingface dataset:
Classification of Pathology Images of Breast Cancer

Diagnosis and grading of breast cancer are done through pathology examination which remains the traditional way in cancer diagnosis. Pathology test is the primary standard for finding severity of abnormality in many cancer diagnoses and also plays the key role in diagnostic assessments. Recently, computerized techniques have been evolving in diagnosing digital pathology for emerging applications related to nuclei detection, segmentation, and classification. Nuclei segmentation remains the challenging task because of cell morphology and architectural distribution. Pathological studies have been conducted for detection and grading many cancers like cervix, lung, brain, prostate and breast cancer and many more. In many of the cancer diagnosis, computer-based diagnostic approaches are playing an important role in reducing human intervention, providing an accurate clinical report. Hence, medical practitioner considers computer-based detection as second opinions. This paper focused on cluster analysis using K-means and classification of breast cancer in the pathological image. The proposed method helps to classify histopathology image in four class by using Support Vector Machine with the combined set of features. The pathology image classified as normal, benign, in situ malignant, invasive malignant by hybrid feature set. The experimental result conducted on publically available dataset INESC-TEC Breast Histology Dataset. The results have proved that the proposed algorithm is suitable for classification of Histopathology image with high accuracy.
El argumento ad verecundiam en la relación médico-paciente: estudio de caso en procedimientos estéticos

This paper addresses the argumentative link that is established in the doctor-patient relationship and elaborates an analysis with the purpose of evaluating the way in which the argument that appeals to the authority or argument ad verecundiam can lead to undesirable medical practices when it is incurred a false authority. In support of these claims, the text presents two moments, on the one hand, it exposes the characteristics of the argument ad verecudiam and its place in the medical environment; and on the other hand, elucidates the argumentative criteria by which the doctor-patient relationship is affected, which entails value consequences on the medical authority.
Psychological Theoretical Framework: A First Step for the Design of Artificial Emotion Systems in Autonomous Agents

Autonomous Agents (AAs) capable of exhibiting emotional behaviors have contributed to the development of natural human-machine interactions in several application domains. In order to provide AAs with emotional mechanisms, their underlying architecture must implement an Artificial Emotion System (AES), a computational model that imitates specific facets of human emotions. Although several AES have been reported in related literature, their design is generally supported on several emotion theories, leading researchers to model and integrate isolated emotion components and mechanisms into the architectures of AES. This theoretical foundation of AES contributes to ambiguities in the analysis and comparison of their underlying architectures, which demands the definition of standards, design guidelines, and integrative frameworks. In this chapter, we present a psychologically inspired theoretical framework designed to serve as a platform for the unification of AES components, the comparison of AES, and the design and implementation of AES in AAs.
Scale Invariant Multi-view Depth Estimation Network with cGAN Refinement

In this paper we propose a deep learning based depth estimation method for monocular RGB sequences. We train a pair of encoder-decoder network to resolve depth information form image pairs and relative camera poses. To solve scale ambiguous of monocular sequences, a conditional generative adversarial network is applied. Experimental results show that the proposed method can overcome the problem of scale ambiguous and therefore is more suitable for a variety of applications.
Supporting Life History Research with Interactive Visualizations

We present two novel interfaces for in-field and self-report data collection and analysis of life histories. LifeHistory interface enables direct input of multifaceted longitudinal data via a timeline grid annotated with pictorial representations of landmark events. TrajectoryView is an interactive visualization of life history data for a side-by-side comparison of parameters of an individual’s life history. The two interfaces are intended as an alternative to paper-based tools and methods currently used by qualitative researchers. Beyond a mere automation of data collection and presentation, the interfaces offer enhancements supporting recall of events and visual analysis of data. We expect that the use of LifeHistory and TrajectoryView will simplify data collection and analysis processes, leading to greater accuracy of data and better opportunities for insights.
Emotion Recognition in Social Media: A Case Study About Tax Frauds

Analyzing and understanding the relation of emotions and human computing interaction has become a necessity today. Indeed, sentiment analysis tools have gained special attention during the last years in order to facilitate and support the understanding and study of human affections. In this paper, we analyze an important Chilean tax fraud case by combining sentiment analysis and critical discourse analysis. We take as a case study, the tweets of the year 2018 that contain the #SQM hashtag. This case involves tax fraud and violations of political campaign laws. People from different political parties created fake invoices, which are then paid by SQM to be illegally used onto political parties violating campaign finance laws. Interesting results are obtained where we identify which topics and persons have a negative or positive connotation in the readers.
The Implications of Text-Messaging for Language Learning

The impact of text-messaging on the form of students' writing was examined using a quantitative approach with a correlational research design to arrive at the findings. A total of 80 students in a Jamaican high school constituted the sample, which was randomly selected. The findings of this research revealed that the Grade 9 students in the sample used a minimal number of text forms in their academic writing. The students who used text forms in their academic writing tend to score high academically. Therefore, texting was found to have a slightly positive impact on students' writing. There was no relationship between the students' gender and their texting patterns. The findings of this study can assist teachers of English to better meet the needs of students who use text-messaging and become a tool for building phonemic awareness and improving literacy.
On the Complexity of Attacking Commercial Authentication Products

In this paper we report on our efforts in attacking commercial authentication products. The challenge in such attacks is that only very limited information on the implementations are given. We were able to reveal information on the processing sequence during the authentication process even as detailed as identifying the clock cycles in which the individual key bits are processed. To summarize the effort of such an attack is significantly higher than the one of attacking a well known implementation.
A Study of “NP de XP” Construction - Centered on X

The article is focused on “NP De XP” construction from three aspects: rhythm, semantics and grammar. For rhythm, no matter verbs or adjectives, two-syllable words are more likely to enter the “NP De XP” construction than monosyllabic and trisyllabic words. For semantics, N is usually the case concerned of X. N can be agent, theme or tool, time, place, method which is related to X. From the grammar point of view, in “NP De XP” construction, some original combination abilities of X is limited. Verbs can’t coexist with tense or aspect marks, and also can’t coexist with objects. As well adjectives can’t coexist with degree adverbs, and also can’t overlap. The function of X is consistent with the function of the “NP De XP” construction and does not violate the theory of endocentric construction.
Development of the complex modelling system for intelligent control algorithms testing

Testing is an essential stage of software and algorithms development, especially in the field of the cooperative control of multi-robot systems, where even one mistake could lead to the whole mission failure. Software-only simulation systems are unable to consider all real-world factors like sensors and motors inaccuracy. We have developed a complex modelling system for the purposes of studying the complex robot behaviour determined by intelligent algorithms. This paper presents basic approaches to general architecture of multi-robot systems, scenarios (missions) formalisation, communication between agents and robot navigation on the scene. The modelling complex is a client-server application, where the server-side allows controlling missions, monitoring and varying parameters of robots and the environment. Also, the application provides the capability for messaging between all participants of a mission, and identifying robots on the scene for navigation and testing purposes. The proposed system makes the development process easier and supports the process of creating reliable control algorithms for multi-robot systems.
A Design of Multifunctional Interfaces to Control Game Screens

Due to the great potential that technological games have regarding interactivity, motor and cognitive development, as well as the notable advances of the technological society, traditional games have been replaced. It was analyzed how object design can connect with new areas like multimedia to create a higher level of interactivity in the games through tangible interfaces to support learning processes based on sensory development. For this study, the researcher has developed games based on multimedia for an audience from six to nine years of age. The games seek to teach the concept of competitiveness.
Side-Channel Attacks on the Mobile Phones: Applicability and Improvements

On one hand, side-channel attacks are a powerful approach to cryptoanalysis, on the other hand, these attacks have a very specific attacker model. As a result, a practical application of SCA is sometimes disputable. The threat level in each case has to be evaluated given individual features of a certain target system. Endpoint client applications like mobile phone applications are especially vulnerable because of an uncomplicated access to the device. More attention has to be paid to their security since they’re more accessible to an attacker and usually carry a large amount of sensitive information. Current research represents the informativity assessment of the side-channel signals obtained from mobile phones. Low-priced accessible equipment was used in order to decrease requirements to an attacker level and consequently increase the applicability of an attack. Logically, it results in a lack of information that can be obtained from such traces, but there are ways to achieve a good interpretation of the signal. For staying within the consumer-segment equipment, the algorithmic approach increasing traces informativity was implemented. The algorithm is Normalized Inter-Class Variance and it allows to distinctly improve the efficiency of trace analysis by reducing the number of points and increasing of the quality of reference traces.
A hybrid between a surrogate-assisted evolutionary algorithm and a trust region method for constrained optimization

This paper develops a hybrid between an RBF-assisted Evolutionary Programming (EP) algorithm and the CONORBIT trust region method for constrained expensive black-box optimization. The proposed hybrid combines the advantages of each approach and results in better performance than either the RBF-assisted EP or CONORBIT alone on test problems from the CEC 2010 benchmark.
Bullet-time image generation without 3-D

We propose a new method for generating bullet-time images without any 3-D information. In a general way, we estimate 3-D camera pose and a 3-D position of a target point, then compute a homography that moves the target point to the image center. In this work, we re-parameterize the homography without 3-D information of the camera and the target point. We also estimate the homography by using an image matching technique. We compare the proposed method with an existing method that uses 3-D information and show the effectiveness of our method.
Research on Integrated Mobile Payment Technology for High Speed

For the actual need for highway mobile payments, the article proposes an intelligent highway integrated mobile payment architecture. Operations basis of the architecture is mobile payment platform payment management center, mobile payment is the main mode of payment. The mobile payment platform is the transfer station of transactions between users and banks, Alipay, WeChat. Through the application on the Guangxi expressway, the validity and feasibility of the proposed architecture are verified.
A Class Specific Representation Learning for Illumination Tolerant Face Recognition

An approach of class specific representation based learning for illumination tolerant face recognition is reported in this paper. Autoencoder based representation and class specific reconstruction along with phase correlation in frequency domain for classification is proposed. Autoencoder based representation is evaluated as very few number of training images are sufficient to handle the entire variation of test face subspace. Phase correlation is used at the classification stage to handle the illumination problem as intensity is the primary concern. This judicial combination of representation and classification shows improved recognition accuracy on benchmark databases. The performance of the proposed approach compared to another state-of-the-art technique on other representation based learning is established with extensive experimental. Advantage of the proposed approach is also shown by the performance analysis with single training image, which is necessary for some real time applications.
How a Tangible User Interface Contributes to Desired Learning Outcomes of the Virtual River Serious Game

Serious games are increasingly used to facilitate stakeholder discussion and collaboration. Much attention is given in the game design literature on how to choose and design a serious game’s scope, content, mechanics, and link to reality in order to achieve the game’s intended learning outcomes. In this paper, we focus on how a serious game’s interface and the interaction it elicits contributes to achieving learning outcomes. We do so in the context of the Virtual River, a serious game focused on river management. Following the design and evaluation of a paper prototype of the Virtual River, a design challenge arose as the highly simplified models of reality used was perceived as a black box by non-expert participants, while expert participants perceived it as oversimplified and unrealistic. As hydrodynamic models used in river management practice are in itself perceived by non-experts as a black box, we decided to look for ways to simplify the interaction with such models in the game. Here, we present a tangible user interface for the Virtual River. The interface enables participants to get a better grip on the hydrodynamics of a river system. The system is set up as a discussion platform where the game board and its tangible game pieces help participants express their thoughts and ideas. We argue that using tangible interaction in Virtual River contributes to social learning outcomes by providing hypotheses based on literature and present how we intend to test these hypotheses.
Local Smoothing Constraint in Convolutional Neural Network for Image Denoising

In this paper, we demonstrate that not only natural images but also their intermediate responses of convolutional neural networks (CNNs) have local smoothing priors. To imposing the local smoothing constraint, we design a local smoothing layer, which is able to suppress noises in a local receptive field. Further, we arrange the local smoothing layer in the early layers of CNNs to effectively capture context information, which is helpful to recovery image details. Experimental results validate that the proposed denoising method outperforms several state-of-the-art methods.
The Classification of Different Situations in a Lecture Based on Students’ Observed Postures

This paper discusses the possibility of identifying different situations related to the students during a lecture from its video by classifying the situations that happen in the lecture based on the similarity in the posture of each student. The recognized situations can be used as indexes for the instructor to watch the video to further improve the lecture. Although it has been shown in a previous work that there are some relations between the postures taken by the students and their understanding of the lecture, it is not clear what types of situations actually happen during the lectures, and the postures taken by the students differ even when they are in the same situations. To deal with these problems, the representative postures of each student in different situations are first obtained by clustering the postures actually taken by the student, and then different situations of the class are obtained by clustering the combinations of representative postures of all the students under the assumptions that similar postures are taken by each student and similar combinations of those postures are observed for the whole group of students when they are in the same situation.
Semi-automatic Aggregation of Multiple Models of Visual Attention for Model-Based User Interface Evaluation

Predicting the distribution of attention to new user interface designs can provide valuable information during the design process, but accurate predictions are difficult to achieve. Recent studies have shown that accuracy can be increased based on the Diversity Prediction Theorem if multiple, independently developed models for the prediction of attention distribution are aggregated. However, aggregating multiple models is a manual task, that takes a lot of effort because a large number of information sources, which are defined as parts of each model, need to be compared among each other. In this work we test two different clustering approaches for automatically aggregating such models. We show that the clustering quality is not sufficient for fully automatic clustering and present a software-supported solution for a semi-automatic clustering process.
Comparing Software Complexity of Monadic Error Handling and Using Exceptions

Exception handling has been the definitive way to handle errors of any kind and exceptional circumstances in modern software. Using exceptions ease comprehension of single-threaded software by reducing complexity, but concurrency and asynchronous programming models require other approaches as the stack at the state of an error may not hold semantically important information. A way to handle such cases has been introduced to standard libraries, for example in Scala, the Try and - to some extent - Option constructs help programmers. Since these display monadic behavior, they provide improved composability compared to exception-based methods. Measuring software complexity can be done using software metrics. There are several well-known definitions: lines of code, cyclomatic complexity or McCabe-metrics, Halstead complexity measures and A-V. In this paper, we use definitions of widely-known metrics that have been extended for the case of exception handling and compare complexity of monadic error handling with standard exception handling techniques.
Investigating the Relationship Between Connection, Agency and Autonomy for Controlling a Robot Arm for Remote Social Physical Interaction

Current telecommunication systems such as Skype cannot allow remote users to interact physically. Thus, we propose installing a robot arm and teleoperating it can realize social physical interaction. Some autonomy may be necessary to realize easy teleoperation because teleoperation requires mental workload. However, too much autonomy can decrease sense of agency, which may cause lack of connection because remote users do not feel they caused actions. Thus, in this study, we investigate the relationship between autonomy level and sense of connection of a remote person with local area and people. We focus on pushing tasks because pushing is one of the major functions in hand and arm use. Sense of agency can be categorized into the Feeling of agency (FOA) which is not conceptual and the Judgement of agency (JOA) which is conceptual. Therefore, we conducted user studies to investigate whether FOA associated with control of trajectories and joint angles affects the sense of connection. The results suggested that higher autonomy could decrease telepresence, and remote users preferred controlling joint angles for fun, but they did not need FOA for performance.
Particle Filter without a Prior Information for Probability Distribution

Kalman filter is one of the most famous state estimation methods. Many state estimations based on Kalman filter have been proposed for various conditions of systems and noise. These estimation methods need to know probability density functions of dynamical systems. However, the probability density functions and a prior information of noise in particular are rarely known in practice. On the other hand, in the field of machine learning, probability density estimations have been studied extensively, and conditional probability density estimations were proposed as extensions of the probability density estimations. In this paper, we propose a direct design method of probability density functions for dynamical systems from data by using conditional probability density estimations because the systems are represented as conditional probability density functions. In addition, we apply the method to particle filter, which is one of nonlinear Kalman filters, and propose a new state estimation method without a prior knowledge for the dynamical systems. Numerical simulations demonstrate the effectiveness of the proposed method.
The Design of the Majority Voting Algorithm Based on Search Engine for the Text Copyright Detection Crawler

With the rapid development of the Internet era, it is very common for the websites to illegally reproduce the written works, which is a special form of copyright infringement with the characteristic of high covertness, fast speed and extensive influence. This behavior seriously violates the information website dissemination copyright of the authors and their authorized publishing websites, which has extremely serious damage to the healthy development of the text industry! To this end, the four state departments of State Copyright Administration, State Internet Information Office, Ministry of Industry and Information Technology, and Ministry of Public Security jointly carry out several times of "Jian Wang" actions to conduct the special harnessing and combating of network piracy. Against the phenomena of online illegally reproduction written works, and through researching the Internet crawler and the websites with illegally reproduction written works, this paper puts forward the text copyright detection crawler system based on search engines and majority voting algorithm.