Reviewerly/exHarmony huggingface dataset:
Evaluating Deep Convolutional Neural Networks as Texture Feature Extractors

Texture is an important visual property which has been largely employed for image characterization. Recently, Convolutional Networks has been the predominant approach on Computer Vision, and their application on texture analysis shows interesting results. However, their popularity steers around object recognition, and several convolutional architectures have been proposed and trained for this task. Therefore, this works evaluates 17 of the most diffused Deep Convolutional Neural Networks when employed for texture analysis as feature extractors. Image descriptors are obtained through Global Average Pooling over the output of the last convolutional layer of networks with random weights or learned from the ImageNet dataset. The analysis is performed under 6 texture datasets and using 3 different supervised classifiers (KNN, LDA, and SVM). Results using networks with random weights indicates that the architecture alone plays an important role in texture characterization, and it can even provide useful features for classification for some datasets. On the other hand, we found that although ImageNet weights usually provide the best results it can also perform similar to random weights in some cases, indicating that transferring convolutional weights learned on ImageNet may not always be appropriate for texture analysis. When comparing the best models, our results corroborate that DenseNet presents the highest overall performance while keeping a significantly small number of hyperparameters, thus we recommend its use for texture characterization.
An Error-Reflective Consistency Model for Distributed Data Stores

Consistency models for distributed data stores offer insights and paths to reasoning about what a user of such a system can expect. However, often consistency models are defined or implemented in coarse-grained manners, making it difficult to achieve precisely the consistency required. Further, many domains are already written to handle anomalies in distributed systems, yet they have little opportunity for expressing or taking advantage of their leniency. We propose reflective consistency-an active solution which adapts an underlying data store to changing loads and resource availability to meet a given consistency level. We implement reflective consistency in Cassandra, an existing distributed data store supporting per-read and perwrite consistency. Our implementation allows users to express their anomaly leniency directly and the system will react to the presence of anomalies, changing Cassandra's consistency only when needed. Users of Reflective Cassandra can expect minimal overhead (anywhere from 1% to 14% depending on configuration) and a 50% decrease in the amount of costly strong reads.
Symmetric Inkball Alignment with Loopy Models

Alignment tasks generally seek to establish a spatial correspondence between two versions of a text, for example between a set of manuscript images and their transcript. This paper examines a different form of alignment problem, namely pixel-scale alignment between two renditions of a handwritten word or phrase. Using loopy inkball graph models, the proposed technique finds spatial correspondences between two text images such that similar parts map to each other. The method has applications to word spotting and signature verification, and can provide analytical tools for the study of handwriting variation.
Mathematical Fallacies and Applications of Graph Theory in Electrical Engineering

A graph is a pair of two sets. A graph is a representation of a system that uses two basic elements of vertices and edges, a node represented by a circle and edge represented by a line connecting two nodes. Graph theory is widely used in every field of engineering. In this paper, an application description of the graph is presented to find the shortest path and circuit network. The purpose of this paper is to connect graphs with engineering and electrical subjects. But beforehand, some fundamental mistakes of thinking in understanding mathematics, also called as fallacies are also introduced.
Pedagogical Constructs and Pedagogical Constructors in the Studies of the Basics of Pedagogical Support in the System of Continuous Education

The present research features the basics of pedagogical support in the system of continuous education in the structure of actualization and generation of various pedagogical constructs and pedagogical constructors that facilitate a holistic understanding of the importance of personality development in the hierarchy of age-appropriate methods, forms, and technologies of educational and socio-professional activities. The authors clarify the concepts of "pedagogical construct", "pedagogical constructor", and "basics of pedagogical support in the system of continuous education". These concepts are described in the context of the adaptive-acmepedagogical, personified-productive, and functional-labor approaches that provide traditional and innovative models of the study and research of scientific-pedagogical phenomena and events, thus improving the quality of pedagogical solutions on micro-, meso-, and macro level. The implemented methods and technologies optimize the quality of production and facilitation, cooperation and self-realization, socialization, and self-actualization. The multidimensionality of the approach reveals the prospects for a teacher who would contribute to the personal development (construct "I want, I can, I must, there is"), as well as holistic and system-mobile formation of the student. In this research, a student is understood as a product that unites all the components of education: science, art, culture, sports, and other areas of socialization and self-realization. Pedagogical constructs and pedagogical constructors in the studies of the basics of pedagogical support in the system of continuous education are determined in various simulated and realized conditions, where productivity, personification, and health saving technologies determine the integrity of the choice of opportunities and results of pedagogical practice and pedagogical activity.
Operationalizing conflict strategies in a board game

The aim of conflict resolution education is to impart essential strategies and skills for resolving conflicts effectively. While these are important life skills, conflict resolution can be difficult to teach because it requires individuals to interact with others, explore new strategies, and receive feedback within a natural social context in order for strong connections to be made. As board games often involve co-located multiplayer interaction and can be effective tools for young learners, we explore the possibility of incorporating learning about conflict resolution into a tabletop game. We describe the process of designing an educational board game - StarStruck - that fosters discussions about conflict management via operationalization of conflict strategies drawn from an instrument founded in social psychology theory. Through in- and out-of-board interactions, StarStruck is designed to provide players with affordances to explore different resolution strategies within their natural social environment. We present examples from initial playtesting sessions to consider the expressive range of conflict scenarios generated by playing the game. This work serves as a preliminary illustration of how to map the vocabulary of conflict resolution to game mechanics, dynamics, and aesthetics so that players can naturally engage with and discuss conflict interactions.
The Sub-Elements of 'Communication Competency' as Core Competence Reflected in the 2015 Revised Curriculum

Every sector of society is looking for ways to raise talented people to live in future society. The education sector is responding to social change through the 2015 revised national curriculum, which puts its core competency at the forefront. Six core competencies such as self-management competence, knowledge information processing competence, creative thinking competence, aesthetic emotional competence, communication competence, and community competency were presented in the 2015 revised curriculum. It is in the early stages to study the status and methods of understanding and utilization of these core competencies in the curriculum at the current level. Core competencies are related to the current system of curriculum and achievement standards in that core competencies are composed of knowledge, skill, and attitude, while possessing inclusiveness and overallness. Considering the actual aspects of education in which the continuity with the past must be maintained, the core competencies that should have practical effects on students
Eye Controlled Wheelchair with Asthenopia Detection

In this paper we present a low cost and real time system which enables a person with quadriplegia to control his wheelchair with eye movements. This system is enabled by voice control. Once enabled, it processes consecutive frames from the web cam to detect the direction of eye movement. Consequently, a signal is sent to the Raspberry pi micro-controller, to turn the motors in the desired direction. Detection is done by pupil monitoring using contours. Additionally, it detects Asthenopia, a disease caused by straining of the eye. This is achieved by monitoring the blink rate of the patient. Blink Detection is carried out using two methods. The first method uses a combination of SURF and Harris corner detection while second method uses facial landmarks.
Research on the Falling Detection System for Elderly-assistant and Walking-assistant Robot

Abstract With the development of living standards, the problems caused by aging population have become more and more serious. At the same time, with the growth of age, the body function of the elderly is degenerating and the body flexibility is reducing, which leads to frequently occurring of falling in elderly. The research data shows that falls are the most common and unpredictable problem for old people, which are even life-threatening. Therefore, a falling detection system used in the walking-assistant robot is proposed to make the walking-assistant robot better service during the outdoor walking. Firstly, a general design plan of the falling detection for old people, which is used in walking-assistant robot, is introduced through the analysis of falling mechanism of elderly. Then, a acceleration sensor ADXL345 and the detection module of touch pressure sensors are used to collect data of acceleration and hand touch force of users. Next, the feature extraction is carried out on the above data, and the falling detection is delivered by time domain feature representation and recognition algorithm. At last, the test system is built for test verification, and the results show that the falling detection system has a stable and reliable performance and high detection accuracy rate, reaching 99.05%. It can detect the falling of elderly effectively.
Methodological approaches to the essential cognition of tolerance within psychosocial theorizing

The peculiarity of this research, as any consciously carried out one on the goals, principles, methods and procedures of methodological search, is a detailed study of the form organizations and the means of effective cognition of the most complex, in our case psychosocial, objects with their diverse specificity and developmentally open everyday life. Undoubtedly, such extremely complex objects of cognition-design include human tolerance as an ontological psychic and, at the same time, a psychosocial phenomenon. In this case, the research optics of the post-nonclassical style of rational theorizing is aimed at methodological approaches to the essential cognition of tolerance in the format of their social and psychological causes, where the main tools of professional methodologization are appropriate analysis, reflection, understanding and the procedure of semantic specification of tolerant enrichment of person’s everyday life flow in the context of his continuous interaction with others. As a result of such intellectual work within psychosocial direction of finding out the phenomenological picture of tolerance, the theoretical substantiation and logic-content filling were given five methodological approaches – sociogenetic, conflict, xenological, information-communicative and personal. Each of them is a specific interpretive invariant of the epistemologically-weighted decoding of the meaning-semantic tissue of tolerance as a stage-by-stage deployed psychosociality, is being self-organized as an important vita-cultural form of conscious presence of joint life in a globalized society and, at the same time, in active dimension of social everyday life, is being approved as socio-cultural norm, also is being characterized by tolerant attitude to other, another or alternative and, as a consequence, optimize the results of collaboration and human communication. And therefore in the realities of today, appears as a framework condition for availability of solidarity processes, established and new forms of productive communicative interaction. In a personal dimension, it ontogenetically emerges as an integral trait-quality of the individual world of personality, which determines his active position in relationships with people regardless of their cultural, social and national identity and is expressed in a humane desire to reach mutual understanding, agreement of different motives, installations, orientations, without resorting to violence, oppression of human dignity and instead using the available humanitarian capabilities – dialogue, clarification, collaboration and mutual assistance.
Per-pixel calibration using CALTag and dense 3D point cloud reconstruction

This paper proposes a multimodal imaging system that allows reconstructing a dense 3D spectral point cloud. The system consists of an Intel RealSense D415 depth camera that includes active infrared stereo and a NuruGo Smart Ultraviolet (UV) camera. RGB and Near Infrared (NIR) images are obtained from the first camera and UV from the second one. The novelty of this work is in the application of a perpixel calibration method using CALTag (High Precision Fiducial Markers for Camera Calibration) that outperforms traditional cameras calibration, which is based on a pinhole-camera model and a checker pattern. The new method eliminates both lens distortions and depth distortion with simple calculations on a Graphics Processing Unit (GPU), using a rail calibration system. To this end, the undistorted 3D world coordinates for every single pixel are generated using only six parameters and three linear equations. The traditional pinhole camera model is substituted by two polynomial mapping models. One handles lens distortions and the other one handles the depth distortions. The use of CALTag instead of traditional checkerboards allows overcoming failures during calibration due to clipping or occlusion of the calibration pattern. Multiple point clouds from different points of view of an object are registered using iterative closest point (ICP) algorithm. Finally, a deep neural network for point set upsampling is used as part of the post-processing to generate a dense 3D point cloud.
Towards Typed Semantics for Parsing Expression Grammars

We describe the formalization of a type system for parsing expression grammars (PEG) which is equivalent to Ford's original fixpoint-based definition of well-formed PEGs. We use our type system definition to implement a functional big-step semantics for typed PEGs using the Agda programming language.
Multimodal Daily Activity Recognition in Smart Homes

Recent research on modeling human daily activities in smart showed substantive challenges due to the heterogeneity of incoming data and the natural interleaving among human activities. This paper introduces an approach for handling multimodal activity recognition systems in smart homes by modeling atomic actions using multi-layer perceptron neural network; enriched with background knowledge from sensors' profiles. The proposed methodology defines the activity recognition problem as multiclass classification problem, in which an atomic action might belong to more than one activity at a given time. For each sensor, a profile of selected features has been created to identify the spatiotemporal aspect of its triggered actions. Then, a multimodal segmentation technique is applied to assemble actions that formulate a given activity into independent blocks of tuples. Such segmentation considers the interleaving among actions by applying multiclass classification. Experiments on multimodal dataset have been conducted to measure the performance of the proposed methodology. Measures such as F-measure and accuracy have been used to compare among different classifiers. The preliminary results showed a significant enhancement especially on activities that share high number of similar actions (performed concurrently).
Maximizing the Organization's Technology Leverage through Effective Conflict Risk Management within Agile Teams

Effective conflict-risk management (ECM) is critical within Agile teams because Agile team effectiveness thrives on effective, constant team interactions as well as frequent customer collaborations. The main goal for ECM is to foster team effectiveness thereby enabling the organization to realize the benefit of adopting and implementing development projects in Agile. Through Agile, organizations develop quality software that enables them to get their products quickly in the market while still being flexible and adaptive to change. This results in improved organization technology competitiveness that enable organizations to keep up with their technological needs and to continually gain from the technology investments they have made, at a faster rate, enabling efficient and cost-effective spending. This is called the organizations' technology leverage. This quantitative study discusses how ECM within agile teams can help maximize an organization technology
Increase Security of IoT Devices Using Multiple One Time Password

Abstract Lately more IoT use of devices both used at home, disaster prone, hospitals, highways and government agencies and many other places that use IoT device for data acquisition in realtime therefore the authors want to examine the security of IoT devices that is increasingly being used by utilizing OTP codes sent by smartphone devices or GSM modules to keep IOT devices from people who want to exploit the IoT device.
AI Entities as AI Agents: Artificial Intelligence Liability and the AI Respondeat Superior Analogy

Artificial Intelligence (AI) based entities are already causing damages and fatalities in today's commercial world. As a result, the dispute about tort liability of AI-based machines, algorithms, agents and robots is exponentially advancing in the scholarly world and outside of it. When it comes to AI accidents, different scholars and key figures in the AI industry advocate for different liability regimes. This ever-growing disagreement is condemning this new emergent technology, soon to be found in almost every home and street in the US and around the world, into a realm of regulatory uncertainty. This obstructs our ability to fully enjoy the many benefits AI has to offer us as consumers and as a society.This Article advocates for the adoption and application of a strict liability regime on current and future AI accidents. It does so by delving into and exploring the realm of legal analogies in the AI context and promoting the agency analogy, and subsequently, the respondeat superior doctrine. This Article explains and justifies why the agency analogy is the best-suited one in contrast to other analogies which have been suggested in the context of AI liability (e.g., products, animals, electronic persons and even slaves). As a result, the intuitive application of the respondeat superior doctrine provides the AI industry with a much-needed underlying liability regime which will enable it to continue to evolve in the years to come, and its victim to receive remedy once accidents occur.
Semantic segmentation in egocentric video frames with deep learning for recognition of activities of daily living

The analysis of videos for the recognition of Instrumental Activities of Daily Living (IADL) through the detection of objects and the context analysis, applied for the evaluation of patient’s capacity with Alzheimer's disease and age related dementia, has recently gained a lot of interest. The incorporation of human perception in the recognition tasks, search, detection and visual content understanding has become one of the main tools for the development of systems and technologies that support the performance of people in their daily life activities. In this paper we propose a model of automatic segmentation of the saliency region where the objects of interest are found in egocentric video using fully convolutional networks (FCN). The segmentation is performed with the information regarding to human perception, obtaining a better segmentation at pixel level. This segmentation involves objects of interest and the salient region in egocentric videos, providing precise information to detection systems and automatic indexing of objects in video, where these systems have improved their performance in the recognition of IADL. To measure models segmentation performance of the salient region, we benchmark two databases; first, Georgia-Tech-Egocentric-Activity database and second, our own database. Results show that the method achieves a significantly better performance in the precision of the semantic segmentation of the region where the objects of interest are located, compared with GBVS (Graph-Based Visual Saliency) method
Software Metrics Artifacts Making Web Quality Measurable

Mining open source repositories introduces an effective approach to put in practice empirical software engineering in a variety of technologies. Kernel development (Linux) first and then Internet (Chromium) and more recently cloud orchestration (Kubernetes) and machine learning (TensorFlow) are fundamental pieces not just for open source ecosystem but also for the industry leading software innovation. Empirical software engineering sustains a better understanding of these projects, reducing even more the barriers for adoption. In this work we focus on empirical quality assessment developing software metrics artifacts to make web components quality measurable. After reviewing the state of the art and main frameworks for software measurement, we will present our proposal for the empirical evaluation of quality metrics for web components, data collection, measurement and prediction, discussing main benefits and some drawback of the selected approach, which will be aimed at future works.
A Density-Based k-Means++ Algorithm for Imbalanced Datasets Clustering

k-means algorithm is popularly used as an effective clustering method. However, existing k-means algorithm usually performs poorly in imbalanced datasets. To address this problem, density-kmeans++ algorithm based on density distance is proposed in this paper. The proposed method incorporates density distance into traditional Euclidean distance-based k-means algorithm when clustering imbalanced dataset. The experimental results on UCI datasets and Western Reserve University Bearing Data indicates that density-kmeans++ has better ability to deal with imbalanced datasets than k-means++.