Reviewerly/exHarmony huggingface dataset:
Blended Automation: The Language-Game of Psychoanalytic Automatism and Cybernetic Automata

Situated within the context of the emerging concept of blended cognition between human and artificial entities, the present brief paper suggest that emphasis should be placed on the concept of automation in humans and (artificially intelligent) machines in order to understand clearly how unclear the opposition between the two categories is. After a short introduction on the relevance of artificial intelligence in various levels of the social fabric, such as policymaking and business strategies, and a discussion on the ill-defined conceptualization of several related terms such as “artificial,” “intelligence,” “consciousness,” the paper investigates “automation,” “automatism,” “automaton,” and “autism,” from psychoanalytical, cybernetic, control systems theoretical, and psychological perspectives, in the context of a Wittgensteinian language-game, drawing comparisons which in turn result to a number of remarks on the relationship between human and artificial agents. Firstly, it appears that human consciousness is deeply associated with non-automatic behavior while its unconsciousness is related to automatic expressions, whereas machine behavior is deeply associated with automatic function with unconsciousness being its only form of expression. Suggesting, however, a flatter ontology based on the concepts of infosphere, treating both humans and machines as inforgs, it is recommended that we think about the unexplained part left in the model of the following quadrant: (a) human non-automatic conscious, (b) human automatic unconscious, (c) machine automatic “conscious,” and (d) X. The paper concludes that by borrowing the algebraic method of cross-multiplication, multiplying (a) to (c) and dividing the result by (b), that is, investigating empirically the relationship between everyday interactions of conscious humans and unconscious machines, and then analyzing the results according to topics concerned with unconscious human behaviors will help creating a rather clearer view of what constitutes X, that is, what is actually feared in the expression of erratic machine behavior sometimes mistakenly understood as conscious.
Bayesian hierarchical model for the estimation of proper receiver operating characteristic curves using stochastic ordering

Diagnostic tests in medical fields detect or diagnose a disease with results measured by continuous or discrete ordinal data. The performance of a diagnostic test is summarized using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The diagnostic test is considered clinically useful if the outcomes in actually-positive cases are higher than actually-negative cases and the ROC curve is concave. In this study, we apply the stochastic ordering method in a Bayesian hierarchical model to estimate the proper ROC curve and AUC when the diagnostic test results are measured in discrete ordinal data. We compare the conventional binormal model and binormal model under stochastic ordering. The simulation results and real data analysis for breast cancer indicate that the binormal model under stochastic ordering can be used to estimate the proper ROC curve with a small bias even though the sample sizes were small or the sample size of actually-negative cases varied from actually-positive cases. Therefore, it is appropriate to consider the binormal model under stochastic ordering in the presence of large differences for a sample size between actually-negative and actually-positive groups.
Toward Privacy-Aware Healthcare Data Fusion Systems

Mobile wearable and sensor-enabled devices offer an opportunity for deluging unprecedented amount of health-related data that is beneficial in health and caregiving research. Fusing data ingested throughout various heterogeneous channels is essential for better provisioning novel healthcare solutions. However, this is typically challenged by privacy-awareness. For example, the European Commission throughout its call-for-proposals always stresses a requirement that provisioned solutions should consider privacy and should boost security- and privacy-awareness in cloud computing environments. Current solutions either do not consider privacy requirements or provide solutions that are mostly ad hoc and patch efforts. In this position paper, we motivate the adoption of Blockchain technologies for providing privacy-awareness to novel healthcare data fusion solutions. Our envisioned solution is proposed on top of current state-of-the-art blockchain and big data representatives, specifically Hyperledger Fabric and Apache Spark.
Automation of Analytical System for Measuring Service Effectiveness by Customer Type

Presently, data analysis has become an indispensable part in various fields of technological applications. The development of analytical methods that do not require analysts to repeatedly analyze data is becoming important. These methods are expected to automate the analysis of large quantities of data gathered on a daily basis. The purpose of this study is to propose and examine a method for automating the process of measuring service effectiveness by customer type. In particular, we propose a method for automating the decisions for a number of factors, applying text mining for automation of interpretation of factor, using decision-making rules for automation of customer classification, and extracting meaningful variables to facilitate an effective feedback on customer satisfaction. The proposed analytical automation system is able to analyze the data and produce results with almost the same amount of information just as analysts do without automation.
A Study on Models and Techniques of Anonymization in Data Publishing

In the era where world runs online the storing and publishing of data online has also increased to a great extent. In this era a large amount of information is collected and published to a network which is publically available. With the exposure of data comes the risk of information leakage of an individual while publishing the data online. Hence for the same we need a security system for preserving the privacy of individual and here the concept of preserving privacy in data publishing came into existence. To achieve this privacy different privacy models and techniques have been proposed which gives different levels of resistance against different attacks by adversaries. In this paper we will discuss about these models and techniques and have a comparative study among them.
Argus Vision: A Tracking Tool for Exhibition Designers

Abstract Contemporary exhibitions are increasingly staged using extensive and often interactive media. To create such exhibitions, exhibition design companies employ professionals from a wide range of different disciplines. The support of interdisciplinary exhibition designers in the design process is one goal of research in Human-Computer Interaction. This includes the deployment of Do-It-Yourself (DIY) Tools that enable professionals from all disciplines involved to design and create interactive media themselves. In this paper, we will present Argus Vision, a DIY Tool, which allows exhibition designers the use of camera-tracking to rapidly prototype and develop immersive exhibitions and interactive installations. We successfully used Argus Vision in two real-world case studies both in the prototyping and in the deployment of two installations in exhibitions. Additionally, we conducted expert interviews with exhibition designers, investigating the tool’s usefulness for them.
Efficient Ate-Based Pairing over the Attractive Classes of BN Curves

This paper proposes two attractive classes of Barreto-Naehrig curve for ate-based pairing by imposing certain condition on the integer $$\chi $$ that parameterizes the curve settings. The restriction results in an unparalleled way to determine a BN curve, its twisted curve coefficients, and obvious generator points. The proposed $$\chi \equiv 11~(\bmod ~12)$$ are found to be more efficient than $$\chi \equiv 7~(\bmod ~12)$$ together with pseudo 8-sparse multiplication in Miller’s algorithm. The authors also provide comparative implementations for the proposal.
Machine learning applied to pack classification

Pack or product classification is a quite common task in market research, particularly for sales tracking audits and related services. Electronic data sources have led to increased volumes, both in the sales volume being tracked and also the number of packs (or stock keeping units). The increase in packs needing to be classified presents a problem, in that, it needs to be done accurately and quickly. Traditional solutions using people for the classifications can be costly, due to the large number of people required to process the classifications in a timely and accurate manner. Reducing the manual work is a priority for audit-based market research businesses, leading to interest in automation, such as through machine learning techniques. In this article, we apply such methods. These include support vector machine, decision tree, XGBoost, AdaBoost, random forest, and neural network–based methods that are trained on the textual descriptions of already classified packs. We also implement a hierarchical classification method to take advantage of the structure of classes of the products. Once the models are trained, they can be used on unclassified data. Where the methods are not confident in their classifications, humans can be asked to classify. The hope is that the methods can learn to classify accurately enough that the manual workloads are reduced to manageable levels. This article reviews various methods and then outlines tests using these methods on two datasets collected by Nielsen, showing good performance.
Type the National Vulnerability Evaluation System Research Based on Fuzzy Comprehensive Evaluation

At the beginning, the report filter six main influential factors which has seriously affect toward the fragile of a country by Grey Correlation Grade model. Second, based on Fuzzy Comprehensive Evaluation, we introduce the idea of Entropy Method into our model and build an evaluation model in valuing the national vulnerability. Finally, select twenty countries around the world evenly to substitute their data into our model and compare our results with the published results of Peace Foundation. The comparison shows that the error in rating is limited in 20%. Therefore, the view that our model has high credibility.
Self-Encounter in Virtual Reality in Robot-Based Telepresence

Abstract Have you ever met yourself? Have you met your past? This report is meant to make a phenomenon known in which VR users at a break-in-presence do not fall back into the lab environment. However, we are not yet able to provide tangible evidence and systematic research about it. Setup: We describe a virtual reality application which originally was intended to provide control for a search and rescue robot. Due to a design requirement to use very limited resources, we developed a sparse representation of the past of the robot. The user encounters the past path of the robot in VR as a collection of 360° photo-spheres which each captures one instant. Multiple users of the application can individually review all past pictures. The most recent picture represents the current perspective of the robot. In addition, each user can interact with virtual objects, e. g., control the robot. Observation: According to perceptual research, breaks-in-presence might occur after sensory conflicts. An encounter of one’s self in VR introduces a perceptual and cognitive conflict. Users were able to realign with their own episodic memory and did not fall back into the lab environment as a result of this new type of break-in-presence.
Learning Database through Developing Database Web Applications

Databases play a very important role in the modern software system, especially in the big data age.It is the centralized repository for all kinds of data.Therefore, all students majoring in technology and computer science related fields should take database courses.However, learning about databases could be challenging without hands-on exercises.It is very important for students to be familiar with database commands and the associated clauses so they can write better database queries.Since the data resides in the database, it is hard for students to imagine the results, especially if there are a lot of records.Visualizing the intermediate results will help students to learn how to write better queries and troubleshoot.It is not practical to just use the command line to interact with the database server.Students must learn the industry standard 3-tier method to retrieve, insert, update and delete thousands of data from databases, and how to write web programs to do so.Designing an E-commerce platform should be the core of fundamental database curriculum -including a friendly graphic user interface for customers to sign up for accounts, search items, place orders and view histories.The instructors should not focus on teaching the database topics, but also show students how to manage the database server and user accounts in the classes.This paper proposes an integrated curriculum for learning about databases through developing database web applications.
Empirical Bounds on the Rate of Secret Bits Extracted from Received Signal Strength

Symmetric cryptography relies on pairs of identical secret keys shared by the legitimate communicating parties. To implement a symmetric-key algorithm for cryptography, a major concern is to develop secure methods for distribution of the secret key. In securing the wireless fading channels by symmetric-key algorithms, the physical layer properties of the channel can be exploited for distribution of the secret keys. In this approach, the channel state provides a common randomness which is shared by the legitimate users but is mostly unknown to an eavesdropper. by means of signal processing techniques, this common randomness is extracted into random secret keys. This paper establishes an information theoretic upper bound on the rate at which the secret keys can be extracted. Instead of the conventional approach that relies on mathematical models for the wireless channel, this paper adopts an experimental approach to estimate this bound from empirical data. A set of signal processing techniques is developed here to numerically estimate this bound for a pair of received signal strength (RSS) recorded by indoor commercial radios.
A Unifying View on Recombination Spaces and Abstract Convex Evolutionary Search

Previous work proposed to unify an algebraic theory of fitness landscapes and a geometric framework of evolutionary algorithms (EAs). One of the main goals behind this unification is to develop an analytical method that verifies if a problem's landscape belongs to certain abstract convex landscape classes, where certain recombination-based EAs (without mutation) have polynomial runtime performance. This paper advances such unification by showing that: (a) crossovers can be formally classified according to geometric or algebraic axiomatic properties; and (b) the population behaviour induced by certain crossovers in recombination-based EAs can be formalised in the geometric and algebraic theories. These results make a significant contribution to the basis of an integrated geometric-algebraic framework with which analyse recombination spaces and recombination based EAs.
Intrafirm Culture of Knowledge Update: Humanistic Approach

The present research features the culture of knowledge in the organization (firm). The purpose of the research is to substantiate the need to apply the humanistic approach to the formation of a culture of knowledge in the personnel policy of a modern firm. The research employed a qualitative analysis of knowledge orientation in the typologies of organizational culture. The author determines the areas of application of types of organizational culture according to their orientation on the knowledge of workers. The paper reveals the main and almost the only way to transfer tacit knowledge – socialization and its form, mentoring. The author proposes to create knowledge bases of intrafirm professional communities as the joint ownership of employees who possess personal knowledge. The results can be used for the development of the personnel and social policies of the firm, taking into account the proposed humanistic approach to the formation of the culture of knowledge and its update. Conclusions: 1. The culture of knowledge as a source of its update should be considered in personnel management, and not in knowledge management, since knowledge is the personal property of employees, not firms. 2. Organizations should create not corporate knowledge bases, but knowledge bases of intrafirm professional communities. 3. The proposed humanistic approach will ensure the creation of the cognitive environment that is adequate to the culture of knowledge, which in turn, will give a positive socio-economic effect to the activities of the organization (firm) by updating the knowledge of its employees.
Problems of standard controlling a scientific student work: legal grounds and practice

The article is devoted to the analysis of the legal basis of standardizing a student’s scientific research presentation in the form of a final qualifying work. There are differences in the federal regulatory frame­work and standardization current practice in this area; insufficiency of norm-definitions in the designated sphere; contradictions between the federal level of regulation and local acts of organizations that do not contain the necessary regulatory content. The author analyzes forms of the organization standards legit­imization in the field of a qualifying work registration. The article clarifies gaps in the standardization control of students’ research by the analysis of the federal standard positions in the bibliography field and an organization standard (higher education institution). The author formulates recommendations on the content of the organization’s standards dictated by the dispositive nature of the state standard norms.
K-Means and Apriori Algorithm for Pharmaceutical Care Medicine (Case Study: Eye Hospital of South Sumatera Province)

The pharmacy at the Eye Hospital of South Sumatra Province is in charge of managing to range from planning, procurement, storage, preparation, compounding, direct services to patients up to medical supplies circulating and drugs circulating in the hospital and providing medicines for patients based on doctor's prescriptions. All transactions carried out by patients are stored in the hospital system database are put into use at the end of 2017. Transaction data that stored is so many and stacked in the database, data mining can be used to extract data and get information from big data. Data mining techniques which used are K-Means algorithm to divide patient and association with Apriori algorithm to determine frequent itemset on drug data. The result obtained from data mining technique applied to the Pharmacy is intended to fulfill the pharmaceutical care medicine in hospitals.
Automatic Detection on Appearance Changes using Local Moment Invariants

A reliable appearance template has to extract a set of invariant features that holds the blueprint of the target image, regardless of being affected by translation, scale, rotation, skew, reflection, contrast and blur. Invariant features are superior in describing a number of visually distinctive and outstanding characteristics of a target but inadequate to identify the appearance changes of the target at a particular surveillance time. Thus, this paper aims to present an automatic detection approach to discover the rate of appearance changes in the target by exploiting the locality property of moment invariants. Unlike the existing local feature descriptors which usually extract the salient features randomly from the target, the proposed approach examines the entire target and subsequently reveals a suspicious region that contains some features that are drifting away from the original template. When the variations of the target's size, shape and orientation are corrupting the features, the reliability of the appearance template will gradually deteriorate and affect the object tracking process. Experiments are conducted to show that a selection of orthogonal moments is applicable to identify the appearance changes of the target by using the generalization relation between geometric monomials and orthogonal polynomial functions. Other than locality property, the proposed approach also preserves the capabilities of distinctiveness and invariance.
Information-bottleneck Based on the Jensen-shannon Divergence with Applications to Pairwise Clustering

The information-bottleneck (IB) principle is defined in terms of mutual information. This study defines mutual information between two random variables using the Jensen-Shannon (JS) divergence instead of the standard definition which is based on the Kullback-Leibler (KL) divergence. We reformulate the information-bottleneck principle using the proposed mutual information and apply it to the problem of pairwise clustering. We show that applying IB to clustering tasks using JS divergences instead of KL yields improved results. This indicates that JS-based mutual information has an expressive power at least as the standard KL-based mutual information.
On Modification of an Adaptive Stochastic Mirror Descent Algorithm for Convex Optimization Problems with Functional Constraints

This paper is devoted to a new modification of a recently proposed adaptive stochastic mirror descent algorithm for constrained convex optimization problems in the case of several convex functional constraints. Algorithms, standard and its proposed modification, are considered for the type of problems with non-smooth Lipschitz-continuous convex objective function and convex functional constraints. Both algorithms, with an accuracy $$\varepsilon $$ of the approximate solution to the problem, are optimal in the terms of lower bounds of estimates and have the complexity $$O\left( \varepsilon ^{-2} \right) $$ . In both algorithms, the precise first-order information, which connected with (sub)gradient of the objective function and functional constraints, is replaced with its unbiased stochastic estimates. This means that in each iteration, we can still use the value of the objective function and functional constraints at the research point, but instead of their (sub)gradient, we calculate their stochastic (sub)gradient. Due to the consideration of not all functional constraints on non-productive steps, the proposed modification allows saving the running time of the algorithm. Estimates for the rate of convergence of the proposed modified algorithm is obtained. The results of numerical experiments demonstrating the advantages and the efficient of the proposed modification for some examples are also given.
Automatic Learner Focus Detection System Over A Live Feed

The world is turning into a technological sphere. The technologies are evolving every second. With everything available in the Internet, people have started preferring to take their lectures and classes online. And so have the seminars turned into webinars, seminars over web. This system mainly concentrates on determining if a viewer attending the webinar is focused or not towards it. The main aim of this system is to recognize the eye gaze of a viewer over a live session with the help of some computer technologies and letting the presenter know if the viewer is focused on the session or not. By this, the presenter will know how well he/she is capable of keeping his viewers focused and he/she can work on his skills if the viewers are not focused. The access to the viewer's webcam is sufficient to achieve the goal.