Reviewerly/exHarmony huggingface dataset:
Cultural transfer – thesaurus of thematization (problematization of transdisciplinarity)

The article analyzes the peculiarities of problematization of transdisciplinary experience in solving interdisciplinary and existential issues of everyday life with the involvement of scientific and value-moral priorities of scientific research. Problematization is considered comprehensively, at the level of thematization of presuppositions of transdisciplinary experience, the formation of explanatory hypotheses (by the type of abduction), taking into account not only direct but indirect evidence, using the heuristics of the thesaurus corresponding to the topic. The formation of the thesaurus is based on a cultural transfer between interdisciplinary discourses with an appeal for mutual understanding to the language of everyday communication in the mode of actual dialogue. Cultural transfer is focused on the translation and understanding of the problems discussed, the removal of speech inconsistencies, contradictions.Problematization, as a mode of conducting transdisciplinary experience, due to its complexly organized nature, does not imply unambiguous solutions once and for all. The latter circumstance plays a positive role, taking into account the chosen methodology for the real presentation of the subject of research. This experience has the ability to challenge the proposed solutions, requiring completeness, evidence, expressiveness and openness, promotes updating depending on the place and time (chronotope) of the research being conducted. Transdisciplinary experience, accumulating skills, always rechecks its methodological approaches: thematization of prerequisites and the creation of an appropriate dictionary, the formation of ways to translate interdisciplinary languages to search for explanatory hypotheses, taking into account not only direct but also indirect evidence.
Deep Ensembles with Hierarchical Diversity Pruning

Diverse deep ensembles hold the potential for improving accuracy and robustness of deep learning models. Both pairwise and non-pairwise ensemble diversity metrics have been proposed over the past two decades. However, it is also challenging to find the right metrics that can effectively prune those deep ensembles with insufficient ensemble diversity, thus failing to deliver effective ensemble accuracy. In this paper, we first compare six popular diversity metrics in the literature, coined as Q metrics, including both pairwise and non-pairwise representatives. We analyze their inherent limitations in capturing the negative correlation of ensemble member models, and thus inefficient in identifying and pruning low quality ensembles. We next present six HQ ensemble diversity metrics by extending the existing Q-metrics with three novel optimizations: (1) We introduce the concept of focal model and separately measure the ensemble diversity among the deep ensembles of the same team size with the concept of focal model, aiming to better capture the negative correlations of member models of an ensemble. (2) We introduce six HQ-diversity metrics to optimize the corresponding Q-metrics respectively in terms of measuring negative correlation among member models of an ensemble using its ensemble diversity score. (3) We introduce a two phase hierarchical pruning method to effectively identify and prune those deep ensembles with high HQ diversity scores, aiming to increase the lower and upper bounds on ensemble accuracy for the selected ensembles. By combining these three optimizations, deep ensembles selected based on our hierarchical diversity pruning approach significantly outperforms those selected by the corresponding Q-metrics. Comprehensive experimental evaluation over several benchmark datasets shows that our HQ-metrics can effectively select high diversity deep ensembles by pruning out those ensembles with insufficient diversity, and successfully increase the lower bound (worst case) accuracy of the selected deep ensembles, compared to those selected using the state-of-the-art Q-metrics.
Online Active Proposal Set Generation for Weakly Supervised Object Detection

To reduce the manpower consumption on box-level annotations, many weakly supervised object detection methods which only require image-level annotations, have been proposed recently. The training process in these methods is formulated into two steps. They firstly train a neural network under weak supervision to generate pseudo ground truths (PGTs). Then, these PGTs are used to train another network under full supervision. Compared with fully supervised methods, the training process in weakly supervised methods becomes more complex and time-consuming. Furthermore, overwhelming negative proposals are involved at the first step. This is neglected by most methods, which makes the training network biased towards to negative proposals and thus degrades the quality of the PGTs, limiting the training network performance at the second step. Online proposal sampling is an intuitive solution to these issues. However, lacking of adequate labeling, a simple online proposal sampling may make the training network stuck into local minima. To solve this problem, we propose an Online Active Proposal Set Generation (OPG) algorithm. Our OPG algorithm consists of two parts: Dynamic Proposal Constraint (DPC) and Proposal Partition (PP). DPC is proposed to dynamically determine different proposal sampling strategy according to the current training state. PP is used to score each proposal, part proposals into different sets and generate an active proposal set for the network optimization. Through experiments, our proposed OPG shows consistent and significant improvement on both datasets PASCAL VOC 2007 and 2012, yielding comparable performance to the state-of-the-art results.
RELEVANT PROFESSIONAL DEVELOPMENT FOR TEACHERS: PRODUCTIVE TRAINING

Elaborating on the previously conducted studies into polar model of sense-making, the authors propose a trialectic model of the way meanings are born.The purpose of this article is to provide theoretical grounds for transition from the dual to trinal model of sense-making strategies and their description.The sense-making strategy determines the direction and quality content of the generated image-meaning.The authors find it expedient to consider trialectic level of the system of senses for more adequate modeling of possible strategies that reflect the key traits of a personality.The above mentioned triad contains hypo-adaptive, hyper-adaptive and pre-adaptive sense-making strategies.The proposed approach enables to identify a personal profile that acts as a multifactor model of possible states of value-notional sphere of an experiencing subject.In terms of modeling behavior patterns, a personal profile of such kind reveals the main direction and principal features of sense-making regulation.
The game of data and privacy

Privacy is a fundamental right and its breach should be punishable offence. But its definition/scope is not uniform globally which gives an opportunity to privacy invaders. Some countries have complex regulation structure for privacy related issues like in U.S. where structure is departmentalized. This makes the system opaque for the citizens and discourages them to exercise their rights. Like any other technology, data analytics domain is swiftly evolving, thus it becomes difficult to consider all the privacy breaching scenarios under the preview of law enforcement and regulation. Even Netflix could not anticipate de- anonymization algorithm and compromised its customers data and paid USD 9 million for lawsuit under VPPA Act 1988. Thus, I would suggest input (training dataset) and output should be encrypted, and algorithm should work on encrypted data for the sequel of such competitions. Ethics is a broader concept than privacy as it deals with the activities which are wrong or right and not necessarily legal or illegal. To be ethically correct, corporates, government, individuals etc. involved in data analytics should follow guidelines, regulations stipulated by various organizations like OECD, concerned/respective regulatory bodies etc. Hence it is unethical to use unregulated software/algorithms such as facial recognition which has low accuracy and higher probability of misuse.
Convective Heat and Mass Transfer to Fluids from a Rotating Sphere

Convective heat and mass transfer from a rotating sphere have been study here.The study cases were divided into the rotating sphere in an axial flow and in still fluid.The heat transfer is a major part in this study because the mass transfer that we got was from analogy.The heat transfer from rotating sphere in an axial flow was solved by taking the advantage of Merk's method, with uniform wall temperature (UWT) and uniform heat flux (UHF) heating conditions, while in still fluid we obtained a new equation for heat transfer for constant wall temperature heating condition.The result was that the heat transfer from the (UHF) case is greater than (UWT) case, while booth cases are increased with the rotation parameter (B) and Prandtl number.Now, for the still fluid case, the heat transfer increases with rotational Reynolds number and with Prandtl number.The mass transfer case had been obtained by the analogy, and because of that we got almost the same equations but with different coefficients and with the same cases that we dealt with heat transfer.A central finite deference method was used to solve fi, gi and θi's parameters for the case of heat and mass transfer from a rotating sphere.
The Study on Energy-Efficient Context-Aware Architecture for Wired or Wireless Communication System

The ever-increasing technical developments in the engineering of embedded systems, along with The proliferation of small sensor design and implementation has allowed mobile devices (e.g. Smartphones) understand everyday human acts, events and experiences. Consequently, inferring a wide spectrum of mobile application user-based behaviours from a very diverse background gathered from a collection of sensory measurements has attracted a great deal of attention in the field of ubiquitous sensing science. The presence and knowledge of the context enables users to be conscious of physical conditions or circumstances surrounding mobile device users, and this enables network networks to react proactively and intelligently on the basis of that knowledge. As smartphones advance, tech engineers are encouraged to build context-conscious apps to understand human-centered or community-based creative social and cognitive behaviours in any circumstance and from wherever. This leads to an ambitious vision of developing an “Internet of Things” society that enables apps that enable consumers that gather, evaluate and exchange local sensory information for large-scale group usage through building a smart network capable of making autonomous rational decisions to function on environmental objects. More significantly, it is believed that the incorporation of information and situational knowledge into the mechanism of identification of human-centered case dynamics may offer a deeper interpretation of human activity and may also provide an incentive for constructive assistance to individuals with a view to enhancing the quality of life. Mobile technologies enabling emerging computationally ubiquitous systems would constrain. However, the medium ware services offered on mobile devices have restricted capacity in terms of strength, memory and bandwidth relative to the capacities of PCs and servers. Power problems are, above all, significant constraints on the deployment of context-aware software. Unfortunately, these specifications minimize the battery life of the system due to the heavy energy usage incurred by both sensor and processor activity. Specifically, constantly recording the user background by sensors puts intense workloads on hardware and computing and, as a result, easily drains the battery capacity. Smart computer batteries, then, do not last a long period when the sensor (s) are working continuously
What are effective labels for augmented data? Improving robustness with AutoLabel

A wide breadth of research has devised data augmentation approaches that can improve both accuracy and generalization performance for neural networks. However, augmented data can end up being far from the clean data and what is the appropriate label is less clear. Despite this, most existing work simply reuses the original label from the clean data, and the choice of label accompanying the augmented data is relatively less explored. In this paper, we propose AutoLabel to automatically learn the labels for augmented data, based on the distance between the clean distribution and augmented distribution. AutoLabel is built on label smoothing and is guided by the calibration-performance over a hold-out validation set. We show that AutoLabel is a generic framework that can be easily applied to existing data augmentation methods, including AugMix, mixup, and adversarial training. Experiments on CIFAR-10, CIFAR-100 and ImageNet show that AutoLabel can improve models' accuracy and calibration performance, especially under distributional shift. Additionally, we demonstrate that AutoLabel can help adversarial training by bridging the gap between clean accuracy and adversarial robustness.
SOME ASPECTS OF CREATION OF LEARNING VIDEO CONTENT IN THE PROCESS OF EDUCATION

The study reveals the problem of organizing the efficient educational process using distance learning technologies based on video content. The paper presents the characteristics of e-learning and distance learning technologies. The study shows that e-learning, being flexible and variable, requires updating the content of education. Teachers need to master new telecommunications technologies, improve digital and information competencies. The authors show that video content genres being the synthesis of various types of visualization allow best solving teaching objectives within the educational process using e-learning technologies. The relevance of the latter is particularly obvious at present. The authors describe such types of video genres as a webinar, video-case, fast, conversation, interview, documentary webinar, and scripted video clip. The paper reveals the process of creation of a learning webinar with the participation of a lecturer and a student, describes the stages of script development, content design, video shooting, and editing. The development and rendering of learning programs to eContent causes the change in the role of a teacher; communication opportunities extend. The lecturer needs to customize innovative technologies to his/her methodological activity. The student’s participation in the process of preparing the materials supporting the teacher’s lecture gives an opportunity both to broaden knowledge in subject matter and to learn the fundamental aesthetic and technological principles and procedures of eContent creation. The presented material allows concluding that joint participation in the development of teacher’s and student’s webinars is an efficient means of formation of a set of skills of eContent communication and processing highly demanded in the modern educational process.
Real Time Person Independent Communication Between Dumb and Blind Person Using Sign and Text Detection

Primary mode of communication is speech by which information can transfer.But hearing-impaired people are not able to communicate with the world.In this paper, low cost prototype model is developed to bridge communication gap for disable people like deaf, dumb and blind.Dump person uses sign language and blind person uses voice, both gives output in text and audio form.System uses atmega-32 as microcontroller, video recording module and flex sensor for developing communication between deaf and dumb with Blind person.For those persons who bothered to express their feelings due to their lack of ability, this device will helpful for them.
A Simple Disaster-Related Knowledge Base for Intelligent Agents

In this paper, we describe our efforts in establishing a simple knowledge base by building a semantic network composed of concepts and word relationships in the context of disasters in the Philippines. Our primary source of data is a collection of news articles scraped from various Philippine news websites. Using word embeddings, we extract semantically similar and co-occurring words from an initial seed words list. We arrive at an expanded ontology with a total of 450 word assertions. We let experts from the fields of linguistics, disasters, and weather science evaluate our knowledge base and arrived at an agreeability rate of 64%. We then perform a time-based analysis of the assertions to identify important semantic changes captured by the knowledge base such as the (a) trend of roles played by human entities, (b) memberships of human entities, and (c) common association of disaster-related words. The context-specific knowledge base developed from this study can be adapted by intelligent agents such as chat bots integrated in platforms such as Facebook Messenger for answering disaster-related queries.
An Approach to Automatically Measure and Visualize Class Cohesion in Object-Oriented Systems

Developing systems with high quality is the motto of software engineering. In object-oriented systems, class cohesion is a significant quality attribute that impacts other quality attributes such as maintainability and reusability. Much time and effort are needed to be spent by the software engineers and developers in order to measure cohesion. Measuring cohesion automatically aids in getting over the manual measurement problem. In this paper, we propose an approach to automatically measure and visualize the cohesion within classes of object-oriented programs. The generated representations give an overall realization of the cohesion in an effective and interactive way. The approach parses the program source code, using an existing tool, into an XML file, and extracts the class tokens according to the definitions of the cohesion metrics. Then, it determines the cohesion relationships through matching these tokens with some class features. Finally, it generates interactive visualizations of the cohesion utilizing several charts. The proposed approach has been validated by conducting a case study. The results showed that the generated visualizations provide a comprehensive recognition of the program cohesion, and facilitate a proper estimation of the software quality based on its cohesion degree.
3D modeling with the ThreeJS Framework of the Colonial Temple of Chuquinga Aymaraes – Perú

This research has the purpose of modeling in 3D of the Colonial Temple of Chuquinga, to prove the rendering time in mobile devices, the investigation is applied, with an explanatory level and an experimental design where the rendering time with the frequency was evaluated of images (FPS, obtaining the frequency of images (FPS), the average is 52.29 FPS, highlighting the most influential GPU, that is, increases the frequency of images (FPS) when the central clock is greater than 500 Mhz in all mobile devices that have an optimum level, while in basic level devices when GPM Mhz decreases, the frequency of images (FPS) decreases, however, it does not cause distortion in animations and textures. The ThreeJS library was used to create 3D objects, 3ds Max for the modeling of temple walls and WebGL for final rendering.
Application of Pulsed Electric Prospecting for Allocation of Zones of Sulfide Mineralization of Copper-Porphyry Type in the Territory of Northern Kazakhstan

Summary Impulse electrical prospecting was used in prospecting and exploration work in the north of Kazakhstan to delineate hidden zones of sulfide mineralization of the copper-porphyry type at a depth of 1 km. One of the stages of the work was to determine the thickness of the weathering crust. The electrical exploration work carried out in the Shuakskaya license area, using the technology of electromagnetic sounding and induced polarization, was aimed at mapping the zones of increased polarizability, identified in Soviet times. Zones of high polarization and low resistivity, which are associated with zones of sulfide mineralization, were confidently identified on the sections built on the basis of the results of one-dimensional inversion. On geoelectric sections in the upper part of the section, a sub-ring structure has been identified that controls most of the mapped poor stockwork mineralization. The results of the electromagnetic survey were used together with geological, geochemical and mineralogical data to draw up a plan for further prospecting and exploration work.
Subspace exploration: Bounds on Projected Frequency Estimation

Given an $n \times d$ dimensional dataset $A$, a projection query specifies a subset $C \subseteq [d]$ of columns which yields a new $n \times |C|$ array. We study the space complexity of computing data analysis functions over such subspaces, including heavy hitters and norms, when the subspaces are revealed only after observing the data. We show that this important class of problems is typically hard: for many problems, we show $2^{Ω(d)}$ lower bounds. However, we present upper bounds which demonstrate space dependency better than $2^d$. That is, for $c,c' \in (0,1)$ and a parameter $N=2^d$ an $N^c$-approximation can be obtained in space $\min(N^{c'},n)$, showing that it is possible to improve on the naïve approach of keeping information for all $2^d$ subsets of $d$ columns. Our results are based on careful constructions of instances using coding theory and novel combinatorial reductions that exhibit such space-approximation tradeoffs.
For a Bottom-Up Approach to the Linguistic and Legal Definition of Hate Speech A Case Study of Offences Against Women

In addressing the legal definition of Hate Speech (HS), some important linguistic issues arise: the necessity of semantic precision of normative texts conflicts with the difficulty of tracing distinctive features of the phenomenon; the balancing with freedom of expression poses the question on the boundaries between opinions and verbal violence. Comparative studies on national laws show significant gaps in definitions, highlighting their negative effects. This paper discusses the problem analysing a corpus of 2,720 Facebook negative comments against women with decisional positions in Europe. Recurring textual strategies are identified testing a bottom-up approach, argued as the appropriate method to meet the current research challenges.