Reviewerly/exHarmony huggingface dataset:
TARA: temperature aware online dynamic resource allocation scheme for energyoptimization in cloud data centres

Cloud data centres, which are characteristic of dynamic workloads, if not optimized for energy consumption, may lead to increased heat dissipation and eventually impact the environment adversely.Consequently, optimizing the usage of energy has become a hard requirement in today's cloud data centres wherein the major part of energy consumption is mostly attributed to computing and cooling systems.Motivated by which this paper proposes an online algorithm for dynamic resource allocation, namely, temperature aware online dynamic resource allocation algorithm (TARA).TARA demonstrates a novel algorithm design to adapt dynamic resource allocation based on the temperature of a data centre using computational fluid dynamics (CFD).Also, TARA demonstrates a new dynamic resource reclaim strategy for making efficient resource allocations leading to efficient energy consumptions in dynamic environments.The proposed algorithm provides optimal resource allocation considering energy efficiency without being overwhelmed by online dynamic workloads.The optimal energy-efficient dynamic resource allocation for online workloads eventually optimizes the computing and cooling energy consumption.We show through theoretical analysis the correctness, efficiency and optimality bounds given as T ARA(P ) ≤ 2OP T (P ) , relative to the optimal solution provided by offline dynamic resource allocation algorithm (OP T (P )) .We show through empirical analysis that the proposed method is efficient and significantly saves energy by 26% when the data centre utilization is 100% compared to batched reclaim.The performance analysis shows significant improvement in optimizing computing and cooling efficiency.TARA can be used in multiple areas of on-demand dynamic resource allocation in cloud computing like resource allocation for virtual machine creation, resource allocation for virtual machine migrations, and virtual resources assignment for elastic cloud applications.
Lantra: Taming Transformers for Robust Facial Landmark Detection

We present Landmark Transformer (LanTra), a powerful method leveraging vision transformers as a backbone in place of convolutional networks, for the facial landmark detection task. Specifically, we deploy a symmetric Encoder-Decoder architecture, with pure transformer networks as encoder and convolutional networks as decoder. In the Transformer-Encoder, the input image is split into several patches, which are seen as tokens and fed into various stages of multi-head self-attention modules. While in the Convolution-Decoder, the output representations of each stage of transformer-encoder are fused into the corresponding stage of the decoder with the proposed Structural-Aware Attention module, which is designed for the feature dimension adaptation and facial structure attention. Owing to the global receptive field and unchanging feature resolution in the proposed transformer-based encoder, we can successfully extract rich context information while maintaining the local information undamaged, which is critical for the robustness of the prediction results. Extensive experiments demonstrate the state-of-the-art results of our method on several popular academic datasets, i.e., WFLW, COFW and 300W, in which we show a tremendous potential of vision transformers in the facial landmark detection task.
Analysis of learning methods in the digital educational environment

In this paper, modern ways of digitalization of the educational process are considered and analyzed. An alternative innovative solution can be a theory of inventive problem solving (TRIZ) a methodology, taking into account the didactic principles of education (continuity, succession, accessibility, etc.). The dynamics of innovative technologies requires constant updating of the educational environment; inclusion of the teacher in the educational activities; active forms of learning (integrated classes, search and research activities); free access to digital repositories (methodological and lecture materials); social networks (Internet communities of teachers, teachers). The introduction of new information and communication technologies (ICT) in the management of the educational system made it possible to achieve a number of goals: modernization in the transfer/acquisition of knowledge (online/distance learning); application of a creative approach in pedagogical activity; the use of methodological and didactic forms of education (personalized learning, meta digital complexes, digital technologies).
DEVELOPMENT OF TECHNICAL STUDENTS’ MOTIVATION FOR INNOVATIVE PROFESSIONAL ACTIVITY

Preparing technical students for innovative professional activity is considered a pressing issue. The community’s burning interest in technical specialists lies in the fact that this activity provides innovative knowledge-intensive, high-tech, and internationally competitive facilities and production technologies recognized as intellectual property. From the ontogenesis standpoint, the motives of technical students’ innovative activity are divided into four groups: material and professional ones, as well as self-assertion, and self-realization. Material motivation has objective nature-aligned pragmatic origins. Launching an ambitious innovative engineering project, many technical specialists wish to benefit from it pragmatically. The source of self-assertion/prestige is the specialist’s nature-aligned desire and willingness to confirm the status of his/her qualification and competence through the results of the innovative engineering activity. Technical specialists’ professional motivation for innovative activity is inseparable from their natural aspiration and desire to use acquired professional knowledge and skills for solving both career and vocational tasks to the best advantage. Self-realization reflects the objective and nature-aligned aspiration of an individual to achieve personal professional self-development.
An Efficient Model to Estimate the Soil Profile and Stratigraphic Uncertainty Quantification

Comprehensive geologic information is very important and needed in underground construction and underwater infrastructure. However, it is quite difficult to reveal the configuration of different soil features underground with sparsely measured data. In this study, a new algorithm was proposed by coupling the General Regression neural network and the Markov Chain method (GR-MC) together to reproduce the characteristics of underground stratigraphy based on rare borehole information. The hypothesis of general regression neural network was introduced to evaluate the variation in soil layers along with the horizontal level, while the Markov chain method was applied to examine the change in soil type along with the depth. The proposed GR-MC method is very intuitive and can be efficiently implemented to predict the geological model using only the coordinates as inputs. Moreover, the proposed method can be smoothly extended into a three-dimensional case and does not require too many computational resources. The spatial coordinates of the target construction site were designed as the input variables, while a vector including the probability information of different soil types was set as the output. The merits of the proposed algorithm were discussed, and a case history was presented by implementing the proposed method. The results are reasonable, and the stratigraphic uncertainty was quantified based on information entropy theory. The possible additional borehole locations were identified and discussed to reduce the stratigraphic uncertainty of a construction site.
Optimized Skeleton graph based CNN for Human Abnormal Detection in Video Streams

Human Action Recognition (HAR) is the process of understanding human actions and behavior. HAR has a broad range of applications, and it has been focused on increasing the attention in various domain of computed vision. Abnormal detection from video stream is vigorous to guarantee the security in both outside spaces with the internal. Furthermore, the abnormal actions are really infrequent and rare, which makes the supervision process more challenging and difficult. In this research, skeleton graph-based Convolutional Neural Network (CNN) is devised for human abnormal activity detection. Here, the skeleton graph-based CNN (Skeleton graph_CNN) is devised based on the concept of classical convolution and skeleton graph generation. The human action recognition classifies the human actions into normal and abnormal class. The abnormal actions from the recognized outcome are detected with Skeleton graph_CNN, which provides the various actions of human as an output. The Skeleton graph_CNNgenerates the skeleton shaped human structure by connecting the joints within the frame to consecutive frames. Moreover, the HAR is carried out using IITB-Corridor Dataset based on metrics, such as testing accuracy of 0.961, sensitivity of 0.956 and specificity of 0.960, correspondingly.
On Target Representation in Continuous-output Neural Machine Translation

Continuous generative models proved their usefulness in high-dimensional data, such as image and audio generation.However, continuous models for text generation have received limited attention from the community.In this work, we study continuous text generation using Transformers for neural machine translation (NMT).We argue that the choice of embeddings is crucial for such models, so we aim to focus on one particular aspect: target representation via embeddings.We explore pretrained embeddings and also introduce knowledge transfer from the discrete Transformer model using embeddings in Euclidean and non-Euclidean spaces.Our results on the WMT Romanian-English and English-Turkish benchmarks show such transfer leads to the best-performing continuous model.
Masmaker: An Open-Source, Portable Software to Create and Integrate Maze-Like Surfaces into Arbitrary Geometries

In recent years, Triply Periodic Minimal Surface (TPMS) structures have raised attention of the scientific community due to their potential applications, not only to lighten structural components, but also to be used in energy applications as absorbers and heat sinks for thermal management, medical applications as porous structures for scaffolds and tissue applications on implants, among others. Nevertheless, from the design point of view, the integration of TPMS into organic or any arbitrary geometry for particular applications, usually faces difficulties associated with the tools available to reach this goal. In this work, an advanced computational tool is presented to automate the design and integration process of TPMS structures into arbitrary geometries. The software tool allows control the main parameters of this kind of structures. Two versions of the tool are shared within the repository, the first version is an executable file with a GUI that allows the users to easily integrate TPMS, and in a second version the source code that will allow advanced users to utilize the software as basis to develop their own implementations. Several illustrative examples about the use of the software are shown using different geometries, cell sizes and voxel resolutions.
Structural Analogies and Homologues Between the West Mediterranean and Black Sea Regions Tectonic Tailoring

Summary An analysis of comparative tectonic evolution of two entailing regions of the Alpine belt - Black Sea and Western Mediterranean - reveals profound analogies and homologues in their structure and development. Though both back-arc basins under comparison have many distinctions and differ each from other due to their linear dimensions (nearly twofold), a certain time shift for the lithological spectra during some epochs, present state of volcanic activity etc., they, nevertheless, reveal a striking morphological coincidence and notable genetic analogy in their spatial, kinematic, undercrustal, palaeogeographic, and other features. The above comparison allows consideration of the circum-Black Sea region as a minor and simplified tectonic copy of the Western Mediterranean area being evolved in similar way and caused by similar tailoring of its structural units that stipulated by uniform stress/fault pattern inherited from the Meso-Tethys tectonic realm demonstrating kind of self-similarity or crustal scaling effect. Two prominent mega-sutures, the Teisseyre-Tornquist Zone and South Armorican Shear zone, represent axial alignments along which a distorted symmetry could be traced for both basins under comparison. The above similarities can help elaborate new trends for oil and gas exploration in the circum-Black Sea basin using geological knowledge on the Western Mediterranean and vice versa.
Human-Computer Interaction Tools and Game Development

As digital technology advances, increasingly complex video games are being brought to market. As this occurs, interface design becomes increasingly important. This chapter explores how to use HCI design in video game user interfaces to meet the game's HCI needs.
Primary School Programming Olympiads in Gomel Region (Belarus)

The content of programming contests for students in grades 1–4 of the Gomel region (Belarus) is described in this article. A general idea of the thematic content of the tasks and examples of the tasks of the city Olympiad, which took place on April 8, 2016, is provided. The methodology of teaching and preparing junior schoolchildren for such Olympiads is also briefly described. A serious technical basis is the instrumental system of distance learning developed under the control of the author (DL.GSU.BY).
Building an R Package

Now we know how to write functions and create classes in R, but neither functions nor classes are the unit we use for collecting and distributing R code. That unit is the package. It is packages you load and import into your namespace when you write
A Brief Introduction to the Major Science and Technology Products and the 2019 Highlights Scientific and Technological Progress of China Seismic Experimental Site (CSES)

The China Seismic Experimental Site (CSES) is a “field laboratory for the research of mainland earthquake science”. In 2019, CSES brought together 28 scientific research teams to participate in the construction and scientific research tasks. At the same time, CSES organized scientific research teams from five research institutes of the China Earthquake Administration to participate in the work. The experimental site also cooperated with the Sichuan Earthquake Administration, the Yunnan Earthquake Administration and other units produced the first batch of products, laying a solid foundation for the experimental field to promote numerical earthquake prediction, strong ground motion simulation and earthquake rupture process simulation in the next step. CSES has formed impressive scientific and technological progress from 90 projects supported by CSES in 2019.
Research on the cooperation of industry-university-research in local universities based on game theory

As the main part of China’s higher education system, local universities aim to serve regional economic and social development and focus on cultivating high-quality talents for the locality. However, compared with universities administrated by the ministry of education and other central agencies, local universities have certain disadvantages in the cooperation of Industry-University-Research. This research analyzes the difficulties and reasons of local universities in the cooperation of Industry-University-Research, and then puts forward corresponding suggestions for local governments and local universities from the perspective of game theory, including optimizing the promotion and regulation functions of the government, optimizing the mechanism of Industry-University-Research cooperation, and optimizing the school-running concept and evaluation system of local universities.
ROLE OF STAFF CAPACITY BUILDING IN IMPROVING JOB PERFORMANCE AMONG STAFF OF NATIONAL COLLEGE OF AVIATION TECHNOLOGY, ZARIA

Staff capacity development entails the training of staff of an organization relevant training intended to improve the overall productivity of that organization and job performance of the trainees. The study determined the role of staff capacity building on job performance of staff of the National College of Aviation Technology, Zaria (NCAT). Primary data was collected using structured questionnaire from 113 staff of NCAT and subjected to descriptive statistical analysis using likert-type scale, frequencies and percentages. The result indicated that 77.9% of the sampled staff are male and also reasonable number (92%) attended post-secondary education. About 41% are youth within the age bracket of 15- 30 years. Staff affirmed that capacity building and staff development improves their job performance and aid in the overall upkeep of their institution mandate. Politicking of training (50%), training inconsistencies (21.2%) and late release of fund (15.9%) were the major challenges affecting the conduct of training. The study recommended mandated trainings attendance before promotion, development of standard protocols for determining the organization training needs, concentrate on staff development and shun politicking of trainings as measures to improve conduct of training in NCAT.
Analyzing the Efficiency of Recommender Systems Using Machine Learning

RedEMC is an e-learning web platform that utilizes a hybrid recommender system to suggest learning resources and online courses to Latin American doctors and other health specialists in the context of Continuing Medical Education. Explicit and implicit feedback were collected in a period of six months to determine the usefulness of personalized recommendations through predictive models, using machine learning approaches and methods. The main contribution of this research is to show how to utilize the feedback given by students who received personalized recommendations in a real study case to create predictive machine learning models that assist organizations to analyze the efficiency of their recommender systems. Once predictive models are generated, educational institutions and companies could also utilize them to make strategic decisions regarding the accomplishment of their organizational goals.
Promoting Elderly Residents' Quality of Life: Design Consideration for Bathing Experiences in the Nursing Home

Bathing elderly residents with assistance is a daily service provided in the Nursing Home (NH). However, it has been widely discussed that assisting bathing is one of the most challenging tasks in caregivers’ service process, and bathing experiences are closely related to the residents’ Quality of Life (QoL). Based on the findings from previous ergonomic studies, elderly residents’ agitated behaviors in the bathing process like repetitious vocalizations, used to communicate or express elderly residents’ unmet needs, are the primary cause of stress for caregivers. Thus, this study proposes design considerations for bathing activities in NHs under the theoretical models of QoL and needs, and it also assumes appropriate design for products and services can minimize the elderly’s agitated behaviors by addressing their unmet needs, which may help improve user experience and release care workers’ workload.
uFACT: Unfaithful Alien-Corpora Training for Semantically Consistent Data-to-Text Generation

We propose UFACT (Un-Faithful Alien Corpora Training), a training corpus construction method for data-to-text (d2t) generation models.We show that d2t models trained on UFACT datasets generate utterances which represent the semantic content of the data sources more accurately compared to models trained on the target corpus alone.Our approach is to augment the training set of a given target corpus with alien corpora which have different semantic representations.We show that while it is important to have faithful data from the target corpus, the faithfulness of additional corpora only plays a minor role.Consequently, UFACT datasets can be constructed with large quantities of unfaithful data, minimising the need for faithful data.We show how UFACT can be leveraged to obtain state-of-the-art results on the WebNLG benchmark using METEOR as our performance metric.Furthermore, we investigate the sensitivity of the generation faithfulness to the training corpus structure using the PARENT metric, and provide a baseline for this metric on the WebNLG (Gardent et al., 2017) benchmark to facilitate comparisons with future work.
On the Possibility of Object-Oriented Speculative Design: A Genderless AI Speaker Based on Anti-correlation Thinking

Intelligent objects in the Internet of Things (IoT) environment have been gradually endowed with many “autonomous” attributes, which inevitably echo the trending thought of speculative realism. Moreover, when confronting the anxiety of sustainable existence brought about by the Anthropocene and the sense of nothingness after the pictorial turn/iconic turn, there is an urgent need for a moment of “human decentralization” and establishing a new stance of “beyond user-centered”. Object-oriented philosophy, as a kind of guerrilla metaphysics, tries to lead us out of a worldview that is no longer anthropocentric and suggests that objects have an inner reality irrelevant to human perception. This article proposes an emerging method as object-oriented speculative design (OOSD) with its anticorrelational thinking, which is based on a hypothesis of the quadruple object by Graham Harman and develops its five steps as an experimental design tool to approach the ontological entity of AI speakers, presenting an object-oriented rather than an anthropomorphic approach that transcends the gendered smart speaker design proposal. It should be noted that the idea of uncovering the inner reality of objects and trying to finally reveal them in the method of OOSD is only a wish with theoretical possibility. The thing-in-itself cannot be accurately speculated because once it is done, it becomes a phenomenon constructed by humans. Therefore, what OOSD offers is a possible pathway for indirect access to the thing in itself, giving us the opportunity to approach it and thus surpass the limitations of human beings.
THE CIRCUMSTANCES OF THE ESTABLISHMENT OF THE MARCHIN LUTHER CHURCH IN KOROLIVSKY GUT

The research conducts a comparative analysis of Ukrainian and European processes in digital technologies, incl. education. The author focuses on significant efforts made in Ukraine to digitalize catalogs and unique funds of ancient Ukrainian literature and resources. Options for the use of digital technologies in historical education are considered, in particular, the importance of acquiring computer skills for text design, creating academic research databases, sources, and catalogs of archives and libraries. The relevant processes have resulted in mobile scientific communication, the format of which the author characterizes as from “desktop” to “desktop”. The article analyzes the historical and cultural monuments, which the State Historical and Cultural Reserve “Tustan” represented in 3D format. The author justified the potential of digital technologies for research and academic activity of students of specialty 032 “History and Archeology”.