Reviewerly/exHarmony huggingface dataset:
THERMAL MANAGEMENT OF DATA CENTERS UNDER STEADY AND TRANSIENT CONDITIONS

Data centers are of crucial importance today in the storage and retrieval of large amounts of data.Most organizations and firms, ranging from banks and online retailers to government departments and internet companies, use data centers to store information that can be recovered efficiently and rapidly.As the deployment of data centers has increased, along with their capacity for data storage, the demands on thermal management have also increased.It is necessary to remove the energy dissipated by the electronic circuitry since the temperature of the components must not rise beyond acceptable levels that could damage them or affect their performance.The energy required for the cooling of data centers is generally quite substantial, with typical data centers requiring hundreds for megawatts for heat removal.Therefore, it is important to develop efficient thermal management systems to reduce power consumption and minimize the environmental impact.Extensive work has been done in recent years on the cooling of data centers.Though most studies have focused on steady state processes, time-dependent behavior is also of considerable interest because the thermal load is generally not constant but varies with time.Fluctuations in the load may arise or the load may peak at certain times.Efficient time-dependent distribution of load between various data centers, if several data centers are available to a given organization, or between different servers of a given data center may be used to improve the efficiency of the heat removal process and thus reduce the overall power consumption.Since environmental conditions vary from location to location, appropriate data centers may be selected, from those available, in order to use the most favorable location to reduce the overall cooling load.These aspects demand a study of both the steady and time-dependent behavior of data centers.The results obtained may be used to optimize the thermal management system and reduce the overall energy consumption.This paper reviews some of the relevant work done on data center cooling, with particular attention given to load distribution and to the data center location that determines the local environmental conditions.These results could form the basis for optimizing the cooling strategy for a system of data centers.
MPTCP+: Enhancing Adaptive HTTP Video Streaming over Multipath

This paper presents a systematic study on adaptive streaming over MPTCP. We start from realworld experiments with Dynamic Adaptive Streaming over HTTP (DASH) and analysis on its performance over MPTCP. We show that DASH can greatly benefit from the improved aggregated throughput by MPTCP; yet the inter-path throughput difference and the intra-path throughput fluctuation have noticeable (negative) impact, too. Without a proper design of path selection and adaptation in MPTCP, they can easily confuse the adaptation logic of DASH, resulting in low bitrates or frequent rebuffering even if high-bandwidth paths are available. We present MPTCP+, an extended multipath TCP solution to offer high quality and smooth playback for adaptive HTTP streaming. MPTCP+ incorporates a path use decision algorithm that smartly disables/enables a path to minimize the inter-path difference, and a novel congestion control algorithm that smooths congestion window evolution with multiple paths. We have implemented MPTCP+ in the MPTCP Linux kernel, with minimum change on the server-side MPTCP module only. It is fully compatible with the existing MPTCP clients and requires no change on the upper-layer protocols, too. Our experiments suggest that MPTCP+ increases the quality of experience (QoE) of DASH by up to 50%.
Review on feature-based method performance in text steganography

The implementation of steganography in text domain is one the crutial issue that can hide an essential message to avoid the intruder. It is caused every personal information mostly in medium of text, and the steganography itself is expectedly as the solution to protect the information that is able to hide the hidden message that is unrecognized by human or machine vision. This paper concerns about one of the categories in steganography on medium of text called text steganography that specifically focus on feature-based method. This paper reviews some of previous research effort in last decade to discover the performance of technique in the development the feature-based on text steganography method. Then, ths paper also concern to discover some related performance that influences the technique and several issues in the development the feature-based on text steganography method.
Recycling a genre for news automation

Abstract The amount of available digital data is increasing at a tremendous rate. These data, however, are of limited use unless converted into a user-friendly form. We took on this task and built a natural language generation (NLG) driven system that generates journalistic news stories about elections without human intervention. In this paper, after presenting an overview of state-of-the-art technologies in NLG, we explain systematically how we identified and then recontextualized the determinant aspects of the genre of an online news story in the algorithm of our NLG software. In the discussion, we introduce the key results of a user test we carried out and some improvements that these results suggest. Then, after relating the news items that our NLG system generates to general aspects of genres and their evolution, we conclude by questioning the idea that journalistic NLG systems should mimic journalism written by humans. Instead, we suggest that developmental work in the field of news automation should aim to create a new genre based on the inherent strengths of NLG. Finally, we present a few suggestions as to what this genre could include.
Explorations into Deep Learning Text Architectures for Dense Image Captioning

Image captioning is the process of generating a textual description that best fits the image scene.It is one of the most important tasks in computer vision and natural language processing and has the potential to improve many applications in robotics, assistive technologies, storytelling, medical imaging and more.This paper aims to analyse different encoder-decoder architectures for dense image caption generation while focusing on the text generation component.Already trained models for image feature generation are utilized with transfer learning.These features are used for describing the regions using three different models for text generation.We propose three deep learning architectures for generating one-sentence captions of Regions of Interest (RoIs).The proposed architectures reflect several ways of integrating features from images and text.The proposed models were evaluated and compared with several metrics for natural language generation.The experimental results demonstrate that injecting image features into a decoder RNN while generating a caption word by word is the best performing architecture among the architectures explored in this paper.
Metareasoning Structures, Problems, and Modes for Multiagent Systems: A Survey

Autonomous multiagent systems can be used in different domains such as agriculture, search and rescue, and fire protection because they can accomplish large missions more quickly and robustly by dividing them into separate tasks. Using multiple agents introduces additional complexity, which makes autonomous reasoning and decision making more challenging, however. Because agents such as ground robots, unmanned air vehicles, and autonomous underwater vehicles may have limited computational resources, they may need computationally efficient yet powerful reasoning algorithms (decision-making processes that perform deliberation and means-end reasoning). Metareasoning, which is reasoning about these reasoning algorithms, offers a way to tackle these challenges by monitoring and controlling reasoning algorithms to improve agent and system performance. Although metareasoning approaches for individual computational agents have been studied, no survey of metareasoning in multiagent systems (MAS) has yet appeared. This survey fills the existing gap by discussing the multiagent metareasoning approaches that have been studied in the literature. It identifies metareasoning structures, applications of metareasoning to reasoning problems, and the modes (techniques) used to control reasoning processes. This survey contributes to the study of MAS by providing a framework for discussing multiagent metareasoning, highlighting successful approaches, and indicating areas where future work may be fruitful.
A Framework for Enhancing Mobile Learner-Determined Language Learning in Authentic Situational Contexts

Mobile technology melds the mobile learner's authentic real and virtual worlds, enabling increasingly untethered personalized, learner-determined language learning opportunities. This article introduces an evidence-based framework founded upon cumulative findings from a number of the authors' recent and ongoing research projects. This framework provides guidance for designing mobile language learning activities within the learner's evolving personal, authentic situational learning context. The framework consists of three learner dimensions and four external contextual affordances that synergistically define the dynamics of this learning context. The merger of these dimensions and external contextual elements yields three interdependent learning concepts—personalization, adaptation, and relevancy—which enhance the mobile learner's motivation and self-determination. Application of these concepts enables instructors and learners to design mobile language activities that consider the interplay of numerous factors impacting language learning in context.
Using of convolutional neural networks for steganalysis of digital images

The article substantiates the relevance of steganalysis, as a determination of the presence of a hidden channel in telecommunication systems, whose nodes exchange digital images. The article deals with the application of convolutional neural networks to solve this problem. It is assumed that the probability of correct image classification using a well-trained convolutional neural network will be comparable or even better than characteristics of statistical algorithms or the RM model. We introduce principles of construction and capabilities of convolutional neural networks in the framework of their applicability to solving the problem of steganalysis. To improve the efficiency and effectiveness of the stegocontainer recognition process, a version of the image classification model for a convolutional neural network is proposed. It is based on combination of several convolutional and fully connected layers. We have developed software for this model version with the ability to train a neural network and evaluate the quality of classification. The analysis of existing software products designed for the task of determining the fact of using steganography in digital images is carried out. The advantage of classifiers based on neural networks in comparison with statistical ones is proved. Using the developed software, an experimental study of classification model on sets of digital images contained in open sources has been carried out. The article presents the results of neural network training, as well as an analysis of the strengths and weaknesses of the selected model.
Implementasi Metode Convolutional Neural Network Menggunakan Arsitektur LeNet-5 untuk Pengenalan Doodle

Recognition of objects to date has been widely applied in various fields, for example in handwritten recognition. This research utilizes the ability of CNN to use LeNet-5 architecture for the introduction of doodle types with 5 object images, namely clothes, pants, chairs, butterflies and bicycles. Each doodle object consists of 30 images with a total dataset of 150 images. The test results show that the first, second and fourth scenarios of bicycle objects are more recognized with an accuracy value of 93% - 98%, recall 86% - 93% and precision 81% - 93%, clothes objects are more recognized in the third scenario with an accuracy value of 94%, 86% recall, and 83% precision.
Cholesterol Detection Based on Eyelid Recognition Using Convolutional Neural Network Method

Lack of public awareness of health will cause serious problems. A small example, people now tend to always consume fatty foods without thinking about the risk of cholesterol levels in the body.  Information on the level of cholesterol suffered by humans can be seen on the human eyelids. The eyelids, one part of the eye, can be known as a person's cholesterol level by observing the eyelids' shape and condition, but many people do not know about this. This application is an application made to detect cholesterol based on the shape of the eyelids. This can determine whether a person is exposed to cholesterol or not, using the Convolutional Neural Network (CNN) method in the classification process. This study provides an output in the form of early detection of cholesterol and prevention so that users can minimize the possibility of illness that will be suffered. This research was conducted to detect cholesterol one eyelid based on digital images. For detecting a cholesterol level, this system got 95.83% of accuracy.
Training of Future Musical Art Teachers: An Ethnocultural Approach

The article reveals theoretical and methodological foundations of implementing the ethnocultural approach in the process of future musical art teachers' professional training.The results of the study have shown that implementing the ethnocultural approach in art education is one of the requirements of the new education paradigm.It is proved that in the process of future musical art teachers, professional training should be taken into account the developmental capabilities of folk music and peculiarities of ethno-musical education development.The article highlights the principles, which regulate the process of future musical art teachers' professional training: ethnocultural basis of students' professional skills formation; using national samples of musical art as learning repertoire; involving students in various types of musical and aesthetic activities, especially in the study of various works of national musical art; personality-centered organization of the educational process; inclusion of ethnocultural component in courses of historical-theoretical and methodological cycles; effective use of various forms of ethno-musical activity.
Multi-Frame Blind Restoration for Image of Space Target With FRC and Branch-Attention

The random noise and anisotropic motion of atmospheric turbulence can cause different degradation patterns, which make images of space targets observed from ground-based stations severely disturbed. In recent years, benefit from the development of convolutional neural networks (CNNs), a large number of effective end-to-end methods were proposed to restore images. However, a single-frame method whose input is just a single image can hardly achieve a further improvement for the restoration image due to the diversified degradation patterns of space-target images. In this paper, we proposed a multi-branch network with a multi-frame input to restore space-target images. The multi-frame input contains space-target images which own different degradation patterns at different moments. In this way, we can fully use the complementary information between input frames. And in this network, two effective technologies are introduced: one is the full resolution convolution module which extracts features by using convolutional layers with different dilation rates to keep feature information complete; the other is the branch-attention module which is used to pass effective information between different branches of the network. Furthermore, we demonstrated the effectiveness of our method by comparing it with those state-of-the-art methods.
A Novel Model of Mimic Defense Based on Minimal L-Order Error Probability

Mimic defense is an active defense theory, which aims to fundamentally change the “easy to attack and difficult to defend” situation of network security. In this paper, we propose an evaluation method based on the probability of being attack successfully, and improve the evaluation scheme of historical confidence. We combine the two evaluation schemes with the TOPSIS (technique for order performance by similarity to ideal solution) algorithm, and finally form a complete heterogeneous variant dynamic scheduling model. Different from traditional multi-mode voting algorithms, the effect of the heterogeneous degree in voting is considered, and we use Bayesian estimation to obtain the optimal result in the probabilistic sense. Finally, simulation results show that the proposed algorithm can effectively enhance the dynamic and security of the mimic defense model, and give full play to the characteristics of mimic defense.
Experimental Inducibility of Supernumerary Phantom Limbs: A Series of Virtual Reality Experiments

Supernumerary phantom limb (SPL) is a rare neuropsychiatric syndrome, under which patients perceive more limbs than they anatomically possess (e.g. two instead of only one right arm). Remarkably, the concurrent limb percepts can thereby differ in respect to their primarily-experienced modality, for instance, that one kinesthetic and one moving limb is experienced. Although SPLs are well documented after various neuropsychiatric conditions, their neurocognitive pathomechanisms still remain elusive. Therefore, an experimental paradigm by which such aberrant body misperceptions could be transiently induced and systematically investigated in healthy participants would be helpful. Inspired by the virtual hand illusion, we developed a virtual supernumerary limb illusion (SLI) version, in which two embodiable virtual hands are presented to the participant via a head-mounted display. One virtual hand is thereby presented medially and the other laterally aside the participant's hidden real hand. Using this general setup, we examined the inducibility of a SLI in three consecutive experiments: Experiment 1 explored by which induction type (visuotactile congruency, visuothermal congruency and visuomotor congruency) SLI experiences can be induced most robustly, Experiment 2 whether SLIs can be induced by a combination of these induction types, and Experiment 3 how visuoproprioceptive congruency influences the inducibility of a SLI. Sense of ownership towards the virtual hands was systematically assessed by means of experience sampling and by an implicit electrodermal embodiment measure. Results reveal a robust effect of stronger sense of ownership towards the medially- than laterally-presented virtual hand, while only a subgroup of participants (on average, across experiments: ~25%) reported concomitant sense of ownership towards both virtual hands (i.e. a full-blown SLI experience). The highest SLI responder rate (~63%) was observed for the combined application of visuotactile and visuothermal congruency, whereas a combined application of visuothermal and visuoproprioceptive congruency induced the lowest SLI responder rate (~4%). In conclusion, our study demonstrates that although under specific experimental conditions, a majority of participants can be made to experience a SLI, under most conditions sense of ownership is only experienced towards one virtual hand. This indicates that multiple type-identical limb percepts are avoided to be co-instantiated by our brain, wherever possible.
The Positive Impact of Teams Games Tournament Learning Model Assisted with Video Media on Students’ Mathematics Learning Outcomes

The low of Mathematics learning outcomes of the fifth-grade students are the main problem in this study. It is because the teacher has not implemented a learning model that can enable students in the learning process, the lack of use of media in the learning process provided by the teacher, students study Mathematics rarely, and the lack of concentration in receiving Mathematics. This study aimed to determine the effect of the video media assisted Teams Games Tournament (TGT) learning model on the Mathematics learning outcomes of the fifth-grade students. This research was a quasi-experimental with using non-equivalent post-test only control group design. The population of this research was 137 students. This study applied an intact group technique so the research sample of fifth grade students was 52 students. The students’ Mathematics learning outcomes were collected by using an objective test method through 24 multiple choice questions. The research hypothesis was tested using t-test. Based on the results of data analysis, the researcher obtained tcount = 14,354 &gt; ttable = 2,021 (at a significance level 5%). The result of hypothesis test indicated that there was a significant difference on students’ Mathematics learning outcomes. Thus, it can be concluded that the video media assisted Teams Games Tournament (TGT) significantly influences the mathematics learning outcomes.
Benchmarking Natural Language Inference and Semantic Textual Similarity for Portuguese

Two sentences can be related in many different ways. Distinct tasks in natural language processing aim to identify different semantic relations between sentences. We developed several models for natural language inference and semantic textual similarity for the Portuguese language. We took advantage of pre-trained models (BERT); additionally, we studied the roles of lexical features. We tested our models in several datasets—ASSIN, SICK-BR and ASSIN2—and the best results were usually achieved with ptBERT-Large, trained in a Brazilian corpus and tuned in the latter datasets. Besides obtaining state-of-the-art results, this is, to the best of our knowledge, the most all-inclusive study about natural language inference and semantic textual similarity for the Portuguese language.
Distributed logistic regression with differential privacy

In this paper, we focus on the privacy protection of sensitive data and develop a distributed logistic regression that satisfies differential privacy. Distributed differential privacy is achieved by perturbing the distributed algorithm output. Further, to prevent privacy leakage occurring during the computer interaction process, we propose a distributed logistic variable perturbation algorithm based on an alternating direction method of multipliers (ADMM) algorithm. Further, the theoretical bounds of the algorithms are provided. Experiments show that the proposed algorithms can effectively analyze distributed storage data and protect their privacy.
Influence of Organisational Culture on Employee Commitment: Evidence from Environmental Protection Agency in Ghana.

This study assesses the influence of culture on employee commitment at Environmental Protection Agency in Accra. The study adopted quantitative method using descriptive survey. One hundred and twenty-six (126) employees were selected using convenience sampling technique. The primary data was gathered using questionnaire and validated through Cronbach Alpha. In analysing the data gathered, descriptive as well as inferential statistics were employed. It was observed from the outcome that, the four culture dimensions used by the researchers all existed at the organisation. Further, training and development; reward and recognition; team work and organisational communication all showed significant relationship with employee commitment at the organisation. Thus, there was a positive effect between cultural practices in the organisation and employee commitment. Management must there ensure the fostering of strong culture to help retained committed staff which eventually reduce high turnover.&#x0D; Citation: Acquah, H. E. A., Sarkodie, N. A., Baffoe, E., Lennox, A., Amon, D. B. N. and Nunoo, J. Influence of Organisational Culture on Employee Commitment: Evidence from Environmental Protection Agency in Ghana, 2020; 5(3): 45-57.&#x0D; Received: May 28, 2020Accepted: September 30, 2020