Reviewerly/exHarmony huggingface dataset:
A new task scheduling strategy based on improved ant colony algorithm in IaaS layer

In the IaaS layer of cloud computing, the cloud platform is required to implement efficient scheduling for large-scale tasks to meet the needs of both users and cloud service providers. In this paper, a multi-objective task scheduling model is established to optimize task completion time, cost and load balancing degree. Furthermore, an improved ant colony algorithm is proposed to solve this problem. The proposed algorithm firstly uses genetic operations to initialize the pheromone, and establishes an ant search model that can speed up convergence, while the pseudo-random proportional rule is used to select the next node of the path. Then, a weighted constraint function of time and cost is established to evaluate the quality of the scheduling's solution, and the Analytic Hierarchy Process (AHP) decision is used to adjust the weighting factors. Moreover, a load balancing method is introduced in the path heuristic function of ant colony algorithm. Compared with other advanced algorithms, the proposed algorithm can significantly reduce task completion time and cost, while accelerating the convergence of the optimal solution and improving load balancing degree greatly.
Blind text images deblurring based on a generative adversarial network

Recently, text images deblurring has achieved advanced development. Unlike previous methods based on hand-crafted priors or assume specific kernel, the authors recognise the text deblurring problem as a semantic generation task, which can be achieved by a generative adversarial network. The structure is an essential property of text images; thus, they propose a structural loss function and a detailed loss function to regularise the recovery of text images. Furthermore, they learn from the coarse-to-fine strategy and present a multi-scale generator, which is utilised for sharpening the generated text images. The model has a robust capability of generating realistic latent images with photo-quality effect. Extensive experiments on the synthetic and real-world blurry images have shown that the proposed network is comparable to the state-of-the-art methods.
Replication Research: Campus Emergency Notification Systems

This paper is an exact replication of the Han, Ada, Sharman and Rao (2015) article on Campus Emergency Notification Systems (ENS).In their study, Information Quality Trust and Subjective Norm were the factors which most commonly induced recipients of ENS notifications to comply with the information and instructions in the notification; Perceived Safety Threat, Perceived Financial Threat, and Past Experience also played a role for some types of ENS notifications.We found essentially the same results, although there were some differences.In our study, Information Quality Trust was again the most important determinant.Subjective norms played a role, but were not a fundamentally important factor as they were in the Han, et al. study; we speculate this may be due to different cultures between our participants and theirs.The other three factors were important for some types of ENS notifications but not others.Our research also suggests that future research should consider past experience with ENS notifications, such as the frequency, location relevance, and the timeliness of past ENS notifications received.
Saliency detection using Multi-layer graph ranking and combined neural networks

In this paper, a new algorithm based on a combined neural network is proposed to improve salient object detection in the complex images. It consists of two main steps. The first step, an objective function which is optimized on a multi-layer graph structure is constructed to diffuse saliency from borders to salient objects, aiming to roughly estimate the location and extent salient objects of an image, meanwhile, color attribute is adopted to rapidly find a set of object-related regions in the image. The second step, establish a combined neural network with Region Net and Local-Global Net. Region Net is adopted to efficiently generate the salient map with the sharp object boundary. Then Local-Global Net based on multi-scale spatial context is proposed to provide strongly reliable multi-scale contextual information, and thus achieves an optimized performance. Experimental results and comparison analysis demonstrate that the proposed algorithm is more effective and superior than most low-level oriented prior methods in terms of precision recall curves, F-measure and mean absolute errors.
Developing Application Android Based For Japanese Language Learning

The purpose of this study is to describe the use of Japanese language learning applications and use them in everyday life.The use of technology is very fast, encouraging every individual to use technology in everyday life, including the use of Japanese.Learning Japanese directly has experienced some difficulties, especially in understanding each word and phrase that is used; it needs help to understand Japanese language learning with the help of media applications.The method used in this study is a descriptive approach to analyze the variables used in application development.The application you normally learn requires compaction of materials to make it easier for application users.Using the application to learn Japanese is expected to be able to produce users who can learn to use Japanese in their daily lives.With the presence of the Japanese language learning application based on the Android operating system, it is expected to produce users who learn Japanese wherever and whenever through the application.
Design Engagements at the Margins of the Global South: De-Centering the “Expert” Within Me

Conceiving everyone as a potential designer and believing in local peoples’ informed understandings and agentic capabilities in addressing local problems, my design research journey was committed to opening up avenues for sustained transformations in the underserved spaces of rural eastern India. In my design engagements, we (community members and I) contributed in three design projects, namely design for community development (DfCD), information design for development (IDfD), and design for grassroots innovation (DfGD). In my design journey, I sincerely questioned/challenged my presumptions and design approaches as well as reflexively reoriented myself to make the design processes culturally meaningful and contextually appropriate. Being constantly cognizant about minute traces of superiority (and sense of indispensability) within me, being willing to challenge, modify, and rediscover myself continually, as well as privileging and situating local and indigenous perspectives and knowledge at the center of design processes, characterize some of the key learning of my design journey.
A Sorting Method: BWMSort II in Interval Type-2 Fuzzy Environment

In this paper, a new multiple criteria sorting method, BWMSort □is applied under fuzzy environment where interval type-2 fuzzy sets (IT2FSs) are used to deal with unclear class assignments. Motivated by the idea of AHPSort □ and Best-Worst method (BWM), we extend the BWM to a sorting method for dividing alternatives into predefined classes and reducing the times of comparison by the limiting profiles and representative points. Furthermore, to better simulate uncertainties and fuzzy information, softening transitions between classes are used to accommodate interval type-2 fuzzy environment. Finally, an example is provided to illustrate effectiveness of the proposed method.
A Piecewise Nonlinear Enhancement Method of Side Scan Sonar Images

Side scan sonar(SSS) imaging is an essential technology for the prospection, monitoring and location of underwater vehicles in marine environment. However, original SSS images are usually poor contrast and contain large amounts of noise, so they can’t be directly used and need to be enhanced. To address these problems, a novel spatial domain enhancement method has been proposed in this paper. We firstly suppress speckle noise with neighborhood smoothing. Through the statistical features of the SSS image histogram, the low-gray region, the middle-gray region and the high-gray region of the SSS image are determined. Then, the three regions are enhanced through a piecewise function according to the imaging characteristics. Our method is easy to be implemented and the parameters can be adjusted automatically. The experiments show that our method has a greater improvement in Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) than existing spatial domain methods, and performs faster processing speed compared to other frequency domain methods. Moreover, the method enhances the gray-value contrast between any adjacent regions and so it is favorable for the subsequent processing.
Virtual reality meets IoT Through Telepresence

Telepresence has become a booming technology because it is a combination of Internet of Things (IOT) and virtual Reality (VR). It is a robotic remote control where humans can sense the current location. where the VR will give the vision and IOT will give the sense of touch and control. Before the telepresence there is only possibility to imagine a virtual world but it is impossible to sense the virtual world, it became possible by using ultrasonic sensor in Telepresence. This paper mainly discuss how VR can be used various sectors. For example, it can be used in agriculture and for the development of education. it can create smart virtual environment like virtual Teleprence robot in smart agriculture and also virtual inclusion via telepresence robot in class Rooms and virtual reality can experienced by user through (VR) Headsets. Telepresence, as mentioned can also be used in diff fields like medical Training, mining maintenance and distance Education.
Librarian’s Information Literacy Skills to Fulfilling Library Users' Needs A Public Library in Bogor, Indonesia

Background of the study: literacy skills of librarianPurpose: This study aims to identify the information literacy skills of librarians in meeting the information needs a user at the public library in the city of Bogor.Method: This study used a qualitative approach with the case study method. Data were collected through observation and interview. The informants of six people selected using purposive sampling, with the criteria set librarian of Library Science education. Information literacy models used in this study are a model Empowering Eight, consisting of identification, exploration, selection, organization, creation, presentation, assessment and application information..Findings: The results showed that the information literacy librarian at the public library in the city of Bogor in meeting the information needs user already applied include a component on the identification, exploration, presentation, assessment, and application.Conclusion: There are some capabilities that cannot be applied to the maximum, including component selection, organization and creation of information.
A STUDY ON IDENTIFYING THE PRE-ANALYTICAL PHASE ERRORS LEADING TO BIOCHEMISTRY AND HEMATOLOGY SAMPLE REJECTION BY USING FMEA &amp; PARETOS PRINCIPLE.

16Sep 2019 A STUDY ON IDENTIFYING THE PRE-ANALYTICAL PHASE ERRORS LEADING TO BIOCHEMISTRY AND HEMATOLOGY SAMPLE REJECTION BY USING FMEA & PARETOS PRINCIPLE. Golla Rachana and Manjunatha R.
University of South Wales national cyber security academy – creating cyber graduates who can ‘hit the ground running’: an innovative project based approach

Higher Education institutions understand that there is a need to embed professional skills within degree programmes. However, evidence shows that graduates, whilst having developed good theoretical knowledge, lack experience and confidence when entering the workplace. Employers view the importance of degree-related skills as only a part of the skills needed to be work ready. This opinion piece discusses an innovative approach to curriculum design in the field of cybersecurity, which uses a project-based approach with limited formal teaching to develop students’ skills, and engages them with industry from the very outset of their course. Early indications show that the course has many successes to celebrate, but that it is not without its challenges.
TOURISTS GEOVISUALIZATION ANALYSIS UTILIZING INSTAGRAM DATA IN CENTRAL JAVA PROVINCE AND SPECIAL REGION OF YOGYAKARTA

Abstract. The growth of Indonesia‟s national economy in the sector of tourism has been improving constantly. Based on BPS data year 2015, tourism has contributed to PDB with the amount of 4,25%. Tourists is an important aspect because they play a role in the economic change in tourism. Along with social media development, tourists‟ distribution can be monitored using big data in social media. Instagram is one of the social media which is often used by tourists to share photos or videos while traveling. Issues that appeared when using big data Instagram to produce spatial data are size, variation, and a big time span. This research is aimed to extract big data Instagram to produce basic tourists’ spatial data and analyze tourist attractions using geovisualization hexagonal tessellation in West Java Province and Special Region of Yogyakarta. Generally, the methods used are divided into three which are data mining, graphic visual analysis and geovisualization classification hexagonal tessellation. The Instagram data extraction process uses a web-based software called Netlytic, while pre-processing data to produce tourists’ spatial data uses QGIS software. Hexagonal tessellation is tested by graphic visual to ensure the most effective measurement in the scale of 1 : 1.250.000. The most effective hexagonal tessellation measurement is used to classify tourist attractions. Based on the results, it shows that the most effective measurement of hexagonal tessellation is 2 Km. The most popular tourist attraction is Malioboro Yogyakarta based on hexagonal tessellation geovisualization analysis.
Contextualized Representations for Low-resource Utterance Tagging

Utterance-level analysis of the speaker's intentions and emotions is a core task in conversational understanding.Depending on the end objective of the conversational understanding task, different categorical dialog-act or affect labels are expertly designed to cover specific aspects of the speakers' intentions or emotions respectively.Accurately annotating with these labels requires a high level of human expertise, and thus applying this process to a large conversation corpus or new domains is prohibitively expensive.The resulting paucity of data limits the use of sophisticated neural models.In this paper, we tackle these limitations by performing unsupervised training of utterance representations from a large corpus of spontaneous dialogue data.Models initialized with these representations achieve competitive performance on utterance-level dialogueact recognition and emotion classification, especially in low-resource settings encountered when analyzing conversations in new domains.
Hjælp! Jeg har mistede min yndlings rød taske. Et studie af grammatikafvigelser

Grammar anomalies are frequent in texts produced by second language learners. When describing these anomalies, two main issues arise: What is anomalous use of grammar? And how are grammar anomalies distinct from orthographic and lexical anomalies? We review earlier error-definitions and suggest defining grammar anomalies according to an explicit norm instead of L1 usage. We propose a broader definition of grammar than in previous studies, based on Boye &amp; Harder (2012). The distinction between grammar, lexicon and orthography is illustrated with data from 28 adult L1 English learners of L2 Danish. In the corpus, 55.9 % of the anomalies were related to grammar. Finally, we discuss how definitions and procedures can be used in future studies of naturally occurring grammar anomalies.
Analysis of swimming pool solar heating using the utilizability method

A study was carried out to predict the long-term energy performance of swimming pool solar heating systems for four Brazilian cities using the utilizability method. The chosen cities were Brasília, Fortaleza, Porto Alegre and São Luís. The pool area was kept constant and both glazed and unglazed solar thermal collectors were considered for the different locations, with a slope equal to the latitude of each city. The meteorological data used in the analysis such as wind speed, ambient temperature, radiation on a horizontal surface and clearness index were specific for each city and obtained from Meteonorm. The geometric parameters of the collectors were constant for all simulations and the results were carried out with a specially developed computational code. In addition, statistical analyses of the data using ANOVA (Analysis of Variance) and Tukey’s test were made.
SAR Image Urban Scene Classification based on an Optimized Conditional Generative Adversarial Network

Classification of urban scenes in SAR images has been challenging in the complex and various behaviors of urban areas in SAR images. Generative Adversarial networks (GANs), which can learn distribution characteristics of image scenes in an alternative training style, have attracted attention. However, these networks all suffer from difficulties in network training and stability. To overcome this issue and extract proper features, this paper introduces two effective solutions. Firstly, employing the residual structure and an extra classifier into the traditional conditional adversarial network to achieve scenes classification. Then gradient penalty is used to optimize the loss convergence during training stage. Last, we select GF-3 and Sentinel-1 SAR images to test the network. The experiment results show the usefulness of our proposed optimization.
Algorithm of web page similarity comparison based on visual block

Phishing often deceives users due to the relative similarity to the true pages on a layout and leads to considerable losses for the society. Consequently, detecting phishing sites has been an urgent activity. By researching phishing web pages using web page screenshots, we discover that this kind of web pages use numerous web page screenshots to achieve the close similarity to the true page and avoid the text and structure similarity detection. This study introduces a new similarity matching algorithm based on visual blocks. First, the RenderLayer tree of the web page is obtained to extract the visual block. Second, an algorithm that will settle the jumbled visual blocks, including the deletion of the small visual blocks and the emergence of the overlapping visual blocks, is designed. Finally, the similarity between the two web pages is assessed. The proposed algorithm sets different thresholds to achieve the optimal missing and false alarm rates.
An Efficient Privacy-Preserving Protocol for Computing kth Minimum Value in P2P Networks

Statistics such as [Formula: see text]th minimum value play a crucial role in our data-driven society, for example by informing decision-making. In this paper, we propose an efficient privacy-preserving protocol that allows a group of users who do not trust each other, for example in a peer-to-peer (P2P) network, to jointly calculate the [Formula: see text]th minimum value. Specifically, in our proposed protocol each user’s data is converted to a binary bit string following a certain rule. Then, the bits at the same position are aggregated from the leftmost to the rightmost. As far as we know, this is the first published scheme to obtain [Formula: see text]th minimum value in a P2P network without affecting users’ privacy. We also remark that the proposed protocol can be easily generalized to compute other statistics, such as maximum value, minimum value, and median value, while achieving high efficiency in a privacy-preserving P2P network. We then demonstrate that the proposed protocol achieves forward security and is resilient to a range of external and internal attacks.
Performance Assessment of Convolutional Neural Networks for Semantic Image Segmentation

Convolutional neural networks are applied successfully for image classification and object detection.Recently, they have been adopted to semantic segmentation tasks and several new network architectures have been proposed.With respect to automotive applications, the Cityscapes dataset is often used as a benchmark.It is one of the biggest datasets in this field and consists of a training, a validation, and a test set.While training and validation allow the optimisation of these nets, the test dataset can be used to evaluate their performance.Our investigations have shown that while these networks perform well for images of the Cityscapes dataset, their segmentation quality significantly drops when applied to new data.It seems that they have limited generalisation abilities.In order to find out whether the image content itself or other image properties cause this effect, we have carried out systematic investigations with modified Cityscapes data.We have found that camera-dependent image properties like brightness, contrast, or saturation can significantly influence the segmentation quality.This papers presents the results of these tests including eight state-of-the-art CNNs.It can be concluded that the out-of-the-box usage of CNNs in real-world environments is not recommended.
On a Key-Based Secured Audio Data-Hiding Scheme Robust to Volumetric Attack with Entropy-Based Embedding

In the data-hiding field, it is mandatory that proposed schemes are key-secured as required by the Kerckhoff’s principle. Moreover, perceptual transparency must be guaranteed. On the other hand, volumetric attack is of special interest in audio data-hiding systems. This study proposes a data-hiding scheme for audio signals, which is both key-based secured and highly perceptually transparent and, thus, robust to the volumetric attack. A modification to a state-of-the-art data-hiding algorithm is proposed to achieve key-based security. Embedding is carried out in the integer discrete cosine transform (DCT) domain; selected samples for embedding are determined by the entropy of the Integer DCT coefficients. Of the two key-based improvements proposed, the multiplicative strategy gives better results, guaranteeing the worst bit error rate when an incorrect key is used. Additionally, the perceptual transparency of the proposed scheme is higher, compared to the state-of-the-art schemes using similar embedding strategies.
Empirical Means on Pseudo-Orthogonal Groups

The present article studies the problem of computing empirical means on pseudo-orthogonal groups. To design numerical algorithms to compute empirical means, the pseudo-orthogonal group is endowed with a pseudo-Riemannian metric that affords the computation of the exponential map in closed forms. The distance between two pseudo-orthogonal matrices, which is an essential ingredient, is computed by both the Frobenius norm and the geodesic distance. The empirical-mean computation problem is solved via a pseudo-Riemannian-gradient-stepping algorithm. Several numerical tests are conducted to illustrate the numerical behavior of the devised algorithm.
Deployment of an IoT Solution for Early Behavior Change Detection

Today, numerous factors are causing a demographic change in many countries in the world. This change is producing a nearly balanced society share between the young and aging population. The noticeable increasing aging population is causing different economical, logistical and societal problems. In fact, aging is associated with chronic diseases in addition to physical, psychological, cognitive and societal changes. These changes are considered as indicators of aging peoples’ frailty. It is therefore important to early detected these changes to prevent isolation, sedentary lifestyle, and even diseases in order to delay the frailty period. This paper presents an experiment deployment of an Internet of Thing solution for the continuous monitoring and detection of elderly people’s behavior changes. The objective is to help geriatricians detect sedentary lifestyle and health-related problems at an early stage.