Reviewerly/exHarmony huggingface dataset:
Edge computing architectures

By considering an edge computing platform as one of the main computing tiers in a fully comprehensive cloudisation model, this chapter first discusses the distinguished features of edge computing compared to other platforms from the perspectives of service, operation and control. In addition, a local view of the intrinsic architecture of an edge computing host is analysed. Subsequently, a standard reference architecture of the edge computing platform is presented, which defines the internal and external interfaces among components at the computing host and system levels. The reference architecture is matched to the network function virtualisation management and orchestration (NFV MANO) model for consideration as a virtualised network function (VNF). In particular, the position and roles of VNF edge computing in the standard mobile network reference model are demonstrated. Finally, the open issues and challenges are presented by way of conclusion.
Visual Spatial Analytics and Trusted Information for Effective Decision Making

Information, not just data, is key to today's global challenges. To solve these challenges requires not only advancing geospatial and big data analytics but requires new analysis and decision-making environments that enable reliable decisions from trustable, understandable information that go beyond current approaches to machine learning and artificial intelligence. These environments are successful when they effectively couple human decision making with advanced, guided spatial analytics in human-computer collaborative discourse and decision making (HCCD). Our HCCD approach builds upon visual analytics, natural scale templates, traceable information, human-guided analytics, and explainable and interactive machine learning, focusing on empowering the decisionmaker through interactive visual spatial analytic environments where non-digital human expertise and experience can be combined with state-of-the-art and transparent analytical techniques. When we combine this approach with real-world application-driven research, not only does the pace of scientific innovation accelerate, but impactful change occurs. I'll describe how we have applied these techniques to challenges in sustainability, security, resiliency, public safety, and disaster management.
Correcting Block Attack on Reduced NEEVA

NEEVA is a hash function algorithm which uses a sponge function as its structure. It is designed for sensor nodes in wireless sensor network (WSN). In this paper, we apply correcting block attack on Reduced NEEVA which is simplification of NEEVA. The result shows that many collision pairs were found in Reduced NEEVA. Based on the analysis, we indicate that the unbalanced Feistel structure give impact on the collision to occur
Performance Comparison of Scanner and Camera-Acquired Data for Bangla Offline Handwriting Recognition

This paper presents a comparison of offline handwriting recognition performance between two different image acquisition devices: scanner and cell-phone camera. Whereas a flat-bed scanner offers higher quality distortion free imaging, a cell-phone camera trumps on the convenience and ease of use. The aim of this research is to quantify how the extra quality obtained from a scanner impacts the offline handwriting recognition. This was evaluated with two classification framework: a segmentation-free offline Bangla handwriting transcription with sequential detection of characters/diacritics [1] and a Bangla handwritten digit recognizer with an SVM classifier [2]. The Boise State Bangla Handwriting dataset is used for the experiments [3]. The highest recognition rate came when both training and testing on scanned images. The network trained with scanned images also exceeded the one trained with camera-acquired images in recognition of the camera acquired images, despite the mismatch in source.
Performance and Cost Analysis of Web Application in Elastic Cloud Environment

In the era of cloud computing, the cost analysis of web applications on the cloud is critical to the quality and profit of cloud services.This paper designs an evaluation model for the performance and cost of web applications that provide a single class of services.The model was developed based on the analytical queuing model, and was provided with multiple evaluation metrics.Next, an experiment was carried out on Amazon AWS, a mature commercial cloud platform.The experimental results agree well with the simulation data of the proposed model in both response time and total cost.The research results provide new insights into the development of e-commerce web applications in the environment of cloud computing.
Knowledge Representation and Answer Evaluation System using Language Processing Algorithm

Automatic Evaluation of answer scripts is an upcoming idea in the field of education. This can be exercised in schools/colleges to enhance the quality of correction and also to reduce human errors. Education plays an important role in the development of a sustainable smart city. In our country, examinations are ultimately used to assess the performance of students. Evaluation of these sheets manually is a very long, tedious and laborious process which in turn becomes less efficient. The main motive of this application is to automate the entire evaluation process and let the students know the areas where they actually lack. The project is basically an Android-based application into which all the answer scripts of students along with its key will be uploaded for each subject individually. The handwriting in the answer sheet is extracted into digital text using Object Character Recognition (OCR) with the help of Tesseract and tokenized using NLTK. The tokenized answer key is compared against the tokenized answer scripts. Marks will be awarded accordingly. Finally, the result/mark is recorded for the respective student along with which a report is being generated with a detailed analysis of a particular student based on his knowledge and understanding of the subject. At the backend, the application uses Firebase for the database.
Improving the Performance of Deep CNNs in Medical Image Segmentation with Limited Resources

Convolutional neural networks (CNNs) have obtained enormous success in image segmentation, which is substantial in many clinical treatments. Even though CNNs have achieved state-of-the-art performances, most researches on semantic segmentation using the deep learning methods are in the field of computer vision, so the research on medical images is much less mature than that of natural images, especially, in the field of 3D image segmentation. Our experiments on CNN segmentation models demonstrated that, with modifications and tuning in network architecture and parameters, modified models would show better performances in the selected task, especially with limited training dataset and hardware. We have selected the 3D liver segmentation as our goal and presented a pathway to select a state-of-the-art CNN model and improve it for our specific task and data. Our modifications include the architecture, optimization algorithm, activation functions and the number of convolution filters. With the designed network, we used relatively less training data than other segmentation methods. The direct output of our network, with no further post-processing, resulted in the dice score of ~99 in training and ~95 in validation images in 3D liver segmentation, which is comparable to the state-of-the-art models that used more training images and post-processing. The proposed approach can be easily adapted to other medical image segmentation tasks.
Using feature-level fusion for multimodal gender recognition for opinion mining videos

Video has rich content and has recently received considerable attention for opinion mining and sentiment analysis, especially with the growing volume of online videos in social networks. However, it has been proved that machine-learning techniques for sentiment analysis are gender biased, i.e. can be more accurate for one gender than the other. Hence, if gender can be recognized early, it can lead to improving the sentiment analysis results. This paper explores multimodal analysis of video data to extract features and build gender recognition models. Different methods are evaluated for unimodal, bimodal and trimodal systems using features from three modalities: visual, audio, and text. The proposed system is evaluated and the results revealed that the fusion of multiple modalities at the feature level can have significant impact on the performance of gender recognition as compared to unimodal models.
Attitude Detection for One-Round Conversation: Jointly Extracting Target-Polarity Pairs

We tackle Attitude Detection, which we define as the task of extracting the replier's attitude, i.e., a target-polarity pair, from a given one-round conversation. While previous studies considered Target Extraction and Polarity Classification separately, we regard them as subtasks of Attitude Detection. Our experimental results show that treating the two subtasks independently is not the optimal solution for Attitude Detection, as achieving high performance in each subtask is not sufficient for obtaining correct target-polarity pairs. Our jointly trained model AD-NET substantially outperforms the separately trained models by alleviating the target-polarity mismatch problem. By employing pointer networks to consider the target extraction task a boundary prediction problem instead of a sequence labelling problem, the model obtained better performance and faster training/inference than LSTM and LSTM-CRF based models. Moreover, we proposed a method utilising the attitude detection model to improve retrieval-based chatbots by re-ranking the response candidates with attitude features. Human evaluation indicates that with attitude detection integrated, the new responses to the sampled queries are statistically significantly more consistent, coherent, engaging and informative than the original ones obtained from a commercial chatbot.
Implementasi Ekstraksi Ciri Histogram dan K-Nearest Neighbor untuk Klasifikasi Jenis Tanah di Kota Banjar, Jawa Barat

Land plays an essential role in the availability of nutrients and water to support our life on earth. Soil quality can be seen from the characteristics of color and texture. By knowing the quality of the soil, we can determine the type of plant that is most suitable for planting. In this study, we conducted a study of soil quality at Langensari. Langensari was chosen because most of the region has an altitude of fewer than 25 meters above sea level, so it is very potent as an agricultural and planta-tion area. The proposed system uses a cross-sectional image of the ground as input. The image is then extracted using histo-gram feature extraction to obtain the intensity, standard deviation, skewness, energy, entropy and smoothness values. K-Nearest Neighbor then used to classify resulting features. The proposed system was tested using 20 test images. Based on the experiment result, the system can classify soil types appropriately with accuracy reaching 60% when value of K = 3.
Uso de repositorios linked data para la creación de recursos educativos en Historia del Arte.

In the context of Art History education, a habitual activity consists of visiting museums and monuments in order to observe and analyze certain characteristics in the exhibited artworks.Each artistic movement is characterized by a series of elements such as used colors, visual effects, types of characters that appear in the works ... In general, this activity is prepared manually, which requires selecting the museum and the artistic movement and plan the visit (what students should do and what they should observe).Afterwards, the students do a resume of the visit and it must perform an evaluation test on what they have visited in order to verify what they have learned.This article describes a tool that automates some aspects of the process of creating and planning a visit and the evaluation of the visit.One of the main contributions of this tool is the use of information about museums and painters extracted directly from the Wikidata linked data repository.In this way, the application is structured as a value-added service that exploits the free information found in Wikidata.
PENERAPAN DATA MINING PADA JUMLAH PELANGGAN PERUSAHAAN AIR BERSIH MENURUT PROVINSI MENGGUNAKAN METODE K-MEANS CLUSTERING

Water is one of the primary needs for humans so that everyone has the right to get clean water for their daily needs. Along with increasing population, the need for water will increase. So with that the PDAM must sell clean / decent water to its customers, clean water becomes the focus of attention and has the greatest power compared to other problems. Because water is a basic necessity, most of the companies impose rates that can be reached by the community and prices are adjusted to the growth in demand. The purpose of this research is to get a grouping of the number of customers of clean water companies in all provinces using the K-Means Algorithm, K-Means is a method for grouping data into a cluster by calculating the closest distance from a data to a centroid point. Clusters used are high level clusters (C1), medium level clusters (C2), and for low level clusters (C3). Centroid data obtained is for high-level clusters (C1) which are as many as 7710154, for medium-level clusters as much as 929586, and for low-level clusters as much as 112462. Based on the calculated data obtained high-level results, namely the province of Indonesia, for the medium level namely province North Sumatra, DKI Jakarta, West Java, Central Java and East Java, and other provinces are low levels. So that this result can be a support for the company in order to increase water needs.
Detecting context abusiveness using hierarchical deep learning

Abusive text is a serious problem in social media and causes many issues among users as the number of users and the content volume increase.There are several attempts for detecting or preventing abusive text effectively.One simple yet effective approach is to use an abusive lexicon and determine the existence of an abusive word in text.This approach works well even when an abusive word is obfuscated.On the other hand, it is still a challenging problem to determine abusiveness in a text having no explicit abusive words.Especially, it is hard to identify sarcasm or offensiveness in context without any abusive words.We tackle this problem using an ensemble deep learning model.Our model consists of two parts of extracting local features and global features, which are crucial for identifying implicit abusiveness in context level.We evaluate our model using three benchmark data.Our model outperforms all the previous models for detecting abusiveness in a text data without abusive words.Furthermore, we combine our model and an abusive lexicon method.The experimental results show that our model has at least 4% better performance compared with the previous approaches for identifying text abusiveness in case of with/without abusive words.
Extended Abstract: Secure and Safe In-Vehicle Device Pairing Using Accelerometer Sensor

Secure pairing of Internet-of-Things (IoT) devices is a challenging problem because many of them lack the typical user interfaces to provide credentials in the authentication process different from conventional computing platforms. The identical problem is observed in a modern automotive environment. Users bring various smart devices besides smartphones to the cabin and want to connect to their vehicles. Moreover, as car/ride-sharing economy is expected to be continuously grown, such an in-vehicle device authentication will soon become a roadblock, without a proper mechanism, before enabling emerging services. In this study, we develop a novel authentication mechanism, which uses the shared context of two pairing ends. In a moving vehicle, for example, a vehicle and an IoT device located in cabin share the motion. The proposed mechanism compares the motion data from accelerometer sensors in both paring ends to determine their spatial co- existence. We implement a proof- of-concept Android app to prove its feasibility and evaluate in practical user scenarios. By minimizing the user interaction in device authentication, the proposed mechanism will improve safety and usability as well as security.
Crowd Risks and Advanced Tools

The objectives of organisations engaged with crowd situations are subject to serious threats that can disrupt and compromise their integrity and prevent desired outcomes. By employing a risk management process, organisations can identify and assess threats, vulnerabilities and weaknesses and also the likelihood and consequences to the security environment. Risk management involves identifying and assessing threats to the operating environment or strategic goals and prioritizing resources to minimise, monitor and control the impact of events (incidents).
Pronunciation Error Detection in Voice Input for Correct Word Suggestion

In this paper, we present a method of pronunciation error detection in voice input for suggesting correct word. With the progress of the speech recognition technology, voice typing has been widely spread in many software and information services. However, to the best of our knowledge, there is no method to correct pronunciation errors for voice input by a user, although the speech recognition function can show only the words whose pronunciations are like the ones that the user input in voice. In this study, we propose a method for correcting pronunciation mistake in voice input by extending an existing method of spell mistake correction based on edit distance. By applying pronunciation score for phoneme sequences, which is proposed in our previous study, it is expected that our method can reduce the number of candidate pronunciation to be suggested in the process of pronunciation error detection.
A Fine-Grained Annotated Multi-Dialectal Arabic Corpus

We present ARAP-Tweet 2.0, a corpus of 5 million dialectal Arabic tweets and 50 million words of about 3000 Twitter users from 17 Arab countries.Compared to the first version, the new corpus has significant improvements in terms of the data volume and the annotation quality.It is fully balanced with respect to dialect, gender, and three age groups: under 25 years, between 25 and 34, and 35 years and above.This paper describes the process of creating the corpus starting from gathering the dialectal phrases to find the users, to annotating their accounts and retrieving their tweets.We also report on the evaluation of the annotation quality using the inter-annotator agreement measures which were applied to the whole corpus and not just a subset.The obtained results were substantial with average Cohens Kappa values of 0.99, 0.92, and 0.88 for the annotation of gender, dialect, and age respectively.We also discuss some challenges encountered when developing this corpus.
Fuzzy Logic Approach for The Simultaneous Analysis of trends in oral Cancer Victims

The purpose of this research is to determine the influences of any two factors that contribute to oral cancer simultaneously. This research is all the more relevant in the Indian context since oral cancer is the 2ND most abundant form of cancer in India. Conventionally several activities or factors are associated with the disease,even though the amount of scientific or mathematical evidence might not be enough. This paper, along with comparing and analysing the contribution of factors also introduces a particular use of a mathematical method to effectively draw conclusions, which might clear out certain misconceptions regarding the causes of the deadly disease. This form of research can be extended to cover factors or causes that were previously not considered without the need of extensive field work. Since the causes and effects do not relate to each other in definitive terms, the need to accommodateuncertainty arises, and to satisfy this need, fuzzy logic is utilized.
Augmented Reality Applications in Early Childhood Education

Today, digital technologies, and mobile technologies in particular, are already part of the everyday life of children even at the preschool age. Joanne G. Sujansky and Jan Ferri-Reed (2009) in their book Keeping the Millennials claim that today’s young people are continuously multitasking for example, working on a laptop while also watching TV, listening to their iPod, and chat or sms at the same time. Therefore, even in pre-primary education, it is important for educators to explore the uses and opportunities of smartphones and other technologies. Among the currently developing mobile technologies are virtual and augmented realities and their uses in various areas of life. The aim of this chapter is to discuss the possibilities of using mobile apps with augmented reality in pre-primary education. We will introduce some of the most popular and widely used applications with examples of uses in kindergarten. We can use the camera of mobile phones or tablets to explore 3D objects. These applications offer an immersive experience and edutainment. AR technology in education is developing rapidly and we think that the alpha generation classrooms could be enhanced and transformed by this technology.
Flashcard for Enriching and Developing the Child Vocabulary with Speech Delay to Improve Lingual Skill

This study aims to enrich and developing vocabulary the child with speech delay by using flashcard. The subject of the research is the child who was late in speaking due to sensory disorders, Kindergarten 1, the age of 4 years old. The research method was single subject research quantitative research (single subject research). Techniques of analysis the data collection through observation of baseline A and B, also documentation. The Results showed that baseline phase (A) phase to the intervention (B) are increasing, which means that the condition has increased after the intervention. Where in the baseline-2 (A2) phase to intervention (B) it is increased which means that the intervention can significantly improve the ability of the subject.
Style Consistency Constrained Fusion Feature Learning for Liver Tumor Segmentation

Due to diversity among tumor lesions and less difference between surroundings, to extract the discriminative features of a medical image is still a challenging job. In order to improve the ability in the representation of these complex objects, the type of approach has been proposed with the encoder-decoder architecture models for biomedical segmentation. However, most of them fuse coarse-grained and fine-grained features directly which will cause a semantic gap. In order to bridge the semantic gap and fuse features better, we propose a style consistency loss to constrain semantic similarity when combing the encoder and decoder features. The comparison experiments are done between our proposed U-Net with style consistency loss constraint in with the state-of-art segmentation deep networks including FCN, original U-Net and U-Net with residual block. Experimental results on LiTS-2017 show that our method achieves a liver dice gain of 1.7% and a tumor dice gain of 3.11% points over U-Net.
Extended reality practice in art &amp; design creative education

As Jerald (2018) states, though virtual reality (VR) has existed for over 50 years, its use as a creative medium is relatively new. In the last four years, as part of the 'second wave of VR', new affordability and accessibility of hardware and software for experiencing and creating VR has incited a surge of interest for the technology from creative industries. Meanwhile, interest and attempts to create VR projects has expanded into other forms of Extended Reality (XR) technologies, like Augmented Reality (AR) and Mixed Reality (MR).
Advanced Topics II: Complexity

This week is the continuation of advanced python concepts and will cover more topics that a developer has to understand on the job.