Reviewerly/exHarmony huggingface dataset:
Keeping the human in the data scientist: Shaping human‐centered data science education

ABSTRACT Human‐centered approaches are still relatively novel for data science practice, where data‐driven analytics are often framed as ways to supersede human judgments. This poster presents core design principles devised by the authors to build a data science curriculum foregrounding ethics and valuing creative human capacities alongside strong data analytic skills. The curriculum design transforms human‐centered principles into practice. The poster visually illustrates the trajectory of these principles in relation to three specific graduate attributes, using a selection of deep dives into the learning design. To illustrate the specific power information theory and practice has to shape data science training, the poster uses evocative quotes from information science theorists and displays how their work significantly influenced the overall philosophy of the program, student activities and assessments. Using ‘gamestorming’ techniques during the poster session, the audience is invited to contribute further insights about ways to nurture the deeply human capacities of the data scientists we train.
Determining Iconic Gesture Forms based on Entity Image Representation

Iconic gestures are used to depict physical objects mentioned in speech, and the gesture form is assumed to be based on the image of a given object in the speaker's mind. Using this idea, this study proposes a model that learns iconic gesture forms from an image representation obtained from pictures of physical entities. First, we collect a set of pictures of each entity from the web, and create an average image representation from them. Subsequently, the average image representation is fed to a fully connected neural network to decide the gesture form. In the model evaluation experiment, our two-step gesture form selection method can classify seven types of gesture forms with over 62% accuracy. Furthermore, we demonstrate an example of gesture generation in a virtual agent system in which our model is used to create a gesture dictionary that assigns a gesture form for each entry word in the dictionary.
The influence of power distance on requirements engineering activities

Requirements Engineering (RE) activities are inherently collaborative, requiring software stakeholders to have intensive communication to identify users’ requirements. As the stakeholders’ culture might heavily impact on the way how they collaborate and communicate, RE might be significantly influenced by their cultural background. To analyse stakeholders’ culture, we use one of the most adopt and comprehensive cultural studies in software engineering: Hofstede’s cultural model. Hofstede’s model has six cultural dimensions, where one of them is Power Distance (PD). The aim of this study is to explore the influence of PD cultural aspects on RE activities. We conducted 32 interviews with software developments/requirements engineer practitioners from two different cultures: Saudi Arabia and Australia. Our results revealed that PD significantly affect RE collaboration among software stakeholders. In this paper, we address the core identified cultural aspects as well as the ways to overcome them. The results that we present in this paper would help to increase awareness of the influence of PD, and thus, improve collaboration within RE activities.
Data protection in cloud computing: A Survey of the State-of-Art

A favorable development of the public cloud infrastructure requires data protection which provides the assurance with the well implement of the infrastructure as supposed, both for the cloud user as well as the cloud provider. Still, data protection is not so far presented with the appropriate attention. In this paper, we have introduced the possible risks that data can experience throughout cloud transfer and also recovery. Hence, we introduced various known attacks and subsequently discussed the advantages as well as the disadvantages of the diverse techniques suggested in the literature for restraining the effects of these last ones.
Software Engineering Practices and Methods in the Game Development Industry

Applying current software engineering practices in the game development industry is a rapidly growing but under researched area. Whether game development studios align to traditional software engineering practices such as agile methodologies to develop their games is not known. It is also unknown how studios perceive their own adherence to such agile development practices. Furthermore, struggling start-up studios could benefit from implementing development practices based on the experiences of established studios. Hence, an exploratory survey was conducted to determine the practice of, and perception of agile game development in New Zealand. The results show that while studios universally state and perceive that they use the agile framework Scrum and sometimes Kanban, their actual practices often differ from these frameworks in key areas. Furthermore, studios collectively overestimated their level of adherence with Scrum. This has general implications for related academic studies as well as for the game industry's own evaluation and improvement of their practices.
Encrypted Cloud Based Personal Health Record Management Using DES Scheme

Cloud-based Personal Health Record systems (CB-PHR)'s majorly used for management of patient's individual health records. Cloud computing are providing real-time healthcare data sharing in a cost-effective manner. Data security and privacy of maintaining personal health records are main obstacles for the wide adoption of CB-PHR systems, since health information is highly sensitive, so it needed to be protected in highly secured manner. In this paper, we propose a novel scheme using Data Encryption Standard (DES) technique for maintaining health record based on challenges and various securities in existing system. In this scheme, indexes are encrypted under different symmetric keys and also the encrypted data indexes from various data providers can be merge by cloud without knowing the index content. It also provides efficient and privacy preserving query processing with a single data query submitted by data user. Encrypted data will be processed by cloud from all related data providers without knowing its query content.
user2Vec: Social Media User Representation Based on Distributed Document Embeddings

Recent improvements in word representations (word embeddings) have improved a wide range of text-based information retrieval applications. Successfully representing many semantic characteristics of words in low dimensional vector spaces with Continuous Bag of Words (CBOW) and Skip-Gram models invoked new techniques for textual input representations. In this study we introduce a neural embedding model for representing social media users using document representation model (doc2vec). We propose a simple method for evaluating the quality of the user vectors. We also share our results on simply averaging user vectors of the same category as category vectors. The experiment results show that our user2vec model creates semantically meaningful representations of users and it is very open for new improvements. We also share the dataset used in this study.
Simultaneously Realization of Image Enhancement Techniques on Real-Time Fpga

Image Processing Techniques have recently become one of the most popular and widely used technologies. Image processing techniques are widely used in many areas. Particularly in real-time applications, imaging and processing in a very short time is of great importance. In this paper, Spatial Domain Techniques which are commonly used in almost all Image Processing Techniques are discussed. Field Programmable Gate Array (FPGA) architecture is used in order to perform the operations as soon as possible while applying image processing techniques. Since the FPGA architecture has the ability to perform parallel processing, it will shorten the processing time and the efficiency will increase. Also, the ability to use FPGA over and over again provides an extra advantage. In this paper, applications are performed using the Vivado program using the verilog hardware identification language. In addition to the simultaneous acquisition of numerical data, corresponding visual figures have been developed in the MATLAB program so that the subject can be better understood.
MapReduce mRMR: Random Forests-Based Email Spam Classification in Distributed Environment

The furthermost standard message transfer system used on the internet for communication is email. These days spam is a serious concern that causes major problems in today’s internet. Spam emails are uninhibited messages that are sent to a large number of beneficiaries arbitrarily. Owing to an overgrowing rise in reputation, the number of unsolicited data has also increased promptly and has led to many security concerns. Although the sufficient number of spam filtering techniques exists, nowadays spammers start discovering innovative practices to escape data that are filtered using the spam filters. Spammers use this communication source for spreading the malware in the name of an executable file. These spam emails waste user’s system memory, computing power, and bandwidth of the network. Spam emails have been initiated to progressively damage the integrity of email and destroy the online experience. The research revealed that if the classification algorithms are used with feature selection then that will return the exact results than the standard classification. In this paper, feature selection is done through minimum redundancy and maximum relevance (mRMR) and the classification is done by means of Random Forests in the MapReduce environment. The performance is compared using various measures, namely sensitivity, correctness, and accuracy with the Random Forests in the distributed environment using Spambase dataset.
srcDiff: A syntactic differencing approach to improve the understandability of deltas

Abstract An efficient and scalable rule‐based syntactic differencing approach is presented. The tool srcDiff is built upon the srcML infrastructure. srcML adds abstract syntactic information into the code via an XML format. A syntactic difference of srcML documents is then taken. During this process, the differences are further refined using a set of rules that model typical editing patterns of source code by developers. Thus, the resulting deltas model edits that are programmer centric versus a purely syntactic tree edit view. Other syntactic differencing approaches focus on obtaining an optimal tree edit distance with the assumption that this will produce an accurate difference. While this may work well for small or simple changes, the differences quickly become unreadable for more complex changes. By contrast, the approach presented here purposely deviates from an optimal tree edit difference in order to create a delta that is both easier to understand and better models changes between the original and modified. To evaluate the approach, a comparison user study against a state‐of‐the‐art syntactic differencing approach and two line‐based differencing tools is conducted as an online within‐participant study with about 70 subjects on 14 sample changes. The results provide support that the rule‐based syntactic differencing produces more accurate and understandable deltas.
Product Recommendation for e-Commerce System based on Ontology

The sale and purchase of goods are now starting to move from being offline to online using the internet, or what is known as e-commerce. With the development of the internet and intelligent computing technology, e-commerce is increasingly being used. The products offered through e-commerce platforms is a matter that needs to be considered because it can influence the user's decision in buying a product. This study aims to build a product recommendation system on e-commerce platform according to user needs. There are several methods that can be used to produce recommendations, one of which is Collaborative Filtering. In this study, the Slope One algorithm is used where the input rating is given based on the domain ontology of the product. Domain ontology is used to represent relationships between products. Thus, the product recommendations are expected to be in accordance with the user's interest. So that product sales are right on target and users get products that suit their needs. This recommendation system will be implemented on e-commerce platforms and is expected to help users and sellers. Based on the case studies conducted, the results of recommendations provided with the ontology approach not only provide recommendations for specific products, but also provide recommendations on categories that may be of interest to the users. Thus, the recommendations will be more varied and are expected to be more in line with user interests.
Group Cost-Sensitive BoostLR With Vector Form Decorrelated Filters for Pedestrian Detection

Pedestrian detection has achieved notable progress in the field of computer vision over the past decade. However, existing top-performing approaches suffer from high computational complexity which prohibits their realization on embedded platforms with low computational capabilities. In this paper, we propose a robust and fast pedestrian detection framework which is based on the Filtered Channel Feature (FCF) approach. The proposed framework exploits vector-form decorrelated filters to extract more discriminative channel features while benefiting from low computational complexity. A novel group cost-sensitive BoostLR (Boosting with Loss Regularization) algorithm is proposed to train the classifier. The proposed training strategy provides more emphasis to the harder samples by exploring the variations of negatives selected from different rounds in hard negative mining processing, and hence is able to boost the overall detection performance. In addition, the proposed method also benefits from the BoostLR framework to achieve better generalization. Experiments on the well-known Caltech, INRIA and CityPersons pedestrian detection datasets show that our proposed approach achieves the best detection performance among all of the state-of-the-art non-deep learning methods and can run one order of magnitude faster than classical FCF methods (e.g. Checkerboards).
Fitting local, low-dimensional parameterizations of optical turbulence modeled from optimal transport velocity vectors

This work exploits a connection between optimal transport theory and the physics of image propagation to yield a locally low-dimensional model of turbulence-corrupted imagery. Optimal transport produces an invertible, pixel-wise linear trajectories to approximate the globally nonlinear turbulence between a clean and turbulence corrupted image pair. We use the low-dimensional model to fit subsets of the optimal transport vector fields and stitch the local models into a surrogate for the global map to be used for image cleaning. Experiments are performed on laboratory generated data of beam propagation using different values of the Fried parameter (a scale measuring turbulence coherence) as well as a toy data set. The results suggest this is a fruitful direction, and first step, towards using multiple realizations of turbulence corrupted images to learn a blind surrogate for the optimal transport vector field for image cleaning.
A 1642 Ma age for the Fraynes Formation, Birrindudu Basin, confirms correlation with the economically significant Barney Creek Formation, McArthur Basin, Northern Territory

An interpreted CA–IDTIMS age of 1642.2 ± 3.9 Ma for a volcanogenic tuffaceous siltstone from the previously undated Fraynes Formation of the Birrindudu Basin in the northwestern Northern Territory enables a rigorous chronostratigraphic correlation to be made with the economically important Barney Creek Formation of the southern McArthur Basin. This result supports previous interpretations that these geographically widely separated formations are probably linked in the subsurface. It also establishes the stratigraphic interval encompassing the Fraynes and Barney Creek formations as a potential target for greenfields base metals and petroleum exploration programs across the greater McArthur Basin.KEY POINTSA new interpreted CA–IDTIMS age provides a chronostratigraphic link between the Fraynes Formation of the Birrindudu Basin and the economically important Barney Creek Formation of the southern McArthur Basin.The Fraynes and Barney Creek formations are probably linked in the subsurface.This stratigraphic level is a potential target for greenfields base metals and petroleum exploration across the greater McArthur Basin.
Automatic illumination control for an endoscopy sensor

Endoscopy and small surgery can be performed with the support of small cameras. These cameras need to be minimal to reduce intrusion, require fast response and automatic illumination. The current work describes the world's smallest camera for endoscopy, NanEye, and the dynamic control algorithm for ROI developed for the illumination LED source coupled to the camera head that allows adjusting light, gain and exposure time. The obtained results show that it is capable of achieving correction speeds under 1 s while maintaining a static error below 3% relative to the total number of pixels of the image. The result of this work will allow the integration of high brightness LED sources of millimeter size on a very small camera enabling its use in endoscopic surgical robotic operations or minimal invasive surgery and NanEye is expected to be supplied at low prices for single time use.
SFour: A Protocol for Cryptographically Secure Record Linkage at Scale

The prevalence of various (and increasingly large) datasets presents the challenging problem of discovering common entities dispersed across disparate datasets. Solutions to the private record linkage problem (PRL) aim to enable such explorations of datasets in a secure manner. A two-party PRL protocol allows two parties to determine for which entities they each possess a record (either an exact matching record or a fuzzy matching record) in their respective datasets - without revealing to one another information about any entities for which they do not both possess records. Although several solutions have been proposed to solve the PRL problem, no current solution offers a fully cryptographic security guarantee while maintaining both high accuracy of output and subquadratic runtime efficiency. To this end, we propose the first known efficient PRL protocol that runs in subquadratic time, provides high accuracy, and guarantees cryptographic security in the semi-honest security model.
Attitudes towards emergent autonomous robots in Austria and Germany

Abstract Autonomous robotic systems are an indispensable component of work in many industries that are on the brink of entering many other areas of people’s lives such as transportation or healthcare. Because attitudes towards new technologies shape consumers’ decision to adopt these innovations, the present study examines the public opinion toward emergent robotic systems in Austria and Germany. The results showed that, in general, attitudes seemed rather positive in both countries. However, for Austria a rather ambivalent picture emerged: although Austrians exhibited the largest adoption rate of robotic technologies in Europe, at the same time they evaluated robots most unfavorably as compared to other European countries. Thus, experiences with robots seemed to have intensified potential downsides of automation that resulted in more concerns regarding the widespread use of robots.
An Improved Algorithm based on KNN and Random Forest

This paper gives an improved algorithm called RFDKNN based on an enhanced KNN (K-Nearest Neighbor) and random forest. First, RFDKNN sorts features based on importance through Gini index and a random forest algorithm. Then it deletes some unimportant features based on this sort in a certain proportion r. Finally, it uses an enhanced KNN algorithm dynamically selecting the optimal nearest neighbor number and distance function to make the distance between two samples closer to true value. Experiments are carried out on the 20 data sets from UCI Machine Learning repository. The results show that compared with other r values, RFDKNN of r=0.7 can obtain a relatively satisfactory classification accuracy. Compared with Naive Bayes, Adaboost, Random Forest, RRSB, W-KNN, dwh-FNN and LI-KNN, RFDKNN has higher classification accuracy on most data sets, especially on large data sets such as Pendigits and Letter.
Deformable models, sparsity and learning-based segmentation for cardiac MRI based analytics

The computational modeling and analysis of cardiac wall motion is a critical step to understand cardiac function and a valuable tool for improved diagnosis of cardiovascular diseases. Although many methods have been developed for cardiac segmentation and wall motion modeling, there are still many unresolved challenges. In this chapter, first we review related techniques for cardiac segmentation and modeling from medical images, mostly CMR. Then, we present a new deep learning method to handle segmentation of multiple structures in cine CMR data. Particularly, we propose a modified deep layer aggregation architecture with channel attention and refinement residual blocks to better fuse appearance information across layers during training and achieve improved results through multiscale analysis of image appearance. The proposed method shows promising results compared to the state-of-the-art approaches for multiple cardiac structures (left ventricle cavity and myocardium, and right ventricle cavity) segmentation in cine CMR data. We also present the methodology for shape refinement and 3D cardiac motion modeling. The shape refinement is achieved by adopting sparse shape composition. Finally, we discuss future research directions and applications of cardiac analytics.
Addressing Byzantine Fault Tolerance in Blockchain Technology

Blockchain technology is considered to be one of the most thriving future Internet technologies with the potential to have a great impact not only on the technical aspects of our lives but also on the social, economic, juristic, security and on many more aspects. Since the appearance of a Bitcoin as the most popular Blockchain based currency a decade ago, the possibilities and strengths of the Blockchain technology have been investigated a lot. The Blockchain technology has a dozen of use cases in different areas of life, whereby one of the most important is Internet of Things and Internet of Everything. This paper concentrates on the vulnerabilities of Blockchain technology, especially on the problem of Byzantine Fault Tolerance. It is one of the crucial problems of Distributed Ledger Technologies in general. Other vulnerabilities of the Blockchain technology analyzed in this paper include the partition and delay attacks.