Reviewerly/exHarmony huggingface dataset:
The Role of the Customer in an Agile Project: A Multi-case Study

In this multi-case study we report the findings from three software projects conducted with SCRUM agile development framework. Each project took approximately a little less than a year to design, develop and test before the launch to the user groups. All project vendors utilized SCRUM framework customized to suit their processes, and included customer as a participant in the overall process. Due to this fact, this study focuses on the role of the customer in daily life of an agile project. The findings show what is actually required from the customer – especially when the sprint length is only one week and the development process is very time-intensive. Although a one week sprint cycle can lead to improved efficiency it required a full time worker from the customer side and it burdened also the developers. Based on our observations, as the developer teams and customer were located in various places around Europe, smooth communication was a key for success. In all cases the asynchronous communication tools, such as Slack, were highly praised, although also direct communications were used to handle more complex issues. According to our findings, these agile projects did not have significant issues caused by the online communication being the preferred way of communication. All of the cases had difficulties in fitting the agile project to the fixed budget, but good collaboration, partnership and trust alleviated most of these problems.
Digital Literacies in Canada

This chapter reviews research on digital technology and digital literacies in the education system in Canada. It reviews empirical research and educational policy documents on digital literacies and technologies, detailing the shifting policy patterns in response to literacy skills needed in the contemporary workforce and our everyday lives. The chapter then moves on to discuss current research and pedagogical trends that have developed as a result of increased emphasis on promoting digitally mediated practices at school and in the workplace. Drawing on research in Canadian classrooms, the chapter highlights the varied policies on digital literacy across diverse educational contexts, and the resultant embedded economic, racial, ethnic, contextual, linguistic, curricular, and pedagogical disparities. The chapter concludes with recommendations for infrastructure, resource and instructional support designed to bridge policy and practice, and develop digital literacy competencies and knowledge.
Does the Oil Revenues Affect the Macro Economic Variables Performance in Africa ? A Panel Data Analysis

Oil revenues play a vital and central role in the economies of developing countries and African countries are among the countries whose macroeconomic variables are affected by oil revenues. The objective of this study is to analyze and investigate the role of oil revenues on the macroeconomic variables in most of the oil-producing African countries. The study employed the Econometric method Pooled Ols model and the fixed and random technique; data used was time sires cross panel data in the period (2000-2018). Based on the literature review, the selected sample study was on countries such as (Nigeria, Algeria, Angola, Gabon, Egypt, Congo, Sudan, Chad, Ghana). The Gross Domestic Product was employed as the target variable and Oil Revenues (Oiler), inflation (CPI), Exchange rate (Ex), exports (Exp), Foreign Direct Invest (FDI), money supply (M2) as independent variables. The study result and analysis of the data indicated that oil revenue has an impact on most of the macroeconomic variables except CPI and M2. A strong positive correlation between oil revenue and GDP growth rate on the countries’ samples study was also indicated. The study concluded that one of the most important macroeconomic variables is oil revenues. It also recommended that African countries need support and need to put more effort in building strong economies that are not dependent on oil revenues; also to work on diversifying the revenues of the states and work to preserve the right of the next generation to exploit natural and oil resources.
Hierarchical object recognition system in machine vision

Abstract Authors propose an efficient architecture of the hierarchical machine vision system and the multimedia transfer algorithm for a distributed client-server system which can adapt to mobile network speed variations. This algorithm is based on the wavelet transform of an input multimedia source and allows for data exchange using all the available bandwidth of the unstable mobile network. The main principle of the hierarchical recognition system is the distributed processing network utilizing the “coarse-fine” paradigm in computer vision. Each source of a video stream is processed in-place by the tiny SoC computer which acts as an Edge Computing Unit and detects the presence of an object fragment in a video frame and crops the bounds of the ROI. The resulting stream containing ROI is directed to the main server if the object is detected, reducing traffic and resource consumption.
Road Sign Detection and Recognition of Thai Traffic Based on YOLOv3

This paper aims to apply a YOLOv3 technique for detecting and recognizing Thai traffic signs in real-time environments. The Thai Traffic Sign Dataset (TTSD) was collected by car cameras to store the video images using the resolution of 1920 × 1080 pixels using 60 frames per second, and a 1280 × 720 pixels and 30 frames per second. In addition, the data was collected in the rural area of Maha Sarakham Province and Kalasin Province. The dataset was generated and distributed for general traffic sign detection and recognition. Two architectures (YOLOv3 and YOLOv3 Tiny) are compared with 50 classes of road signs and 200 badges in each class, containing 9,357 images. The experiment shows that the mean average precision (mAP) of YOLOv3 (88.10%) is better than YOLOv3 Tiny (80.84%) while the speed of YOLOv3 marginally is better than YOLOv3.
Strategy to Use Local Government’s Facebook Page to Improve Public Services

The population of internet users in Indonesia increase continuously form year to year along with the development of information technology infrastructure in Indonesia. This underlies the management of social media in the government agencies. The government sector could communicated with the citizens through social media such as Facebook as a form of direct communication to disseminate information for the citizens. Through current government social media accounts, it could be seen that the posting of official government Facebook account were still rare. Based on existing studies, it has not yet been reviewed the relationship of post and comment categorization on Facebook to improve the government's Facebook management strategy and public service improvement strategy. Therefore, this study was conducted so that the results could help the local government to maximize the management of Facebook accounts to be right on target, provide trustful information, and get a good response from the citizens. There were 30 Local Government Facebook accounts that have been analysed, resulting number of statuses and comment ranks in last 2 months. Then performing frequency analysis on 30-often-used words which will be used as a trigger to categorize. The results of the study are in the form of strategic suggestions for local governments to improve the content of each category through Facebook. Local governments are suggested to convey information from many aspects in a balanced manner in accordance with the needs of the citizens.&#x0D; &#x0D; Keywords– social media; categorization; strategy; local government
Deep Feature-Preserving Based Face Hallucination: Feature Discrimination Versus Pixels Approximation

Face hallucination aims to produce a high-resolution (HR) face image from an input low-resolution (LR) face image, which is of great importance for many practical face applications, such as face recognition and face verification. Since the structure features of face image is complex and sensitive, obtaining a super-resolved face image is more difficult than generic image super-resolution (SR). To address these limitations, we present a novel GAN (Generative adversarial network) based feature-preserving face hallucination approach for very low resolution ( $$16 \times 16$$ pixels) faces and large scale upsampling (8 $$\times $$ ). Specifically, we design a new residual structure based face generator and adopt two different discriminators - an image discriminator and a feature discriminator, to encourage the model to acquire more realistic face features rather than artifacts. The evaluations based on both PSNR and visual result reveal that the proposed model is superior to the state-of-the-art methods.
Designing a Chatbot Social Cue Configuration System

Social cues (e.g., gender, age) are important design features of chatbots. However, choosing a social cue design is challenging. Although much research has empirically investigated social cues, chatbot engineers have difficulties to access this knowledge. Descriptive knowledge is usually embedded in research articles and difficult to apply as prescriptive knowledge. To address this challenge, we propose a chatbot social cue configuration system that supports chatbot engineers to access descriptive knowledge in order to make justified social cue design decisions (i.e., grounded in empirical research). We derive two design principles that describe how to extract and transform descriptive knowledge into a prescriptive and machine-executable representation. In addition, we evaluate the prototypical instantiations in an exploratory focus group and at two practitioner symposia. Our research addresses a contemporary problem and contributes with a generalizable concept to support researchers as well as practitioners to leverage existing descriptive knowledge in the design of artifacts.
Biometric Authentication in Cloud

Cloud computing is used to store information and supply computing services and resources to humans available all over the internet. Huge amount of data is being saved in data centers, which could therefore create chance for records leakage and get a right of entry to develop illegitimate customers. Authentication of users therefore helps to make sure and confirm a person’s identification earlier than granting him/her get admission into cloud Storage system. The main goal of this method is to authenticate at client-side server, server side, or as third party, and the use of special techniques to maintain the information in a relaxed and secure way. We present a overview on the exceptional authentication mechanisms used in cloud. Our Main focus is on providing biometric authentication for cloud-based structures. In addition, we develop awareness on physical and behavioral based biometric authentication techniques and offer a comparative analysis as which method is better in each instance and thereby provides a better authentication mechanism for cloud.
Real-time incremental recommendation for streaming data based on apache flink

Collaborative filtering (CF), one of the most famous methods for building recommendation systems, recommends relevant items to users or predicting ratings of users’ unknown items. Matrix factorization (MF) models are well-known model to deal with predicting the rating problem. However, the recommen dation system based on matrix factorization is hard to keep up with the rapidly changing real-world data. When ratings on new users or new items come, the static model can not fit well on new data. As a consequence, if the current thing does not apply, the prediction accuracy will lose. In addition, it is a significant computation cost to rebuild the model on the whole data. To capture these changes, in this paper, we construct an online-and-offline Collaborative Filtering with a multi-method model to improve the traditional CF method, called Online SGD with Offline Knowledge (OSGDO for short). Besides, we propose a real-time incremental recommendation framework on Apache Flink, which is a scalable stream and batch data processing platform. Meanwhile, we implement our proposed method on our proposed framework. Our method proves to be good at online training when new observations arrive. And the results of experiments show that the dynamic training process we proposed is more efficient than rebuild the model on all the data. At the same time, our algorithm performs well in practice and can achieve impressive accuracy quickly when it is tested with the well-known data sets of MoviesLens and Netflix.
Foreground Extraction and Motion Recognition Technology for Intelligent Video Surveillance

With the rapid development of computer technology and network technology, it has become possible to build a large-scale networked video surveillance system. The video surveillance system has become a new type of infrastructure necessary for modern cities. In this paper, the problem of foreground extraction and motion recognition in intelligent video surveillance is studied. The three key sub-problems, namely the extraction of motion foreground in video, the deblurring of motion foreground and the recognition of human motion, are studied and corresponding solutions are proposed. A background modeling technique based on video block is proposed. The background is modeled at the block level, which greatly reduces the spatial complexity of the algorithm. It solves the problem that the traditional Gaussian model (GMM) moving target enters the static state and is integrated into the background process. The target starts to move for a long time and there are ghosts and other problems, which reduce the processing efficiency of the lifting algorithm. The test results on the Weizmann dataset show that the proposed algorithm can achieve high human motion recognition accuracy and recognition with low computational complexity. The rate can reach 100%; the local constrained group sparse representation classification (LGSRC) model is used to classify it. The experimental results on Weizmann, KTH, UCF sports and other test datasets confirm the validity of the algorithm in this chapter. KNN, SRC voting classification accuracy.
Real or Fake? Spoofing State-Of-The-Art Face Synthesis Detection Systems.

The availability of large-scale facial databases, together with theremarkable progresses of deep learning technologies, in particular GenerativeAdversarial Networks (GANs), have led to the generation of extremely realisticfake facial content, which raises obvious concerns about the potential formisuse. These concerns have fostered the research of manipulation detectionmethods that, contrary to humans, have already achieved astonishing results insome scenarios. In this study, we focus on the entire face synthesis, which isone specific type of facial manipulation. The main contributions of this studyare: i) a novel strategy to remove GAN fingerprints from synthetic fakeimages in order to spoof facial manipulation detection systems, while keepingthe visual quality of the resulting images, ii) an in-depth analysis ofstate-of-the-art detection approaches for the entire face synthesismanipulation, iii) a complete experimental assessment of this type of facialmanipulation considering state-of-the-art detection systems, remarking howchallenging is this task in unconstrained scenarios, and finally iv) a novelpublic database named FSRemovalDB produced after applying our proposedGAN-fingerprint removal approach to original synthetic fake images. The observed results led us to conclude that more efforts are required todevelop robust facial manipulation detection systems against unseen conditionsand spoof techniques such as the one proposed in this study.
A Comparative Study of CNN and FCN for Histopathology Whole Slide Image Analysis

Automatic analysis of histopathological whole slide images (WSIs) is a challenging task. In this paper, we designed two deep learning structures based on a fully convolutional network (FCN) and a convolutional neural network (CNN), to achieve the segmentation of carcinoma regions from WSIs. FCN is developed for segmentation problems and CNN focuses on classification. We designed experiments to compare the performances of the two methods. The results demonstrated that CNN performs as well as FCN when applied to WSIs in high resolution. Furthermore, to leverage the advantages of CNN and FCN, we integrate the two methods to obtain a complete framework for lung cancer segmentation. The proposed methods were evaluated on the ACDC-LungHP dataset. The final dice coefficient for cancerous region segmentation is 0.770.
Estimating Missing Values in Multivariate-Time-Series Clinical Data using Gradient Boosting Tree on Temporal and Cross-variable Features

Estimation of missing values in multivariate-time-series clinical data is of great importance for data analysis. It is common to use mean value and forward/backward value of a time-series to filling in the missing parts. However, the simply statistical values can hardly capture the rich information in multi-time-series for estimating miss values.
Directed Lovász local lemma and Shearer’s lemma

Moser and Tardos (J. ACM (JACM) 57(2), 11 2010) gave an algorithmic proof of the lopsided Lovász local lemma (LLL) in the variable framework, where each of the undesirable events is assumed to depend on a subset of a collection of independent random variables. For the proof, they define a notion of a lopsided dependency between the events suitable for this framework. In this work, we strengthen this notion, defining a novel directed notion of dependency and prove the LLL for the corresponding graph. We show that this graph can be strictly sparser (thus the sufficient condition for the LLL weaker) compared with graphs that correspond to other extant lopsided versions of dependency. Thus, in a sense, we address the problem “find other simple local conditions for the constraints (in the variable framework) that advantageously translate to some abstract lopsided condition” posed by Szegedy (2013). We also give an example where our notion of dependency graph gives better results than the classical Shearer lemma. Finally, we prove Shearer’s lemma for the dependency graph we define. For the proofs, we perform a direct probabilistic analysis that yields an exponentially small upper bound for the probability of the algorithm that searches for the desired assignment to the variables not to return a correct answer within n steps. In contrast, the method of proof that became known as the entropic method, gives an estimate of only the expectation of the number of steps until the algorithm returns a correct answer, unless the probabilities are tinkered with.
Coin Recovery from Inaccessible Cryptocurrency Wallet Using Unspent Transaction Output

In 2008, the blockchain technology was invented to create a modern electronic cash transaction protected through cryptographic mechanisms in peer to peer or decentralized network. The first success cryptocurrency based on blockchain technology is Bitcoin, which core processes are based on public key cryptography. While the public key is transformed and used as an address for payment, the private key is used to claim ownership to access the funds stored in the correspondent address. The processes of generating addresses, signing transactions, and checking the balance are parts of the cryptocurrency core processes. With these activities, the wallet application was created to assist and handle the execution of those processes.The private key plays the most important component to interact with assets inside cryptocurrency blockchain database. Without the private key, the asset in blockchain will not be managed or controlled, thus all cryptocurrency coins in correspondent wallet will be lost. This paper provides a solution to recovery cryptocurrency coin that lost according to inability to login to wallet application. The main component used to recover coins in this paper is the unspent transaction output.
Overview of Flight Guidance, Navigation, and Control for the DLR Reusability Flight Experiment (ReFEx)

This paper gives an overview of the proposed GNC architecture for DLR's Reusability Flight Experiment (ReFEx), which is split into two main subsystems: The Guidance and Control (G&C) subsystem, which determines the desired trajectory from the current flight state to a designated target and issues the necessary commands to the actuators (canards and rudder, as well as a reaction control system for flight phases outside the atmosphere) by calculating the necessary forces and torques based on the required changes of velocity and attitude for following this trajectory; the Hybrid Navigation System (HNS), which is responsible for estimating position, velocity, attitude, angular rates, and other parameters of the flight state. It does so by real-time fusion of measurements of inertial sensors (accelerometers and gyroscopes) with the measurements of GNSS receivers, Sun sensors, laser and radar altimeters. The requirements and boundary conditions set by the mission, the current design baseline for both subsystems, the stability analyses for flight control, the basic guidance strategy chosen as well as the navigation performance assessment through covariance analysis are described and discussed.
Extracting Symptom Names and Disease-Symptom Relationships from Web Texts Using a Multi-Column Convolutional Neural Network.

We propose a method to create large-scale Japanese medical dictionaries that include symptom names and information about the relationship between a disease and its symptoms using a large web archive that includes large amounts of text written by non-medical experts. Our goal is to develop a diagnosis support system that makes a diagnosis according to the natural language (NL) inputs provided by patients. To achieve this, two medical dictionaries need to be constructed: one that includes a wide variety of symptom names expressed in NL and another that includes information about the relationship between a disease and its symptoms. Dictionaries will then be used to predict the patient's disease via two developed methods that extract symptom names and disease-symptom relationships. Both methods retrieve sentences using WISDOM X and then apply neural classifiers to them. Our experimental results show that our methods achieved 93.8% and 88.3% in the F1-score, respectively.
Multi-Modal Deep Learning for Vehicle Sensor Data Abstraction and Attack Detection

Mulimodal learning is the process of reasoning with different types of data simultaneously using a neural network. This is no easy feat. Challenges to tackle include mapping features from one modality to another and finding a joint representation. As applied to connected vehicles, we propose a multimodal module that detects manipulations in the image sensor through verifying its consistency with the LiDAR sensor.A state-of-the-art multimodal network, Regnet, is adapted and trained for attack detection. Adaptation includes replacing the input layer and abstracting the multimodal input process. The KITTI dataset is extended to represent two attacks on connected vehicles: inpainting and translation. The end product is a smart multimodal module that abstracts connected-vehicle sensor input and guards against data integrity breaches.
Displacing big data

Many technical approaches for detecting and preventing cybercrime utilise big data and machine learning, drawing upon knowledge about the behaviour of legitimate customers and indicators of cybercrime. These include fraud detection systems, behavioural analysis, spam detection, intrusion detection systems, anti-virus software, and denial of service attack protection. However, criminals have adapted their methods in response to big data systems. We present case studies for a number of different cybercrime types to highlight the methods used for cheating such systems. We argue that big data solutions are not a silver bullet approach to disrupting cybercrime, but rather represent a Red Queen’s race, requiring constant running to stay in one spot.