Reviewerly/exHarmony huggingface dataset:
Adaptation of Open Up in the Scrum Framework to Improve Compliance in Scope, Risk Management and Delivery Times in Software Development Projects

This study consisted of adapting artifacts and practices from the Open Up development method in Scrum to improve compliance in scope, risk management and delivery times in software development projects. The practices (administrative, technical and deployment) and artefacts of Open Up were adapted in planning events, Sprint and Scrum retrospectives. Improvements were measured by indicators with the following results; in scope: delivery deadlines met (66% to 95%) and missed milestones (80% to 0%); in risk management: number of risks (from 4 to 3) and risk identification (from 4 to 9) which means a reduction in incidence from 100% to 33%; and, in time compliance: project delay (53% to 13%) and in delayed tasks (42.50% to 31.75%). The effects of the adaptation were: the development of plans was improved, the control of the evolution of the architecture of the product was improved as well as the deliveries were better planned. This study is relevant because it allows orienting software development projects where there is a need to follow plans based on prescriptive and normative contexts of an organization and at the same time have an agile approach. The results conclude that the adaptation improved the project with a surpassed learning curve; it is useful in prescriptive and agile development environments. It is suggested to replicate the study under similar conditions and/or other indicators.
Image Tampering Detection Using Convolutional Neural Network

Pictures are considered the most reliable form of media in journalism, research work, investigations, and intelligence reporting. With the rapid growth of ever-advancing technology and free applications on smartphones, sharing and transferring images is widely spread, which requires authentication and reliability. Copy-move forgery is considered a common image tampering type, where a part of the image is superimposed with another image. Such a tampering process occurs without leaving any obvious visual traces. In this study, an image tampering detection method was proposed by exploiting a convolutional neural network (CNN) for extracting the discriminative features from images and detects whether an image has been forged or not. The results established that the optimal number of epochs is 50 epochs using AlexNet-based CNN for classification-based tampering detection, with a 91% accuracy.
Technological Model for Implementation of m-Learning in Higher Education in Republic of Yemen

In the study we present extension of the Koole's FRAME model for mobile learning, and technological model for m-learning implementation with pilot study in Republic of Yemen. Also several frameworks and models for implementation of mobile learning are discussed. The proposed technological model is applicable in developing countries with not well developed e-learning ecosystem and problems in communication infrastructure.
Optimization of Energy Consumption in Activated Sludge Process Using Deep Learning Selective Modeling

Optimization of Energy consumption in activated sludge plants. Selection approach of the best observation that will be used in a supervised deep learning model. The model was applied to learning and validation period in order to show its performance.
Online Feature Selection via Deep Reconstruction Network

This paper addresses the feature selection problems in the setting of online learning of data streams. Typically this setting imposes restrictions on computational resources (memory, processing) as well as storage capacity, since instances of streaming data arrive with high speed and with no possibility to store data for later offline processing. Feature selection can be particularly beneficial here to selectively process parts of the data by reducing the data dimensionality. However selecting a subset of features may lead to permanently ruling out the possibilities of using discarded dimensions. This will cause a problem in the cases of feature drift in which data importance on individual dimensions changes with time. This paper proposes a new method of online feature selection to deal with drifting features in non-stationary data streams. The core of the proposed method lies in deep reconstruction networks that are continuously updated with incoming data instances. These networks can be used to not only detect the point of change with feature drift but also dynamically rank the importance of features for feature selection in an online manner. The efficacy of our work has been demonstrated by the results of experiments based on the MNIST database.
Influence of Model Refactoring on Code Debt

Context: The term Technical Debt (TD) is associated with the effect of immature artifacts in the development of software. Although TD may incur in different stages of development, few pieces of research have addressed the relationship between TD in models and its influence on their respective source codes. Goal: To investigate the influence of model refactoring on code debt and vice-versa. Method: A replicated study was conducted considering the refactoring of a Unified Modeling Language (UML) class diagram for the removal of model smells, code issues, and code debt items. Besides, a different round-trip engineering tool was used, other than that employed in the original study. Findings: The findings of the original study were confirmed, but the usage of different engineering tools may influence the quantification of code debt. Conclusion: New replicated studies are necessary to further investigate the influence of model refactoring on code debt, considering other code issues, detection tools and class diagrams.
Foveated Vision for Biologically Inspired Continuous Face Authentication

In everyday life whenever people observe, interact or speak to each other, visual attention is mostly directed toward the other person’s face, particularly to the eyes and the nearby periocular regions. This is naturally reflected when the user interacts with their mobile phones in several usual activities, such as web access, payments and video calls. For this reason, the functionality of mobile devices is strongly affected by the design of the user interface. In this chapter, we propose a biologically inspired approach for continuous user authentication based on the analysis of the ocular regions. The proposed system is based on a modified version of the HMAX visual processing module. HMAX is a hierarchical model which has been conceived to mimic the basic neural architecture of the ventral stream of the visual cortex. The original HMAX model consists of four layers: S1, C1, S2 and C2. S1 and C1 represent the responses to a bank of orientation-selective Gabor filters. S2 and C2 represent the responses of simple and complex cells to other textural features. The discrimination power of HMAX in recognizing classes of objects is invariant to rotation and scale. The C1 layer, which is mainly responsible for the scale and rotation invariance, is implemented using a max-pooling operation, which may lose some spatial information. To overcome this problem while preserving the maximal visual acuity and hence the localization accuracy, we propose to augment the model by applying a retinal log-polar mapping. The log-polar mapping is an approximation of the retino-cortical mapping that is performed by the early stages of the primate visual system. Due to the high density of the cones in the fovea, the log-polar approximation of the space-variant distribution model of the photoreceptors can only be applied outside the foveal region. Therefore, the log-polar mapping is added to the HMAX model as a complementary stage to process the peripheral region of the grabbed images. In order to demonstrate the feasibility of the proposed approach to mobile scenarios, experimental results obtained from publicly available databases and image streams grabbed from mobile devices will be presented.
ClothSurface: Exploring a Low-Cost Prototyping Tool to Support Ideation for Shape Displays

A shape-changing user interface is a type of interface that interacts with users by changing its physical form. Although researchers have been extensively studying shape-changing user interfaces, relevant research on its various design aspects—including tools and methods—remains limited. Prototyping shape-changing interfaces often requires sophisticated equipment and knowledge, which makes this sphere of design unwelcoming for designers with limited resources and technical knowledge (e.g., design students). In this study, we propose ClothSurface—a simple and low-cost prototyping tool to design for shape displays—and explore its use through a series of design sessions. The results reveal that ClothSurface can allow inexperienced designers to illustrate their ideas and to explore the design space of shape displays.
Vectorization of Architectural Floor Plans

This paper proposes a method to vectorize the architectural floor plans by recognizing the graphic primitives used in such drawings. The method consists of thin and thick layer separation of the input image, followed by the identification of thin lines, arcs, and circles. The thick lines are identified as boundary wall and the vector information of these lines are stored separately after identification. Morphological operations and geometric analysis has been used for separation of thin-thick layers and identification of lines and circular arcs respectively. The proposed algorithm has been tested on various datasets and the experimental results demonstrates the efficiency and robustness of the presented algorithm. Finally, the reconstructed architectural floor plans from the computed vector information shows the accuracy of the proposed method.
Swarm Optimization Clustering for Image Segmentation of Insulators

To keep the steady operation of the power system, it is an effective way to find the fault of insulators from the their images, in which image segmentation of the insulators is an important prerequisite. However, due to the noise existing in the images of insulators, traditional methods do not play well in image segmentation of insulators. This paper proposes a swarm optimization clustering algorithm for image segmentation of insulators. This method uses ant colony clustering algorithm for image clustering and segmentation of insulators. To improve the effectiveness of the algorithm, the particle swarm algorithm is used to optimize the parameters of the ant colony clustering algorithm. Compared to traditional algorithms, this method can do image segmentation of insulators more effectively.
Influencing Factors of Mobile Technology Adoption by Small Holder Farmers in Zimbabwe

Agriculture is the primary occupation of the majority of Zimbabweans living in rural areas which constitutes of about 70% of Zimbabwe’s population. In the present times of technological development, mobile technology particularly mobile phones have become the most important tools of communication which can be accessed by farmers for agricultural related information and knowledge, hence understanding the adoption of this technology is important. This paper contributes to the existing literature by comprehensively reviewing the role of mobile technology in agriculture and the factors that influence the adoption of this technology among small holder farmers in Zimbabwe. The study informs policy makers and technology startup companies to adopt appropriate strategies to encourage the adoption of mobile technology among small holder farmers having different demographic characteristics and other influencing factors highlighted in the study.
Study on the reform of integrating project teaching method into the teaching of C programming

In order to meet the new teaching requirements of programming courses and improve students' practical programming ability, the actual practice projects will run through the course 'C programming', which is conducive to students' basic knowledge and students' learning interest.
Index of Difficulty Measurement for Handedness with Biometric Authentication

This study attempts to identify the variance between right- and left-handed users when utilizing touch screen devices. Three experiments were created in Flutter to gather data and identify the differences in functionality by left- and right-handed users on touch screen devices. These experiments focused on speed and accuracy while also using mirrored images. Experiment one focused on Fitts' law while two and three focused on steering law. This paper focuses primarily on the Fitts' law experiment. The collected data were analyzed and visualizations created to provide insight into whether these biometric experiments prove or disprove the existence of deficiencies in touch screen devices for left-handed users. Data was collected from the native touchscreen and timing features for each participant. A preliminary visual analysis of the data indicates a significant variance between right- and left-handed users, but a machine learning analysis is necessary to verify this hypothesis definitively.
Web-based canteen payment system with RFID technology

Abstract The development of Modern Technology is very fast, along with the existence of paper money as a global exchange instrument used as a means of payment began to be replaced by magnetic card cards. The process of payment at the Bilingual Krian Canteen Krian Still Less Efficient, causing quite long queues that cause feelings of uncomfortable, complicated change change makes the transaction process becomes longer and change money in the form of unequal amount of candy. To overcome the problem required a system that is expected to solve the problem, where the RFID card can be used as a means of payment to replace cash. General system design includes design flowchart, context diagram, data flow diagram, relationships between tables and design output or system design and detailed design includes database design. The system created aims to design a Payment system. It consists of several components namely RFID card and reader. The test results of this system when RFID closer to the reader then the system will display data in accordance with the data received from the RFID and can make a transaction. The read distance of each RFID to the reader after 5 times is 6 cm. On the barrier or object determines the success of data readings detected or not. This RFID payment system is potentially good enough to be applied in the Canteen environment.
Control Synthesis Through Deep Learning

Deep learning has gained unprecedented rapid popularity in computer science in recent years. It is used in tasks that were previously considered highly challenging for computers, such as speech and image recognition and natural language processing. While deep learning is often associated with complicated tasks, we look at the much more mundane task of refining a system behavior through control that is constructed with the use of learning techniques. We compare the use of deep learning for this task with other techniques such as automata learning and genetic programming.
Classification of Multi Source Ultrasonogram Image of Steatosis

Aim of supervised classification is to obtain an algorithm that works across the data sets collected from different sources. Ideally a classifier, trained with sufficient variations in the input data, should be able to make reasonably good prediction on test data, obtained from arbitrary independent source. Medical images are sensitive to imaging devices and imaging conditions; therefore, classification of medical images is, typically restricted to a single data set where images are collected under similar conditions. In the present communication human normal and fatty liver ultrasonogram images from two different sources are used. When one source is employed for training and the other for testing, the classification accuracy is low. However, addition of a small fraction of data from the testable source to the training set drastically improves the classification accuracy indicating an algorithm to develop a classifier that works on data from different independent sources.
Radar Tracking Using State Estimation and Association

Lecture 1: Fundamentals of Trajectory Estimation Lecture 2: Dealing With Real World Problems (Single Target, Single Radar) Lecture 3: Multiple Target Tracking Lecture 4: Tracking with a Multiple Sensor System.
SEnD: A Social Network Friendship Enhanced Decentralized System to Circumvent Censorships

While the Internet is open by design, it is still the case that users can be subject to censorship by governments or enterprises in accessing Web services and data. In this paper we propose SEnD, a fully-distributed censorship circumvention system built upon an overlay, where users have peer-to-peer virtual private IP tunnels to proxies within their social network. With SEnD, users in an uncensored area can act as proxy servers for their social friends in a censored area, allowing them to bypass the censorship. SEnD is able to outperform the current censorship techniques, such as IP address blocking and active probing attacks. We assessed the effectiveness of SEnD through extensive simulations based on a synthetic dataset, as well as experiments based on a prototype implementation. We built our synthetic dataset based on parameters obtained from questionnaires administered both inside and outside China (we consider China as a case study of censorship area). The results show that SEnD is feasible, efficient and scalable. For example, when the proportion of concurrent active users is less than 60, 99.9 percent of these users are able to find proxy servers.
Multi-Objective GP Strategies for Topical Search Integrating Wikipedia Concepts

Genetic Programming techniques have demonstrated great potential in dealing with the problem of query generation. This work explores different Multi-Objective Genetic Programming strategies for evolving a collection of topic-based Boolean queries. It compares three approaches to build topical Boolean queries: using terms, incorporating Wikipedia semantics (Wikipedia concepts) and a hybrid approach, using a combination of both terms and concepts. In addition, different fitness functions are combined giving rise to seven multi-objective schemes. In particular, we investigate the use of the proposed strategies in conjunction with novel fitness functions aimed at attaining high diversity based on the information-theoretic notion of entropy and Jaccard similarity. Experiments were completed using 25 topics from a dataset consisting of approximately 350,000 webpages classified into 448 topics. The results reveal that the use of Wikipedia concepts does not result in statistically significant improvements in precision, global recall or diversity when compared to the term-based approaches. However, the use of concepts has a positive effect on query interpretability since the use of terms leads to artificial queries that are hard to interpret by humans. In the meantime, concept-based queries contain a smaller number of operands than the term-based ones, hence resulting in better execution times without a loss in retrieval performance.
The CNN-Corpus in Spanish

This paper details the development and features of the CNN-corpus in Spanish, possibly the largest test corpus for single document extractive text summarization in the Spanish language. Its current version encompasses 1,117 well-written texts in Spanish, each of them has an abstractive and an extractive summary. The development methodology adopted allows good-quality qualitative and quantitative assessments of summarization strategies for tools developed in the Spanish language.
Improving target selection accuracy for vehicle touch screens

When operating the touch screen in a car, the touch point can shift due to the vibration, resulting in selection errors. Using larger target is a possible solution, but this significantly limits the amount of content that can be displayed on the touch screen. Therefore, we propose a method for in-vehicle touch screen target selection that can be used with a variety of sensors to increase selection accuracy. In this method, the vibration feature is learned by Variational AutoEncoder based model, and it is used for estimating touch point distribution. Our experimental results demonstrate that the proposed method allows users to achieve higher target selection accuracy than conventional methods.
Security, Privacy, and Usability Challenges in Selfie Biometrics

From biometric image acquisition to matching to decision making, designing a selfie biometric system is riddled with security, privacy, and usability challenges. In this chapter, we provide a discussion of some of these challenges, examine some real-world examples, and discuss both existing solutions and potential new solutions. The majority of these issues will be discussed in the context of mobile devices, as they comprise a major platform for selfie biometrics; face, voice, and fingerprint biometric modalities are the most popular modalities used with mobile devices.
Characterization of splicing in digital images using gray scale co-occurrence matrices

Image forgery has become the main concern of the society over the past years due to an increase in the number of fraudulent image manipulations. Therefore, it has become a necessity to build an effective forgery detection method to check the integrity and authenticity of images. In this paper, an unsupervised method for classification of forged images and a supervised method for localization of forged regions is proposed. The methods use GLCM, a textural feature descriptor that uses pixel correlation in image to extract features from it. Further, for feature matching, Euclidean and Hellinger distance is used and finally, the localization of tampered region is performed. Euclidean and Hellinger distance is calculated separately on each image for comparison of the results. The experimental results showed that Hellinger outperformed Euclidean distance in feature matching and gave better localization results. In another approach, a bounding box is taken from the image to get the testing area and the training area. SVM classifier is used for classification of forged blocks and the authentic blocks in the image. The experimentation is performed on CASIA v1.0 dataset.
Computer Mediated Communication and Vocabulary Learning: The Case of Facebook

Nowadays, Computer-Mediated Communication (CMC) becomes the most dominant mode of communication as computers, smart phones, tablets are worldwide used devices for communication especially with the internet facilities through different platforms. However, despite the spread of computer mediated communication modes and devices, it is noticed that people still lack vocabulary in the English language. Therefore, this research intends to investigate Facebook use as a shared online space to learn vocabulary in English language. In addition, it aims at developing, within Facebook users, the possible vocabulary learning strategies that would enhance their English vocabulary learning. Therefore, two research tools were used with a group of facebook users: a questionnaire and an experiment. The obtained results confirmed that the participants under investigation have significantly improved their vocabulary learning via Facebook. In addition, vocabulary learning has been greatly affected by certain training on some strategies. Hence, Facebook proved to be not only a source of entertainment, but also an effective informal learning tool of English vocabulary.