Reviewerly/exHarmony huggingface dataset:
Appscrumfall: APP Development Methodology Based on ScrumFall

Agile is a combination of iterative and changeable process which is adopted by IT organization. All software projects are specialized. It is necessary to choose a software model to build any software but a single software development model may not be suitable for all types of projects. Developers face difficulties with existing models for the duration of development. They tackle challenges by balancing the software development lifecycle according to their needs. Therefore, in this paper, we have introduced a software process methodology that features both the scrum and waterfall approaches and is named “appscrumfall.” Software development of mobile app using the model ““Scrum-Fall” (scrum blended with waterfall)” is practicing in the company to solve the deficiencies of the traditional model. We have analyzed the performance and availability to implement this process model. The result shows that this process model is effective for software projects. The aim of this model is to provide a formula for better implementation in large IT sectors.
MATURE: Recommender System for MAndatory FeaTURE Choices

In this research work, we propose a content-based approach MATURE, utilizing the additional information about both the user and item to predict the items for such a user who has current mandatory needs to accommodate as opposed to the past preferences. To the best of our knowledge, MATURE is the first such recommender system that guarantees and ensures the inclusion of the specified mandatory features, when recommending the items to the user and justifies the recommendation by using the information enclosed in mandatory features.
Extraction of Leukocyte Section from Digital Microscopy Picture with Image Processing Method

Appraisal of leukocyte sections is essential to predict the disease in humans. The increase in the leukocyte indirectly represents the disease in humans. This work proposes a methodology to extract the various leukocyte segments from the Microscopic Blood Smear (MBS) image with better accuracy. This work implemented an entropy assisted thresholding and morphology based segmentation to mine the segment. The threshold is executed with the Kapur’s function with a Social Group Optimization (SGO). The mining of leukocyte is done with morphological extraction. The mined segment is then compared against the ground-truth (GT) and the performance metrics are then computed. Further, a confusion matrix is also constructed to confirm the performance of the proposed technique. In this analysis, the benchmark images of Leukocyte Images for Segmentation and Classification (LISC) is considered and the attained outcomes are authenticated with existing similar results in literature. The experimental outcome confirms that, proposed technique is efficient in extracting all the five leukocytes from the LISC catalog with improved image quality parameters. This confirms that, proposed system can be used to examine the clinical rank pictures in future.
Dual Stream Segmentation Network for Real-Time Semantic Segmentation

Modern real-time segmentation methods employ two-branch framework to achieve good speed and accuracy trade-off. However, we observe that low-level features coming from the shallow layers go through less processing, producing a potential semantic gap between different levels of features. Meanwhile, a rigid fusion is less effective due to the absence of consideration for two-branch framework characteristics. In this paper, we propose two novel modules: Unified Interplay Module and Separate Pyramid Pooling Module to address those two issues respectively. Based on our proposed modules, we present a novel Dual Stream Segmentation Network (DSSNet), a two-branch framework for real-time semantic segmentation. Compared with BiSeNet, our DSSNet based on ResNet18 achieves better performance 76.45% mIoU on the Cityscapes test dataset while sharing similar computation costs with BiSeNet. Furthermore, our DSSNet with ResNet34 backbone outperforms previous real-time models, achieving 78.5% mIoU on the Cityscapes test dataset with speed of 39 FPS on GTX1080Ti.
An Analysis of Cognitive Elements and Effects of Video Commercials

This chapter starts with an analysis of differences between the indexes attached to still images in the image database and cognitive elements of still images extracted from the cognitive experiment. It then analyzes the relationship between cognitive elements of each video shot generated based on still images and cognition of the video commercial produced by combining them. Lastly, it discusses how to index the video shot and production methods of video commercials based on the analyses. The image database tends to attach the records of the timing and place of shooting and features of the persons as indexes. In order to produce an attractive video commercial, it is necessary that video shots convey rich cognitive elements that are not too simple. This ensures that when combining several video shots, their cognitive elements have some consistency and consistent cognitive elements are woven into the video commercial to constitute a rich semantic network.
Formal Specification and Verification of Data Separation for Muen Separation Kernel

Introduction: Development of integrated mixed-criticality systems is becoming increasingly popular for application-specific systems, which needs separation mechanism for available onboard resources and the processors equipped with hardware virtualization. Hardware virtualization allow the partitions to physical resources, which include processor cores, memory, and I/O devices, among guest virtual machines (VMs). For building mixed criticality computing environment, traditional virtual machine systems are inappropriate because they use hypervisors to schedule separate VMs on physical processor cores. In this article, we discuss the design of an environment for mixed-criticality systems: The Muen an x86/64 separation kernel for high assurance. The Muen Separation Kernel is an Open Source microkernel which has no runtime errors at the source code level. The Muen separation kernel has been designed precisely to encounter the challenging requirements of high-assurance systems built on the Intel x86/64 platform. Muen is under active development, and none of the kernel properties of it has been verified yet. In this paper, we present a novel work of verifying one of the kernel properties formally. Method: The CTL used in NuSMV is a first-order modal along with data-depended processes and regular formulas. CTL is a branching-time logic, meaning that its model of time is a tree-like structure in which the future is not determined; there are different paths in the future, any one of which might be an actual path that is realized . This section shows the verification of all the requirements mentioned in section 3. In NuSMV tool the command used for verification of the formulas written in CTL is checkctlspec -p ”CTL-expression”. The nearest quantifier binds each occurrence of a variable in the scope of the bound variable, which has the same name and the same number of arguments. Result: Formal methods have been applied to various projects for specification and verification purpose. Some of them are the SCOMP , SeaView , LOCK,and Multinet Gateway projects. The TLS was written formally. Several mappings were done between the TLS and the SCOMP code: Informal English language to TLS, TLS to actual code , and TLS to pseudo-code. The authors present an ACL2 model for a generic separation kernel also known as GWV approach. Conclusion: We consider the formal verification of data separation property which is one of the crucial modules to achieve the separation functionality. The verification of the data separation manager is carried out on the design level using the NuSMV tool. Furthermore, we present the complete model of the data separation unit along with its code written in the NuSMV modelling language. Finally, we have converted the non-functional requirements into the formal logic, which then has verified the model formally.
MENTORING EFFECT ON STUDENTS ACADEMIC ACHIEVEMENT AT SECONDARY SCHOOL LEVEL

The students are measured the basic element in all education systems. They are the central hub through which educational goals are achieved. Therefore, it becomes mandatory that their needs are assessed and encountered constantly to improve their learning and achievement of academic objectives. This is an actual phenomenon that mentoring is dignified as a “learning support” which is provided to the students. Mentoring may effect in the student’s advanced educational triumph. This research study aimed to investigate the mentoring effect on the academic achievement of the students at secondary level. In the study true experimental design was used to evaluate data. The population of the research study was comprising on 137, Govt. boy’s schools besides the 46,189, learners registered in these schools at district Chakwal. Mentor teacher acquired four weeks mentoring training as per, continuous professional development (CPD) framework, includes; lesson plan, audio visual aids, activity based learning, student’s & teacher communication, classroom organisation, and assigning home task. Mentor teacher delivered four-week treatment to mentee students. Whereas no mentoring intervention was provided to the controlled group. The students were randomly assigned the group on the basis of pre-test score. Pre-test & post-test were administered to obtain student’s score. To measure the means and analysis of the data; t-test plus Levene’s equality test were practised. It was concluded that mentoring effect was very dynamic in increasing academic achievement of students.
An English-Swahili parallel corpus and its use for neural machine translation in the news domain

This paper describes our approach to create a neural machine translation system to translate between English and Swahili (both directions) in the news domain, as well as the process we followed to crawl the necessary parallel corpora from the Internet. We report the results of a pilot human evaluation performed by the news media organisations participating in the H2020 EU-funded project GoURMET.
Research on UAV Image Recognition Based on Convolutional Neural Network

As an important visual function of drones, image recognition has helped drones accomplish a variety of tasks in complex environments. At present, most of drone image detection uses the traditional method of artificial feature extraction. It is difficult to meet the increasing accuracy requirements. Based on this, this paper applies convolutional neural networks to UAV image recognition to meet high-precision requirements. This article first introduced the development process of convolutional neural networks to facilitate their overall integration. And then summarized the “visual function” of the drone, then analyzed the future development trend of the drone image recognition technology, expounded the key technology of the drone image recognition, and finally explained the image recognition technology based on convolutional neural network, discussed its recognition process and recognition method, and it has a certain significance for deepening the research of UAV image recognition.
Twitter Data Mining for Targeted Marketing

The ascendancy of social media has enabled a large number of users all around the world to share information and their beliefs regarding various topics. Various social media websites like Google+, Facebook, Instagram, Twitter, LinkedIn, etc. allow the users to share their opinions and get connected to other users in any part of the world. People openly share information about their interests in products and purchases, thus giving organizations a considerable opportunity to extract valuable insights and get an idea of customer behavior. These insights can, in turn, be used to target customers at specific locations based on their preferences. This paper aims to provide an approach to target users on Twitter by discerning their preferences with the use of unique keywords in the real estate sector. This approach consists of multiple stages that extract data from Twitter to collect usernames and carry out further classification and elimination of usernames through different stages to qualify them based on their probability of affording a property in the target regions. The accounts of qualified usernames are further crawled to find extra contact information to increase the audience reachability. This approach can thus enable organizations to send targeted advertisements to a set of users based on their locations and preferences, thus allowing them to revamp their strategy and enhance their marketing output.
Planogram Design Analytics using Image Processing

A planogram is a tool for visual merchandising of retail stores. It displays a detailed view of the retail store with the major intent for product placement. The planogram is highly useful for examining the point of sale as it demonstrates the exact positioning of products. Two major benefits are there for building planograms while planning the store layout such as maximizing sales and space of the retail store.An important part of Planogram management is to read planogram drawings and images and convert these to representational data in CSV or database formats.In this part, Contour analysis of image using OpenCV along with combination of text detection and recognition is performed using Connectionist Text Proposal Network and Convolutional Recurrent Neural Network deep learning models respectively. The proposed planogram design analytics system is implemented using real data. The customer had tested the system as per different cases. The feedback obtained from them confirms that the system meets the requirements to their satisfaction.
A Case Study in Multi-Emotion Classification via Twitter

Social media platforms generate continuously tremendous quantities of valuable knowledge for users’ perspectives towards our global societies for example, Twitter. Sentiment analysis reveals its vital role to take the advantage of these different perspectives for different applications like, political votes, business domains, financial risks, and etc. Most traditional approaches in sentiment analysis predict a single attitude from the users’ tweets. This is not considered a quiet correct approach, due to multiple of implied feelings in the users’ tweets towards a specific topic, person, or event. This research presents hybrid machine learning approach, that can predict multiple feelings in the same tweet. It applies two methods, which are Binary relevance based on four machine learning algorithms in addition to Convolutional neural networks. The tweets preprocessed and converted into feature vectors. Word embedding, emotion lexicons, and frequency distribution probability are used to extract features from the input tweets. The paper finally presents a case study of two experiments to show the multi emotion prediction classifiers workflow on real tweets. The applied dataset is on SemEval2018 Task E-c. Binary relevance method has hamming score 0.53, and Convolutional neural network method has score 0.54.
Histopathology Image Classification using Enhanced Fuzzy Min-Max network

Histopathology image classification can offer great support towards breast cancer identification. In this paper, an Enhanced fuzzy min max (EFMM) neural network is considered and applied to histopathological images. Gray level co-occurrence matrix (GLCM) based feature extractor is employed to extract the textural information from each histopathology image and then these information are applied on EFMM model to accurately classify the histopathology images. In this work, Breast cancer histopathology (BreaKHis) database is considered, which comprises malignant and benign histopathology images at four different magnifications. Simulation outcomes depict that the magnification factor of 100× provides greater recognition rate than other magnifying factors.
Tandem: Securing Keys by Using a Central Server While Preserving Privacy

Abstract Users’ devices, e.g., smartphones or laptops, are typically incapable of securely storing and processing cryptographic keys.We present T andem , a novel set of protocols for securing cryptographic keys with support from a central server. T andem uses one-time-use key-share tokens to preserve users’ privacy with respect to a malicious central server. Additionally, T andem enables users to block their keys if they lose their device, and it enables the server to limit how often an adversary can use an unblocked key. We prove T andem ’s security and privacy properties, apply T andem to attributebased credentials, and implement a T andem proof of concept to show that it causes little overhead.
A Hybrid Architecture for Semantic Image Similarity Learning

Differentiating between similar image inputs has always been one of the key tasks in machine learning. Inspired by the recent progress in the area of natural language processing, we introduce the image similarity learning model that considers the semantic scene similarity in its decision process. The architecture of the model is organized in a way to consider the similarity in the feature vectors of the images, as well as the semantic similarity in their generated captions, which are later combined to reach a more accurate result. We use Siamese-like network structure for parallel image processing and receiving the accurate results. Our model confirmed to improve the accuracy of a standard convolutional neural network and was validated on INRIA Holidays Dataset.
Identity Verification and Fraud Detection During Online Exams With aPrivacy Compliant Biometric System

Distant learning is an alternative solution to education when the learner is far from the school or cannot attendcourses for professional or medical reasons. The main objective of this work is to design a smart applicationof remote exams, using a multibiometric system combining face with deep learning and keystroke dynamicsto verify the identity of the learner. Privacy protection is consider in this work as an important issue becausemany personal data are processed in the proposed solution. We consider in this paper experiments under real-life conditions to identify abnormal behaviours with confidence indicators. We show the system ability tomake the correct decision while preserving learner’s privacy.
The Use of the Incomplete Tetrad Method for Measuring the Similarities in Nonmetric Multidimensional Scaling

Abstract Research background: So far, many methods of direct measurement of similarity in multidimensional scaling have been developed (e.g. ranking, sorting, pairwise comparison and others). The method selection affects the subjective feelings of the respondents, i.e. fatigue, weariness resulting from making numerous assessments, or difficulties in expressing similarity assessments. Purpose: In the proposed method, for all four-element sets (tetrads) of objects a respondent is asked to pick out the most similar and the least similar pair. Because the number of tetrads increases very rapidly with the number of objects, the aim of the study is to indicate the possibility of measuring similarities based on the reduced number of tetrads. Research methodology: In order to make scaling results independent from respondents’ subjective effects the analysis was made on the basis of the given distance matrix. To construct perceptual maps based on tetrads, multidimensional scaling with the use of the MINISSA program was performed. The quality of matching the resulting points configuration to the configuration determined based on the distance matrix was tested by a Procrustes statistic. Results: It was demonstrated that the choice of the incomplete set of tetrads has no significant effect on the results of multidimensional scaling, even when all pairs of objects in tetrads cannot be presented equally frequently. Novelty: An original method for calculating similarities in nonmetric multidimensional scaling.
PaaS Optimization of Apache Applications Using System Parameter Tuning of Big Data Platforms in Distributed Computing

Widely used data processing platforms use distributed systems to process huge data efficiently. The aim of this article is to optimize the platform services by tuning only the relevant, tunable, system parameters and to identify the relation between the software quality metrics. The system parameters of data platforms based on the service level agreements can be defined and customized. In the first stage, the most significant parameters are identified and shortlisted using various feature selection approaches. In the second stage, the iterative runs of applications are executed for tuning these shortlisted parameters to identify the optimal value and to understand the impact of individual input parameters on the system output parameter. The empirical results imply significant improvement in performance and with which it is possible to render the proposed work optimizing the services offered by these data platforms.
Correlation Filtering Tracking Algorithm Based on Adaptive Model update

Aiming at the problem that the kernel correlation filter is prone to tracking failure and tracking drift in complex scenes, this paper proposes object tracking algorithm that pays attention to adaptive model updating based on the method from [5]. The model updating strategy based on object speed changes and response value of object enhances the adaptability of model in the process of object tracking. Through the dynamic adjustment of the threshold, the applicability of the judgment of object tracking results is increased. The experiments on the OTB-2013 dataset show that the algorithm proposed in this paper has better tracking accuracy for objects in complex scenes and has real-time performance.
Registering Historical Context for Question Answering in a Blocks World Dialogue System

Task-oriented dialogue-based spatial reasoning systems need to maintain history of the world/discourse states in order to convey that the dialogue agent is mentally present and engaged with the task, as well as to be able to refer to earlier states, which may be crucial in collaborative planning (e.g., for diagnosing a past misstep). We approach the problem of spatial memory in a multi-modal spoken dialogue system capable of answering questions about interaction history in a physical blocks world setting. We employ a pipeline consisting of a vision system, speech I/O mediated by an animated avatar, a dialogue system that robustly interprets queries, and a constraint solver that derives answers based on 3D spatial modelling. The contributions of this work include a semantic parser competent in this domain and a symbolic dialogue context allowing for interpreting and answering free-form historical questions using world and discourse history.
Monwatch: A fuzzy application to monitor the user behavior using wearable trackers

Nowadays, the proliferation of wearable devices has enabled monitoring user behaviours and activities in a non-invasive, autonomous and straightforward way. Moreover, new trend analysis has been boosted by biosignal sensors from wearable trackers, such as inertial or heart rate sensors. The knowledge of such user activity presents a personalized monitoring to prevent any kind of physical or neurological disorders through the sensor evaluation by an expert. To this end, the definition of key indicators and the display of results and relevant analyses require of agile and effective tools. Therefore, this proposal presents a novel web application where the data obtained from a Fitbit Ionic smartwatch wearable are collected, synchronized and compiled to present a summary of an user's daily activity, which is defined by a linguistic description using fuzzy logic to represent the most relevant Health Key Indicators (HKI). Moreover, an analysis of the user's behaviour over time is proposed by inferring relevant patterns from a fuzzy clustering algorithm.