Reviewerly/exHarmony huggingface dataset:
The effect of Test-Driven Development and Behavior-Driven Development on Project Success Factors: A Systematic Literature Review Based Study

Agile testing approaches test-driven development (TDD), behavior-driven development (BDD); have gained popularity since the introduction of agile practices. The concept of using these approaches in software projects is to help projects succeed. The effect of TDD and BDD on Project success factors namely, cost, time, and customer satisfaction/external quality, remains an area of investigation. Therefore, this study investigates the results of previously published articles on the effect of TDD and BDD on project success factors. A systematic literature review has been conducted considering articles published between 1999 up to 2020. Thirty-one studies were selected and analyzed. 74.1% of studies are experiments, 25.8% are case studies. 67.7% of these studies were performed in an industrial setting, 29.0% were performed in an academic setting and only one study with 3.22% was mixed academic and industrial settings. The result shows that the TDD and the BDD consume more time and cost compared to traditional testing (TLD). Moreover, the TDD as TLD may not necessarily satisfy the customer unlike the BDD appears that achieve customer satisfaction. Although, when TDD compared to BDD, TDD consumed more time and lower customer acceptance than the BDD.
Hierarchical Similarity Learning for Language-Based Product Image Retrieval

This paper aims for the language-based product image retrieval task. The majority of previous works have made significant progress by designing network structure, similarity measurement, and loss function. However, they typically perform vision-text matching at certain granularity regardless of the intrinsic multiple granularities of images. In this paper, we focus on the cross-modal similarity measurement, and propose a novel Hierarchical Similarity Learning (HSL) network. HSL first learns multi-level representations of input data by stacked encoders, and object-granularity similarity and image-granularity similarity are computed at each level. All the similarities are combined as the final hierarchical cross-modal similarity. Experiments on a large-scale product retrieval dataset demonstrate the effectiveness of our proposed method. Code and data are available at https://github.com/liufh1/hsl.
Crank That Feed: A Physical Intervention for Active Twitter Users

Passively consuming digital social media content often precludes users from mindfully considering the value they derive from such experiences as they engage in them. We present a system for using Twitter that requires users to continuously turn a hand crank to power their social media screen. We evaluate the device and its effects on how users value Twitter with 3 participants over 3 weeks, with the middle week of Twitter usage directed exclusively through our system. Using our device caused a dramatic decrease in Twitter usage for all participants, which either persisted or rebounded in the post-intervention week. Our analysis of diary studies and qualitative interviews surfaced three themes indicating shifting focus on content, shifting awareness about the role of social media, and new social dynamics around content-sharing.
Automated Detection and Classification of Breast Cancer Tumour Cells using Machine Learning and Deep Learning on Histopathological Images

Cancer is the result of abnormal growth of cells in a specific body part. It can outspread to other body parts rapidly if not diagnosed in a timely manner. Breast cancer is caused due to development of cancer cells in the breast tissue of women. Breast Cancer is the most frequent cause of death in women after lung cancer. If detected at primary stages, the breast cancer can be cured and the chances of survival drastically increases. Advances in screening and treatment for breast cancer have improved survival rates dramatically since 1989[1]. In this paper we have applied machine learning for image classification and further segmentation algorithms is applied for detection of the tumorous cells. The designing of the model began with classification of histopathological image dataset into Cancerous and Non - cancerous classes using Support Vector Machine (SVM) and Convolutional Neural Network (CNN) algorithms. Both the classifiers are examined on the basis of sensitivity, specificity, accuracy, precision, f1-score parameters. The resulting image i.e., Cancerous obtained from the classification algorithms are further used as an input for Image segmentation models. Genetic Algorithm (GA) and K-Means are used for the segmentation of the histopathological images. Experimental results showed that CNN for image classification in combination with GA for image segmentation gave more precise results with accuracy 99%.
Learning to Boost the Efficiency of Modern Code Review

Modern Code Review (MCR) is a standard in all kinds of organizations that develop software.MCR pays for itself through perceived and proven benefits in quality assurance and knowledge transfer. However, the time invest in MCR is generally substantial. The goal of this thesis is to boost the efficiency of MCR by developing AI techniques that can partially replace or assist human reviewers. The envisioned techniques distinguish from existing MCR-related AI models in that we interpret these challenges as graph-learning problems. This should allow us to use state-of-science algorithms from that domain to learn coding and reviewing standards directly from existing projects. The required training data will be mined from online repositories and the experiments will be designed to use standard, quantitative evaluation metrics. This research proposal defines the motivation, research-questions, and solution components for the thesis, and gives an overview of the relevant related work.
Designing a Self-Efficacy Game for Health Literacy in Marginalized Communities

Health inequity is a critical problem in the United States and one that primarily affects marginalized communities. One critical aspect to interventions addressing this issue is the aim to increase health literacy, so that members of these communities can make informed decisions and feel empowered to take charge of their personal health. Our team developed a transformational game that uses self-efficacy to address players’ health literacy in context. Using properties from both visual novel and strategy simulation genres, we present a game in which players take on the role of a community manager who works to better their community by completing quests from non-player character (NPC) community members. The current paper contributes our research and iterative design processes, and highlights future directions utilizing focus groups and playtesting with community members, game development, and evaluation studies to assess impact.
Autonomous Navigation Assurance with Explainable AI and Security Monitoring

With the rise of disruptive artificial intelligence (AI) technology, assuring autonomous systems is of the utmost importance. The drive to spread autonomous vehicles to all whether military or the commercial space is makes autonomous navigation a must for assurance. In this paper, we introduce the concept of using explainable AI to assure the operational autonomy of a vehicle while monitoring the security of the system. We demonstrate our paradigm by developing a system monitor using AI (i.e., Random Forest Tree algorithm) to assure the native autonomy of an autonomous Turtlebot3 and monitor security status of the Turtlebot3 computer system and sensor data. Then we dig into the decision trees of the AI algorithm and extract information to help explain the navigation autonomy and decisions made by our monitor. The explainability of the security monitor is not part of this experiment but the security monitor is useful for helping the autonomous navigation make decisions in the instance of attack. Further, we demonstrate the efficacy of the monitor by subjecting the Turtlebot3 to a "Sharp Turn Maze", which was designed to be hard to solve by the native autonomy, and a barrage of security attacks. These experiments allow the AI-Monitor to demonstrate its ability to identify and explain unproductive or wrong decisions made by the native autonomy and identify cyber-security related faults in order to trigger navigation contingencies.
Machine Learning, Compression, and Image Quality

The current drive toward incorporating machine learning (ML) models within automated closed-loop workflows renews existing issues for image quality prediction; specifically, maintaining the operator-in-the-loop's trust in system operation. This paper reviews methods to assess information content in the reconstruction process to maintain trust in closed-loop workflows. We structure commonly used ML architectures in the form of Autoencoders. Auotencoders are commonly studied for image compression and reconstruction. For manual exploitation, the interpretability of an image indicates the potential intelligence value of the data. Historically, the National Imagery Interpretability Rating Scale (NIIRS) has been the standard for quantifying the intelligence potential based on image analysis by human observers. Empirical studies have demonstrated that spatial resolution is the dominant predictor of the NIIRS level of an image and that compression to 1 bit per pixel can maintain that NIIRS level. However, with modern ML that digests images rather than extracted features, what is the corresponding size of the latent space required to maintain the NIIRS levels? To those ends, we will operate on moderate size images, 480x480 pixels, to provide realistic generalizable estimates over those experiments against similar sprite size (<; 100x100 pixels) images.
Analysis of constraints on data management of intelligent platform for smart building in the era of big data

Abstract With the rapid development of science and technology in China, data has become the core of various fields and industries. The construction industry is the first to bear the brunt. The merits and demerits of data management can directly determine whether smart buildings are worthy of the term “smart”. However, at present, the management level of most intelligent platform data of smart buildings is low, which cannot well adapt to and meet the development needs of smart buildings. Therefore, in view of the problems existing in the data management of the intelligent platform of the existing smart building in the era of big data, this paper conducts in-depth research and exploration, and points out the main restraining factors of the data management of the intelligent platform of the smart building. This paper expounds how to achieve more fine, safe and intelligent management of the data of intelligent platform of smart buildings in the era of big data.
Pose-robust Face Recognition by Deep Meta Capsule network-based Equivariant Embedding

Despite the exceptional success in face recognition related technologies, handling large pose variations still remains a key challenge. Current techniques for pose-robust face recognition either, directly extract pose-invariant features, or first synthesize a face that matches the target pose before feature extraction. It is more desirable to learn face representations equivariant to pose variations. To this end, this paper proposes a deep meta Capsule network-based Equivariant Embedding Model (DM-CEEM) with three distinct novelties. First, the proposed RB-CapsNet allows DM-CEEM to learn an equivariant embedding for pose variations and achieve the desired transformation for input face images. Second, we introduce a new version of a Capsule network called RB-CapsNet to extend CapsNet to perform a profile-to-frontal face transformation in deep feature space. Third, we train the DM-CEEM in a meta way by treating a single overall classification target as multiple sub-tasks that satisfy certain unknown probabilities. In each sub-task, we sample the support and query sets randomly. The experimental results on both controlled and in-the-wild databases demonstrate the superiority of DM-CEEM over state-of-the-art.
An Ensemble Approach for Handling Class Imbalanced Disease Datasets

It is always very much helpful for the doctors if early diagnosis report of some chronic disease is available. Most of the disease datasets, prepared by some means, are imbalanced in nature, which implies that number of instances belonging to one class (i.e. the minority class) is exceptionally less compared to the number of instances in the other class (i.e. the majority class). Hence, if we directly feed such data to a classification model, it would mislead the model performance. To address this problem researchers apply different undersampling approaches. One undersampling method may not fulfil the requirement as it may fail to select the true representative samples of the majority class, thereby losing many important data. To this end, we have proposed an ensemble approach where we have initially considered three different undersampling methods (Condensed Nearest Neighbour (CNN), NearMiss (version-1) and NearMiss (version-3)) and formed a union of data undersampled by those methods. This approach ensures better balance between the majority and minority classes which is beyond the reach of the individual methods considered here. We have used AdaBoost classifier to note the final classification results. On evaluating our method on three datasets namely WDBC, New-thyroid and BUPA, we have obtained 99.7%, 99.56% and 86.59% AUC score respectively which are better than the contemporary methods. Source code of this work is available at https://github.com/Sayansurya/Project.
Comparative Analysis for Predicting Non-Functional Requirements using Supervised Machine Learning

Functional and non-functional requirements are two important aspects of the requirements gathering phase (RGP) in any system development lifecycle (SDLC) model. The FRs are much simpler to understand and easily extractable from the user stories at RGP. On the other hand, the non-functional requirements (NFRs) are critical but play a significant role to improve the quality of the product and are used in determining the acceptance of a designed system. Inside the NFR, several quality factors focus on the specific quality attribute of a system, like security, performance, reliability, etc. To classify the NFRs for each category is a challenging task. This paper mainly focuses on the prediction of the requirements classification of NFRs by using supervised machine learning (ML) algorithms followed by comparative analysis on five different ML algorithms: decision tree, k-nearest neighbor (KNN), random forest classifier (RFC), naïve Bayes and logistic regression (LR). The study has been conducted in two phases. In the first phase, the model has been designed which accepts a dataset containing textual data where 11 quality attributes are focused for prediction, and evaluation is done based on 15% of test data and 85% of training data, while in the second phase, the performance of each algorithm is evaluated based on four different evaluation metrics: precision, recall, accuracy, and confusion matrix. The exhaustive results of the comparative analysis demonstrate that the performance of the LR algorithm is the best of all algorithms with very high prediction rates and 75% accuracy. Besides, the naïve Bayes resulted in 66% accuracy at second place, the decision tree provided 60% accuracy and marked third, the RFC with 53% accuracy being at fourth, and KNN with 50% accuracy being lowest of all. The LR algorithm should be preferred for the prediction of the classification of NFRs
Neural Utterance Confidence Measure for RNN-Transducers and Two Pass Models

In this paper, we propose methods to compute confidence score on the predictions made by an end-to-end speech recognition model in a 2-pass framework. We use RNN-Transducer for a streaming model, and an attention-based decoder for the second pass model. We use neural technique to compute the confidence score, and experiment with various combinations of features from RNN-Transducer and second pass models. The neural confidence score model is trained as a binary classification task to accept or reject a prediction made by speech recognition model. The model is evaluated in a distributed speech recognition environment, and performs significantly better when features from second pass model are used as compared to the features from streaming model.
Research on Text to Image Based on Generative Adversarial Network

In recent years, Generative Adversarial Network (GAN) has quickly become the most popular deep generative model framework, and it is also the most popular topic in the current deep learning research field. Although the generative adversarial network has achieved remarkable results from text description to image generation, when a complex image containing multiple objects, the position of each object will be blurred and overlapped, and the edges of the generated image will be blurred and local textures will be unclear. Usually given text description can generate the corresponding rough image, but there are still some problems in the image details. In order to solve the above problems, on the basis of Stack GAN, a scene graph-based stacked generative confrontation network model (Scene graph stack GAN, SGS-GAN) is proposed, which converts the text description into The scene graph uses the scene graph as the condition vector and inputs the random noise into the generator model to obtain the result image. The experimental results show that the Inception store of the SGS-GAN model on the Visual Genome and COCO data sets reached 6.64 and 6.52, respectively, which were increased by 0.212 and 0.219 compared to Sg2Im. This proves that the diversity and vividness of the generated samples and the sharpness of the image are obviously improved after the number of times of training and the input of the scene graph.
Automatically Authoring Regression Tests for Machine-Learning Based Systems

Two key design characteristics of machine learning (ML) systems-their ever-improving nature, and learning-based emergent functional behavior - create a moving target, posing new challenges for authoring/maintaining functional regression tests. We identify four specific challenges and address them by developing a new general methodology to automatically author and maintain tests. In particular, we use the volume of production data to periodically refresh our large corpus of test inputs and expected outputs; we use perturbation of the data to obtain coverage-adequate tests; and we use clustering to help identify patterns of failures that are indicative of software bugs. We demonstrate our methodology on an ML-based context-aware Speller. Our coverage-adequate, approx. 1 million regression test cases, automatically authored and maintained for Speller (1) are virtually maintenance free, (2) detect a higher number of Speller failures than previous manually-curated tests, (3) have better coverage of previously unknown functional boundaries of the ML component, and (4) lend themselves to automatic failure triaging by clustering and prioritizing subcategories of tests with over-represented failures. We identify several systematic failure patterns which were due to previously undetected bugs in the Speller, e.g., (1) when the user misses the first letter in a short word, and (2) when the user mistakenly inserts a character in the last token of an address; these have since been fixed.
From early biological models to CNNs: do they look where humans look?

Early hierarchical computational visual models as well as recent deep neural networks have been inspired by the functioning of the primate visual cortex system. Although much effort has been made to dissect neural networks to visualize the features they learn at the individual units, the scope of the visualizations has been limited to a categorization of the features in terms of their semantic level. Considering the ability humans have to select high semantic level regions of a scene, the question whether neural networks can match this ability, and if similarity with humans attention is correlated with neural networks performance naturally arise. To address this question we propose a pipeline to select and compare sets of feature points that maximally activate individual networks units to human fixations. We extract features from a variety of neural networks, from early hierarchical models such as HMAX up to recent deep convolutional neural netwoks such as Densnet, to compare them to human fixations. Experiments over the ETD database show that human fixations correlate with CNNs features from deep layers significantly better than with random sets of points, while they do not with features extracted from the first layers of CNNs, nor with the HMAX features, which seem to have low semantic level compared with the features that respond to the automatically learned filters of CNNs. It also turns out that there is a correlation between CNN's human similarity and classification performance.
Library Intelligent Book Recommendation System Using Facial Expression Recognition

To solve the problem of information overload, the recommendation system has developed in various fields. Faced with a variety of forms and rich masses of books, the book recommendation system based on various recommendation methods is applied in the library. In the traditional book recommendation system, there are some problems, such as single recommendation mode, lack of pertinence, too centralized recommended books and so on. In order to solve these problems, this paper proposes a personalized book recommendation system. Through user expression recognition to obtain user preferences, according to user preferences to recommend books to user. Facial expression recognition is realized by using convolution neural network model. This kind of recommendation method has real-time and authenticity. The book recommendation system based on facial expression recognition can improve users' sense of use when applied to library robots.
NLP on YouTube: A Look on Feminism

Feminism is a complex phenomenon that has evolved in recent times. We can see it through different optics such as social media or traditional media. The aim of this project is to analyze, descriptively, how it has grown on a platform like YouTube. We are using new computer techniques and trying to understand how the concept evolves. Hence, in this paper, we will provide a language analysis that examines deliberative frameworks and online roles. Our study focuses on adding new methods that offer insights into the Digital Agora. The main outcomes show that the deliberation on YouTube, with regard to Spanish Feminism, has received an important and enormous increase especially since 2016, revealing two possible souls within that debate.
Image Captioning with Attention Based Model

Defining the content of an image automatically in Artificial Intelligence is basically a rudimentary problem that connects computer vision and NLP (Natural Language Processing). In the proposed work, a generative model is presented by combining the recent developments in machine learning and computer vision based on a deep recurrent architecture that describes the image using natural language phrases. By integrating the training picture, the trained model maximizes the likelihood of the target description sentence. The efficiency of the model, its accuracy and the language it learns is only dependent on the image descriptions, which was demonstrated by experiments performed on several datasets.