Reviewerly/exHarmony huggingface dataset:
Tracking of Moving Objects Based on Video Image

The article presents six different algorithms for tracking objects operating in real time on the video image of a visible light camera. Tracking based on a video image defines the process of locating a moving object or many objects in subsequent frames of the film. The evaluation of the effectiveness of the analyzed object tracking and detection algorithms was made on the basis of numerical calculations carried out on the basis of two video films. The calculation results are illustrated in figures and tables.
A Distortion or ‘Our’ Default?

Introduction. In his 2015 examination of propaganda, How Propaganda Works, Jason Stanley writes: Speech that is silencing has the effect of restricting our free speech rights; this is why states that seem closer to embodying liberal democratic ideals, such as Canada and United States, have considerably less hate speech in the public domain than states further away from such ideals, such as Hungary. (Stanley 2015, p. 37) Given the global events since, I was taken aback by this claim when recently reading Stanley’s book. To me, Stanley seemed to be wrong about the United States more closely embodying liberal democratic ideals and containing considerably less hate speech than (say) Hungary. Or at the very least, his claim seemingly no longer obtains, and the change in the states of affairs has been both rapid and shocking. Having said that, and perhaps with less pessimism than my initial...
A Quest of Software Process Improvements in DevOps and Kanban:

A good software process improves software products. In the case of a small software company, software development is a matter of survivability due to its limited resources to develop software. XYZ Company is a very small software company that adopted Kanban and DevOps and faced software delivery delays. It is necessary to recommend the software process improvements to solve this problem. Software process improvements are the outcomes of measurement and analysis of maturity levels using the ISO 29110 framework in a qualitative study. They are then analyzed using the Lean Six Sigma tools, namely gap analysis, root cause analysis, and Pareto analysis. Delphi method validated them and resulted in 18 improvement recommendations within four domains, namely (a) product, (b) people, (c) technology, and (d) process. The improvements span across two main processes within software development, namely (a) Project Management (PM) and (b) Software Implementation (SI). The XYZ Company or any agile-based software company could adopt the 18 improvement recommendations to enhance the software process and quality.
Classification of Quranic Topics Using Ensemble Learning

the real datasets in the world usually are imbalanced; the number of samples for their classes is not equal. Classifying these datasets makes the classifiers pay attention to the class with more samples than the classes with fewer samples. The Qur'anic dataset can be considered an imbalanced dataset because verses of the Qur'anic topics are not equal. Many studies have been performed to classify Qur'anic text using different classifiers. However, few studies classified the Qur'anic verses based on Imbalanced Learning (IL). So, this work aims to classify the Qur'anic text using Ensemble methods, Boosting and Bagging. The base classifiers of these methods were LibSVM, Naïve Bayes, KNN, and J48. Three techniques are conducted in this paper based on the standard classifiers. The three techniques are: implementing the base classifiers alone, implementing these classifiers with the Boosting method, and implementing the classifiers with the Bagging method. The results showed that the Quranic classification performance was improved when the ensemble methods were applied for the imbalanced Qur'anic verses in the standard classifiers.
RVPLAN: a general purpose framework for replanning using runtime verification

We present RVPLAN, a general purpose framework for replanning that combines classical planning with runtime verification. RVPLAN uses runtime monitors to report violations of the planner’s assumptions over the system, effectively detecting plan failures. Re-planning is triggered when such violations are detected, using the feedback from the monitors to update the model of environment. We illustrate the use of our framework with a remote inspection running example.
Crowdsourced Urban Annotations and Augmented Reality as Design Thinking Tools to Navigate and Interact with Urban Data

Urban data and urban analytics have become global phenomena as modern cities move towards institutionalizing the collection and the availability of statistical and urban-sensing data. Projects like the Citizen Sense, Smart Citizen, SONYC, and the New York City Open Data projec are but a few of many global initiatives that study and highlight the importance of working with urban data as means to annotate, measure, and build a smart city (Picon 2015) or a sentient city (Shepard 2011) whose present and futures rely heavily on the policies and strategies of collecting and analyzing urban data. Are urban data objective and un-opinionated snapshots of reality? Will presentation strategies and mediums influence how data is interpreted and used in designing the futures of the city? Are statistical and sensor data true representations of urban reality or are there other data that also needs to be considered, like oral histories and inter-city migration trails, to present a more accurate picture? This paper presents an HCI centric design research that addresses some of the questions and concerns regarding urban statistics and urban data as meta descriptors of the city, and as blueprints of the metropolis of the future.
Enhanced CNN-Based Gaze Estimation on Wireless Capsule Endoscopy Images

Wireless capsule endoscopy (WCE) is a modality used for the non-invasive examination of the gastrointestinal (GI) tract. Physicians diagnose pathologies in images derived from Capsule Endoscopy (CE) using specific gaze patterns to observe pathologically related visual cues. Lately, deep learning has advanced in the domain of human eye-fixation estimation in natural images. However, the potentials of predicting the eye related patterns, such as eye fixations, in medical images has not been thoroughly investigated. In this work, we propose a CNN auto-encoder model, that is capable of predicting saliency maps estimating the gaze-patterns, in terms of eye-fixations, of physicians in CE images. The proposed model outperforms other approaches for visual saliency estimation based on physicians' eye fixation by providing an AUC-J of 0.726 among CE images depicting various pathological and normal cases.
Querying for Interactions

Deep Learning and Computer Vision advances enabled sophisticated information extraction out of images and videos. Recent research aims to make objects, their types and relative locations, first class citizens for query processing purposes. We initiate research to explore declarative queries for real time video streams involving objects and their interactions. We seek to efficiently identify frames in which an object is interacting with another in a specific way. We propose progressive filters (PF) algorithm which deploys a sequence of inexpensive and less accurate filters to detect the presence of query specified objects on frames. We demonstrate that PF derives a least cost sequence of filters given the query objects' current selectivities. Since selectivities may vary as the video evolves, we present a statistical test to determine when to trigger filters' re-optimization. Finally, we present Interaction Sheave, a filtering approach that uses learned spatial information about objects and interactions to prune frames that are unlikely to involve the query specified action between them, thus improving the frame processing rate. We present the results of a thorough experimental evaluation involving real datasets. We experimentally demonstrate that our techniques can improve query performance (up to an order of magnitude) while maintaining competitive F1-score.
Neural mixture models with expectation-maximization for end-to-end deep clustering

Any clustering algorithm must synchronously learn to model the clusters and allocate data to those clusters in the absence of labels. Mixture model-based methods model clusters with pre-defined statistical distributions and allocate data to those clusters based on the cluster likelihoods. They iteratively refine those distribution parameters and member assignments following the Expectation-Maximization (EM) algorithm. However, the cluster representability of such hand-designed distributions that employ a limited amount of parameters is not adequate for most real-world clustering tasks. In this paper, we realize mixture model-based clustering with a neural network where the final layer neurons, with the aid of an additional transformation, approximate cluster distribution outputs. The network parameters pose as the parameters of those distributions. The result is an elegant, much-generalized representation of clusters than a restricted mixture of hand-designed distributions. We train the network end-to-end via batch-wise EM iterations where the forward pass acts as the E-step and the backward pass acts as the M-step. In image clustering, the mixture-based EM objective can be used as the clustering objective along with existing representation learning methods. In particular, we show that when mixture-EM optimization is fused with consistency optimization, it improves the sole consistency optimization performance in clustering. Our trained networks outperform single-stage deep clustering methods that still depend on k-means, with unsupervised classification accuracy of 63.8% in STL10, 58% in CIFAR10, 25.9% in CIFAR100, and 98.9% in MNIST.
Genetic Operators and Their Impact on the Training of Deep Neural Networks

Neuroevolution is an area that recently has received more attention due to the increasing accessibility to computing power. Neuroevolution aims to combine the field of artificial neural networks (ANN) with meta-heuristic techniques. Several approaches exist in the field of neuroevolution, from different ways of training ANNs to find whole optimal architectures for a given purpose. In this chapter, four genetic algorithm variations are used to train a deep neuronal network (DNN) model to solve a maze, i.e., to go from a starting position to a goal position. This is done by giving as input to the DNN the current state of the maze in image form, and then, the network outputs the next movement action to take in order to change the current position in the maze and reach the goal. Each variation of the genetic algorithm presents changes in its operators, so the advantages or disadvantages of certain operators can be analyzed. The aim of this chapter is to analyze how the operators of the genetic algorithm impact the training and the results of the DNN model.
A Comparative Study on Single Image Dehazing Using Convolutional Neural Network

In many computer vision tasks, dehazing is an important preprocessing step such as in various application areas of tracking and object recognition. Dehazing is the task of removing haze content or haze effect from the image under consideration. Nowadays, learning-based methods are used widely for dehazing. Among them, a convolutional neural network is one such method. After the successful use of CNN in different fields of image processing, it is being applied with modifications in various algorithms for dehazing as well. Deep CNNs are good at obtaining feature information automatically in a data-driven manner. In this paper, we have discussed various methods that use CNN for dehazing and their performance is measured by PSNR and SSIM.
COSPEX: A Program Comprehension Tool for Novice Programmers

Developers often encounter unfamiliar code during software maintenance which consumes a significant amount of time for comprehension, especially for novice programmers. Researchers have come up with automated techniques that can provide effective code comprehension and summaries by analyzing a source code and present key information to the developers. Existing debuggers represent the execution states of the program but they do not show the complete execution at a single point. Studies have revealed that the effort required for program comprehension can be reduced if novice programmers are provided with worked examples. Hence, we propose COSPEX (Comprehension using Summarization via Program Execution) - an Atom plugin that dynamically extracts key information for every line of code executed and presents it to the developers in the form of an interactive example-like dynamic information instance. As a preliminary evaluation, we presented 14 undergraduates having Python programming experience up to 1 year with a code comprehension task in a user survey. We observed that COSPEX helped novice programmers in program comprehension and improved their understanding of the code execution. The source code and tool are available at: https://github.com/rishalab/COSPEX, and the demo on Youtube is available at: https://youtu.be/QQY-8KuDaEM.
On the use of Deep Learning Enabled Face Mask Detection For Access/Egress Control Using TensorFlow Lite Based Edge Deployment on a Raspberry Pi

This paper presents a face mask detection system that can be easily retrofitted to a standalone Access and Egress control system and can be deployed using any Edge processing subsystem. In this work, the widely available Raspberry Pi is used incorporating novel TensorFlow lite based training methods to demonstrate the utility of the approach. This subsystem achieves real-time face mask detection with high accuracy of over 89% in face mask detection at a confidence level of greater than 90% with excellent detection speeds of less than 3ms. The performance of the Raspberry Pi based implementation compares very favorably with GPU desktop-based alternative approaches that require excellent real time cloud enabled communications. The system is shown to work robustly with minimal human intervention and can be easily deployed at entrance points to lab or other employee facilities where face masks are mandatory for employee safety. The proposed algorithm consists of a single stage object detector together with an adaptive training dataset. This dataset comprises a hybrid combination of locally collected training data together with publicly available datasets and it is shown to be most appropriate for the face mask detection use case at hand.
Positive-unlabeled classification under class-prior shift: a prior-invariant approach based on density ratio estimation

Learning from positive and unlabeled (PU) data is an important problem in various applications. Most of the recent approaches for PU classification assume that the class-prior (the ratio of positive samples) in the training unlabeled dataset is identical to that of the test data, which does not hold in many practical cases. In addition, we usually do not know the class-priors of the training and test data, thus we have no clue on how to train a classifier without them. To address these problems, we propose a novel PU classification method based on density ratio estimation. A notable advantage of our proposed method is that it does not require the class-priors in the training phase; class-prior shift is incorporated only in the test phase. We theoretically justify our proposed method and experimentally demonstrate its effectiveness.
A Method for Evaluating Food Image Complexity from the Photographer’s Perspective

How complex should the food photographs of a restaurant menu be? Studies on image complexity have been conducted using objective and subjective approaches. Nevertheless, neither approach considers the factors that photographers commonly manipulate during image creation. Therefore, this paper proposes a methodology to evaluate the impression of complexity considering these factors. Through interviews, factors like the Camera Angle, Background, Lighting, Light Color, and the Number of Decorative Elements were identified and defined. Based on these definitions, food images were analyzed and tested by participants who assessed their impression of complexity. The results suggested that the amount of decorative element is a factor with a significant effect on the impression of complexity. Furthermore, the correlation between the number of decorative elements and the evaluations suggested that measuring food image complexity according to the decoration in the image is a closer representation of the impression of complexity judged by human subjects.
Human posture estimation and correction based on the CPM and the Pearson correlation coefficient

This paper proposes a mechanism for estimating and correcting fitness posture based on deep learning. First, use the Convolutional Pose Machine (CPM) to estimate the human body posture from the collected human motion images. Then 14 key bone points of the human body can be obtained after correction, and the pixel positions of these 14 bone points are used as the criterion for judging the motion posture. Subsequently, citing the Pearson correlation coefficient as a correlation basis, compared Pearson correlation coefficients of each area of the standard posture with the posture to be measured, then get the weighted sum of the comparison results of each area which is used as a criterion for judging whether the human body posture is standard. Experimental results show that this mechanism has the accuracy of high degree.
Normal Forms for Semantically Witness-Based Learners in Inductive Inference

In inductive inference, we study learners (computable devices) inferring formal languages. In particular, we consider semantically witness-based learners, that is, learners which are required to justify each of their semantic mind changes. This natural requirement deserves special attention as it is a specialization of various important learning paradigms. As such, it has already proven to be fruitful for gaining knowledge about other types of restrictions. In this paper, we provide a thorough analysis of semantically converging, semantically witness-based learners, obtaining normal forms for them. Most notably, we show that set-driven globally semantically witness-based learners are equally powerful as their Gold-style semantically conservative counterpart. Such results are key to understanding the, yet undiscovered, mutual relation between various important learning paradigms of semantically converging learners.
Piecewise empirical mode Bayesian estimation – A new method to denoise electrooculograms

Electrooculograms (EOG) indicate the magnitude and direction of eye movements. The EOG contain several types of noise and interference, which makes their analysis difficult. The existing denoising methods fail to preserve certain eye movement signatures, mainly in regions where there is a change of eye movement type, for e.g., fixation to saccades. Hence, the prime aim of the study is to denoise EOG signals, while preserving the signatures in such conditions. In this paper, we propose a new denoising method that addresses the aforementioned issue. In this algorithm, we first divide the signal into sub-signals based on the region of changes in the eye movement type. For each sub-signal, we separate the intrinsic mode oscillations from noise. Finally, the filtered sub-signals are concatenated to form the reconstructed signal. The reconstruction artifacts at the joining region are removed by Bayesian estimation. We name this algorithm as the Piecewise Empirical Mode Bayesian Estimation (PEMBE). We demonstrate that PEMBE is more effective in the denoising process as compared to the existing methods, as tested on the Physiosig EOG. The PEMBE is also found to be useful in the removal of the baseline wander.
On The Gap Between Software Maintenance Theory and Practitioners’ Approaches

The way practitioners perform maintenance tasks in practice is little known by researchers. In turn, practitioners are not always up to date with the proposals provided by the research community. This work investigates the gap between software maintenance techniques proposed by the research community and the software maintenance practice. We carried out a survey with 112 practitioners from 92 companies and 12 countries. We concentrate on analyzing if and how practitioners understand and apply the following subjects: bad smells, refactoring, software metrics, and change impact analysis. This study shows that there is a large gap between research approaches and industry practice in those subjects, especially in change impact analysis and software metrics.
Handling Complexity in Some Typical Problems of Distributed Systems by Using Self-organizing Principles

Today’s software systems are continuously becoming more complex. Main factors that determine software complexity are huge amounts of distributed components, heterogeneity, problem size and dynamic changes of the environment. These challenges are especially emphasized in distributed software systems. To cope with unforeseen dynamics in the environment and vast number of unpredictable dependencies on participating components, employing of self-organization principles at different levels in the software architecture can be beneficial. This could help in shifting complexity from one central coordinator component to many distributed, autonomously acting software components. Swarm intelligence represents a self-organizing biological system. Therefore, swarm-inspired algorithms play an important role in the design of self-organizing software for distributed systems and enable different kinds of self-organization. This chapter is based on my keynote at IJCCI 2019 with the purpose to provide a brief overview of the significance and power of swarm intelligence in coping with some typical distributed systems’ problems as well as findings about how and in which use cases the principles of self-organization can contribute to reduce software complexity.
On Linear Time Decidability of Differential Privacy for Programs with Unbounded Inputs

We introduce an automata model for describing interesting classes of differential privacy mechanisms/algorithms that include known mechanisms from the literature. These automata can model algorithms whose inputs can be an unbounded sequence of real-valued query answers. We consider the problem of checking whether there exists a constant d such that the algorithm described by these automata are dϵ-differentially private for all positive values of the privacy budget parameter ϵ. We show that this problem can be decided in time linear in the automaton's size by identifying a necessary and sufficient condition on the underlying graph of the automaton. This paper's results are the first decidability results known for algorithms with an unbounded number of query answers taking values from the set of reals.
Emotions in Computer Vision Service Q&amp;A

Software developers are increasingly using cloud-based services that provide machine learning capabilities to implement `intelligent' features. Studies show that incorporating machine learning into an application increases technical debt, creates data dependencies, and introduces uncertainty due to their non-deterministic behaviour. We know very little about the emotional state of software developers who have to deal with such issues; and the impacts on productivity. This paper presents a preliminary effort to better understand the emotions of developers when experiencing issues with these services with the wider goal of discovering potential service improvements. We conducted a landscape analysis of emotions found in 1,425 Stack Overflow questions about a specific and mature subset of these cloud-based services, namely those that provide computer vision techniques. To speed up the emotion identification process, we trialled an automatic approach using a pre-trained emotion classifier that was specifically trained on Stack Overflow content, EmoTxt, and manually verified its classification results. We found that the identified emotions vary for different types of questions, and a discrepancy exists between automatic and manual emotion analysis due to subjectivity.