Reviewerly/exHarmony huggingface dataset:
Adaptive Immunohistochemical Image Pre-processing Method

In this research study, the authors analyzed immunohistochemical images used to diagnose breast cancer pathologies. The authors developed an adaptive method of immunohistochemical image pre-processing. This method allows improving the quality of immunohistochemical image processing at the following stage of segmentation and calculating the quantitative characteristics of micro-objects. The method is based on computer vision algorithms.
Viewing the 360° Future: Trade-Off Between User Field-of-View Prediction, Network Bandwidth, and Delay

Predicting a user's field-of-view (FoV) accurately can help to significantly reduce the high bandwidth requirements for 360° video streaming, as it enables sending only the tiles corresponding to the predicted FoV. Since many approaches for user head-orientation (i.e., FoV) prediction have been proposed in the literature, ranging from simple linear regression to more complex neural networks, it is difficult to comprehensively decide which method to use. Towards resolving this gap in knowledge, in this work we benchmark user prediction algorithms over an aggregation of multiple datasets and study the implications of this analysis. Our results demonstrate that it is indeed difficult for any prediction algorithm to accurately predict a user's FoV beyond a very short future time window of approximately 300 ms. We also observe that users' viewing behavior is dominated by sideways head movement, rather than up-and-down. These findings have implications on network bandwidth, latency, and playback buffering at the client: (1) Extra "padding" tiles are needed around the user's FoV in order to correct for prediction errors; in particular, a rectangular padding achieves lower stall rate than square padding, for the same bandwidth usage; (2) Video playout buffers, network delay, and jitter need to be small in order to avoid stale predictions of the user's field-of-view, which are only valid 300 ms into the future; (3) Per-video and per-user personalization of the padding can save bandwidth for slow-moving users or videos. We mathematically quantify these tradeoffs and present simulation results to demonstrate these findings and implications. Our results have implications for FoV prediction methods in future 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> streaming systems.
Managing data constraints in database-backed web applications

Database-backed web applications manipulate large amounts of persistent data, and such applications often contain constraints that restrict data length, data value, and other data properties. Such constraints are critical in ensuring the reliability and usability of these applications. In this paper, we present a comprehensive study on where data constraints are expressed, what they are about, how often they evolve, and how their violations are handled. The results show that developers struggle with maintaining consistent data constraints and checking them across different components and versions of their web applications, leading to various problems. Guided by our study, we developed checking tools and API enhancements that can automatically detect such problems and improve the quality of such applications.
Motion Planning and Task Allocation for a Jumping Rover Team

This paper presents a cooperative robotic team composed of unmanned ground vehicles (UGVs) with hybrid operational modes to tackle the multiple traveling salesman problem (mTSP) with obstacles. The hybrid operational modes allow every UGV in the team to not only travel on a ground surface but also jump over obstacles. We name these UGVs jumping rovers. The jumping capability provides a flexible form of locomotion by leaping and landing on top of obstacles instead of navigating around obstacles. To solve the mTSP, an optimal path between any two objective points in an mTSP is determined by the optimized rapidly-exploring random tree method, named RRT*, and is further improved through a refined RRT* algorithm to find a smoother path between targets. We then formulate the mTSP as a mixed-integer linear programming (MILP) problem to search for the most cost-effective combination of paths for multiple UGVs. The effectiveness of the hybrid operational modes and optimized motion with assigned tasks is verified in an indoor, physical experimental environment using the customized jumping rovers.
Implicit Discriminator in Variational Autoencoder

Recently generative models have focused on combining the advantages of variational autoencoders (VAE) and generative adversarial networks (GAN) for good reconstruction and generative abilities. In this work we introduce a novel hybrid architecture, Implicit Discriminator in Variational Autoencoder (IDVAE), that combines a VAE and a GAN, which does not need an explicit discriminator network. The fundamental premise of the IDVAE architecture is that the encoder of a VAE and the discriminator of a GAN utilize common features and therefore can be trained as a shared network, while the decoder of the VAE and the generator of the GAN can be combined to learn a single network. This results in a simple two-tier architecture that has the properties of both a VAE and a GAN. The qualitative and quantitative experiments on real-world benchmark datasets demonstrate that IDVAE performs better than the state of the art hybrid approaches. We experimentally validate that IDVAE can be easily extended to work in a conditional setting and demonstrate its performance on complex datasets.
Using Virtual Reality and Music in Cognitive Disability Therapy

Traditional therapy methods for people with cognitive disabilities involve long periods of time performing exercises that can be tedious or unattractive for both patients and the professionals who work with them in specialized centers. In this work we aim at investigating the possibilities that combining music and virtual reality technology might offer for improving this scenario. Following this purpose, we applied an action research approach to co-develop with stakeholders a highly-configurable and accessible application to support exercising abilities usually targeted in rehabilitation therapy, such as coordination and motor skills, memory, and spatial perception. The results of an experiment carried out with both professionals and patients showed promising results, suggesting that this type of technology could improve the rehabilitation process, and make it more engaging.
YOLO-ASC: You Only Look Once And See Contours

YOLO is a useful, one-stage tool for object detection and classification. In this paper, we consider the application of grocery product detection. The grocery stores have a significant amount of product classes, so it is beneficial to postpone the classification into a second, specialized neural network with a higher capacity. Extracting bounding boxes for a classification network is not precise enough as the detected area includes redundant information about the background. We propose YOLO-ASC, which, for rectangular-based objects, detects bounding boxes together with object contour using a quadrangular. This approach allows detecting objects more accurately and without the background. For the quadrangular detection functionality, YOLO-ASC shares the feature maps that are already present in the network, and therefore its inference time is almost identical to the original YOLO. YOLO reaches high detection precision by using YOLO apriori knowledge, anchors extracted from data. In this work, we present two experiments where we demonstrate that YOLO-ASC training converges faster due to the symbiosis between the bounding box detection and quadrangular detection. Finally, we propose a tool for generating synthetic datasets with quadrangular labels that is helpful for transfer learning.
Partial Multi-label Learning with Label and Feature Collaboration

Partial multi-label learning (PML) models the scenario where each training instance is annotated with a set of candidate labels, and only some of the labels are relevant. The PML problem is practical in real-world scenarios, as it is difficult and even impossible to obtain precisely labeled samples. Several PML solutions have been proposed to combat with the prone misled by the irrelevant labels concealed in the candidate labels, but they generally focus on the smoothness assumption in feature space or low-rank assumption in label space, while ignore the negative information between features and labels. Specifically, if two instances have largely overlapped candidate labels, irrespective of their feature similarity, their ground-truth labels should be similar; while if they are dissimilar in the feature and candidate label space, their ground-truth labels should be dissimilar with each other. To achieve a credible predictor on PML data, we propose a novel approach called PML-LFC (Partial Multi-label Learning with Label and Feature Collaboration). PML-LFC estimates the confidence values of relevant labels for each instance using the similarity from both the label and feature spaces, and trains the desired predictor with the estimated confidence values. PML-LFC achieves the predictor and the latent label matrix in a reciprocal reinforce manner by a unified model, and develops an alternative optimization procedure to optimize them. Extensive empirical study on both synthetic and real-world datasets demonstrates the superiority of PML-LFC.
Identification of cultural influences on requirements engineering activities

Requirements Engineering (RE) involves critical activities to ensure the accurate elicitation and documentation of clients' requirements. RE is a socio-technical activity and requires intensive communication with several clients. RE activities might be considerably influenced by individuals' cultural background because culture has a deep impact on the way in which people communicate. However, there has been limited exploration of this issue. We present a framework that identifies and analyses cultural influences on RE activities. To build the framework, we employed Hofstede's cultural model and a mixed-methods design comprising two case studies involving two cultures: Saudi Arabia and Australia. The evaluation highlighted that the framework provides high accuracy to identify cultural influences in other cultures as well.
Agile Requirements Prioritization in Practice: Results of an Industrial Survey

Agile software development stresses the importance of providing the customer with a product of a maximized business value. To achieve that, requirements prioritization is used. Agile development methods like Scrum define guidelines for prioritization, however practitioners do not necessarily have to follow them. Our goal was to investigate the industry practice related to requirements prioritization process, including its timing, participants, criteria used and prioritization techniques applied. We designed an on-line questionnaire (based on literature review) and conducted a survey involving practitioners from Polish IT industry. We received 69 valid responses indicating requirements prioritization practices in industrial Agile projects. We found out that despite the fact that business value is the most common criterion used to prioritize requirements, other criteria like complexity, stability and mutual interdependencies are considered as well. Other findings indicate that consideration of such multiple criteria requires different viewpoints, thus making requirements prioritization a process that has to involve many participants of different roles and competencies.
Research on group behavior model based on neural network computing

Abstract Compute is a term coined from the etymology of French and Latin words computer and computare respectively, so is computing. This field of computing has grown enormously over the years. From the simple, traditional Turing machine invented in 1936 by Alan Turing to the current neural network (NN) computing. NNs, a field of artificial intelligence (AI) was exhilarated from the structure and inner workings of the brain. Just as the brain is, that is, an interconnection of neurons, so is the NN which is an interconnection of basic structures known as the perceptron. They do not differ much in structure. Their only difference is that one is artificial while the other is entirely biological. The hierarchical intricacies of the NN can be represented in three layers: the perceptron, artificial NN (ANN), and deep NN (DNN). With the influx of mental and behavioral disorders, basic surveillance, and the urgency to improve the mental health of people, studying the behavioral dynamics of people is requisite. CCTV and street cameras can only do so much, thus the need to employ the field of NN which makes use of supervised learning in training the models to perfect and automate surveillance. The results of this retrospective research indicate that the use of the NN model surpasses those of traditional methods in terms of efficiency and reliability.
Feature Aggregation Attention Network for Single Image Dehazing

Due to its ill-posed nature, single image dehazing is a challenging problem. In this paper, we propose an end-to-end feature aggregation attention network (FAAN) for single image dehazing. It incorporates the idea of attention mechanism and residual learning and can adaptively aggregate different level features. In particular, in the proposed FANN, we design a novel block structure consisting of feature attention module, smoothed dilated convolution and local residual learning. The local residual learning allows the less useful information to be bypassed through multiple skip connections. The feature attention module is designed to assign more weight to important features. The smoothed dilated convolution is adopted to enlarge the receptive field without the negative influence of gridding artifacts. The experiments on the RESIDE dataset show that the proposed approach acquires state-of-the-art performance in both qualitative and quantitative measures.
Why do Software Teams Deviate from Scrum?

Human, social, organizational, and technical aspects are intertwined with each other in software teams during the software development process. Practices that teams actually adopt often deviate from those of the used frameworks, such as Scrum. However, currently there is little empirical insight explaining typical deviations, including their reasons and consequences. In this paper we use observations to investigate selected activities of the software development process in two companies that use Scrum. We study identified deviations to understand their reasons and consequences, using a survey and interviews. We identify 13 deviations and we categorize reasons based on type. The deviations' consequences are investigated in terms of their impact. Most deviations can be found in multiple teams. Reasons are doubts of the teams, organizational structures and complexity of the work. Consequences of deviations affect product development and team work.
Designing of Facial Emotion Recognition System Based on Machine Learning

Facial expression recognition is still ongoing research area in the era of new revolution of the latest technology. Facial expression presents the emotion or eternal beauty of a human being. Facial emotion recognition plays an important role in computer vision and machine learning area. There are lots of application are formed based on those technology. It can be used in many application like Human Machine Interface (HMI), robotics, security, entertainment etc. Facial emotion recognition is the most important cognitive function which is performs by our brain very efficiently. Facial expression is a posture of human being face which is located beneath the face skin. Though all the emotions are reflect in voice, hand or body movement gesture but all the major types of expression are expressed only through the face of the human being. There are almost 300 identified emotions are found but in our work we have considered six basic emotions like all the three primary colours, the emotions are-anger, disgust, fear, happiness, sadness and surprise. For our case we have first train the system with some identical facial expression images or some normal images. Then we take the sample image as our input. After that we have pre-processed the image and use the feature extraction method. Then we use the classification algorithm to compare the training data and testing data. Thus we can easily got the facial emotion as our results. We have done all our simulation using MATlab software.
Scale-Invariant Multi-Oriented Text Detection in Wild Scene Image

Automatic detection of scene texts in the wild is a challenging problem, particularly due to the difficulties in handling (i) occlusions of varying percentages, (ii) widely different scales and orientations, (iii) severe degradation in the image quality etc. Here, we propose a deep architecture consisting of a novel Feature Representation Block (FRB) capable of efficient abstraction of the input information. Also, we consider curriculum learning with respect to difficulties in image samples and gradual increase in pixel-wise blurring. Our framework is capable of detecting texts of different scales and orientations. It can tackle blurring from multiple possible sources, non-uniform illumination as well as partial occlusions of varying percentages. Text detection performance of the proposed framework on various benchmark datasets including ICDAR 2015, ICDAR 2017 MLT, MSRA-TD500 and COCO-Text has significantly improved the respective state-of-the-art results.
Augmenting Banking and FinTech with Intelligent Internet of Things Technology

Banks and Financial institutions play a crucial role in economic development of country albeit slow to embrace IoT technology. IoT has the potential to revolutionize financial services by enabling a deeper understanding of economic trends, customer preferences and capturing their needs and preferences in real time. Integrating Artificial Intelligence in IoT enhances IoT performance by analysing the information and extracting knowledge therefore making them more intelligent. The FinTech(Financial Technology) needs to be amalgamated with Intelligent IoT to improve customer services, gain customer's perspicacity and to improve efficiency. This paper proffers a cloud centric Intelligent Internet of Things conceptual architecture for banking and financial sector. Furthermore a case study of implementing Intelligent IoT for vehicle loans has also been presented.
Context-Aware Cyber-Physical Assistance Systems in Industrial Systems: A Human Activity Recognition Approach

The increasing demand for product customisation is leading to higher complexities within manufacturing. This imposes new challenges for the workforce. One way to support operators' productivity may be context-aware, human-centred cyber-physical assistance systems. Human Activity Recognition (HAR) is a promising approach to enable context-awareness. However, standardised approaches to integrate HAR into existing manufacturing environments are rare. Particularly, there is a lack of available datasets of manufacturing activities. Moreover, comparative studies of inertial and visual HAR approaches are still rare. This work therefore proposes Methods-Time Measurement (MTM) as a standardised foundation for creating a manufacturing activity dataset. Subsequently, five different machine learning algorithms are tested for their recognition performance based on the dataset captured with an inertial sensor suit and an RGB-D sensor. A proof-of-concept is delivered for both sensor categories applied to the scope of 18 MTM-1 activities, whereas inertial data outperformed depth data. K-Nearest Neighbour and Bagged Tree algorithms revealed the best classification accuracy results in this context.
Role of machine learning and artificial intelligence algorithms for teaching reform of linguistics

The teaching of linguistics is limited by the influence of various factors, which leads to poor teaching effect, and the teaching process is difficult to evaluate. In order to improve the efficiency of linguistics teaching, this paper uses improved machine learning algorithms to construct a linguistics artificial intelligence teaching model. According to the teaching needs of linguistics, the efficiency of the teaching process is improved, and the teaching evaluation is performed, and the root cause analysis algorithm based on MCTS is optimized. Moreover, according to the frequent item set algorithm in data mining, a layered pruning strategy is proposed to further reduce the search space and improve the efficiency of the model. In addition, this study combines with the comparative teaching experiment to study the efficiency of artificial intelligence models in linguistics teaching. The statistical results show that the model proposed in this paper has a certain effect.
Gender and Age based Census System for Metropolitan Cities

This paper presents a smart computer-vision based system which helps ensure disciplined entry of people in places by ascertaining the allowable age and gender of the person, as well as storing the data for census use. The system involves deep learning based facial image classifiers, which predict the gender and age from the image of face captured by the camera in real-time. The actuating response is generated according to the prediction of the computer vision algorithm, which further keeps the record of encountered faces with the respective counts of persons with corresponding facial traits. The log of the entire surveillance process can be monitored online through the integrated IOT capability, thereby can be used as primary data source. The successful experimental runs demonstrate the system's usability in the real world applications.
Efficient Planning for High-Speed MAV Flight in Unknown Environments Using Online Sparse Topological Graphs

Safe high-speed autonomous navigation for MAVs in unknown environments requires fast planning to enable the robot to adapt and react quickly to incoming information about obstacles within the world. Furthermore, when operating in environments not known a priori, the robot may make decisions that lead to dead ends, necessitating global replanning through a map of the environment outside of a local planning grid. This work proposes a computationally-efficient planning architecture for safe high-speed operation in unknown environments that incorporates a notion of longer-term memory into the planner enabling the robot to accurately plan to locations no longer contained within a local map. A motion primitive-based local receding horizon planner that uses a probabilistic collision avoidance methodology enables the robot to generate safe plans at fast replan rates. To provide global guidance, a memory-efficient sparse topological graph is created online from a time history of the robot's path and a geometric notion of visibility within the environment to search for alternate pathways towards the desired goal if a dead end is encountered. The safety and performance of the proposed planning system is evaluated at speeds up to 10m/s, and the approach is tested in a set of large-scale, complex simulation environments containing dead ends. These scenarios lead to failure cases for competing methods; however, the proposed approach enables the robot to safely reroute and reach the desired goal.