Reviewerly/exHarmony huggingface dataset:
A Hybrid Contrast Limited Adaptive Histogram Equalization (CLAHE) for Parathyroid Ultrasonic Image Enhancement

Histogram Equalization (HE) and its variations have been extensively used in image enhancement because of its easy realization and small computation. Although these methods may enhance image contrast effectively, they usually have some defects, for instance, lack of image details, noise amplification and undue enhancement. In this research, a strong hybrid algorithm is proposed to enhance the ultrasound parathyroid images. The algorithm introduces human-computer interaction to specify the region of interest (ROI), and combines Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm, Gamma transform and High Pass detail enhancement to get the final enhanced image. Experimental results show that the proposed hybrid γ-CLAHE algorithm is able to enhance the ultrasonic image in an effective and efficient way and get an output image of greater clarity for medical detection and diagnosis.
Real-time Human Segmentation using Pose Skeleton Map

In this paper, an algorithm for real-time human segmentation is proposed. This algorithm uses connection relations of human joints provided by pose estimation as prior knowledge which brings striking enhancement for the accuracy of human segmentation. High-quality segmentation results can be produced with real-time speed dealing with large range of human pose under complicated scenes.
Information encountering in the humanities: Embeddedness, temporality, and altruism

ABSTRACT Humanities research informs people's movements across time and reshapes cultural worldviews. While we know that humanists integrate information resources into their research program and that serendipitous discoveries enable truly transformative knowledge creation, our picture of discovery in the humanities is unclear. There is an important role for information professionals to better support humanistic discovery as routine practice. To fully comprehend the complex research activities of humanities scholars it is necessary to expand upon existing models of information encountering (IE) by analyzing discovery episodes and the temporal and topical factors in play. We take particular interest in studying how scholars in the art history and archeology disciplines conceptualize, pursue, and often complete research projects. Ultimately we present an amended IE model that reveals intricate connections between IE, rediscovery, and altruistic collaboration mutually evolving over distinct timespans.
Face morphing using average face for subtle expression recognition

Current facial expression classifiers are created based on face images with high-intensity expressions. Therefore, the recognition accuracy is low for subtle facial expressions. In this paper, we proposed a preprocessing method of face morphing to improve the ability of the classifier based on machine learning. That is, by using the average face, it removes person-specific face differences and magnifies the signal of subtle expressions. In addition, we created an artificial subtle facial expression image dataset by using average and morphed faces for both uses in our algorithm and its verification. Finally, our proposed method, as a preprocessing in a machine learning based recognizer, significantly improved the recognition accuracy of a facial expression recognition system such as CNN.
Flexible Automatic Football Filming and Summarization

We propose a method aimed at reducing human intervention in football video shooting and highlights editing, allowing automatic highlight detection together with panning and zooming on salient areas of the playing field. Our recognition subsystem exploits computer vision algorithms to perform automatic detection, pan and zoom and extraction of salient segments of a recorded match. Matches are elaborated offline, extracting and analyzing motion and visual features of the elements in salient zones of the scene, i.e. midfield circle and penalty areas. Automatic summarization is performed by classifying subsequences of a match with machine learning algorithms, which are pretrained on previously acquired and annotated videos of other matches. Among salient actions, special attention is given to goal events, but also other generic highlights are identified. The only assumption for our method to work is to employ a pair of cameras which should frame the football pitch splitting the field in two halves. We demonstrate the functioning of our approach using two ultra high definition cameras, building a system which is also able to collect various metadata of the matches to extrapolate other salient information.
An automatic calibration approach for a multi-camera-robot system

In this paper we present an automated and precise calibration approach to enable a vision-based robot control with a multi-camera setup. In our case we use a setup consisting of four fixed cameras above the workplace (eye-to-hand configuration) and a collaborative robot. The robot will be used to automate the intrinsic camera calibration and the necessary process of capturing images of a calibration pattern from a maximum number of different viewpoints. For the extrinsic calibration of the cameras and the calibration to the robot we use only two small markers, which are permanently attached to the base of the robot. This allows a fast online calibration of the setup. With the help of first experiments, we can show that the calibration method works well under laboratory conditions. Furthermore, we will discuss the achieved accuracy.
Image and surface registration

Image registration is one of the most useful and practical applications of image analysis. Among its many tasks are the tracking of changes between data from different time points or motion correction. Moreover, superimposing complementary information across image modalities is needed, as new imaging modalities emerge in the field. This chapter presents a review of the fundamental ideas and models in the field. The goal is to reflect the current state of the art of image registration (IR) to motivate the readers to refine these models, and to enable the tackling of new challenges as they arise. After discussing the background (Section 2), we review main components of a variational model: a distance measure or data fidelity term (Section 3), regularization to ensure existence of solutions and constraints to further restrict the wanted transformation (Section 4). We also discuss diffeomorphic approaches which ensure local invertibility. We present the surface registration (SR) modelling in the same framework of variational models in Section 5 where the close relationship between IR and SR is also discussed, while we briefly discuss the numerical methods for IR and SR in Section 6. Finally in Section 7, we touch upon the main ideas in deep learning-based approaches for registration.
Secure Health Record Sharing for Mobile Healthcare in Privacy Preserving Cloud Environment

Using mobile healthcare (mHealth) application, a patient can receive consultation from healthcare providers anywhere without visiting hospital. Thus mHealth helps in extending the healthcare services outside the hospitals. Cloud computing is proving itself crucial in healthcare by helping in storing and maintaining the patient's health record which can be accessed from any location. But patient's health record is highly vulnerable and has to be secured by encrypting before storing it in the cloud. Hence this paper encompasses the study and proposal of certain approaches applicable to mHealth application which lets patients send health records to doctors and doctors to further share the record and negotiate with specialists and also helps patients in finding friends suffering from common symptoms in privacy preserving environment ensuring the health record's integrity.
Mining Patterns in Source Code Using Tree Mining Algorithms

Discovering regularities in source code is of great interest to software engineers, both in academia and in industry, as regularities can provide useful information to help in a variety of tasks such as code comprehension, code refactoring, and fault localisation. However, traditional pattern mining algorithms often find too many patterns of little use and hence are not suitable for discovering useful regularities. In this paper we propose FREQTALS, a new algorithm for mining patterns in source code based on the FREQT tree mining algorithm. First, we introduce several constraints that effectively enable us to find more useful patterns; then, we show how to efficiently include them in FREQT. To illustrate the usefulness of the constraints we carried out a case study in collaboration with software engineers, where we identified a number of interesting patterns in a repository of Java code.
Two fast prototypes of web-based augmented reality enhancement for books

Purpose The purpose of this paper is to identify a light and scalable augmented reality (AR) solution to enhance library collections. Design/methodology/approach The author first did research to identify the major obstacle in creating a scalable AR solution. Next, she explored possible workaround methods and successfully developed two prototypes that make the current Web-based AR work with ISBN barcode. Findings Libraries have adopted AR technology in recent years mainly by developing mobile applications for specific education or navigation programs. Yet a straight-forward AR solution to enhance a library's collection has not been seen. One of the obstacles lies in finding a scalable and painless solution to associate special AR objects with physical books. At title level, books already have their unique identifier – the ISBN number. Unfortunately, marker-based AR technology only accept two-dimensional (2-D) objects, not the one-dimensional (1-D) EAN barcode (or ISBN barcode) used by books, as markers for technical reasons. In this paper, the author shares her development of two prototypes to make the Web-based AR work with the ISBN barcode. With the prototypes, a user can simply scan the ISBN barcode on a book to retrieve related AR content. Research limitations/implications This paper mainly researched and experimented with Web-based AR technologies in the attempt to identify a solution that is as platform-neutral as possible, and as user-friendly as possible. Practical implications The light and platform-neutral AR prototypes discussed in this paper have the benefits of minimum cost on both the development side and the experience side. A library does not need to put any additional marker on any book to implement the AR. A user does not need to install any additional applications in his/her smartphone to experience the AR. The prototypes show a promising future where physical collections inside libraries can become more interactive and attractive by blurring the line of reality and virtuality. Social implications The paper can help initiate the discussion on applying Web-based AR technologies to library collections.
New Approach to Human AI Interaction to Address Digital Divide&amp; AI divide: Creating an Interactive AIplatform to Connect Teachers &amp; Students

This paper explores the potential for artificial intelligence(AI) to transform the digital divide, especially education for children with disabilities,and girl children in under-developed areas of the world.AnAI based framework for coaching students in their mother tongue using AI based interactive voice conversations over an ordinary telephone is proposed and implemented in this paper. The proposed framework interacts everyday with students for a few minutes in their local language, asking questions on class subjects and checking answers using chatbot inspired AI based conversational speech interfaces.This framework proposed differs from today's approaches in 6 major dimensions:1)Proposed AI platform gives the ability to reach out to the unreached children. For instance, visually impaired children can dial &talk to AI teacherover ordinary telephone calls.2)To cross the literacy barrier, this new AI platform allows students to interact in their mother tongue.3)Using a novel Human AI interaction paradigm, this AI platform attempts to cross the uncanny valley. Each student hears their actual teacher's voice as they converse with this AI.4)Students can be motivated using anemotionally intelligent modelofHuman AI interaction via affective computing technique.5)To cross financial affordability, this paper demonstrates an approach which can be deployed at absolutely zero financial burden for the children.6)A user interface to construct AI mediated dialogs& hyper-link multiple dialogs.The proposed system architecture combines the power of advances in AI with an interesting Human AI interaction design, that enables to address the divide - both AI Divide and Digital Divide. A vision for applying AI for good for next billion lives is the central contribution of this paper.
Impact of artificial intelligence on patent law. Towards a new analytical framework – [ the Multi-Level Model]

Inventions and new ideas are at the center of societal transformation. Inventions have been historically protected by a system of intellectual property law of which patents are at the heart. Whilst patent law is still deeply moored in its roots in the industrial revolution, to a greater extend it has been able to adapt to the successive revolutions like the computing albeit with some challenges. The world is now at an unprecedented threshold of the most far reaching revolution whose consequences to patent law in particular are so far reaching that its impact is still unknown. This is the AI revolution. This paper begins with an analysis of the AI revolution from available literature and research. It notes that there are divergent views among scholars about the impact of AI on various elements of patent law. It posits that this dissonance might be a result of most scholars treating AI as one homogenous block without distinguishing the various phases in the evolution of AI. It thus proposes a new sui generis conceptual framework – [ the Multi-Level Model] as a suitable basis for insightful conceptual analysis of AI impact on patent law with focus on two key questions; patentability and inventorship.
Contextual High-Level Uncertainty Modeling Reducing Surprises in Decision Making

In life of animals and humans, there are different stages where uncertainty plays a key role. These uncertainties create risks and surprises which will certainly impact decision making. In ideal scenario, pattern-based or rule-based systems will surely work, as Decision making is completely dependent on known and predictive inputs from environments as well as intelligent agents. But when it comes to high level decision making, these surprises bring down the accuracy. To identify precisely and model such uncertainty correctly is a challenge. There are different techniques used by researchers like fuzzy logic based, probability theory based and many more to model uncertainty. This paper illustrates these techniques with a new model proposed to learn from the uncertainty for decision making.
Unraveling Recursion: Compiling an IR with Recursion to System F

Lambda calculi are often used as intermediate representations for compilers. However, they require extensions to handle higher-level features of programming languages. In this paper we show how to construct an IR based on $$\text {System}\ F_{\omega }^\mu $$ which supports recursive functions and datatypes, and describe how to compile it to $$\text {System}\ F_{\omega }^\mu $$ . Our IR was developed for commercial use at the IOHK company, where it is used as part of a compilation pipeline for smart contracts running on a blockchain.
pyjeo: A Python Package for the Analysis of Geospatial Data

A new Python package, pyjeo, that deals with the analysis of geospatial data has been created by the Joint Research Centre (JRC). Adopting the principles of open science, the JRC strives for transparency and reproducibility of results. In this view, it has been decided to release pyjeo as free and open software. This paper describes the design of pyjeo and how its underlying C/C++ library was ported to Python. Strengths and limitations of the design choices are discussed. In particular, the data model that allows the generation of on-the-fly data cubes is of importance. Two uses cases illustrate how pyjeo can contribute to open science. The first is an example of large-scale processing, where pyjeo was used to create a global composite of Sentinel-2 data. The second shows how pyjeo can be imported within an interactive platform for image analysis and visualization. Using an innovative mechanism that interprets Python code within a C++ library on-the-fly, users can benefit from all functions in the pyjeo package. Images are processed in deferred mode, which is ideal for prototyping new algorithms on geospatial data, and assess the suitability of the results created on the fly at any scale and location.
Virtual Fitting Space for Dress Trials

Abstract People buy clothes in stores which may take a lot of time for them to try their fitting and in online shopping there is no possibility where people can try-on the clothes which they want to buy. Our intention is that by creating a virtual fitting room environment we can improvise approachability of trying clothes on and need not have to waste time we need to have the correct measurement of the user and the dress model with correct rotation, measurement, location. Even people are interested more in online shopping and do buy a lot of things, but buying clothes online is still considered as ineffective way because people don’t have enough knowledge about dress fittings in online shopping and it is not even possible to try- on the clothes. In order to improvise this drawback we can use the virtual fitting room which would provide the online customers with a wide range of options and a great delight to the vendors online. This concept of e-clothing helps you decide whether the dress fits you or suits you in an easy and efficient manner. Our analysis about customers provides a great fitting of dress to the person as per their requirement. To identify a human we use skeleton detection and to find the outline of human we use human contour detection method. Different positions of human body are detected using Haar cascade classification method and warping process is used to resize the outfit according to the size of the human body.
Pedestrian Re-Recognition Algorithm Based on Optimization Deep Learning-Sequence Memory Model

Pedestrian re-recognition is an important research because it affects applications such as intelligent monitoring, content-based video retrieval, and human-computer interaction. It can help relay tracking and criminal suspect detection in large-scale video surveillance systems. Although the existing traditional pedestrian re-recognition methods have been widely applied to address practical problems, they have deficiencies such as low recognition accuracy, inefficient computation, and difficulty to adapt to specific applications. In recent years, the pedestrian re-recognition algorithms based on deep learning have been widely used in the pedestrian re-recognition field because of their strong adaptive ability and high recognition accuracy. The deep learning models provide a technical approach for pedestrian re-recognition tasks with their powerful learning ability. However, the pedestrian re-recognition method based on deep learning also has the following problems: First, the existing deep learning pedestrian re-recognition methods lack memory and prediction mechanisms, and the deep learning methods offer only limited improvement to pedestrian re-recognition accuracy. Second, they exhibit overfitting problems. Finally, initializing the existing LSTM parameters is problematic. In view of this, this paper introduces a revertive connection into the pedestrian re-recognition detector, making it more similar to the human cognitive process by converting a single image into an image sequence; then, the memory image sequence pattern reidentifies the pedestrian image. This approach endows deep learning-based pedestrian re-recognition algorithms with the ability to memorize image sequence patterns and allows them to reidentify pedestrians in images. At the same time, this paper proposes a selective dropout method for shallow learning. Selective dropout uses the classifier obtained through shallow learning to modify the probability that a node weight in the hidden layer is set to 0, thereby eliminating the overfitting phenomenon of the deep learning model. Therefore, this paper also proposes a greedy layer-by-layer pretraining algorithm for initializing LSTM and obtains better generalization performance. Based on the above explanation, this paper proposes a pedestrian re-recognition algorithm based on an optimized LSTM deep learning-sequence memory learning model. Experiments show that the pedestrian re-recognition method proposed in this paper not only has strong self-adaptive ability but also identifies the average accuracy. The proposed method also demonstrates a significant improvement compared with other mainstream methods because it can better memorize and learn the continuous motion of pedestrians and effectively avoid overfitting and parameter initialization in the deep learning model. This proposal provides a technical method and approach for adaptive pedestrian re-recognition algorithms.
Combined approach to forecasting of manufacturing system target indicators in a changing external environment

The paper presents a combined approach to forecasting of the manufacturing system target indicators, depending on the key factors determining the functioning of this system and business environmental factors. The approach includes: (i) identification of key factors and weak signals influencing the target indicators by analyzing the system functioning model in the external environment represented by a cognitive map of the situation; (ii) analysis of dynamics of the identified key factors and manufacturing system target indicators and the construction of a forecast model using time series analysis methods; (iii) monitoring of the heterogeneous information space in order to detect changes in trends and the composition of parameters of the forecast model; (iv) correction of the forecast model and cognitive map of the situation according to the results of the detected changes. The approach is focused on the formation of long-term forecasts of the target indicators and increasing the accuracy of these forecasts by identifying significant influencing events whose influence does not have time to reflect in time series.
Audio-replay Attacks Spoofing Detection for Automatic Speaker Verification System

Speaker verification, as one of the most convenient methods in biometric systems, has been widely applied. However, most automatic speaker verification systems are vulnerable to a variety of attacks, especially audio-replay attacks. Therefore, this paper focuses on the detection method of audio-replay attacks. It is found that using a hybrid feature can achieve better results than a single feature. As for the classifier in the backend, the use of the Gaussian mixture model cannot obtain convincing results, and the traditional convolution neural network architecture can easily lead to the model overfitting. Therefore, this paper chooses DenseNet architecture. The experimental results show that the hybrid feature and DenseNet architecture can achieve 46.06% relative improvement than the baseline system.