Reviewerly/exHarmony huggingface dataset:
Deep neural network in medical image processing

Computer-assisted analysis for better interpretation of biomedical images has been a long-standing concern in the field of medical imaging. On the image interpretation front, recent developments in machine learning, especially in the field of deep learning, have made a major leap forward to help with the identification, classification, and quantification of patterns in biomedical images. Specifically, the leveraging of hierarchical structures based entirely on data, instead of handcrafted features mostly based on domain-specific knowledge, lies at the heart of the advances. In this way, deep learning is rapidly proving to be the state-of-the-art platform for achieving superior performance in various biomedical applications. The most important point in efficacious treatment of a disease is an accurate diagnosis, which by and large is not an easy feat to achieve. There are many reasons for an inaccurate diagnosis such as inexperienced doctors, scarcity of trained radiologists, lack of proper equipment, and so on. But technology (specifically machine learning) provides a silver lining, as it has been proven quite successful in aiding and assisting diagnosis by interpreting biomedical images such as X-ray, computerized tomography scans, mammograms, and so on. The unprecedented success of deep learning frameworks in the interpretation of biomedical images is mainly due to the following factors: •The progress of high-tech central processing units, graphics processing units, and most recently tensor processing units. •The availability of a large amount of data •Advances in deep learning algorithms There has been tremendous interest in the use of image processing and analysis techniques for computer-aided detection (CAD)/diagnostics (CADx) in biomedical images. The goal has been to increase diagnostic accuracy as well as the reproducibility of biomedical image interpretation. CAD/CADx systems can aid radiologists/doctors by providing a second opinion and may be used in the first stage of examination in the near future, providing the reduction of the variability among radiologists in the interpretation of biomedical images. In this chapter, we aim to discuss fundamentals of image processing first including segmentation and edge detection followed by identifying critical areas in biomedical images, denoising, and applying image processing technique on various available biomedical image data sets. The authors focus on tissue segmentation, application of convolutional neural network in interpreting biomedical images, usage of different deep learning libraries for identifying areas of interest in a biomedical image, computer-aided disease diagnosis or prognosis, and so on. We will conclude by raising research issues and suggesting future directions for further improvements.
A Robust Driver’s Gaze Zone Classification using a Single Camera for Self-occlusions and Non-aligned Head and Eyes Direction Driving Situations

Distracted driving is one of the most common causes of traffic accidents around the world. Recognizing the driver's gaze direction during a maneuver could be an essential step for avoiding the matter mentioned above. Thus, we propose a gaze zone classification system that serves as a base of supporting systems for driver's situation awareness. However, the challenge is to estimate the driver's gaze inside not ideal scenarios, specifically in this work, scenarios where may occur self-occlusions or non-aligned head and eyes direction of the driver. Firstly, towards solving miss classifications during self-occlusions scenarios, we designed a novel protocol where a 3D full facial geometry reconstruction of the driver from a single 2D image is made using the state-of-the-art method PRNet. To solve the miss classification when the driver's head and eyes direction are not aligned, eyes and head information are extracted. After this, based on a mix of different data pre-processing and deep learning methods, we achieved a robust classifier in situations where self-occlusions or non-aligned head and eyes direction of the driver occur. Our results from the experiments explicitly measure and show that the proposed method can make an accurate classification for the two before-mentioned problems. Moreover, we demonstrate that our model generalizes new drivers while being a portable and extensible system, making it easy-adaptable for various automobiles.
Digital Assistant with Augmented Reality

From CRTs to virtual displays, the perception of the digital world we know is continually evolving. Technologies like computer vision and object recognition can be used in conjunction with augmented reality to create an interactive and enhanced user experience of the real world. People are familiar with digital assistants like Siri on iPhone, Google Assistant on android, Cortana on Windows, or Alexa and Google Home in households. As the digital assistant market increases, so does their functionalities and capabilities. They perform a wide variety of tasks ranging from answering the most basic questions, to playing a song, telling jokes, setting alarms, reading audiobooks, and even take control of IoT devices in a smart home system. With this paper, we aim at incorporating the same in an environment where the user can see and interact with the agent, which increases user satisfaction and enhances the overall experience.
Future Challenges in the Next Generation of Voice User Interface

With the development of artificial intelligence technology, artificial interactions come up for providing powerful assistance to our lives. Among them, voice user interface (VUI) plays important roles in assisting the disabled and complex interaction scenarios. This paper mainly introduces the key elements and core technics in VUI. Also, future challenges will be discussed from the perspective of empathy, ethics, and accessibility. This paper serves as a summary for future study in VUI.
Human-In-The-Loop Construction of Decision Tree Classifiers with Parallel Coordinates

How can there be Human-In-the-Loop-Learning (HILL) if datasets aimed at building classifiers have ever more dimensions?We make two contributions.First, we examine the few early results on the effectiveness of HILL for building autonomous classifiers and report on our own experiment that validates the merits of HILL.Second, we introduce a HILL system (by using parallel coordinates) for learning of decision tree classifiers (DTCs).DTCs importantly emphasise the relevance of attributes and enable attribute selection, and therefore are appreciated for their transparency.The proposed system addresses a number of the shortcomings of the many HILL systems and allows for easy exploration of datasets.In particular, we incorporate parallel coordinates effectively in our tool for visualisation of high dimensional datasets.We can not only focus the learning on the accuracy of classifiers, but we can enhance performance in other important factors such as system's interpretability and the ability to gain insight into datasets.Finally, we show the advantages of our HILL system in the application area of mobile robotics using the case study of image segmentation in robotic soccer.
Towards Analyzing Contributions from Software Repositories to Optimize Issue Assignment

Most software teams nowadays host their projects online and monitor software development in the form of issues/tasks. This process entails communicating through comments and reporting progress through commits and closing issues. In this context, assigning new issues, tasks or bugs to the most suitable contributor largely improves efficiency. Thus, several automated issue assignment approaches have been proposed, which however have major limitations. Most systems focus only on assigning bugs using textual data, are limited to projects explicitly using bug tracking systems, and may require manually tuning parameters per project. In this work, we build an automated issue assignment system for GitHub, taking into account the commits and issues of the repository under analysis. Our system aggregates feature probabilities using a neural network that adapts to each project, thus not requiring manual parameter tuning. Upon evaluating our methodology, we conclude that it can be efficient for automated issue assignment.
Locality Preserving Projection Based Multiple Copy-Paste Forgery Detection

Digital images are the dominant media of information in the digital world and used to convey the desired information. It has been noticed that manipulated images are put to wrong use to create a false perception about the image or story in support. Copy-paste forgery is one of the most exploited image manipulation approaches. In recent years the research community has proposed many methods for the detection of such forgery. In this work, the copy-paste forgery has been detected and localized by the proposed scheme using locality preserving projection (LPP). As the processing level increases, changes in pixel intensity and statistical changes in image pixels increase, which reduces the accuracy of localization. Similarity preserving property of LPP is used for developing a multifaceted method for copy-paste forgery detection for images attacked with various post-processing.
Augmented Reality for Immersive and Tactile Flight Simulation

Flight simulators based on wrap-around displays provide an immersive experience for pilot training, but not only are they expensive and bulky, they typically only simulate a single aircraft configuration. Virtual reality (VR) is an interesting alternative, providing an immersive experience with lower cost and space requirements, as well as the ability to simulate arbitrary aircraft. Unfortunately, current VR systems do not provide natural tactile interaction with instruments and avionics in the cockpit environment. This article explores augmented reality (AR) as an alternative to VR and wrap-around display flight simulators, delivering an immersive experience for pilot training and tactile interaction with aircraft controls. The concept is demonstrated through a basic general aviation simulator setup, showing that a relatively low-cost personal computer and graphics processing unit can provide AR simulations at useful frame rates.
How can instructors strengthen students’ motivation to learn complex 3D concepts in an engineering classroom?

The effect of Virtual Reality (VR)-based lectures on student motivation to learn complex 3D materials science concepts is discussed in this Research Full Paper. With the growing classroom size and less personalized attention to students, STEM instructors are challenged with finding ways to strengthen student motivation and sustainably transform the educational experience of current and future learners. Students' ability to visualize and imagine complex 3D concepts in multiple engineering disciplines can be a barrier to learning for diverse learners. This research uses VR technology to study the impact on student motivation to learn materials science concepts based on John Keller's ARCS model of motivation. As a benchmark, student participants (n=26) were given a slides-based lecture, and the initial motivation level was assessed using the Instructional Materials Motivation Survey (IMMS) and reflection questions. Next, the VR intervention was given using low-cost and scalable Cardboard Viewers fitted with personal smartphones, followed by IMMS survey and reflection questions. A mixed-methods approach including the use of a t-test to analyze quantitative data, and Chi's (1997) verbal data analysis to identify emergent themes among qualitative data was applied in this study. Overall student participants' motivation was found to increase due to VR intervention. Among the four components of the ARCS motivation model of instruction, attention and satisfaction components showed a significant increase after the VR intervention, while relevance and confidence components remained unchanged. This finding suggests the use of scalable VR technology as a suitable option for capturing students' attention in order to facilitate learning about complex 3D concepts in engineering classrooms.
Rank based Approach for Extracting Unit Pixel Width Skeleton

Abstract Skeletonization is a transformation of a digital picture component into a subset of the original component. The skeleton is a qualitative tool for matching and analyzing 2D objects. Skeletonization is considered as an important issue because of the close link between an object’s skeleton and its boundary. Arank based skeleton extraction algorithm is presented which iterates itself to remove all the skeleton branches corresponding to the unimportant shape regions depending on the boundary extraction method. The method proposed performance is compared with various methods in the literature such as ZS, LW, and MZS skeletonization algorithms and identified that proposed method out performs well with respect to the performance measures elapsed time, thinning rate, thinning speed, and connectivity.
Bangla Real-Word Error Detection and Correction Using Bidirectional LSTM and Bigram Hybrid Model

Real-word error detection and correction in Bangla sentence is now more relevant topic of enormous interest nowadays. The complex character structure and grammatical rules in Bangla yield difficulty in processing the language. This paper proposes a hybrid method for real-word error detection and correction in Bangla which involves a combination of two different approaches like N-gram language model such as bigram and bidirectional long short-term memory (LSTM), a special type of recurrent neural network (RNN). We have initially collected Bangla dataset from different sources which is further reprocessed as the available datasets have valid output but does not provide input set with real-word error. The proposed model generates bigram sequences from the data corpus after accomplishing tokenization. Afterward, the sequences are fed into bidirectional LSTM model to predict the accurate word in order to replace inconsistent word. An advantage of this proposed procedure is that bidirectional LSTM remembers forward and backward relationship among words which creates better understanding of context for the network. Moreover, the system works on word length matching so that the output word length does not vary from the original one. The system shows significantly promising performance conveying 82.86% accuracy.
A New M-voting System for COVID-19 Special Situation in Iraq

Voting is the important procedure in different countries worldwide and is essential to any consensus-dependent society [1].Especially in the context of COVID pandemic where people stays home, such systems are very relevant today.Elections can happen for different reasons, involving choosing leaders, finishing political problems, choosing a substantial government, etc. [2].The phenomenal using of Information Technology in the process of election is quickly earning momentum.This growth in voting systems indicated to as evoting comes with more benefits than the voting system based on paper.While there are many efforts in different parts of world regarding implementations and trials for electronic voting, there are not many researches regarding this in Asia Minor, especially Iraq.It can be noted that Iraq has several regions with difficult access and where sometime it is difficult to collect votes in the traditional way because of access reasons and post-war conditions.We want to fill this gap through the research presented in this paper.In this paper we describe an application for m-voting targeting the specific conditions of Iraq in the COVID situation.In the current society, the application of which we are talking about, can also be seen as a significant help for a numerous amount of countries during the pandemic of COVID-19.The application is based on Mobile technology.We choose mobile technology motivated by the fact that although people do not have computers, almost everyone has a mobile phone in Iraq.Moreover, the usefulness of mobiles can represent a proper way for people with disabilities to execute their legal right to vote for the future of their country.
Morphosyntactic Patterns Follow Monotonic Mappings

Apart from being a system of structures, language is a system of relations. Understanding the particular regularities underlying these relations helps us predict both possibilities and gaps in linguistic organization. This paper follows Graf’s work [13] in positing monotonicity as a substantial underlying restriction on possible patterns in morphosyntactic paradigms. This approach not only extends the notion of monotonicity outside semantics, but also combines this formal explanation with extralinguistic motivations. The tense hierarchy I propose for syncretism in verbal paradigms is independently motivated by Reichenbach’s tense system [22]. The gender hierarchy used for gender resolution rules is directly extracted from the organization of the linguistic data. The restriction on both types of paradigms is readily explained by the fact that they only allow monotonic mappings from a base hierarchy to output forms.
A Critique of the Google Apple Exposure Notification (GAEN) Framework

As a response to the COVID-19 pandemic, digital contact tracing has been proposed as a tool to support the health authorities in their quest to determine who has been in close and sustained contact with a person infected by the coronavirus. In April 2020, Google and Apple released the Google Apple Exposure Notification (GAEN) framework, as a decentralised and more privacy-friendly platform for contact tracing. The GAEN framework implements exposure notification mostly at the operating system layer, instead of fully at the app(lication) layer. In this chapter, we study the consequences of this approach. We argue that this creates a dormant functionality for mass surveillance at the operating system layer. We show how it does not technically prevent the health authorities from implementing a purely centralised form of contact tracing (even though that is the stated aim). We highlight that GAEN allows Google and Apple to dictate how contact tracing is (or rather is not) implemented in practice by health authorities and how it introduces the risk of function creep.
Identifying and Analyzing Implicit Interactions in a Wastewater Dechlorination System

Critical infrastructures consist of numerous components, and even more interactions, many of which may not be expected or foreseen by the system designers. The existence of these so-called implicit interactions indicates design flaws that, if not mitigated, could result in losses of system stability, safety, and security. In this paper, we apply a formal methods-based approach for identifying and analyzing implicit interactions in a real-world Wastewater Dechlorination System provided by a municipal wastewater treatment facility. A system model is developed using the C $$^2$$ KA modeling framework and the analysis is automated using a software prototype. The analysis results include a summary of the identified implicit interactions and a calculation of their severity and exploitability, which helps to inform mitigation efforts at early stages of system design. We validate the results with a questionnaire which shows that the rigorous, practical approaches applied in this case study have the potential to improve overall system security and resilience.
Eating Like an Astronaut: How Children Are Willing to Eat

How food is presented and eaten influences the eating experience. Novel gustatory interfaces have opened up new ways for eating at the dining table. For example, recent developments in acoustic technology have enabled the transportation of food and drink in mid-air, directly into the user's tongue. Basic taste particles like sweet, bitter and umami have higher perceived intensity when delivered with acoustic levitation, and are perceived as more pleasant despite their small size (approx. 20 L or 4mm diameter droplets). However, it remains unclear if users are ready to accept this delivery method at the dining table. Sixty-nine children aged 14 to 16 years did a taste test of 7 types of foods and beverages, using two delivery methods: acoustic levitation, and knife and fork (traditional way). Children were divided into two groups: one group was shown a video demonstrating how levitating food can be eaten before the main experiment whereas the other group was shown the videos after. Our results showed no significant differences in liking of the foods and beverages between the two delivery methods. However, playing the video prior to the test significantly increased the liking and willingness to eat vegetables in the levitation method. Evaluative feedback suggested that a bigger portion size of levitating foods could be the game-changer to integrate this novel technology into real-life eating experiences.
A Cluster Based System for Analyzing Ethereum Blockchain Transaction Data

Ability to perform fast analysis on massive public blockchain transaction data is needed in various finance applications such as tracing of fraudulent activities. The blockchain data that is synced as a node is accessible as a sequence of blocks containing transactions. This way of accessing transaction data, however, is too slow for applications that require a transaction graph to be constructed. We develop a cluster based system that constructs a distributed transaction graph in parallel. Since blockchain data is continuously growing, our parallel system also offers the advantage of being able to scale by simply increasing the number of nodes in the cluster. Our system has been developed using the MPI message passing interface. We report performance results from our system operating on the whole 9.5 million block (roughly 4 year) Ethereum mainnet blockchain data. We report timings obtained from tests involving distributed transaction graph construction, partitioning, page ranking of addresses, degree distribution and token transaction counting on a 16 node economical cluster set up on the Amazon cloud. In particular, our system is able to construct distributed graph of 658 million ether and 31 major token transactions in 188 seconds.
A probabilistic modeling approach for interpretable data inference and classification

In this paper, we propose a new probabilistic modeling approach for interpretable inference and classification using the maximum likelihood evidential reasoning (MAKER) framework. This approach integrates statistical analysis, hybrid evidence combination and belief rule-based (BRB) inference, and machine learning. Statistical analysis is used to acquire evidence from data. The BRB inference is applied to analyze the relationship between system inputs and outputs. An interdependence index is used to quantify the interdependence between input variables. An adapted genetic algorithm is applied to train the models. The model established by the approach features a unique strong interpretability, which is reflected in three aspects: (1) interpretable evidence acquisition, (2) interpretable inference mechanism, and (3) interpretable parameters determination. The MAKER-based model is shown to be a competitive classifier for the Banana, Haberman’s survival, and Iris data set, and generally performs better than other interpretable classifiers, e.g., complex tree, logistic regression, and naive Bayes.
Denoising High Density Impulse Noise using Duo-Median Filter for Mammogram Images

Mammogram plays a vital role on cancer detection in the breast. Computerized processing enhances readability of digital mammogram. Impulse noise is one of the major noise which affects mammogram. Duo-median filter is anticipated to removes high density impulse noise. The projected filter checks for impulse noise and replace the pixel value by median of 2x2 neighboring pixel value. Results of proposed algorithm are compared with median filter, adaptive median filter and super mean filter using various parameters such as peak signal to noise ratio and structural similarities for general image and mammogram. The proposed Duo-median shows significantly better result with AMF and SUMF for higher noise percentage.