Reviewerly/exHarmony huggingface dataset:
Interactive Endoscopy: A Next-Generation, Streamlined User Interface for Lung Surgery Navigation

Computer generated graphics are superimposed onto live video emanating from an endoscope, offering the surgeon visual information that is hiding in the native scene—this describes the classical scenario of augmented reality in minimally invasive surgery. Research efforts have, over the past few decades, pressed considerably against the challenges of infusing a priori knowledge into endoscopic streams. As framed, these contributions emulate perception at the level of the surgeon expert, perpetuating debates on the technical, clinical, and societal viability of the proposition. We herein introduce interactive endoscopy, transforming passive visualization into an interface that allows the surgeon to label noteworthy anatomical features found in the endoscopic video, and have the virtual annotations remember their tissue locations during surgical manipulation. The streamlined interface combines vision-based tool tracking and speech recognition to enable interactive selection and labeling, followed by tissue tracking and optical flow for label persistence. These discrete capabilities have matured rapidly in recent years, promising technical viability of the system; it can help clinicians offload the cognitive demands of visually deciphering soft tissues; and supports societal viability by engaging, rather than emulating, surgeon expertise. Through a video-assisted thoracotomy use case, we develop a proof-of-concept to improve workflow by tracking surgical tools and visualizing tissue, while serving as a bridge to the classical promise of augmented reality in surgery.
VAPOR: A Value-Centric Blockchain that is Scale-out, Decentralized, and Flexible by Design

Blockchains is a special type of distributed systems that operates in unsafe networks. In most blockchains, all nodes should reach consensus on all state transitions with Byzantine fault tolerant algorithms, which creates bottlenecks in performance. In this paper, we propose a new type of blockchains, namely Value-Centric Blockchains (VCBs), in which the states are specified as values (or more comprehensively, coins) with owners and the state transition records are then specified as proofs of the ownerships of individual values. We then formalize the "rational" assumptions that have been used in most blockchains. We further propose a VCB, VAPOR, that guarantees secure value transfers if all nodes are rational and keep the proofs of the values they owned, which is merely parts of the whole state transition record. As a result, we show that VAPOR enjoys significant benefits in throughput, decentralization, and flexibility without compromising security.
Fast Nonparametric Mutual-Information-based Registration and Uncertainty Estimation

In this paper we propose a probabilistic model for multi-modal non-linear registration that directly incorporates the mutual information (MI) metric into a demons-like optimization scheme. In contrast to uni-modal registration, where the demons algorithm uses repeated spatial filtering to obtain very fast solutions, MI-based registration currently relies on general-purpose optimization schemes that are much slower. The central idea of this work is to reformulate an often-used histogram interpolation technique in MI implementations as an explicit spatial interpolation step within a generative model. By exploiting the specific structure of this model, we obtain a dedicated and fast expectation-maximization optimizer with demons-like properties. This also leads to an easy-to-implement Gibbs sampler to infer registration uncertainty in high-dimensional deformation models, involving very little additional code and no external tuning. Preliminary experiments on multi-modal brain MRI images show that the proposed optimizer can be both faster and more accurate than the free-form deformation method implemented in Elastix. We also demonstrate the sampler’s ability to produce direct uncertainty estimates of MI-based registrations – to the best of our knowledge the first method in the literature to do so.
New Copy-Move Forgery Detection Algorithm

In the era of digitization, the digital image plays a documentary role and consolidates a belief. Therefore, the image has to be trustworthy as important as the belief it influences. However, the digital world also came accompanied with the easiness of image manipulation for fun or forgery by available simple or professional image editing programs or even by cell phone apps. Accordingly, the necessity resulted in setting a goal to invent robust forgery detection systems that enable the computer to automatically recognize the tampering and make a tangible decision about image integrity. This paper plans a new algorithm for detecting the most popular forgery attack that is the copy-move forgery. The purpose of using the new algorithm is to make Scale invariant feature transform and the geometric transformation work together to achieve the best results in the forgery detection. The algorithm consists of five stages: image acquisition, the preprocessing for gray level production and dividing the input image, feature extraction of objects using Scale invariant feature transform algorithm, matching and indexing using a k-d tree and two Neural Network test, and geometric transformation. The result of matching determines whether the image is forged or not.
Are Humans Biased in Assessment of Video Interviews?

Supervised systems require human labels for training. But, are humans themselves always impartial during the annotation process? We examine this question in the context of automated assessment of human behavioral tasks. Specifically, we investigate whether human ratings themselves can be trusted at their face value when scoring video-based structured interviews, and whether such ratings can impact machine learning models that use them as training data. We present preliminary empirical evidence that indicates there are biases in such annotations, most of which are visual in nature.
Weakly Supervised Segmentation by a Deep Geodesic Prior

The performance of the state-of-the-art image segmentation methods heavily relies on the high-quality annotations, which are not easily affordable, particularly for medical data. To alleviate this limitation, in this study, we propose a weakly supervised image segmentation method based on a deep geodesic prior. We hypothesize that integration of this prior information can reduce the adverse effects of weak labels in segmentation accuracy. Our proposed algorithm is based on a prior information, extracted from an auto-encoder, trained to map objects’ geodesic maps to their corresponding binary maps. The obtained information is then used as an extra term in the loss function of the segmentor. In order to show efficacy of the proposed strategy, we have experimented segmentation of cardiac substructures with clean and two levels of noisy labels (L1, L2). Our experiments showed that the proposed algorithm boosted the performance of baseline deep learning-based segmentation for both clean and noisy labels by $$4.4\%$$ , $$4.6\%$$ (L1), and $$6.3\%$$ (L2) in dice score, respectively. We also showed that the proposed method was more robust in the presence of high-level noise due to the existence of shape priors.
Undefinability in Inquisitive Logic with Tensor

Logics based on team semantics, such as inquisitive logic and dependence logic, are not closed under uniform substitution. This leads to an interesting separation between expressive power and definability: it may be that an operator O can be added to a language without a gain in expressive power, yet O is not definable in that language. For instance, even though propositional inquisitive logic and propositional dependence logic have the same expressive power, inquisitive disjunction and implication are not definable in propositional dependence logic. A question that has been open for some time in this area is whether the tensor disjunction used in propositional dependence logic is definable in inquisitive logic. We settle this question in the negative. In fact, we show that extending the logical repertoire of inquisitive logic by means of tensor disjunction leads to an independent set of connectives; that is, no connective in the resulting logic is definable in terms of the others.
Quantitative Multimodal Interaction Analysis for the Assessment of Problem-Solving Skills in a Collaborative Online Game

We propose a novel method called Quantitative Multimodal Interaction Analysis to understand the meaning of interactions from a set of multimodal observable behavior. We apply this method for the measurement of collaborative problem-solving skills in a dyadic online game specially designed for this purpose. We outline our assumptions and describe the machine learning approach that help us tag multimodal behaviors connecting the theoretical construct with the empirical evidence.
Single-view and Multi-view Methods in Marker-less 3D Human Motion Capture

Abstract Human motion capture has now played a pivotal role in more and more applications, including biomechanics, sports, image segment, animation, robotics, etc. Although commercial marker-based human motion capture models have matured, the shortcomings, such as obtrusion, expense, errors due to damage to the marker trajectories, long set-up times and etc. exposed by this approach are becoming more and more apparent. Marker-less human motion capture analysis is likely to provide inexpensive and efficient solutions to solving these problems for the reconstruction of human motion in the future. In this paper, we discuss and compare the background and characteristics of marker-based and marker-less human motion capture models. Then we divide the marker-less human motion capture into single view and multi view and display some popular models. These methods are also categorized according to their internal logic and algorithms. Finally, we present some of the major shortcomings of the current marker-less human motion capture models and the future direction of development.
Nutritional booklet and social media: Their effects on adolescents’ fattening-food knowledge and consumption

Abstract One cause of adolescents’ fat-consumption imbalance is inadequate knowledge of nutrition and socialization about good eating habits. Adolescents like searching for food information through social media like Facebook and Instagram before consuming. This study was to analyse the effect of nutrition booklet and social media on the adolescents’ fattening-food knowledge and consumptions. The sample of this quasi-experiment was 96 eleventh-grade Science students of SMAN 1, 3, and 5 Surakarta selected using probability proportional size sampling. This study was conducted in one month. Nutritional education intervention with social media and nutritional booklet was conducted six times in two weeks. The data were analysed using Wilcoxon, Kruskal Wallis, and Logistic Binary Regression with SPSS. Nutritional education with nutrition booklet influenced the fattening-food knowledge ( p = 0.000) and consumption ( p &lt; 0.05). Nutritional education with social media influenced the fattening-food knowledge ( p = 0.001) and consumption ( p &lt; 0.05). There were different scores of the fattening-food knowledge ( p = 0.001) and consumption ( p = 0.005) in the nutritional education group with nutritional booklet and social media. Nutritional booklet and social media influenced adolescents’ fattening-food knowledge and consumption. There were different fattening-food knowledge and consumption in the adolescents using nutritional booklet and social media.
Evaluating a voice-based interaction

Natural User Interface provides the possibility of interaction with software through direct actions of human body, such as gesture and voice. In the case of voice, it is possible for the user to interact with the system quickly and directly, making the interaction simple and thus providing accessibility. Several studies were conducted with voice as a form of interaction between the user and system. Nevertheless, these studies are generally analyzed from a quantitative perspective and few perform a detailed qualitative analysis. The goal of this paper was to evaluate qualitatively a exploratory study with voice-based interaction. For this, two steps of the Grounded Theory (GT) method were used to obtain solid results. From these results, guidelines were proposed for the creation of digital text by voice so that possible users have this possibility of interaction when they write their texts.
Scala implicits are everywhere: a large-scale study of the use of Scala implicits in the wild

The Scala programming language offers two distinctive language features implicit parameters and implicit conversions, often referred together as implicits. Announced without fanfare in 2004, implicits have quickly grown to become a widely and pervasively used feature of the language. They provide a way to reduce the boilerplate code in Scala programs. They are also used to implement certain language features without having to modify the compiler. We report on a large-scale study of the use of implicits in the wild. For this, we analyzed 7,280 Scala projects hosted on GitHub, spanning over 8.1M call sites involving implicits and 370.7K implicit declarations across 18.7M lines of Scala code.
An Innovative Approach for Data Recovery Using Robust Reversible Watermarking

Watermarking is a technique which provides ownership rights on shared data. Watermarks are primirily used for copyright protection and content authetication. Many reversible watermarking methods are proposed recently to protect rights of owner along with recovering original data. In this paper, we proposed a fragile watermarking technique based on Robust Reversible Watermarking and sharing of image with motivation to maintain quality of image. Watermarked data is embedded into the image using LSB algorithm. This algorithm uses Random key Generator to encrypt data using binary XOR and both watermark image with secret key are send to receiver using Mail facility. Results indicate that proposed watermarking scheme reduces image distortion and preserves ownership right with the additional mail facility.
Interval Algorithm for Random Number Generation: Information Spectrum Approach

The problem of exactly generating a general random process (target process) by using another general random process (coin process) is studied. The performance of the interval algorithm, introduced by Han and Hoshi, is analyzed from the perspective of information spectrum approach. When either the coin process or the target process has one point spectrum, the asymptotic optimality of the interval algorithm among any random number generation algorithms is proved, which demonstrates utility of the interval algorithm beyond the ergodic process. Furthermore, the feasibility condition of exact random number generation is also elucidated. Finally, the obtained general results are illustrated by the case of generating a Markov process from another Markov process.
Optimized Classification Model for Efficient Recognition of Meal-Related Activities in Daily Life Meal Environment

Increasing the number of mastications can help suppress obesity, but it is difficult to keep constant awareness of it in everyday life. Besides, the conventional mastication number measurement apparatus is large and non-portable such it is difficult to use it in daily life. This research proposes a system that may support both consciousness improvement feedback in real-time and accurate quantified monitoring of mastication, swallowing and such other meal-related activities. It is composed of a cheap and small bone conduction microphone to collect intra-body sound signal, and a smartphone that can process it and provides feedback. Though meal-related activities quantification using wearable devices has been studied for many years, proposed systems have only been tested in experimental environment. In this study, we collected dietary sound data in a natural eating environment, evaluated the performances of different models, and proposed an optimization for classification of chewing, swallowing, and speaking activities.
Scale Invariant Feature Transform Based Method for Objects Matching

During the last decades, object recognition reported a widespread area of researchers. The reason for that is a numerous mixture of applications, where each application has its specific requirements and constraints. This paper uses the Scale Invariant Feature Transform (SIFT) for representation and extraction features. After that, a similarity algorithm is used for determining the matching object exactly. According to the results, the classifier succeeds in getting the accuracy of 91%. The results are acceptable.
Domain Selection of Transfer Learning in Fuzzy Prediction Models

Transfer learning has emerged as a solution for the cases where little or no labeled data are available in the training process.It leverages the previously acquired knowledge (a source domain with a large amount of labeled data) to facilitate solving the current tasks (a target domain with little labeled data).Many transfer learning methods have been proposed, and especially fuzzy transfer learning method, which is based on fuzzy systems, has been developed because of its capability to deal with the uncertainty in transfer learning.However, there is one issue with fuzzy transfer learning that has not yet been resolved: the domain selection problem, which is heavily depended on the knowledge transfer method and the applied prediction model.In this work, we explore the domain selection problem in Takagi-Sugeno fuzzy model when multiple source domains are accessible, and define the similarity between the source and target domains to provide guidance for the domain selection.The experiments on synthetic datasets are designed to simulate the situations of multiple sources in transfer learning, and demonstrate the rationality of the proposed similarity in selecting the source domain for the target domain.Further, the real-world datasets are used to validate the proposed domain adaptation method, and verify its capability in solving practical situations.
Poacher Detection in African Game Parks and Reserves with IoT: Machine Learning Approach

Extinction of wildlife animals is one of the well-documented problems the world is battling with currently. Africa which harbors a good number of these species is one of the regions most hit by this problem. To a greater extent, this is due to the continuous poaching practices in various African countries. The emergency of Internet-of-Things (IoT) technology has had a number of promising solutions to problems in many areas such as; environmental monitoring, traffic monitoring, smart health, waste management, e.t.c. Thus in this work, we design an IoT framework to curb the poaching practice in Africa. To improve the effectiveness of our system, we integrate a machine learning model to perform image analysis and classification task for the poacher detection purpose. A trial implementation of the framework is carried out and the results show a significant potential of IoT being used to enhance surveillance in game parks and reserves and hence, control the poaching problem.
Exploiting EuroVoc’s Hierarchical Structure for Classifying Legal Documents

Multi-label document classification is a challenging problem because of the potentially huge number of classes. Furthermore, real-world datasets often exhibit a strongly varying number of labels per document, and a power-law distribution of those class labels. Multi-label classification of legal documents is additionally complicated by long document texts and domain-specific use of language. In this paper we use different approaches to compare the performance of text classification algorithms on existing datasets and corpora of legal documents, and contrast the results of our experiments with results on general-purpose multi-label text classification datasets. Moreover, for the EUR-Lex legal datasets, we show that exploiting the hierarchy of the EuroVoc thesaurus helps to improve classification performance by reducing the number of potential classes while retaining the informative value of the classification itself.
3D Interaction with Virtual Objects in Real Water

The objective of this research was to evaluate and compare perceived fatigue and usability of 3D user interfaces in and out of the water. Virtual Reality (VR) in the water has several potential applications, such as aquatic physical rehabilitation, where patients are typically standing waist or shoulder deep in a pool and performing exercises in the water. However, there have been few works that developed waterproof VR/AR systems and none of them have assessed fatigue, which has previously been shown to be a drawback in many 3D User Interfaces above water. This research presents a novel prototype system for developing waterproof VR experiences and investigates the effect of submersion in water on fatigue as compared to above water. Using a classic selection and docking task, results suggest that being underwater had no significant effect on performance, but did reduce perceived fatigue, which is important for aquatic rehabilitation. Previous 3D interaction methods that were once thought to be too fatiguing might still be viable in water.
Multi-Objective Control of Distributed Parameter Systems in the Case of Interval Uncertainty of the Plant Characteristics

The previously developed technology of multi-objective optimization of deterministic systems with distributed parabolic parameters can be applied in typical situations with interval uncertainties of parametric characteristics of controlled plants. Minimax problems of mathematical programming are formed in accordance with a control strategy based on the principle of the best guaranteed result of a expanded set of arguments, which includes all permissible realizations of uncertain factors, in the case of estimating a given accuracy of approximation to a required finite system state in uniform metric. These problems are solved by an alternance method that uses the Chebyshev properties of desired extermals and fundamental laws of the problem domain. This paper also presents an example of multi-objective optimization of temperature modes of induction heating of metallic semifinished products before plastic deformation operations in technological complexes for pressure treatment of metal, which is of independent interest.
Cardiovascular Conditions Classification Using Adaptive Neuro-Fuzzy Inference System

ECG (electrocardiogram) signals have been widely used to determine the cardiovascular conditions of individuals. In this research, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is designed to identify six different heart conditions: normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), left bundle branch block (LBBB), right bundle branch block (RBBB), and paced beats. The inputs to ANFIS are the seven time-domain features extracted from ECG signals, such as the Q amplitude, the QRS interval, etc. The proposed approach is tested on the ECG signals of MIT-BIH database; and computer simulation results indicate this method is promising with an average accuracy of 98.39%, and an average specificity of 99.67%.