Reviewerly/exHarmony huggingface dataset:
Comparison of manual and semi-automatic registration in augmented reality image-guided liver surgery: a clinical feasibility study

Abstract Background The laparoscopic approach to liver resection may reduce morbidity and hospital stay. However, uptake has been slow due to concerns about patient safety and oncological radicality. Image guidance systems may improve patient safety by enabling 3D visualisation of critical intra- and extrahepatic structures. Current systems suffer from non-intuitive visualisation and a complicated setup process. A novel image guidance system (SmartLiver), offering augmented reality visualisation and semi-automatic registration has been developed to address these issues. A clinical feasibility study evaluated the performance and usability of SmartLiver with either manual or semi-automatic registration. Methods Intraoperative image guidance data were recorded and analysed in patients undergoing laparoscopic liver resection or cancer staging. Stereoscopic surface reconstruction and iterative closest point matching facilitated semi-automatic registration. The primary endpoint was defined as successful registration as determined by the operating surgeon. Secondary endpoints were system usability as assessed by a surgeon questionnaire and comparison of manual vs. semi-automatic registration accuracy. Since SmartLiver is still in development no attempt was made to evaluate its impact on perioperative outcomes. Results The primary endpoint was achieved in 16 out of 18 patients. Initially semi-automatic registration failed because the IGS could not distinguish the liver surface from surrounding structures. Implementation of a deep learning algorithm enabled the IGS to overcome this issue and facilitate semi-automatic registration. Mean registration accuracy was 10.9 ± 4.2 mm (manual) vs. 13.9 ± 4.4 mm (semi-automatic) (Mean difference − 3 mm; p = 0.158). Surgeon feedback was positive about IGS handling and improved intraoperative orientation but also highlighted the need for a simpler setup process and better integration with laparoscopic ultrasound. Conclusion The technical feasibility of using SmartLiver intraoperatively has been demonstrated. With further improvements semi-automatic registration may enhance user friendliness and workflow of SmartLiver. Manual and semi-automatic registration accuracy were comparable but evaluation on a larger patient cohort is required to confirm these findings.
A Semi-supervised Approach to Generate the Code-Mixed Text using Pre-trained Encoder and Transfer Learning

Code-mixing, the interleaving of two or more languages within a sentence or discourse is ubiquitous in multilingual societies.The lack of code-mixed training data is one of the major concerns for the development of end-to-end neural network-based models to be deployed for a variety of natural language processing (NLP) applications.A potential solution is to either manually create or crowd-source the code-mixed labelled data for the task at hand, but that requires much human efforts and often not feasible because of the language specific diversity in the code-mixed text.To circumvent the data scarcity issue, we propose an effective deep learning approach for automatically generating the code-mixed text from English to multiple languages without any parallel data.In order to train the neural network, we create synthetic code-mixed texts from the available parallel corpus by modelling various linguistic properties of code-mixing.Our codemixed text generator is built upon the encoderdecoder framework, where the encoder is augmented with the linguistic and task-agnostic features obtained from the transformer based language model.We also transfer the knowledge from a neural machine translation (NMT) to warm-start the training of code-mixed generator.Experimental results and in-depth analysis show the effectiveness of our proposed code-mixed text generation on eight diverse language pairs.
Cryptocurrency Market: Behavioral Finance Perspective

The cryptocurrency market has received immense consideration in media and academia since the beginning of 2013 because of its huge price fluctuation.This study focuses on Arab investors who invest in the cryptocurrency market by investigating the influence of behavioral finance factors on investment decisions in the cryptocurrency market.A quantitative approach was used by employing a snowball sampling method through 112 questionnaires.The results show that herding theory, prospect theory, and heuristic theory have a significant effect on investors' investment decisions in the cryptocurrency market.This emphasizes the significant role of the proposed behavioral factors as determinants of the investors' investment decisions.This study contributes to the existing research by consolidating the results of different researches in this study.It also contributes to the investors' understanding of the dynamics of the cryptocurrency market and it enhances the ability to make informed decisions based on their understanding.The implication of the findings will prepare hit and run investors to be progressively prepared to stay in the cryptocurrency market and develop their abilities on the most proficient method to settle on sound venture choices.Furthermore, the findings of this study will encourage financial specialists to realize that information on the traditional finance theory is not adequate to excel in the cryptocurrency market.
Mitotic Figures—Normal, Atypical, and Imposters: A Guide to Identification

Counting mitotic figures (MF) in hematoxylin and eosin-stained histologic sections is an integral part of the diagnostic pathologist's tumor evaluation. The mitotic count (MC) is used alone or as part of a grading scheme for assessment of prognosis and clinical decisions. Determining MCs is subjective, somewhat laborious, and has interobserver variation. Proposals for standardizing this parameter in the veterinary field are limited to terminology (use of the term MC) and area (MC is counted in an area measuring 2.37 mm2). Digital imaging techniques are now commonplace and widely used among veterinary pathologists, and field of view area can be easily calculated with digital imaging software. In addition to standardizing the methods of counting MF, the morphologic characteristics of MF and distinguishing atypical mitotic figures (AMF) versus mitotic-like figures (MLF) need to be defined. This article provides morphologic criteria for MF identification and for distinguishing normal phases of MF from AMF and MLF. Pertinent features of digital microscopy and application of computational pathology (CPATH) methods are discussed. Correct identification of MF will improve MC consistency, reproducibility, and accuracy obtained from manual (glass slide or whole-slide imaging) and CPATH approaches.
A Deep Generative Distance-Based Classifier for Out-of-Domain Detection with Mahalanobis Space

Detecting out-of-domain (OOD) input intents is critical in the task-oriented dialog system. Different from most existing methods that rely heavily on manually labeled OOD samples, we focus on the unsupervised OOD detection scenario where there are no labeled OOD samples except for labeled in-domain data. In this paper, we propose a simple but strong generative distance-based classifier to detect OOD samples. We estimate the class-conditional distribution on feature spaces of DNNs via Gaussian discriminant analysis (GDA) to avoid over-confidence problems. And we use two distance functions, Euclidean and Mahalanobis distances, to measure the confidence score of whether a test sample belongs to OOD. Experiments on four benchmark datasets show that our method can consistently outperform the baselines.
An integrated emotional and physiological assessment for VR-based active shooter incident experiments

Unfortunately, active shooter incidents are on the rise in the United States. With the recent technological advancements, virtual reality (VR) experiments could serve as an effective method to prepare civilians and law enforcement personnel for such scenarios. However, for VR experiments to be effective for active shooter training and research, such experiments must be able to evoke emotional and physiological responses as live active shooter drills and events do. The objective of this study is thus to test the effectiveness of an active shooter VR experiment on emotional and physiological responses. Additionally, we consider different locomotion techniques (i.e., walk-in-place and controller) and explore their impact on users' sense of presence. The results suggest that the VR active shooter experiment in this study can induce emotional arousal and increase heart rate of the participants immersed in the virtual environment. Furthermore, compared to the controller, the walk-in-place technique resulted in a higher emotional arousal in terms of negative emotions and a stronger sense of presence. The study presents a foundation for future active shooter experiments as it supports the ecological validity using VR for active shooter incident related work for the purposes of training or research.
Soil Temperature Prediction Using Convolutional Neural Network Based on Ensemble Empirical Mode Decomposition

Soil temperature plays an important role in agriculture, industry and other fields.Accurate soil temperature prediction can help improve productivity and avoid risks in many fields.At present, many machine learning methods have been applied to soil temperature prediction such as support vector regression (SVR), artificial neural network (ANN), long short-term memory neural network (LSTM) and others.In this article, we propose a machine learning model called convolutional neural network based on ensemble empirical mode decomposition (EEMD-CNN) to predict soil temperature.In this model, ensemble empirical mode decomposition (EEMD) is used to decompose original soil temperature series into several intrinsic mode functions (IMFs).After decomposition, the original series are combined with IMFs to get new twodimension input data as the input of the convolutional neural network (CNN).By comparing the results which is predicted by the trained model with the original soil temperature series and other four models of persistence forecast (PF), backpropagation neural network (BPNN), LSTM and EEMD-LSTM these, the result shows that EEMD-CNN has the better performance than other four models.EEMD-CNN shows good performance not only on predicting next day's soil temperature but also on predicting several days delay's temperature also has good performance.It is concluded that the proposed EEMD-CNN model in this study is a suitable tool for soil temperature prediction.
DecisionTree for Classiﬁcation and Regression: A State-of-the Art Review

Classification and regression are defined under the umbrella of the prediction task of data mining. Discrete values are predicted using classification techniques whereas regression techniques are most suitable for predicting continuous data. Analysts from different research areas like data mining, statistics, machine learning, pattern recognition, and big data analytics preferred decision trees over other classifiers as it is simple, effective, efficient, and its performance is competitive with others. In this paper, we review extensively many popularly used state-of-the-art decision tree-based techniques for classification and regression. We present a survey of more than forty years of research that has been emphasized on the application of decision trees in both classification and regression. This survey could be the potential source for all the researchers who are keenly interested to apply the decision tree classifier/regressor for their research work.
Secret data modification based image steganography technique using genetic algorithm having a flexible chromosome structure

Abstract In all forms of confidential communication, the most significant element is security. Cryptography can be used to secure the information, but it discloses the presence of covert communication. Hence, steganography was invented; steganography is an art of concealed communication. Steganography is performed by inserting the secret information in a cover media to camouflage the presence of covert communication. In image steganography, an image is used as the cover media for secure communication. There are various image steganography techniques invented by many researchers, but a small number of them focus on improving visual quality and increasing payload capacity simultaneously. This paper presents a secret data modification based high capacity image steganography technique using genetic algorithm (GA). This new technique uses the least significant bit (LSB) replacement steganography, to embed the secret data. However, the secret data is rearranged and modified before embedding it in the LSBs of the cover image. The parameters used to rearrange and modify the secret data are controlled by GA. A unique concept called flexible chromosome is introduced which allows GA to interpret the chromosome value in different ways. GA attempts to find the best possible parameter value that yields high visual quality of the stego images. The stego images produced by the proposed technique have an average PSNR value of 46.41 dB and 40.83 dB for 2 bit per pixel (bpp) and 3 bpp data hiding capacity respectively.
On the Stochastic Analysis of a Quantum Entanglement Distribution Switch

In this article, we study a quantum entanglement distribution switch that serves k users in a star topology.We model variants of the system as continuous-time Markov chains and obtain expressions for switch capacity, expected number of qubits stored in memory at the switch, and the quantum memory occupancy distribution.We obtain a number of analytic results for systems in which measurements are imperfect, the links are homogeneous or heterogeneous and for switches that have an infinite or finite number of quantum memories or buffers.In addition, we model the effect of decoherence of quantum states and associated cutoff times on their storage using a simple model.From numerical observations, we discover that decoherence-associated cutoff times have little effect on capacity and expected number of stored qubits for homogeneous systems.For heterogeneous systems, especially those operating near the boundaries of their stability regions (i.e., systems that are nearly unstable), buffer size and decoherence can have significant effects on performance metrics.We also learn that in general, increasing the buffer size from one to two qubits per link is advantageous to most systems, whereas increasing the buffer size further yields diminishing returns.The analytical results obtained in this work can serve as a useful guide toward the future design of quantum switches-e.g., by allowing the designer to determine how many quantum memories suffice for a given number of users-as well as provide valuable insight on the performance of these and similar devices.
Using extended complexity theory to test SMEs’ adoption of Blockchain-based loan system

Blockchain-based loan system can be summed up as: information exchange between various government departments; information exchange between enterprises and various financial institutions; detection of the actual use of loans in the form of encrypted currency. This technology is supposed to reduce a lot of financing costs for SMEs on average. Therefore, this research extends complexity theory to discover the factors that affect the use of Blockchain loan systems by SMEs. Complexity, perceived risk, perceived fairness and reward sensitivity prove to have significant effects on usage intention. Complexity proves to have moderating effects on other relationships. This research may contribute to the system performance improvement and provide opportunities for SMEs to share information with financial institutions or individuals around the world, thereby providing investors with equal opportunities for competition.
Blockchain-Based Verifiable Credential Sharing with Selective Disclosure

Sharing credentials could raise privacy concerns. For digital credentials to be widely accepted, there is a need for an end-to-end system that provides (i) secure verification of the participant identities and credentials to increase trust, and (ii) a data minimisation mechanism to reduce the risk of oversharing the credential data. This paper proposes CredChain, a blockchain-based Self-Sovereign Identity (SSI) platform architecture that allows secure creation, sharing and verification of credentials. Beyond the verification of identities and credentials, a flexible selective disclosure solution is proposed using redactable signatures. The credentials are managed through a decentralised application/wallet which allows users to store their credential data privately under their full control and re-use as necessary. Our evaluation results show that CredChain architecture is feasible, secure and exhibits the level of performance that is within the expected benchmarks of the well-known blockchain platform, Parity Ethereum.
State-of-the-Art Software-Based Remote Attestation: Opportunities and Open Issues for Internet of Things

The Internet of Things (IoT) ecosystem comprises billions of heterogeneous Internet-connected devices which are revolutionizing many domains, such as healthcare, transportation, smart cities, to mention only a few. Along with the unprecedented new opportunities, the IoT revolution is creating an enormous attack surface for potential sophisticated cyber attacks. In this context, Remote Attestation (RA) has gained wide interest as an important security technique to remotely detect adversarial presence and assure the legitimate state of an IoT device. While many RA approaches proposed in the literature make different assumptions regarding the architecture of IoT devices and adversary capabilities, most typical RA schemes rely on minimal Root of Trust by leveraging hardware that guarantees code and memory isolation. However, the presence of a specialized hardware is not always a realistic assumption, for instance, in the context of legacy IoT devices and resource-constrained IoT devices. In this paper, we survey and analyze existing software-based RA schemes (i.e., RA schemes not relying on specialized hardware components) through the lens of IoT. In particular, we provide a comprehensive overview of their design characteristics and security capabilities, analyzing their advantages and disadvantages. Finally, we discuss the opportunities that these RA schemes bring in attesting legacy and resource-constrained IoT devices, along with open research issues.
Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review

Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making is a challenging task. Machine Learning (ML) techniques in ICUs are making headway in the early detection of high-risk events due to increased processing power and freely available datasets such as the Medical Information Mart for Intensive Care (MIMIC). We conducted a systematic literature review to evaluate the effectiveness of applying ML in the ICU settings using the MIMIC dataset. A total of 322 articles were reviewed and a quantitative descriptive analysis was performed on 61 qualified articles that applied ML techniques in ICU settings using MIMIC data. We assembled the qualified articles to provide insights into the areas of application, clinical variables used, and treatment outcomes that can pave the way for further adoption of this promising technology and possible use in routine clinical decision-making. The lessons learned from our review can provide guidance to researchers on application of ML techniques to increase their rate of adoption in healthcare.
Optimization of Computer-Aided English Translation Teaching Based on Network Teaching Platform

With the continuous development of science and technology, more and more technologies such as computers have been put into actual teaching work.Li's computer-assisted music teaching model can just make up for the single teaching model of exam-oriented education.Based on computer-assisted teaching technology, this article carries out the design and development of a digital music course teaching system.The system can easily digitally process the recorded sound and present it in the form of sound waves on the computer.It can visually edit the audio and save the specific training status in the database comprehensively, providing more information for teaching and training.Intuitive visual information and all parameters transform the theory and methods of skills into vivid image data information.At the same time, analysis and discussion on computer technology are carried out.Using computers, music production can be operated in an intuitive and visual environment, clarifying the theme of music, developing the structure, and post-production.It has been proved through practice that this system can not only provide for teaching and training More intuitive and vivid visual information and all parameters, which transform skill theory and methods into vivid image data information, can also significantly expend efficiency of music learning and the training of professional skills, which is worthy of vigorous promotion and practice.
FedSens: A Federated Learning Approach for Smart Health Sensing with Class Imbalance in Resource Constrained Edge Computing

The advance of mobile sensing and edge computing has brought new opportunities for abnormal health detection (AHD) systems where edge devices such as smartphones and wearable sensors are used to collect people's health information and provide early alerts for abnormal health conditions such as stroke and depression. The recent development of federated learning (FL) allows participants to collaboratively train powerful AHD models while keeping their health data private to local devices. This paper targets at addressing a critical challenge of adapting FL to train AHD models, where the participants' health data is highly imbalanced and contains biased class distributions. Existing FL solutions fail to address the class imbalance issue due to the strict privacy requirements of participants as well as the heterogeneous resource constraints of their edge devices. In this work, we propose FedSens, a new FL framework dedicated to address the class imbalance problem in AHD applications with explicit considerations of participant privacy and device resource constraints. We evaluate FedSens using a real-world edge computing testbed on two real-world AHD applications. The results show that FedSens can significantly improve the accuracy of AHD models in the presence of severe class imbalance with low energy cost to the edge devices.
Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation

Recent studies in the field of Machine Translation (MT) and Natural Language Processing (NLP) have shown that existing models amplify biases observed in the training data.The amplification of biases in language technology has mainly been examined with respect to specific phenomena, such as gender bias.In this work, we go beyond the study of gender in MT and investigate how bias amplification might affect language in a broader sense.We hypothesize that the 'algorithmic bias', i.e. an exacerbation of frequently observed patterns in combination with a loss of less frequent ones, not only exacerbates societal biases present in current datasets but could also lead to an artificially impoverished language: 'machine translationese'.We assess the linguistic richness (on a lexical and morphological level) of translations created by different data-driven MT paradigms -phrase-based statistical (PB-SMT) and neural MT (NMT).Our experiments show that there is a loss of lexical and morphological richness in the translations produced by all investigated MT paradigms for two language pairs (EN↔FR and EN↔ES).