Reviewerly/exHarmony huggingface dataset:
Semi-automatic liver segmentation based on probabilistic models and anatomical constraints

Abstract Segmenting a liver and its peripherals from abdominal computed tomography is a crucial step toward computer aided diagnosis and therapeutic intervention. Despite the recent advances in computing methods, faithfully segmenting the liver has remained a challenging task, due to indefinite boundary, intensity inhomogeneity, and anatomical variations across subjects. In this paper, a semi-automatic segmentation method based on multivariable normal distribution of liver tissues and graph-cut sub-division is presented. Although it is not fully automated, the method minimally involves human interactions. Specifically, it consists of three main stages. Firstly, a subject specific probabilistic model was built from an interior patch, surrounding a seed point specified by the user. Secondly, an iterative assignment of pixel labels was applied to gradually update the probabilistic map of the tissues based on spatio-contextual information. Finally, the graph-cut model was optimized to extract the 3D liver from the image. During post-processing, overly segmented nodal regions due to fuzzy tissue separation were removed, maintaining its correct anatomy by using robust bottleneck detection with adjacent contour constraint. The proposed system was implemented and validated on the MICCAI SLIVER07 dataset. The experimental results were benchmarked against the state-of-the-art methods, based on major clinically relevant metrics. Both visual and numerical assessments reported herein indicated that the proposed system could improve the accuracy and reliability of asymptomatic liver segmentation.
Challenges and Approaches to Scaling the Global Commons

The re-emergence of commoning over the last decades is not incidental, but rather indicative of a large-scale transition to a more “generative” organization of society that is oriented toward the planet’s global carrying capacity. Digital commons governance frameworks are of particular importance for a new global paradigm of cooperation, one that can scale the organization of communities around common goals and resources to unprecedented levels of size, complexity and granularity. Distributed Ledger Technologies (DLTs) such as blockchain have lately given new impetus to the emergence of a new generation of authentic “sharing economy,” protected from capture by thorough distribution of power over infrastructure, that spans not only digital but also physical production of common value. The exploration of the frontiers of DLT-based commoning at the heart of this article considers three exemplary cases for this new generation of commons-oriented community frameworks: the Commons Stack, Holochain and the Commons Engine, and the Economic Space Agency. While these projects differ in their scope as well as in their relation to physical common-pool resources (CPRs), they all share the task of redefining markets so as to be more conducive to the production and sustainment of common value(s). After introducing each of them with regards to their specificities and commonalities, we analyze their capacity to foster commons-oriented economies and “money for the commons” that limit speculation, emphasize use-value over exchange-value, favor equity in human relations, and promote responsibility for the preservation of natural habitats. Our findings highlight the strengths of DLTs for a federated scaling of CPR governance frameworks that accommodates rather than obliterates cultural differences and creates webs of fractal belonging among nested communities.
Sensor Fusion Basketball Shooting Posture Recognition System Based on CNN

In recent years, with the development of wearable sensor devices, research on sports monitoring using inertial measurement units has received increasing attention; however, a specific system for identifying various basketball shooting postures does not exist thus far. In this study, we designed a sensor fusion basketball shooting posture recognition system based on convolutional neural networks. The system, using the sensor fusion framework, collected the basketball shooting posture data of the players’ main force hand and main force foot for sensor fusion and used a deep learning model based on convolutional neural networks for recognition. We collected 12,177 sensor fusion basketball shooting posture data entries of 13 Chinese adult male subjects aged 18–40 years and with at least 2 years of basketball experience without professional training. We then trained and tested the shooting posture data using the classic visual geometry group network 16 deep learning model. The intratest achieved a 98.6% average recall rate, 98.6% average precision rate, and 98.6% accuracy rate. The intertest achieved an average recall rate of 89.8%, an average precision rate of 91.1%, and an accuracy rate of 89.9%.
Arabic Tweets Sentiment Analysis about Online Learning during COVID-19 in Saudi Arabia

The COVID-19 pandemic can be considered as the greatest challenge of our time and is defining and reshaping many aspects of our life such as learning and teaching, especially in the academic year of 2020.While some people could adapt quickly to online learning, others consider it to be inefficient.The re-opening of schools and universities is currently under consideration.However, many experts in many countries suggested that at least one semester should be online, during the pandemic.Understanding the public's emotional reaction to online learning has become significant.This paper studies the attitude of people of Saudi Arabia towards online learning.We have used a collection of Arabic tweets posted in 2020, collected mainly via hashtags that originated in Saudi Arabia.Our sentiment analysis has shown that people have maintained a neutral response to online learning.This study will allow scholars and decision makers to understand the emotional effects of online learning on communities.
A New Ensemble Method for Detecting Anomalies in Gene Expression Matrices

One of the main problems in the analysis of real data is often related to the presence of anomalies. Namely, anomalous cases can both spoil the resulting analysis and contain valuable information at the same time. In both cases, the ability to detect these occurrences is very important. In the biomedical field, a correct identification of outliers could allow the development of new biological hypotheses that are not considered when looking at experimental biological data. In this work, we address the problem of detecting outliers in gene expression data, focusing on microarray analysis. We propose an ensemble approach for detecting anomalies in gene expression matrices based on the use of Hierarchical Clustering and Robust Principal Component Analysis, which allows us to derive a novel pseudo-mathematical classification of anomalies.
A Dimensional Comparison between Evolutionary Algorithm and Deep Reinforcement Learning Methodologies for Autonomous Surface Vehicles with Water Quality Sensors

The monitoring of water resources using Autonomous Surface Vehicles with water-quality sensors has been a recent approach due to the advances in unmanned transportation technology. The Ypacaraí Lake, the biggest water resource in Paraguay, suffers from a major contamination problem because of cyanobacteria blooms. In order to supervise the blooms using these on-board sensor modules, a Non-Homogeneous Patrolling Problem (a NP-hard problem) must be solved in a feasible amount of time. A dimensionality study is addressed to compare the most common methodologies, Evolutionary Algorithm and Deep Reinforcement Learning, in different map scales and fleet sizes with changes in the environmental conditions. The results determined that Deep Q-Learning overcomes the evolutionary method in terms of sample-efficiency by 50–70% in higher resolutions. Furthermore, it reacts better than the Evolutionary Algorithm in high space-state actions. In contrast, the evolutionary approach shows a better efficiency in lower resolutions and needs fewer parameters to synthesize robust solutions. This study reveals that Deep Q-learning approaches exceed in efficiency for the Non-Homogeneous Patrolling Problem but with many hyper-parameters involved in the stability and convergence.
The Effectiveness of Using Technology in Learning English

This study found out about the effectiveness of using technology in learning English. However, few studies have been shown the effectiveness of technology in all English skills: Speaking, listening, reading, and writing skills. Third-year students of the Foreign Language Department at Van Lang University participated in this study for 2 weeks. The questions revolve around learning English and using technology in learning English. The researchers will then collect all the data of the responses to come to general conclusions for the study. The goal of using technology in learning English brings a strong potential to enhance language skills for learners and promote the process of learning English quickly. After using technology in learning English, we will see the effectiveness of the four skills, and this effect is expressed as a percentage (30%, 50%, 80%) based on the results obtained from the questionnaires.
Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous Driving

Path planning research plays a vital role in terms of safety and comfort in autonomous driving systems. This paper focuses on safe driving and comfort riding through path planning in autonomous driving applications and proposes autonomous driving path planning through an optimal controller integrating obstacle-dependent Gaussian (ODG) and model prediction control (MPC). The ODG algorithm integrates the information from the sensors and calculates the risk factors in the driving environment. The MPC function finds vehicle control signals close to the objective function under limited conditions, such as the structural shape of the vehicle and road driving conditions. The proposed method provides safe control and minimizes vehicle shaking due to the tendency to respond to avoid obstacles quickly. We conducted an experiment using mobile robots, similar to an actual vehicle, to verify the proposed algorithm performance. The experimental results show that the average safety metric is 72.34%, a higher ISO-2631 comport score than others, while the average processing time is approximately 14.2 ms/frame.
Probing for idiomaticity in vector space models

Contextualised word representation models have been successfully used for capturing different word usages, and they may be an attractive alternative for representing idiomaticity in language.In this paper, we propose probing measures to assess if some of the expected linguistic properties of noun compounds, especially those related to idiomatic meanings, and their dependence on context and sensitivity to lexical choice, are readily available in some standard and widely used representations.For that, we constructed the Noun Compound Senses Dataset, which contains noun compounds and their paraphrases, in context neutral and context informative naturalistic sentences, in two languages: English and Portuguese.Results obtained using four types of probing measures with models like ELMo, BERT and some of its variants, indicate that idiomaticity is not yet accurately represented by contextualised models.
Legaltech and Lawtech: Global Perspectives, Challenges, and Opportunities

Legaltech refers to the application of new technologies to the world of law, to carry out tasks that, until recently, were performed by lawyers or other personnel working in law firms. From 2015 onwards the Lawtech alternative has emerged. In this work, the concepts of Legaltech and Lawtech have been analyzed by searching the two main scientific information databases such as Scopus and Wed of Science (WoS). There has been a clear trend to use the concept of Legaltech against Lawtech. Six clear research lines have been detected from the whole of the published documents regarding these concepts. These are the related to Computer Science, Justice, Legal profession, Legal design, Law firms, and Legal Education. It is proposed to use the term Legaltech to include all technological advances in the legal field. From the point of view of opportunities, the irruption of Legaltech will be able to offer accurate legal advice to the public, reducing the price of this and on the other hand, analyze large amounts of data that law firms and legal advisors will use to improve their management and increase their productivity. In short, Legaltech and Lawtech are opening up new opportunities in the legal sector encouraging technological innovation, giving greater access to legal services, even try to achieve the goal of universal access to justice.
Secure Cloud Storage Architecture for Digital Medical Record in Cloud Environment Using Blockchain

Medicinal services and genomics are the absolute most significant sorts of information for cross-organization prescient displaying that appraisals understanding results by dissecting watched information and producing logical proof utilizing information from different establishments. Decentralized protection safeguarding prescient displaying empowers numerous establishments to become familiar with an increasingly generalizable model on medicinal services or genomic information by sharing the halfway prepared models rather than patient level information, while dodging dangers, for example, single purpose of control. Restorative consideration has turned out to be one of the most crucial pieces of human lives, prompting a sensational increment in therapeutic huge information. To streamline the conclusion and treatment process, human services experts are presently receiving Internet of Things (IoT) - based wearable innovation. In any case, these advances likewise posture grave protection dangers and security worries about the information move and the logging of information exchanges. These security and protection issues of medicinal information could result from a postponement in treatment advance, notwithstanding imperilling the patient's life. We propose the utilization of a Blockchain into give secure administration and investigation of human services enormous information. Be that as it may, square chains are computationally costly, request high data transmission and extra computational power, and are in this manner not totally appropriate for most asset obliged IoT gadgets implied for shrewd urban communities. In this work, we attempt to determine the previously mentioned issues of utilizing Blockchain with Healthcare Data. In this way, we propose a novel system of Blockchain models appropriate for Healthcare continuous information that depend on their conveyed nature and other extra protection and security properties of the system and distributed storage. First information will be characterized utilizing information grouping. This arranged information will be sent to Blockchain for extra protection and security properties. The arrangements given here make application information and exchanges increasingly secure and unknown over a Blockchain-based system.
Exploring the Determinants of Service Quality of Cloud E-Learning System for Active System Usage

E-Learning through a cloud-based learning management system, with its various added advantageous features, is a widely used pedagogy at educational institutions in general and more particularly during and post Covid-19 period. Successful adoption and implementation of cloud E-Learning seems difficult without significant service quality. Aims: This study aims to identify the determinant of cloud E-Learning service quality. Methodology: A theoretical model was proposed to gauge the cloud E-Learning service quality by extensive literature search. The most important factors for cloud E-Learning service quality were screened. Instruments for each factor were defined properly, and its content validity was checked with the help of Group Decision Makers (GDMs). Empirical testing was used to validate the proposed theoretical model, the self-structured closed-ended questionnaire was used to conduct an online survey. Findings: Internal consistency of the proposed model was checked with reliability and composite reliability and found appropriate α ≥ 0.70 and CR ≥ 0.70. Indicator Reliability was matched with the help of Outer Loading and found deemed fit OL ≥ 0.70. To establish Convergent Validity Average Variance Extracted, Factor Loading and Composite Reliability were used and found deemed suitable with AVE ≥ 0.50. The HTMT and Fornell–Lacker tests were applied to assess discriminant validity and found appropriate (HTMT ≤ 0.85). Finally, the Variance Inflation Factor was used to detect multicollinearity if any and found internal and external VIF &lt; 3. Conclusions: Theoretical model for cloud E-Learning service quality was proposed. Information Quality, Reliability, Perceived ease of use and Social Influence were considered as explanatory variables whereas actual system usage was the dependent variable. Empirical testing on all parameters stated that the proposed model was deemed fit in evaluating cloud E-Learning service quality.
Color-Optimized One-Pixel Attack Against Digital Pathology Images

Modern artificial intelligence based medical imaging tools are vulnerable to model fooling attacks. Automated medical imaging methods are used for supporting the decision making by classifying samples as regular or as having characters of abnormality. One use of such technology is the analysis of whole-slide image tissue samples. Consequently, attacks against artificial intelligence based medical imaging methods may diminish the credibility of modern diagnosis methods and, at worst, may lead to misdiagnosis with improper treatment. This study demonstrates an advanced color-optimized one-pixel attack against medical imaging. A state-of-the-art one-pixel modification is constructed with minimal effect on the pixel's color value. This multi-objective approach mitigates the unnatural coloring of raw none-pixel attacks. Accordingly, it is infeasible or at least cumbersome for a human to see the modification in the image under analysis. This color-optimized one-pixel attack poses an advanced cyber threat against modern medical imaging and shows the importance of data integrity with image analysis.
“Nice Try, Kiddo”: Investigating Ad Hominems in Dialogue Responses

Ad hominem attacks are those that target some feature of a person's character instead of the position the person is maintaining.These attacks are harmful because they propagate implicit biases and diminish a person's credibility.Since dialogue systems respond directly to user input, it is important to study ad hominems in dialogue responses.To this end, we propose categories of ad hominems, compose an annotated dataset, and build a classifier to analyze human and dialogue system responses to English Twitter posts.We specifically compare responses to Twitter topics about marginalized communities (#Black-LivesMatter, #MeToo) versus other topics (#Vegan, #WFH), because the abusive language of ad hominems could further amplify the skew of power away from marginalized populations.Furthermore, we propose a constrained decoding technique that uses salient n-gram similarity as a soft constraint for top-k sampling to reduce the amount of ad hominems generated.Our results indicate that 1) responses from both humans and DialoGPT contain more ad hominems for discussions around marginalized communities, 2) different quantities of ad hominems in the training data can influence the likelihood of generating ad hominems, and 3) we can use constrained decoding techniques to reduce ad hominems in generated dialogue responses.
Emotion Detection in Twitter Social Media Using Long Short-Term Memory (LSTM) and Fast Text

&#x0D; &#x0D; &#x0D; &#x0D; Emotion detection is important in various fields such as education, business, employee recruitment. In this study, emotions will be detected with text that comes from Twitter because social media makes users tend to express emotions through text posts. One of the social media that has the highest user growth rate in Indonesia is Twitter. This study will use the LSTM method because this method is proven to be better than previous studies. Word embedding fast text will also be used in this study to improve Word2Vec and GloVe that cannot handle the problem of out of vocabulary (OOV). This research produces the best accuracy for each word embedding as follows, Word2Vec produces an accuracy of 73,15%, GloVe produces an accuracy of 60,10%, fast text produces an accuracy of 73,15%. The conclusion in this study is the best accuracy was obtained by Word2Vec and fast text. The fast text has the advantage of handling the problem of out of vocabulary (OOV), but in this study, it cannot improve the accuracy of word 2vec. This study has not been able to produce very good accuracy. This is because of the data used. In future works, to get even better results, it is expected to apply other deep learning methods, such as CNN, BiLSTM, etc. It is hoped that more data will be used in future studies.&#x0D; &#x0D; &#x0D; &#x0D;
Learning a Tree-Structured Channel-Wise Refinement Network for Efficient Image Deraining

Significant advances have been made in image deraining due to the use of kinds of deep neural networks. However, existing deep neural network-based methods usually contain significant abundant network parameters and thus leads to expensive computation cost, which limits the application of deraining technology in high-level vision tasks. In this paper, we propose a compact and flexible Tree-structured Channel-wise Refinement Block (TCRB) for efficient image deraining, which contains augmentation, refinement, and aggregation modules to better explore features. Specifically, the refinement module can progressively extract groups of more discriminative features from the channel augmented inputs, and then the aggregation module adaptively fuses features from the refinement module to preserve image details by leveraging the Enhanced Channel Attention (ECA) method. Moreover, we present a Tree-structured Channel-wise Refinement Network (TCRN) by stacking multiple TCRBs, which could achieve competitive performance as the complicated networks. We embed the TCRB into a Multi-scale Tree-structured Channel-wise Refinement Network (MTCRN) based on an encoder and decoder network architecture and show that it performs favorably against state-of-the-art deraining algorithms on both synthetic datasets and real-world rainy images, while reaching a better trade-off in terms of model parameters and inference time.
A Secure Three-Input AND Protocol with a Standard Deck of Minimal Cards

Card-based protocols are used to perform cryptographic tasks such as secure multiparty computation using a deck of physical cards. While most of the existing protocols use a two-colored deck consisting of red cards and black cards, Niemi and Renvall in 1999 constructed protocols for securely computing two-input Boolean functions (such as secure logical AND and XOR computations) using a commonly available standard deck of playing cards. Since this initial investigation, two-input protocols with fewer cards and/or shuffles have been designed, and by combining them, one can perform a secure computation of any Boolean circuit. In this paper, we directly construct a simple card-based protocol for the three-input AND computation. Our three-input AND protocol requires fewer cards and shuffles compared to that required when applying any existing two-input AND protocol twice to perform the three-input AND computation. Our protocol is unique in the sense that it is card minimal if we use two cards to encode a single bit.