Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparison of Data Mining Techniques for Suicide Attempt Characteristics Mapping and Prediction

According to the report by Khon Kaen Rajanagarin-dra Psychiatric hospital, there are many factors such as poverty, loss, disappointment, alcohol addiction, addiction, chronic physical illness, depression, economic problems and major life changes that cause suicide attempts. However, there are some unsuccessful suicides. These individuals might try to repeatedly commit suicide attempts. In this paper, the aims of this research are to compare ID3, C4.5 and naïve Bayes and predict the characteristics of individuals who have suicidal ideation to repeatedly commit suicide attempts by using data mining techniques. After data preprocessing, three data mining techniques were selected to compare the efficiency of the single of classification models and the ensemble models. The researchers also adopted synthetic minority over-sampling technique (SMOTE) to balance the suicidal ideation of reattempt and no attempt classes. The researchers used 57 attributes from self-harm surveillance report (RP.506S) of Khon Kaen Rajanagarindra Psychiatric hospital. The experimental results of the single method showed that C4.5 is the best model with the highest percentage of correctly classified instances rate of 90.69%, while the ensemble model, voting method showed the best results of 91.26. We found the number of characteristics from C4.5 is 474 patterns. There are people, who have suicidal ideation over 500 in each case is 42 patterns. In 42 patterns, there are 22 patterns that more than 90% accuracy.
Improving Image Classification Performance with Automatically Hierarchical Label Clustering

Image classification is a common and foundational problem in computer vision. In traditional image classification, a category is assigned with single label, which is difficult for networks to learn better features. On the contrary, hierarchical labels can depict the structure of categories better, which helps network to learn more hierarchical features and improve the classification performance. Though many datasets contain images with multi-labels, the labels in these datasets usually lack of hierarchy. To overcome this problem, we propose a new method to improve image classification performance with Automatically Hierarchical Label Clustering (AHLC). Firstly, AHLC calculates the similarity between each pair of original categories by how easily they are misclassified with a pre-trained classifier. Secondly, AHLC obtains hierarchical labels by merging similar categories using hierarchical clustering. Finally, AHLC trains a new classifier with hierarchial labels to improve the original classification performance. We evaluate our method on MNIST and CIFAR-100 datasets and the results demonstrate the superiority of our method. The main contribution of this work is that we can simply improve an existing classification network by AHLC without extra information or heavy architecture redesign.
Semantic Image Inpainting Through Improved Wasserstein Generative Adversarial Networks

Image inpainting is the task of filling-in missing regions of a damaged or incomplete image. In this work we tackle this problem not only by using the available visual data but also by incorporating image semantics through the use of generative models. Our contribution is twofold: First, we learn a data latent space by training an improved version of the Wasserstein generative adversarial network, for which we incorporate a new generator and discriminator architecture. Second, the learned semantic information is combined with a new optimization loss for inpainting whose minimization infers the missing content conditioned by the available data. It takes into account powerful contextual and perceptual content inherent in the image itself. The benefits include the ability to recover large regions by accumulating semantic information even it is not fully present in the damaged image. Experiments show that the presented method obtains qualitative and quantitative top-tier results in different experimental situations and also achieves accurate photo-realism comparable to state-of-the-art works.
Linking Arrhythmias and Adipocytes: Insights, Mechanisms, and Future Directions

Obesity and atrial fibrillation have risen to epidemic levels worldwide and may continue to grow over the next decades. Emerging evidence suggests that obesity promotes atrial and ventricular arrhythmias. This has led to trials employing various strategies with the ultimate goal of decreasing the atrial arrhythmic burden in obese patients. The effectiveness of these interventions remains to be determined. Obesity is defined by the expansion of adipose mass, making adipocytes a prime candidate to mediate the pro-arrhythmogenic effects of obesity. The molecular mechanisms linking obesity and adipocytes to increased arrhythmogenicity in both the atria and ventricles remain poorly understood. In this focused review, we highlight areas of potential molecular interplay between adipocytes and cardiomyocytes. The effects of adipocytes may be direct, local or remote. Direct effect refers to adipocyte or fatty infiltration of the atrial and ventricular myocardium itself, possibly causing increased dispersion of normal myocardial electrical signals and fibrotic substrate of adipocytes that promote reentry or adipocytes serving as a direct source of aberrant signals. Local effects may originate from nearby adipose depots, specifically epicardial adipose tissue (EAT) and pericardial adipose tissue, which may play a role in the secretion of adipokines and chemokines that can incite inflammation given the direct contact and disrupt the conduction system. Adipocytes can also have a remote effect on the myocardium arising from their systemic secretion of adipokines, cytokines and metabolites. These factors may lead to mitochondrial dysfunction, oxidative stress, autophagy, mitophagy, autonomic dysfunction, and cardiomyocyte death to ultimately produce a pro-arrhythmogenic state. By better understanding the molecular mechanisms connecting dysfunctional adipocytes and arrhythmias, novel therapies may be developed to sever the link between obesity and arrhythmias.
Negative Appendectomy Rate and Risk Factors That Influence Improper Diagnosis at King Abdulaziz University Hospital

Introduction: Acute Appendicitis is the most common emergent abdominal surgery worldwide. diagnosis based on clinical assessment, laboratory and radiological investigations and appendectomy is the treatment of choice. Removing a normal appendix is a relatively common surgical issue, defined as negative appendectomy (NA). Multiple risk factors contribute to NA; female gender, normal WBC, normal CRP count, and CT scan unavailability. However, recently NA is decreasing in incidence after CT scan and Alvarado scoring. Aim: We aimed to estimate the rate of negative appendectomy, and determine possible risk factors among King Abdulaziz University Hospital. Patients and Methods: Article has a retrospective character and included non-incidental 441 patients who undergo an appendectomy, during period 2008 to 2018. Results: Negative Appendectomy incidence (9.5%) was higher among females at (64.3%). Gynecological complaints were seen in (22.2%) of cases with a sign for Negative Appendectomy. Surgery reports documented (29.6%) of female ovarian cyst diseases. Alvarado scoring at presentation was less than 7 in (69%) of cases with statistical significance value. Normal WBC count (50%), for automated neutrophil (45.2%) was high, and same number were recorded with the normal neutrophil count, all of them have statically significant relation with NA. Conclusion: Proper clinical evaluation involves documenting Alvarado score, using CRP, efficient radiological utilization. Also, considering more referrals to gynecological specialists of similar presentation especially females at reproductive age. Investing in time and equipment for proper clinical assessment can avoid the unnecessary burden and save our resources for better use.
The Geant4-DNA Project

The Geant4-DNA project proposes to develop an open-source simulation software based and fully included in the general-purpose Geant4 Monte-Carlo simulation toolkit. The main objective of this software is to simulate biological damages induced by ionizing radiations at the cellular and sub-cellular scale. This project was originally initiated by the European Space Agency for the prediction of the deleterious effects of radiations that may affect astronauts during future long duration space exploration missions. In this paper, the Geant4-DNA collaboration presents an overview of the whole on-going project, including its most recent developments that are available in the Geant4 toolkit since December 2009 (release 9.3), as well as an illustration example simulating the direct irradiation of a biological chromatin fiber. Expected extensions involving several research domains, such as particle physics, chemistry and cellular and molecular biology, within a fully interdisciplinary activity of the Geant4 collaboration are also discussed.
Blockade of the Adenosine 2A Receptor Mitigates the Cardiomyopathy Induced by Loss of Plakophilin-2 Expression

Background: Mutations in plakophilin-2 (PKP2) are the most common cause of familial Arrhythmogenic Right Ventricular Cardiomyopathy, a disease characterized by ventricular arrhythmias, sudden death, and progressive fibrofatty cardiomyopathy. The relation between loss of PKP2 expression and structural cardiomyopathy remains under study, though paracrine activation of pro-fibrotic intracellular signaling cascades is a likely event. Previous studies have indicated that ATP release into the intracellular space, and activation of adenosine receptors, can regulate fibrosis in various tissues. However, the role of this mechanism in the heart, and in the specific case of a PKP2-initiated cardiomyopathy, remains unexplored. Objectives: To investigate the role of ATP/adenosine in the progression of a PKP2-associated cardiomyopathy. Methods: HL1 cells were used to study PKP2- and Connexin43 (Cx43)-dependent ATP release. A cardiac-specific, tamoxifen-activated PKP2 knock-out murine model (PKP2cKO) was used to define the effect of adenosine receptor blockade on the progression of a PKP2-dependent cardiomyopathy. Results: HL1 cells silenced for PKP2 showed increased ATP release compared to control. Knockout of Cx43 in the same cells blunted the effect. PKP2cKO transcriptomic data revealed overexpression of genes involved in adenosine-receptor cascades. Istradefylline (an adenosine 2A receptor blocker) tempered the progression of fibrosis and mechanical failure observed in PKP2cKO mice. In contrast, PSB115, a blocker of the 2B adenosine receptor, showed opposite effects. Conclusion: Paracrine adenosine 2A receptor activation contributes to the progression of fibrosis and impaired cardiac function in animals deficient in PKP2. Given the limitations of the animal model, translation to the case of patients with PKP2 deficiency needs to be done with caution.
Optimal Sensor and Actuator Placement using Balanced Model Reduction

Optimal sensor and actuator placement is a central challenge in high-dimensional estimation and control. Nearly all subsequent control decisions are affected by these sensor/actuator locations, and optimal placement amounts to an intractable brute-force search among the combinatorial possibilities. In this work, we exploit balanced model reduction and greedy optimization to efficiently determine sensor and actuator placements that optimize observability and controllability. In particular, we determine locations that optimize scalar measures of observability and controllability via greedy matrix QR pivoting on the dominant modes of the direct and adjoint balancing transformations. Pivoting runtime scales linearly with the state dimension, making this method tractable for high-dimensional systems. The results are demonstrated on the linearized Ginzburg-Landau system, for which our algorithm approximates well-known optimal placements computed using costly gradient descent methods.
Enablers for Advancement of Women into Leadership Position: A Study Based on IT/ITES Sector in India

Women in a leadership position has been a matter of concern the world over especially in information technology (IT)/ information technology enabled services (ITES). However, for the advancement of women in leadership positions, individual characteristics are not enough. Besides individual factors, it is the detection of organizational factors that enable the advancement of women into leadership positions. The present article develops a multidimensional scale on the perceived enablers for the advancement of women in leadership position in the IT/ITES sector. The scale considers both individual factors (characteristics) and organizational factors, such as welfare schemes, career development support, and training. The article also reveals that individual factors, such as self-confidence, ambition, and perceived competency are also enablers of advancing women to leadership positions. Researchers could examine the considered dimensions of the proposed scale in other sectors and with respect to other constructs related to women's work-life balance.
Practical Text Classification With Large Pre-Trained Language Models

Multi-emotion sentiment classification is a natural language processing (NLP) problem with valuable use cases on real-world data. We demonstrate that large-scale unsupervised language modeling combined with finetuning offers a practical solution to this task on difficult datasets, including those with label class imbalance and domain-specific context. By training an attention-based Transformer network (Vaswani et al. 2017) on 40GB of text (Amazon reviews) (McAuley et al. 2015) and fine-tuning on the training set, our model achieves a 0.69 F1 score on the SemEval Task 1:E-c multi-dimensional emotion classification problem (Mohammad et al. 2018), based on the Plutchik wheel of emotions (Plutchik 1979). These results are competitive with state of the art models, including strong F1 scores on difficult (emotion) categories such as Fear (0.73), Disgust (0.77) and Anger (0.78), as well as competitive results on rare categories such as Anticipation (0.42) and Surprise (0.37). Furthermore, we demonstrate our application on a real world text classification task. We create a narrowly collected text dataset of real tweets on several topics, and show that our finetuned model outperforms general purpose commercially available APIs for sentiment and multidimensional emotion classification on this dataset by a significant margin. We also perform a variety of additional studies, investigating properties of deep learning architectures, datasets and algorithms for achieving practical multidimensional sentiment classification. Overall, we find that unsupervised language modeling and finetuning is a simple framework for achieving high quality results on real-world sentiment classification.
Differences in persistence between dogs and wolves in an unsolvable task in the absence of humans

Despite being closely related, dogs perform worse than wolves in independent problem-solving tasks. These differences in problem-solving performance have been attributed to dogs’ greater reliance on humans, who are usually present when problem-solving tasks are presented. However, more fundamental motivational factors or behavioural traits such as persistence, motor diversity and neophobia may also be responsible for differences in task performance. Hence, to better understand what drives the differences between dogs’ and wolves’ problem-solving performance, it is essential to test them in the absence of humans. Here, we tested equally raised and kept dogs and wolves with two unsolvable tasks, a commonly used paradigm to study problem-solving behaviour in these species. Differently from previous studies, we ensured no humans were present in the testing situation. We also ensured that the task was unsolvable from the start, which eliminated the possibility that specific manipulative behaviours were reinforced. This allowed us to measure both persistence and motor diversity more accurately. In line with previous studies, we found wolves to be more persistent than dogs. We also found motor diversity to be linked to persistence and persistence to be linked to contact latency. Finally, subjects were consistent in their performance between the two tasks. These results suggest that fundamental differences in motivation to interact with objects drive the differences in the performance of dogs and wolves in problem-solving tasks. Since correlates of problem-solving success, that is persistence, neophobia, and motor diversity are influenced by a species’ ecology, our results support the socioecological hypothesis, which postulates that the different ecological niches of the two species (dogs have evolved to primarily be scavengers and thrive on and around human refuse, while wolves have evolved to primarily be group hunters and have a low hunting success rate) have, at least partly, shaped their behaviours.
Test-Based Security Certification of Composite Services

The diffusion of service-based and cloud-based systems has created a scenario where software is often made available as services, offered as commodities over corporate networks or the global net. This scenario supports the definition of business processes as composite services, which are implemented via either static or runtime composition of offerings provided by different suppliers. Fast and accurate evaluation of services’ security properties becomes then a fundamental requirement and is nowadays part of the software development process. In this article, we show how the verification of security properties of composite services can be handled by test-based security certification and built to be effective and efficient in dynamic composition scenarios. Our approach builds on existing security certification schemes for monolithic services and extends them towards service compositions. It virtually certifies composite services, starting from certificates awarded to the component services. We describe three heuristic algorithms for generating runtime test-based evidence of the composite service holding the properties. These algorithms are compared with the corresponding exhaustive algorithm to evaluate their quality and performance. We also evaluate the proposed approach in a real-world industrial scenario, which considers ENGpay online payment system of Engineering Ingegneria Informatica S.p.A. The proposed industrial evaluation presents the utility and generality of the proposed approach by showing how certification results can be used as a basis to establish compliance to Payment Card Industry Data Security Standard.
An Indel-Resistant Error-Correcting Code for DNA-Based Information Storage

Synthetic DNA can in principle be used for the archival storage of arbitrary data. Because errors are introduced during DNA synthesis, storage, and sequencing, an error-correcting code (ECC) is necessary for error-free recovery of the data. Previous work has utilized ECCs that can correct substitution errors, but not insertion or deletion errors (indels), instead relying on sequencing depth and multiple alignment to detect and correct indels -- in effect an inefficient multiple-repetition code. This paper describes an ECC, termed "HEDGES", that corrects simultaneously for substitutions, insertions, and deletions in a single read. Varying code rates allow for correction of up to ~10% nucleotide errors and achieve 50% or better of the estimated Shannon limit.
A Combined Motion-Audio School Bullying Detection Algorithm

School bullying is a common social problem, which affects children both mentally and physically, making the prevention of bullying a timeless topic all over the world. This paper proposes a method for detecting bullying in school based on activity recognition and speech emotion recognition. In this method, motion and voice data are gathered by movement sensors and a microphone, followed by extraction of a set of motion and audio features to distinguish bullying incidents from daily life events. Among extracted motion features are both time-domain and frequency-domain features, while audio features are computed with classical MFCCs. Feature selection is implemented using the wrapper approach. At the next stage, these motion and audio features are merged to form combined feature vectors for classification, and LDA is used for further dimension reduction. A BPNN is trained to recognize bullying activities and distinguish them from normal daily life activities. The authors also propose an action transition detection method to reduce computational complexity for practical use. Thus, the bullying detection algorithm will only run, when an action transition event has been detected. Simulation results show that the combined motion-audio feature vector outperforms separate motion features and acoustic features, achieving an accuracy of 82.4% and a precision of 92.2%. Moreover, with the action transition method, the computation cost can be reduced by half.
An integrative machine learning approach for prediction of toxicity-related drug safety

This method allows the prediction of toxicity-related drug clinical trial failures, withdrawals from the market, and idiosyncratic toxicity risk by combining biological network analysis with machine learning. Recent trends in drug development have been marked by diminishing returns caused by the escalating costs and falling rates of new drug approval. Unacceptable drug toxicity is a substantial cause of drug failure during clinical trials and the leading cause of drug withdraws after release to the market. Computational methods capable of predicting these failures can reduce the waste of resources and time devoted to the investigation of compounds that ultimately fail. We propose an original machine learning method that leverages identity of drug targets and off-targets, functional impact score computed from Gene Ontology annotations, and biological network data to predict drug toxicity. We demonstrate that our method (TargeTox) can distinguish potentially idiosyncratically toxic drugs from safe drugs and is also suitable for speculative evaluation of different target sets to support the design of optimal low-toxicity combinations.
Multilayer Hybrid Deep-Learning Method for Waste Classification and Recycling

This study proposes a multilayer hybrid deep-learning system (MHS) to automatically sort waste disposed of by individuals in the urban public area. This system deploys a high-resolution camera to capture waste image and sensors to detect other useful feature information. The MHS uses a CNN-based algorithm to extract image features and a multilayer perceptrons (MLP) method to consolidate image features and other feature information to classify wastes as recyclable or the others. The MHS is trained and validated against the manually labelled items, achieving overall classification accuracy higher than 90% under two different testing scenarios, which significantly outperforms a reference CNN-based method relying on image-only inputs.