Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting event mentions based on a semantic analysis of microblogs for inter-region relationships

An ability to predict people’s interests in different regions would be valuable to many applications including marketing and policymaking. We posit that social media plays an important role in capturing collective user interests in different regions and their dynamics over time and across regions. Event mentions in microblogs of social media like Twitter not only reflect the people’s interests in different regions but also affect the posting of future messages as the content of microblogs propagates to others through an online social network. Differentiating from the various network analysis techniques that have been developed to capture people’s interests and their propagation patterns, we propose an event mention prediction method that utilises an analysis of inter-region relationships. We first obtain regional user interests for each topic by applying Latent Dirichlet Allocation (LDA) to region-specific collections of tweets and then compute pairwise similarities among regions. The resulting similarity-based region network becomes the basis for constructing region groups through Markov Cluster Algorithm, which helps removing noise relationships among regions. We then propose a relatively simple regression technique to predict future event mentions in different regions. We demonstrate that the proposed method outperforms the state-of-the-art event prediction method, confirming that the novel method of constructing groups from region-based sub-topic interests indeed contributes to the increase in the prediction accuracy.
Efficient exploration of molecular paths from As-Rigid-As-Possible approaches and motion planning methods. (Exploration efficace de chemins moléculaires par approches aussi rigides que possibles et par méthodes de planification de mouvements)

Proteins are macromolecules participating in important biophysical processes of living organisms. It has been shown that changes in protein structures can lead to changes in their functions and are found linked to some diseases such as those related to neurodegenerative processes. Hence, an understanding of their structures and interactions with other molecules such as ligands is of major concern for the scientific community and the medical industry for inventing and assessing new drugs.In this dissertation, we are particularly interested in developing new methods to find for a system made of a single protein or a protein and a ligand, the pathways that allow changing from one state to another. During past decade, a vast amount of computational methods has been proposed to address this problem. However, these methods still have to face two challenges: the high dimensionality of the representation space, associated to the large number of atoms in these systems, and the complexity of the interactions between these atoms.This dissertation proposes two novel methods to efficiently find relevant pathways for such biomolecular systems. The methods are fast and their solutions can be used, analyzed or improved with more specialized methods. The first proposed method generates interpolation pathways for biomolecular systems using the As-Rigid-As-Possible (ARAP) principle from Computer Graphics. The method is robust and the generated solutions preserve at best the local rigidity of the original system. An energy-based extension of the method is also proposed, which significantly improves the solution paths. However, in scenarios requiring complex deformations, this geometric approach may still generate unnatural paths. Therefore, we propose a second method called ART-RRT, which combines the ARAP principle for reducing the dimensionality, with the Rapidly-exploring Random Trees from Robotics for efficiently exploring possible pathways. This method not only gives a variety of pathways in reasonable time but the pathways are also low-energy and clash-free, with the local rigidity preserved as much as possible. The mono-directional and bi-directional versions of the ART-RRT method were applied for finding ligand-unbinding and protein conformational transition pathways, respectively. The results are found to be in good agreement with experimental data and other state-of-the-art solutions.
High‐precision control of tracked field robots in the presence of unknown traction coefficients

Accurate steering through crop rows that avoids crop damage is one of the most important tasks for agricultural robots utilized in various field operations, such as monitoring, mechanical weeding, or spraying. In practice, varying soil conditions can result in off‐track navigation due to unknown traction coefficients so that it can cause crop damage. To address this problem, this paper presents the development, application, and experimental results of a real‐time receding horizon estimation and control (RHEC) framework applied to a fully autonomous mobile robotic platform to increase its steering accuracy. Recent advances in cheap and fast microprocessors, as well as advances in solution methods for nonlinear optimization problems, have made nonlinear receding horizon control (RHC) and receding horizon estimation (RHE) methods suitable for field robots that require high‐frequency (milliseconds) updates. A real‐time RHEC framework is developed and applied to a fully autonomous mobile robotic platform designed by the authors for in‐field phenotyping applications in sorghum fields. Nonlinear RHE is used to estimate constrained states and parameters, and nonlinear RHC is designed based on an adaptive system model that contains time‐varying parameters. The capabilities of the real‐time RHEC framework are verified experimentally, and the results show an accurate tracking performance on a bumpy and wet soil field. The mean values of the Euclidean error and required computation time of the RHEC framework are equal to 0.0423 m and 0.88 ms, respectively.
Sentiment Expression Boundaries in Sentiment Polarity Classification

We investigate the effect of using sentiment expression boundaries in predicting sentiment polarity in aspect-level sentiment analysis. We manually annotate a freely available English sentiment polarity dataset with these boundaries and carry out a series of experiments which demonstrate that high quality sentiment expressions can boost the performance of polarity classification. Our experiments with neural architectures also show that CNN networks outperform LSTMs on this task and dataset.
Properties of adv-1 - Adversarials of Adversarials

Neural networks are very successful in the domain of image processing, but they are still vulnerable against adversarial images – carefully crafted images to fool the neural network during image classiﬁ-cation. There are already some attacks to create those adversarial images, therefore the transition from original images to adversarial images is well understood. In this paper we apply adversarial attacks on adversarial images. These new images are called adv − 1 . The goal is to investigate the transition from adversarial images to adv − 1 images. This knowledge can be used to 1. ) identify adversarial images and 2. ) to ﬁnd the original class of adversarial images.
Computer vision system for characterization of pasta (noodle) composition

Abstract. Noodle is a type of pasta, mainly composed of wheat flour (WF), widely consumed due to its easy preparation. Recently, there has been a growing concern in the food industry about nutritionally enriched processed wheat products, and the analytical methods used to characterize these products. We implemented a computer vision system (CVS) using image analysis and prediction algorithms, to predict three different components in pasta: hydrolyzed soy protein (HSP), fructo-oligosaccharide (FOS), and WF. Pasta samples used in the experiments were produced with 12 different combinations of these components, varying the amounts of HSP, FOS, and WF. Microscopy images of samples were acquired, preprocessed, and segmented to extract image features. We investigated 56 image features from four types (color, intensity, texture, and border) along with four machine learning algorithms (gradient boost machine, multilayer perceptron artificial neural network, support vector machine, and random forest) and partial least-squares to predict the quantity of noodle components. Accurate results were obtained for HSP and WF, with coefficient of regression (R2) of 0.82 and 0.75, and root mean square error (RMSE) of 0.12 and 0.15, respectively. On the other hand, FOS was not accurately identified (R2 = 0.39, RMSE = 0.21). The results support the potential application of CVS in the processing industry for noodle production.
Security Analysis for Randomness Improvements for Security Protocols

Many cryptographic mechanisms depend on the availability of secure random numbers. In practice, the sources of random numbers can be unreliable for many reasons. There exist ways to improve the reliability of randomness, but these often do not work well with practical constraints. One proposal to reduce the impact of untrusted randomness is the proposal by Cremers et al. [4], which aims to be effective in existing deployments. In this document, we provide a security analysis of the construction in [4] (Revision 3) and elaborate on design choices and practical interpretations.
Improving code summarization by combining deep learning and empirical knowledge (S)

Code summaries are human-readable text that describes the functionality of code blocks. Software developers use code summaries to understand the specification of API while code retrieve system relies on code summaries for effective code search. However, code summaries are often written by software developers. Writing good code summaries usually requires great effort. It could be helpful if developers use automatic code summarization system to generate code summaries. Recently, some works have applied deep learning methods to generate code summaries for code snippets. However, those deep learning methods treat code snippets as streams of text tokens while ignoring the inherent code structure information. In this paper, we propose a novel code summarization method named the CDEModel (Code summarization by Deep learning and Empirical knowledge) that combines inherent code structure information with deep learning models. The CDE-Model proposes several empirical strategies to transform code snippets to refined code representation and feeds them into an encoder-decoder neural network for text generation. We conduct large-scale experiments on 1500 popular Java projects on GitHub with 396,184 pairs of code snippets and summaries. Experimental results show that the quality of code summaries generated by our CDE-Model is better than other two methods. To the best of our knowledge, this paper is the first to combine code structure information with deep learning.
Revisiting FISTA for Lasso: Acceleration Strategies Over The Regularization Path

In this work we revisit FISTA algorithm for Lasso showing that recent acceleration techniques may greatly improve its basic version, resulting in a much more competitive procedure. We study the contribution of the diﬀerent improvement strategies, showing experimentally that the ﬁnal version becomes much faster than the standard one.
Orthostatic hypotension in internal medicine wards

Abstract Objectives: Most studies of orthostatic hypotension (OH) have focused on community-dwelling and institutionalized patient populations. Less is known about OH in hospitalized patients. Moreover, a comprehensive review of OH in internal medicine wards has not been published in the English literature. Our purpose is to provide current information regarding OH in internal medicine inpatients. Methods: A comprehensive search of medical databases was performed for potentially relevant articles, using the following keywords: postural or orthostatic hypotension, with the combination of hospitalization or internal medicine. Inclusion criteria were: population of patients hospitalized for acute disorders in internal medicine or geriatric wards with a sample size of ≥50 and publication as an original full-length article in the English language. Data from 14 selected studies are reviewed, including: pathophysiology, evaluation, prevalence, manifestations, risk factors, prognosis, and management. Results: OH is a common and often symptomatic disorder in elderly internal medicine patients. The prevalence of OH in this population ranges from 22–75%. There are substantial discrepancies between the studies reviewed regarding definitions and means of evaluating OH. OH in internal medicine wards is largely non-neurogenic and multifactorial. The main predisposing factors for OH are prolonged bed rest, hypertension, and heart failure. OH in internal medicine wards is managed mainly with non-pharmacologic interventions, and is frequently reversible. Conclusions: In internal medicine inpatients, OH warrants attention because this disorder is common, potentially dangerous, and treatable. In the hospital setting, OH should be routinely assessed on ambulation, following the current guidelines for OH definition and meaning.
Joint classification of complementary features based on multitask compressive sensing with application to synthetic aperture radar automatic target recognition

Abstract. We propose a synthetic aperture radar (SAR) automatic target recognition (ATR) method by jointly classifying three complementary features based on multitask compressive sensing (MtCS). The principal component analysis features, elliptical Fourier descriptors and the azimuthal sensitivity image, are extracted or constructed to describe the intensity distribution, target shape, and electromagnetic characteristics of the original SAR images, respectively. The three features describe the original SAR image from different aspects, thus their joint use can provide more discrimination for distinguishing different classes of targets. Afterward, the three features are jointly classified based on MtCS, which can properly represent individual tasks, and also exploit their inner correlations. Therefore, it is promising that the discriminability of different features can be better exploited to improve the ATR performance. Extensive experiments are conducted on the moving and stationary target acquisition and recognition dataset under both the standard operating condition and several typical extended operating conditions, i.e., configuration variance, large depression angle variance, noise corruption, and partial occlusion. The results demonstrate the effectiveness and robustness of the proposed method in comparison with several state-of-the-art SAR ATR methods.
DNNBuilder: an Automated Tool for Building High-Performance DNN Hardware Accelerators for FPGAs

Building a high-performance FPGA accelerator for Deep Neural Networks (DNNs) often requires RTL programming, hardware verification, and precise resource allocation, all of which can be time-consuming and challenging to perform even for seasoned FPGA developers. To bridge the gap between fast DNN construction in software (e.g., Caffe, TensorFlow) and slow hardware implementation, we propose DNNBuilder for building high-performance DNN hardware accelerators on FPGAs automatically. Novel techniques are developed to meet the throughput and latency requirements for both cloud- and edge-devices. A number of novel techniques including high-quality RTL neural network components, a fine-grained layer-based pipeline architecture, and a column-based cache scheme are developed to boost throughput, reduce latency, and save FPGA on-chip memory. To address the limited resource challenge, we design an automatic design space exploration tool to generate optimized parallelism guidelines by considering external memory access bandwidth, data reuse behaviors, FPGA resource availability, and DNN complexity. DNNBuilder is demonstrated on four DNNs (Alexnet, ZF, VGG16, and YOLO) on two FPGAs (XC7Z045 and KU115) corresponding to the edge- and cloud-computing, respectively. The fine-grained layer-based pipeline architecture and the column-based cache scheme contribute to 7.7x and 43x reduction of the latency and BRAM utilization compared to conventional designs. We achieve the best performance (up to 5.15x faster) and efficiency (up to 5.88x more efficient) compared to published FPGA-based classification-oriented DNN accelerators for both edge and cloud computing cases. We reach 4218 GOPS for running object detection DNN which is the highest throughput reported to the best of our knowledge. DNNBuilder can provide millisecond-scale real-time performance for processing HD video input and deliver higher efficiency (up to 4.35x) than the GPU-based solutions.
A Selective and Incremental Backup Scheme for Task Pools

Checkpointing is a common approach to prevent loss of a program's state after permanent node failures. When it is performed on application-level, less data need to be saved. This paper suggests an uncoordinated application-level checkpointing technique for task pools. It selectively and incrementally saves only those tasks that have stayed in the pool during some period of time and that have not been saved before. The checkpoints are held in a resilient in-memory data store. Our technique applies to any task pool variant in which workers operate at the top of local pools, and work stealing operates at the bottom. Furthermore, the tasks must be free of side effects, and the final result must be calculated by reduction from individual task results. We implemented the technique for the lifeline-based global load balancing variant of task pools. This variant couples random victim selection with an overlay graph for termination detection. A fault-tolerant realization already exists in the form of a Java library, called JFT_GLB. It uses the APGAS and Hazelcast libraries underneath. Our implementation modifies JFT_GLB by replacing its nonselective checkpointing scheme with our new one. In experiments, we compared the overhead of the new scheme to that of JFT_GLB, with UTS, BC and two synthetic benchmarks. The new scheme required slightly more running time when local pools were small, and paid off otherwise.
Engineering human megakaryocytic microparticles for targeted delivery of nucleic acids to hematopoietic stem and progenitor cells

Cell-derived microparticles, with their native recognition abilities, can be used as novel carriers for targeted cargo delivery to stem cells. Hematopoietic stem and progenitor cells (HSPCs) are important target cells for gene therapy applications. Current genetic modifications of HSPCs rely on viral vectors in vivo or electroporation ex vivo. Here, we developed a nonviral system based on megakaryocytic microparticles (MPs) for targeted delivery of plasmid DNA (pDNA) and small RNAs to HSPCs. We have previously shown that megakaryocytic MPs, the most abundant MPs in blood circulation, target specifically and deliver cargo to HSPCs both in vitro and in vivo. With an optimized electroporation protocol, an average of 4200 plasmid copies per MP were loaded into MP, thus enabling effective delivery of green fluorescent protein (GFP)–encoding pDNA to HSPCs and HSPC nuclei, with up to 81% nuclei containing pDNA. Effective functional small interfering RNA (siRNA) and microRNA (miRNA) delivery were also demonstrated. As patient-specific or generic megakaryocytic MPs can be readily generated and stored frozen, our data suggest that this system has great potential for therapeutic applications targeting HSPCs.
Prevalence of Shiga toxin‐producing Escherichia coli in pasture‐based dairy herds

Shiga toxin‐producing Escherichia coli strains (STEC) are food‐borne pathogens. While E. coli O157:H7 is commonly associated with cattle, less is known about the prevalence of non‐O157 STEC serogroups in bovines. This study evaluated the prevalence and virulence status of O157:H7 and six E. coli O‐serogroups (O26, O103, O45, O145, O121, O111) in New Zealand dairy farms using molecular as well as culture‐based methods. Fresh farm dairy effluent (FDE) (n = 36) and composite calf faeces (n = 12) were collected over three samplings from 12 dairy farms. All seven target serogroups were detected through molecular techniques. Of the 202 isolates which were serologically confirmed following traditional culturing and immunomagnetic separation (IMS), O103, O26, O45 and O121 were the most common serogroups, being found in 81, 47, 42 and 32% of the FDE and in 17, 33, 25 and 9% of the calf faeces respectively. The majority (157/202) of the isolates were negative for stx and eae virulence genes. The prevalence of the seven target STEC was low, and only nine O26 isolates (4%) were recovered from four of the farms. The study has highlighted the need for improving the isolation of Top 7 STEC from the stx‐negative populations present in fresh dairy effluent and calf faeces.
Visualizing genome synteny with xmatchview

In genomics research, the visual representation of DNA sequences is of prime importance. When displayed with additional information, or tracks, showing the position of annotated genes, alignments of sequence of interest, etc., these displays facilitate our understanding of genome and gene structure, and become powerful tools to assess the relationship between various sequence data. They can be used for troubleshooting sequence assemblies, in-depth sequence analysis, and eventually find their way in publications and oral presentations as they often translate complex and abundant data succinctly, with esthetically pleasing images. Here, I introduce xmatchview and xmatchview-conifer, two python applications for comparing genomes visually and assessing their synteny. Availability: https://github.com/warrenlr/xmatchview
TREMO: A dataset for emotion analysis in Turkish

This study presents a new dataset to be used in emotion extraction studies in Turkish text. We consider emotion extraction as a supervised text classification problem, which thereby requires a dataset for the training process. To satisfy this requirement, we aim to create a new dataset containing data for the six emotion categories: happiness, fear, anger, sadness, disgust and surprise. To gather this dataset, we conducted a survey and collected 27,350 entries from 4709 individuals. In the next step, we performed a validation process in which annotators validated each entry one by one by assigning a related emotion category. As a result of this process, we obtained two datasets, one raw and the other validated. Subsequently, we generated four versions of these two datasets using two different stemming methods and then modelled them using a vector space model. Then, we ran machine learning algorithms, including complement naive Bayes (CNB), random forest (RF), decision tree C4.5 (J48) and an updated version of support vector machines (SVMs), on the models to calculate the accuracy, precision, recall and F-measure values. Based on the results we obtained, we concluded that the SVM classifier yielded the highest performance value and that the models trained with a validated dataset provide more accurate results than the models trained with a non-validated dataset.