Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Dataset and Performance Evaluation of a Region-based CNN for Urban Object Detection

In the last years, we have seen a large growth in the number of applications which use deep learning-based object detectors. Autonomous Driving Assistance Systems (ADAS) is one of the areas where it has more impact. In this work, we present a novel study that evaluates a state-of-the-art technique for urban object localization. In particular, we investigate the performance of the Faster R-CNN method to detect and localize urban objects in a variety of outdoor urban videos involving pedestrians, cars, bicycles and other objects moving in the scene. We propose a new dataset that is used for benchmarking the accuracy of a real-time object detector (Faster R-CNN). Part of the data was collected using an HD camera mounted in a vehicle. Besides, some of the data is weakly annotated so it can be used for testing weakly-supervised learning techniques. We have carried out extensive experiments demonstrating the effectiveness of the baseline approach, which achieved a 74.2% accuracy on the proposed dataset. Moreover, we have evaluated a baseline approach for traffic sign recognition achieving an accuracy of 98.1%. A ResNet-based architecture was trained and used for this purpose as a second stage of our object detector. The full dataset is available for download at http://www.rovit. ua.es/dataset/traffic/
A Qualitative Study on using GuideGen to Keep Requirements and Acceptance Tests Aligned

Software requirements constantly change, thus impacting all other artifacts of an evolving system. In order to keep the system in a consistent state, changes in requirements should be documented and applied accordingly to all affected artifacts, including acceptance tests. In practice, however, changes in requirements are not always documented nor applied to the affected acceptance tests. This is mostly due to poor communication, lack of time or work overload, and eventually leads to project delays, unintended costs and unsatisfied customers. GuideGen is a tool-supported approach for keeping requirements and acceptance tests aligned. When a requirement is changed, GuideGen automatically generates guidance in natural language on how to modify impacted acceptance tests and communicates this information to the concerned parties. In this paper, we evaluate GuideGen in terms of its perceived usefulness for practitioners and its applicability to real software projects. The evaluation was conducted via interviews with 23 industrial practitioners from ten companies based in Europe. The results indicate that GuideGen is a useful approach that facilitates requirements change management and the communication of changes between requirements and test engineers. The participants also identified potential for improvement, in particular for using GuideGen in large projects.
Expression of Toll-like Receptors, Pro-, and Anti-inflammatory Cytokines in Relation to Gut Microbiota in Irritable Bowel Syndrome: The Evidence for Its Micro-organic Basis

Background/Aims A Subset of patients with irritable bowel syndrome (IBS) may have mild inflammation due to immune activation. Toll-like receptors (TLRs) and cytokines may cause intestinal inflammation. We studied their expression in relation to gut microbiota. Methods Expression of TLRs and cytokines was assessed in 47 IBS patients (Rome III) and 25 controls using quantitative real-time polymerase chain reaction. Immunohistochemistry was further performed to confirm the expression of TLR-4 and TLR-5. Results Of 47 patients with IBS, 20 had constipation (IBS-C), 20 diarrhea (IBS-D), and 7 unclassified (IBS-U). The mRNA levels of TLR-4 and TLR-5 were up-regulated in IBS patients than controls (P = 0.013 and P < 0.001, respectively). Expression of TLR-4 and TLR-5 at protein level was 4.2-folds and 6.6-folds higher in IBS-D than controls. The mRNA levels of IL-6 (P = 0.003), C-X-C motif chemokine ligand 11 (CXCL-11) (P < 0.001) and C-X-C motif chemokine receptor 3 (CXCR-3) (P < 0.001) were higher among IBS patients than controls. Expression of IL-6 (P = 0.002), CXCL-11 (P < 0.001), and CXCR-3 (P < 0.001) were up-regulated and IL-10 (P = 0.012) was down-regulated in IBS-D patients than controls. Positive correlation was seen between TLR-4 and IL-6 (P = 0.043), CXCR-3, and CXCL-11 (P = 0.047), and IL-6 and CXCR-3 (P = 0.003). Stool frequency per week showed positive correlation with mRNA levels of TLR-4 (P = 0.016) and CXCR-3 (P = 0.005), but inversely correlated with IL-10 (P = 0.002). Copy number of Lactobacillus (P = 0.045) and Bifidobacterium (P = 0.011) showed correlation with IL-10 in IBS-C, while Gram-positive (P = 0.031) and Gram-negative bacteria (P = 0.010) showed correlation with CXCL-11 in IBS-D patients. Conclusions Altered immune activation in response to dysbiotic microbiota may promote intestinal inflammation in a subset of patients with IBS.
The distinguishable DNA whole genome methylation profile of 2 cases of pediatric precursor B acute lymphoblastic leukaemia (BCP ALL) with prodromal, preleukemic phase

Rationale: A prolonged, prodromal phase before definitive paediatric precursor B acute lymphoblastic leukaemia (BCP ALL) diagnosis is rarely observed. Patients concerns: In the first, the patient presented with an aplastic preleukemic phase, whilst the second presented with a rheumatic-like preliminary phase. Diagnoses: The case reports of two patients with BCP ALL with a prodromal phase lasting a few weeks are presented. Interventions and outcomes: DNA whole genome profile methylation analysis of bone marrow cells obtained at diagnosis revealed a pattern of methylation that was readily distinguishable from both healthy and standard course BCP ALL bone marrow samples. Lessons: The biological implication of this observation remains unclear, with many differentially methylated loci involved in many processes like neurogenesis, cell projection organization and adhesion along with leucocyte activation and apoptosis. The prevalence and clinical significance of these methylation changes is unknown but this data indicates that the epigenetic basis of BCP ALL with a prolonged, prodromal phase requires a more detailed assessment.
Attention-Based BiLSTM Network with Lexical Feature for Emotion Classification

Emotion classification is an important task for identifying users’ emotional expressions in text. Though a variety of neural models have been proposed nowadays, these models mainly focus on modeling the content of words or characters without fully employing the emotional features in lexical features, especially the features of part-of -speech (POS). In this paper, we reveal that the information of POS as well as that of words is important for identifying the type of emotion in a given text. We propose two simple models to fully learn the emotional features of the POS of words. Every model consists of the long short-term memory (LSTM) network as input encoders and the component of attention mechanism. One model is to concatenate the POS tags of vectors into the hidden states of representations generated by LSTM as raw feature representations and put them into the component of attention mechanism to generate the text representation toward a special emotion. The other is to use both LSTM and attention mechanism to model the context representation of words and those of POS tags respectively and concatenate these context representations as the text representation toward a special emotion. We conduct some experiments on datasets for evaluation and demonstrate the effectiveness of our model, where the datasets consist of the open-source dataset from NLPCC& 2014 and the dataset of manual annotation. Experimental results show that our models can achieve outstanding performance for emotion classification in Chinese Weibo texts and outperform classical baselines.
NEGAN:Network Embedding based on Generative Adversarial Networks

Network embedding, also known as graph representation, is a classical topic in data mining. It has been widely used in real-world network applications such as node classification and community detection. However, it remains open to find a method that is scalable and preserves both structure and content information. Based on generative adversarial networks, we propose an unsupervised network embedding framework NEGAN, which is featured by combining graph topology and node content. In NEGAN, network nodes are mapped to the target space in a highly flexible non-linear way, guided by the content of the nodes. This mapping is learned from the generator of the generative adversarial networks, and node adjacency in the input network is preserved. Experiments on real datasets show that NEGAN outperforms all the existing methods on many scenarios including node classification, visualization and community detection tasks.
Prototype Selection Using Self-Organizing-Maps and Entropy for Overlapped Classes and Imbalanced Data

The k nearest neighbor kNN is a traditional supervised classifier used in data mining tasks. However, when used in real applications, mainly in a dataset with class imbalance or class overlap, kNN suffers with problems in accuracy performance. In this paper, we propose three prototype selection methods using self-organizing maps (SOM) and information entropy to increase the effectiveness of the kNN classifier in datasets with these conditions. The methods, named SOMEntropyKnn, were able to increase the effectiveness of the kNN classifier in all the 14 datasets used in the experiment, increasing the accuracy performance from datasets with imbalance or overlap problems.
A Time-Sensitive Hybrid Learning Model for Patient Subgrouping

Heterogeneity among patients always leads to different progression patterns and may require different types of therapy in clinical diagnosis. Therefore, it is crucial to study patient subgrouping. Normally, patient subgrouping is an unsupervised work due to the lack of labeled data. Analysing patients with complex medical data is challenging because of the data multiformity and time irregularity. To handle these issues, we propose a time-sensitive hybrid learning model to subgroup patients. First, we divide the multiform clinical data into two parts: non-time series data and time series data. Then we utilize basic autoencoder (AE) which is a commonly used unsupervised algorithm to learn patients’ representations from non-time series data, and we use a recurrent neural network (RNN) based AE to extract representations from time series data. To capture the time irregularity in time series data, we propose a time-sensitive RNN which utilizes the time intervals to control the decaying degree of history memories. Finally, we present a weighted k-means method to subgroup patients with the pairwise representations. Experiments on real world medical datasets demonstrate that our proposed model can effectively improve the validity of patient subgrouping.
Berberine ameliorates experimental varicocele‐induced damages at testis and sperm levels; evidences for oxidative stress and inflammation

The present study was performed to show the ameliorative effect of berberine (BBR), as an antioxidant and anti‐inflammatory agent, against experimental varicocele (VCL)‐induced molecular and histological damages. For this purpose, 50 mature Wistar rats were divided into control, control‐sham, VCL‐sole, 50 mg/kg and 100 mg/kg BBR‐treated VCL‐induced groups. The tissue levels of interleukin‐6 (IL‐6), tumour necrosis factor‐α (TNF‐α), nitric oxide (NO), total antioxidant capacity (TAC), malondialdehyde (MDA), superoxide dismutase (SOD) and gluthatione peroxidase (GSH‐px) as well as the mRNA levels of testicular CuZn SOD, MnSOD, EC‐SOD and GSH‐px were evaluated. The serum concentration of testosterone and germ cells mRNA damage were analysed. Finally, the sperm viability, motility, DNA integrity and chromatin condensation were analysed. Observations revealed that, the BBR significantly downregulated VCL‐increased IL‐6, TNF‐α and NO levels, upregulated the CuZn SOD, MnSOD, EC‐SOD and GSH‐px mRNA level, decreased testicular MDA content, enhanced serum testosterone level and ameliorated testicular TAC, SOD and GSH‐px levels. The animals in BBR‐treated groups exhibited diminished mRNA damage versus non‐treated VCL‐induced group. The BBR has significantly (p < 0.05) improved sperm parameters. In conclusion, the BBR by promoting testicular antioxidant potential and by downregulating inflammatory reactions fairly promotes spermatogenesis and upregulates the sperm quality.
The regulation of cytokine signaling by retinal determination gene network pathway in cancer

Tumor environment plays a pivotal role in determining cancer biology characteristics. Cytokine factors, as a critical component in tumor milieu, execute distinct functions in the process of tumorigenesis and progression via the autocrine or paracrine manner. The retinal determination gene network (RDGN), which mainly comprised DACH, SIX, and EYA family members, is required for the organ development in mammalian species. While the aberrant expression of RDGN is involved in the proliferation, apoptosis, angiogenesis, and metastasis of tumors via interacting with different cytokine-related signals, such as CXCL8, IL-6, TGF-β, FGF, and VEGF, in a cell- or tissue-dependent manner. Thus, joint detection of this pathway might be used as a potential biomarker for the stratification of target therapy and for the precision prediction of the prognosis of cancer patients.
Using Deep Learning to Recommend Discussion Threads to Users in an Online Forum

Using comments made in discussion threads on the social media aggregation website Reddit, the topics of conversation were identified using the probabilistic topic model Latent Dirichlet Allocation (LDA). Employing these topics as features for a neural network, several different neural network frameworks were trained on the data to serve as models to identify which threads a given user would be interested in contributing to based on their previously shown interests. This was done on a sample set of 30 users using 10 different initial random weights for each framework. The ideal model for each user was identified as being the one that scored the highest F2-Score, the harmonic mean of precision and recall with a bias towards recall, on a development set. Testing these ideal models on a test set, they achieved an average F2-Score of 0.825, as well as an average precision of 0.542 and an average recall of 0.956 for a sample set of users.
A Redescriptive Approach to Autonomous Perceptual Classification in Robotic Cognitive Architectures

This paper is concerned with the problem of perceptual classification in the framework of life-long learning developmental cognitive architectures. Perceptual classification is the process by which autonomous entities organize their, usually continuous, perceptual streams into classes of perceptions that are relevant to the different contexts in which they find themselves. In particular, here we describe an approach based on context related generalization or categorization of perceptions in an autonomous manner within embodied systems. This approach involves the introduction of a new type of knowledge nuggets, Pnodes, within the long term memory structure of a cognitive architecture. Taking inspiration from the hippocampus-cortex relationships in real brains, P-nodes are initially described by means of a set of representative perceptual points which are subsequently generalized in a cortex like neural representation. This approach is tested in a series of experiments on a real robot.
Genetic trade-offs between male reproductive traits in Drosophila melanogaster

In Drosophila melanogaster, males engage in both extensive pre- and post-copulatory competition for the opportunity to mate with females and subsequently sire offspring. The selection pressure for increased male reproductive success has resulted in the evolution of a wide diversity of sexual traits. However, despite strong selection, individuals often exhibit considerable phenotypic variation in the expression of these traits, and it is unclear if any of this variation is owing to underlying genetic trade-offs. Here, using hemiclonal flies, we examine how male reproductive success covaries with their ability to induce long-term stimulation of oogenesis and oviposition in their mates, and how this relationship may change over time. We found that males from hemiclone lines with phenotypes that were more successful in a short-term reproductive ‘scramble’ environment were less effective at stimulating long-term fecundity in females. Furthermore, we observed that males from hemiclone lines which showed the most improvement over a longer reproductive interaction period also tended to stimulate higher long-term fecundity in females. Together, these results indicate the presence of genetic trade-offs between different male reproductive traits and offer insights into the maintenance of their variation.
Anesthesia protocols in laboratory animals used for scientific purposes

Background: A suitable, effective and free of complications anesthetic protocol is very important in experimental studies on animal models since it could bias the outcome of a trial. To date there is no universally accepted protocol for induction, maintenance and recovery from anesthesia. The endotracheal intubation with the use of inhalation anesthesia is used very especially in the from of large size laboratory animals, because it is a secure and easy control mode. However, it is not common for small laboratory animals because of the high technical skills required. Aim: The aim of this paper is a review of the main methods of induction of anesthesia in laboratory animals. Materials and methods:We performed an electronic search of MEDLINE (PubMed interface), ISI Web of Science and Scopus using the keywords “anesthesia” and “animal (s)” or “protocol (s)” or “surgery”, without the data or the language restriction. We consider only the most common laboratory animals (rats, mice, rabbits, pigs). We identify all the scientific articles that refer to the use of anesthetics for studies on laboratory animals in all areas: experimental surgery, CT, MRI, PET. All documents identified the search criteria are subject to review only by identifying relevant studies. Conclusions:There is a strong need for application of existing guidelines for research on experimental animals; specific guidelines for anesthesia and euthanasia should be considered and reported in future studies to ensure comparability and quality of animal experiments. (www.actabiomedica.it)
From Machine Learning to Explainable AI

The success of statistical machine learning (ML) methods made the field of Artificial Intelligence (AI) so popular again, after the last AI winter. Meanwhile deep learning approaches even exceed human performance in particular tasks. However, such approaches have some disadvantages besides of needing big quality data, much computational power and engineering effort; those approaches are becoming increasingly opaque, and even if we understand the underlying mathematical principles of such models they still lack explicit declarative knowledge. For example, words are mapped to high-dimensional vectors, making them unintelligible to humans. What we need in the future are context-adaptive procedures, i.e. systems that construct contextual explanatory models for classes of real-world phenomena. This is the goal of explainable AI, which is not a new field; rather, the problem of explainability is as old as AI itself. While rule-based approaches of early AI were comprehensible “glass-box” approaches at least in narrow domains, their weakness was in dealing with uncertainties of the real world. Maybe one step further is in linking probabilistic learning methods with large knowledge representations (ontologies) and logical approaches, thus making results re-traceable, explainable and comprehensible on demand.
Practical Nonlinear Model Predictive Control Using an Echo State Network Model

This paper presents a novel suboptimal nonlinear model predictive controller which makes use of Echo State Network (ESN) models. The controller is based on the Practical Nonlinear Predictive Controller algorithm, a general framework which can be used for the implementation of nonlinear model predictive controllers using almost any class of nonlinear model. Echo State Networks explore the Reservoir Computing learning paradigm, which is a computationally efficient strategy for training recurrent artificial neural networks. In the proposed control algorithm, the ESN model is used to obtain on-line a nonlinear prediction of the system free response and a linearized version of the model is used to obtain a local approximation of the systems step response, which is used to build the dynamic matrix of the system at each sampling instant. The proposed controller was tested in a real nonlinear process, comprising the automatic control of the discharge pressure of a refrigerant compressor in a test rig. This process was identified using an ESN and the results are compared to linear and Hammerstein nonlinear models. The controller was implemented in the real test rig and compared with a classical PID, being tested at distinct operating conditions. The modeling approach presented good results, with the Echo State Network model outperforming the linear and Hammerstein nonlinear models. The controller implementation results show that the Practical Nonlinear Model Predictive Controller algorithm with Echo State Networks enables the application of nonlinear model predictive control with recurrent neural networks with low computational burden, both in the process modeling phase and control signal optimization.
Towards a Security Requirements Management Framework for Open-Source Software

Security refers to a class of non-functional requirements (NFRs) related to system confidentiality, integrity, and availability. It plays a critical role in many open source software (OSS) projects. Experience indicates that considering security early in the software life cycle can help address security problems, such as reducing information breach and unauthorized data access. However, unlike up-front requirements engineering (RE), requirements are fully discussed and become elaborated in OSS projects only after the implementation begins. Therefore, security management approaches which based on up-front RE shall be modified or improved while applied to OSS projects. To make OSS projects more secure, this research extends existing security requirements management approaches and proposes a new security management framework for OSS projects. I also describe obstacles for building our framework and formulate their conquering as research questions. Analysis and discussion of research questions will enable me to gain valuable insights, which I will use to improve the proposed framework.
Improving Hamming distance-based fuzzy join in MapReduce using Bloom Filters

Join operation is one of the key ones in databases, allowing to cross data from several tables. Two tuples are crossed when they share the same value on some attribute(s). A fuzzy or similarity join combines all pairs of tuples for which the distance is lower than or equal to a prespecified threshold ε from one or several relations. Fuzzy join has been studied by many researchers because its practical application. However, join is the most costly and may even not be possible to compute on large databases. In this paper, we thus propose the optimization for MapReduce algorithms to process fuzzy joins of binary strings using Hamming Distance. In particular we propose to use an extension of Bloom Filters to eliminate the redundant data, reduce the unnecessary comparisons, and avoid the duplicate output. We compare and evaluate analytically the algorithms with a cost model.