Miracle-dz/UniFAR-FTUs huggingface dataset:
Becoming Agile while preserving software product lines: an Agile transformation model for large companies

Software process improvement has always been an essential part of software projects. Current market trends and the rapid pace of changing requirements demand fast development and adaptability. Agile software development is a popular possibility to react on these trends. Implementing agile practices promises for example a shorter time-to-market, satisfied customers and increased software quality. Consequently many companies strive for an integration of agile methods or for an agile transformation. High-technological environments such as the automotive domain also want to benefit from the advantages promised by agile software development. Even more than smaller companies, these large ones have to deal with software systems getting more and more complex. One established solution facing this problem is an effective and managed way to reuse software at least partially. Software product lines provide an efficient way to manage software reuse and to handle the high complexity. Consequently, they are widely distributed in large and high-technological environments. In most companies in the automotive domain, software product lines are already present and agile development methods should be introduced. Hence, there is a need for a transformation model preserving the benefits of software product lines. We conducted a literature review to achieve an overview and a better understanding of agile transformation models in large companies. In total, we analyzed 367 papers. None of them addresses the agile transformation in large companies with existing software product lines. In consideration of this research gap, we propose a transformation model preserving the benefits of already existing models and extending aspects which are important for existing software product lines.
An Efficient Ranking Scheme for Frequent Subgraph Patterns

Frequent Subgraph Mining (FSM) is an active research field and is considered as the essence of graph mining. FSM is extensively used in graph clustering, classification and building indices in the databases. In literature, different FSM approaches are suggested such as AGM, FSG, SPIN, SUBDUE, gSpan, FFSM, CloseGraph, FSG, GREW. Most of these FSM techniques perform very well for small to medium size graph datasets, but the computational cost of FSM becomes very critical when the graph size is increased. In accession to this, the number of frequent subgraphs patterns grows exponentially with the increasing size of graph datasets. Consequently, in this research work, a conceptual framework called A RAnked Frequent pattern-growth Framework (A-RAFF) is proposed. A-RAFF achieved efficiency by embedding the ranking of discovered frequent subgraphs during the mining process. The experiments on real and synthetic graph datasets demonstrated that the mining results of A-RAFF are very promising as compared to the existing FSM techniques.
ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models

This paper describes our submissions to SemEval 2018 task 1. The task is affect intensity prediction in tweets, including five subtasks. We participated in all subtasks of English tweets. We extracted several traditional NLP, sentiment lexicon, emotion lexicon and domain specific features from tweets, adopted supervised machine learning algorithms to perform emotion intensity prediction.
Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity

Interventional therapy using brain-computer interface (BCI) technology has shown promise in facilitating motor recovery in stroke survivors; however, the impact of this form of intervention on functional networks outside of the motor network specifically is not well-understood. Here, we investigated resting-state functional connectivity (rs-FC) in stroke participants undergoing BCI therapy across stages, namely pre- and post-intervention, to identify discriminative functional changes using a machine learning classifier with the goal of categorizing participants into one of the two therapy stages. Twenty chronic stroke participants with persistent upper-extremity motor impairment received neuromodulatory training using a closed-loop neurofeedback BCI device, and rs-functional MRI (rs-fMRI) scans were collected at four time points: pre-, mid-, post-, and 1 month post-therapy. To evaluate the peak effects of this intervention, rs-FC was analyzed from two specific stages, namely pre- and post-therapy. In total, 236 seeds spanning both motor and non-motor regions of the brain were computed at each stage. A univariate feature selection was applied to reduce the number of features followed by a principal component-based data transformation used by a linear binary support vector machine (SVM) classifier to classify each participant into a therapy stage. The SVM classifier achieved a cross-validation accuracy of 92.5% using a leave-one-out method. Outside of the motor network, seeds from the fronto-parietal task control, default mode, subcortical, and visual networks emerged as important contributors to the classification. Furthermore, a higher number of functional changes were observed to be strengthening from the pre- to post-therapy stage than the ones weakening, both of which involved motor and non-motor regions of the brain. These findings may provide new evidence to support the potential clinical utility of BCI therapy as a form of stroke rehabilitation that not only benefits motor recovery but also facilitates recovery in other brain networks. Moreover, delineation of stronger and weaker changes may inform more optimal designs of BCI interventional therapy so as to facilitate strengthened and suppress weakened changes in the recovery process.
Magnesium Ions Inhibit the Expression of Tumor Necrosis Factor α and the Activity of γ-Secretase in a β-Amyloid Protein-Dependent Mechanism in APP/PS1 Transgenic Mice

Alzheimer’s disease (AD) is a neurodegenerative disease characterized by cognitive impairment. The neuropathological features of AD are the aggregation of extracellular amyloid β-protein (Aβ) and tau phosphorylation. Recently, AD was found to be associated with magnesium ion (Mg2+) deficit and tumor necrosis factor-alpha (TNF-α) elevation in the serum or brains of AD patients. To study the relationship between Mg2+ and TNF-α, we used human- or mouse-derived glial and neuronal cell lines or APP/PS1 transgenic (Tg) mice as in vitro and in vivo experimental models, respectively. Our data demonstrates that magnesium-L-threonate (MgT) can decrease the expression of TNF-α by restoring the levels of Mg2+ in glial cells. In addition, PI3-K/AKT and NF-κB signals play critical roles in mediating the effects of Mg2+ on suppressing the expression of TNF-α. In neurons, Mg2+ elevation showed similar suppressive effects on the expression of presenilin enhancer 2 (PEN2) and nicastrin (NCT) through a PI3-K/AKT and NF-κB-dependent mechanism. As the major components of γ-secretase, overexpression of presenilin 1 (PS1), PEN2 and NCT potentially promote the synthesis of Aβ, which in turn activates TNF-α in glial cells. Reciprocally, TNF-α stimulates the expression of PEN2 and NCT in neurons. The crosstalk between TNF-α and Aβ in glial cells and neurons could ultimately aggravate the development and progression of AD.
Acciones de promoción de la salud organizacional y su relación con variables estructurales y el bienestar de los trabajadores: Análisis desde un modelo de organizaciones saludables

El bienestar de los trabajadores es un imperativo categorico para las organizaciones tanto por razones eticas como por razones de competitividad. La creacion de organizaciones o entornos laborales que promuevan el bienestar de los trabajadores constituye un gran reto para las organizaciones y un ambito de estudio academico. Objetivo: Describir la relacion que existe entre un conjunto de factores organizacionales y su impacto en el bienestar de un grupo de trabajadores, expresado atraves del engagement, la satisfaccion con el trabajo y la ansiedad relacionada con el trabajo. Metodo: Se empleo un modelo de ecuaciones estructurales. Resultados: Senalan que las acciones de promocion de la salud organizacional tiene un impacto significativo en los valores culturales y en la adopcion de practicas organizacionales. Conclusiones: En la investigacion se evidenciaron relaciones estadisticamente significativas entre las acciones de promocion de la salud y los valores culturales. Es necesario continuar investigando sobre las organizaciones saludables y su impacto en el bienestar no solo de los empleados, sino en todos sus grupos de interes: proveedores, clientes, comunidad.
A hybrid 9-level inverter with minimum number of switches for single phase grid connected solar PV system

This paper proposes a new hybrid 9-level inverter topology for single phase grid connected PV systems. This inverter has combined the features of neutral point clamped (NPC), flying capacitor (FC) multilevel inverters (MLI) and coupled inductor phenomenon. It produces 9-level output voltage with minimum number of power components, which improves the efficiency of the system and reduces the cost as well. Another advantage of this topology is that there is no need of additional balancing circuit to regulate the input DC capacitors and auxiliary capacitor. The proposed topology is investigated through MATLAB simulations for 1000W system and all the results are presented.
TakeLab at SemEval-2018 Task 7: Combining Sparse and Dense Features for Relation Classification in Scientific Texts

We describe two systems for semantic relation classification with which we participated in the SemEval 2018 Task 7, subtask 1 on semantic relation classification: an SVM model and a CNN model. Both models combine dense pretrained word2vec features and hancrafted sparse features. For training the models, we combine the two datasets provided for the subtasks in order to balance the under-represented classes. The SVM model performed better than CNN, achieving a F1-macro score of 69.98% on subtask 1.1 and 75.69% on subtask 1.2. The system ranked 7th on among 28 submissions on subtask 1.1 and 7th among 20 submissions on subtask 1.2.
Anxiety sensitivity cognitive concerns drive the relation between anxiety sensitivity and symptoms of depression

Abstract Depression is typically treated as a homogeneous construct despite evidence for distinct cognitive, affective, and somatic symptom dimensions. Anxiety sensitivity (AS; the fear of consequences of anxiety symptoms) is a cognitive risk factor implicated in the development of depressive symptoms. However, it is unclear how lower order AS dimensions (i.e. physical, cognitive, and social concerns) relate to depressive symptom factors. Confirmatory factor analysis, followed by structural equation modeling, were conducted to examine the factor structure of depression and to then examine the relations between these factors and the lower order factors of AS. This study was conducted in a sample of 374 adults (M age = 35.5, 54.3% female) with elevated levels of psychopathology (89.2% meeting criteria for at least one DSM-5 diagnosis, 25.6% primary depressive disorder). In this study a two-factor model of depression, composed of Cognitive and Affective/Somatic factors, was superior to one- and three-factor solutions. AS cognitive concerns were related to both cognitive and affective/somatic symptoms of depression. Neither of the other AS dimensions was related to depression symptom dimensions. These findings provide a better understanding of the relations between AS and depression symptoms.
Shortening of the Pittsburgh Sleep Quality Index Survey Using Factor Analysis

Objective/Introduction Lengthy surveys have the potential to burden users and can lead to inaccuracies. Conducting analyses to shorten existing validated surveys is beneficial. The objective, therefore, was to shorten the Pittsburgh Quality Sleep Index (PSQI) for young adults. Methods PSQI data from 1246 college students were used. An exploratory factor analysis (FA) was utilized to shorten survey after dropping select items. Nonparametric correlation analysis (Spearman's rho) was conducted between the global sleep scores of the shortened and original surveys. Agreements tests (Kappa and McNemar's test) measured the agreement of the surveys and sensitivity and specificity were evaluated. Results Six factors were examined using maximum likelihood factoring method, applying squared multiple correlations with Promax rotation to allow for correlated variables. FA with six factors explained 100% of shared variance based on eigenvalues and accounted for 61% of variability based on variables. The FA resulted in 13 selected questions (“shortPSQI”), corresponding to 5 of the 7 components of the original survey. High correlation was found between the global scores of the original survey and the “shortPSQI” (rho = 0.94, p < 0.001). When the global score was converted to the categorical variable of good or poor sleepers, the agreement test indicated strong agreement (Kappa 0.83, 95% CI 0.79–0.86, p < 0.0001). Conclusion The validated, 19-item PSQI survey was shortened to 13 items. Tests of correlation and agreement indicate the “shortPSQI” may be an acceptable alternative to the original survey for young adults. Clinical Trial Registration Data for this study was taken from the Get Fruved study, registered on October 21, 2016, on clinicaltrials.gov (NCT02941497).
Distributed Data Aggregation at Scale for Large Community of Users

The eCommerce world is facing increasingly huge data volumes and bigger user community. This paper presents an architecture to enable highly performant and highly scalable queries for large community of external customers. The architecture explores the unique pattern of external customer activities: in a big data store hosting a big community of large number of users, in the range of tens or hundreds of millions, each user's data is a fraction of the whole but the community as a whole demands extremely high volume of concurrent analytical queries with sub-second response. In the system, a key-value store is utilized to maximize read concurrency, a custom compression algorithm is developed to minimize data transfer, and a custom query engine is developed to provide aggregation on the fly. Scalability and other potential applications are discussed in the end.
Closing the Income Gap : Evaluating the Effectiveness of Employee Stock Ownership Programs

Since the 1950s, the disparity between executives and employees in America has been on the rise and has increased to staggering levels, stalling income mobility and affecting individuals and the economy at large. This paper examines the impact of Employee Stock Ownership Plans (ESOPs) and Employee Stock Purchasing Plans (ESPPs) on the pay structure within publicly traded companies. Economists have been studying the effectiveness of ESOPs since 1974 and have found that ESOPs improve employee morale, productivity, and firm performance, but few have examined the role that ESOPs play in wealth inequality, and none have utilized the gap between CEO and average worker pay as a mechanism for measuring disparity. This study is the first to utilize panel data from the last decade to study the impact of stock ownership plans on CEO pay, and to examine whether or not companies with ESOPs have a smaller CEO to average worker pay gap. The findings suggest that overall CEO pay sensitivity is higher in ESOP companies than it is in non-ESOP companies, as evidenced by higher increases in ESOP CEO pay than in non-ESOP CEO pay to the same increases in firm performance metrics. On the other hand, there is no significant difference between the ways CEOs are compensated in companies with ESPPs versus in those without. Lastly, there is inconclusive evidence to support the claim that companies with ESOPs have a lower CEO to average worker pay ratio.
Security review on the Internet of Things

This paper looks at Internet of Things devices from a security perspective to help provide an understanding on the impact Plug and Play Internet of Things devices have on organizations and Home networks. Such devices which are common within IoT are Internet connected devices such as mobile phones, routers or fridges. The papers key areas are to focus on the attack surface such as Malware within the Internet of Things domain, poor web interfaces which are exposed to the Internet, improper network connectivity and default credentials being used after purchase. In addition to discussing the attack surface, the paper looks at security best practices and outlines how to properly administer and manage IoT devices within controlled networks such as private organizations, government and home networks. This focuses on reasons for allowing such devices as well as guidance for best practices through the developing, manufacturing and consumer life cycle.
Hardware-Based Online Self-Diagnosis for Faulty Device Identification in Large-Scale IoT Systems

Thanks to advances in semiconductor and communication technologies, a multitude of devices can be connected over a network. This widespread interconnectivity among disparate devices has ushered the era of Internet-of-Things (IoT). After IoT devices are developed and tested, they are integrated within a system and eventually deployed. Due to the complex nature of IoT systems, however, they may fail even after deployment. In a large-scale IoT system, an automatic diagnosis technique is imperative, because it may take too much time and effort to investigate a large number of devices. In this paper, a faulty device identification technique is proposed that is based on very lightweight processor-level architectural support. A hardware-based monitoring agent is incorporated within a processor, and connected to a separate monitoring program when an examination is required. By analyzing information collected by the agent, the monitoring program determines whether the device under monitoring is working correctly, or not. The experimental results demonstrate that the proposed technique can detect 92.66% of failures, with merely 1.55% false alarms.
Attribute-based Face Recognition and Application in Safety Detection of Intelligent Factory

In order to meet intelligent factory safety requirements, a safety monitoring system based on multi-attribute face recognition is introduced in this paper. The multi-attribute face recognition model is obtained by fine-tuning Resnet-50, which is applied in the mobile robot platform. When the target appears in the field of monitoring area, the multiple attributes of the target can be detected by the model. Then, the system makes the appropriate decision according to the predicted result. The experiments show that the multiple attributes of the target face can be recognized by the model. In particular, whether the target wears a helmet or not can be detected by the monitoring system. Further, the safety of intelligent factory would be improved, reducing the reliance on labor force.
Gefitinib Inhibits Bleomycin-Induced Pulmonary Fibrosis via Alleviating the Oxidative Damage in Mice

Pulmonary fibrosis (PF) is a life-threatening interstitial lung disease. In this study, we tried to reveal the model of action between high-mobility group box 1 (HMGB1) and α-smooth muscle actin (α-SMA) and the protective role of gefitinib in pulmonary fibrosis induced by the administration of bleomycin aerosol in mice. For the mechanism study, lung tissues were harvested two weeks after modeling to detect the coexpression of HMGB1 and α-SMA by immunohistochemistry and immunofluorescence staining. Protein-DNA interactions were analyzed using a pulldown assay to study the relationship between HMGB1 and α-SMA. For the gefitinib treatment study, the mice were divided into three groups: phosphate-buffered saline (PBS) control group, PBS-treated PF group, and gefitinib-treated PF group. Gavage of gefitinib or PBS (20 mg/kg/day) was performed after bleomycin treatment for two weeks until the mice were sacrificed. Lung and blood samples were collected to assess the histological changes, oxidative stress, and expression of NOXs, HMGB1, EGFR, MAPKs, AP-1, and NF-κB to determine the curative effect and related molecular mechanisms. The results revealed the high coexpression of α-SMA and HMGB1 in some interstitial cells in the fibrotic lung. The DNA-protein pulldown analysis proved that HMGB34367 acted as a novel transcriptional factor for the α-SMA promoter and participated in the eventual development of pulmonary fibrosis. Second, gefitinib could significantly decrease lung fibrotic changes and the level of MDA and recover the T-AOC level. Meanwhile, gefitinib could also reduce the NOX1/2/4, HMGB1, p-EGFR, p-ERK, p-JNK, p-P38, p-NF-κB, p-c-Jun, and p-c-Fos expression levels in fibrotic lungs. The present study suggested that gefitinib could alleviate lung fibrosis through the HMGB1/NOXs-ROS/EGFR-MAPKs-AP-1/NF-κB signal in bleomycin-induced pulmonary fibrosis.
Link Prediction for Authorship Association in Heterogeneous Network Using Streaming Classification

Prediction of links or relations between the objects in any network is no longer a new task these days; in fact it has become a high rated area of research and has attracted many researchers seeking their contribution to the mentioned area. Research has seen an exponential growth over the passing years, and the active researchers do not hesitate in linking with fellow researchers working in same domain irrespective of their geographic location. However this in turn has generated a very complex network of objects and links which are needed to be analyzed and dealt with. Prediction of co-authorship is the sub domain of link prediction and with the increasing complexity of co-authorship network the authors are treated as heterogeneous entity not as homogeneous ones. The rule is simple analyze the data preprocess it, train the classifier according to desired classification rules and then get the classified form of data. But irrelevant features always reflect various impacts and issues on generation of a classifier and consequently the impact is sustained to further classification results. Therefore, this paper proposes streaming classification algorithm combined with Correlation based Feature selection as a solution to the stated problem. The consistent and relevant features are selected with the help of feature selection algorithm and then these features are classified with the help of streaming classification algorithm- Very Fast Decision Tree (VFDT). VFDT is a streaming classification algorithm and it takes the dataset in the form of continuous stream as an input. Finally the effectiveness of the proposed algorithm can be seen in the experimental results.
Turkic Languages ( TURKLANG 2015 ) Turkic language support in Sketch Engine

Sketch Engine is a corpus manager tool which allows building own text corpora from user-uploaded files or from Internet by downloading and cleaning web pages in a particular language and domain. It also provides many functions to explore the corpus data. We present the level of current support of Turkic languages (namely Azeri, Kazakh, Kyrgyz, Tatar, Turkish, Turkmen, Urdu and Uzbek) in Sketch Engine. It is currently possible to use features of Sketch Engine like concordancing, filtering, sampling, sorting of query searches, wordlist generating, collocation lists extraction, keyword extraction, finding good dictionary examples for words and phrases and some other features. Additionally, we discuss possible developments for improving Turkic language support in Sketch Engine, starting with incorporating existing tagging tools for Turkic languages, adding terminology extraction and building word sketches and thesauri.We invite Turkic language specialists to join us in our efforts of building large scale and at the same time high quality resources for Turkic languages.