Miracle-dz/UniFAR-FTUs huggingface dataset:
Personality profiles of global software developers

Context: Individuals' personality traits have been shown to influence their behavior during team work. In particular, positive group attitudes are said to be essential for distributed and global software development efforts where collaboration is critical to project success. Objective: Given this, we have sought to study the influence of global software practitioners' personality profiles from a psycholinguistic perspective. Method: Artifacts from ten teams were selected from the IBM Rational Jazz repository and mined. We employed social network analysis (SNA) techniques to identify and group practitioners into two clusters based on the numbers of messages they communicated, Top Members and Others, and used standard statistical techniques to assess practitioners' engagement in task changes associated with work items. We then performed psycholinguistic analysis on practitioners' messages using linguistic dimensions of the LIWC tool that had been previously correlated with the Big Five personality profiles. Results: For our sample of 146 practitioners, we found that the Top Members demonstrated more openness to experience than the Other practitioners. Additionally, practitioners involved in usability-related tasks were found to be highly extroverted, and coders were most neurotic and conscientious. Conclusion: High levels of organizational and inter-personal skills may be useful for those operating in distributed settings, and personality diversity is likely to boost team performance.
Probabilistic Programming Models for Dispatching and Resource Allocation in Traffic Incident Management

This paper proposes mathematical programming models with probabilistic constraints in order to address incident response and resource allocation problems for traffic incident management. In incident response problems, we introduce the concept of quality of service during a potential incident to give the operator the flexibility to determine the optimal dispatching policy in response to various situations. An integer programming model with probabilistic constraints is proposed to address the dispatching problem with consideration given to the stochastic resource requirements at the sites of the potential incidents. For the resource allocation problem, assuming that the stochastic distribution of incidents over a network is given, we introduce a mathematical model to determine the number of service vehicles allocated to each depot to meet the requirements of the potential incidents by taking into account the stochastic nature of the resource requirement and incident occurrence probabilities. For completeness, relevant concepts and solution algorithms in stochastic programming are also presented. Several examples are included to demonstrate the applications of these models to real-world problems. This paper concludes with a summary of results and recommendations for future research.
Authenticating users through their arm movement patterns

In this paper, we propose four continuous authentication designs by using the characteristics of arm movements while individuals walk. The first design uses acceleration of arms captured by a smartwatch's accelerometer sensor, the second design uses the rotation of arms captured by a smartwatch's gyroscope sensor, third uses the fusion of both acceleration and rotation at the feature-level and fourth uses the fusion at score-level. Each of these designs is implemented by using four classifiers, namely, k nearest neighbors (k-NN) with Euclidean distance, Logistic Regression, Multilayer Perceptrons, and Random Forest resulting in a total of sixteen authentication mechanisms. These authentication mechanisms are tested under three different environments, namely an intra-session, inter-session on a dataset of 40 users and an inter-phase on a dataset of 12 users. The sessions of data collection were separated by at least ten minutes, whereas the phases of data collection were separated by at least three months. Under the intra-session environment, all of the twelve authentication mechanisms achieve a mean dynamic false accept rate (DFAR) of 0% and dynamic false reject rate (DFRR) of 0%. For the inter-session environment, feature level fusion-based design with classifier k-NN achieves the best error rates that are a mean DFAR of 2.2% and DFRR of 4.2%. The DFAR and DFRR increased from 5.68% and 4.23% to 15.03% and 14.62% respectively when feature level fusion-based design with classifier k-NN was tested under the inter-phase environment on a dataset of 12 users.
Enhancing coverage and capacity for multiuser MIMO systems by utilizing scheduling

Recent studies have revealed that the remarkable capacity improvement resulting from an open-loop multiple-input-multiple-output (MIMO) spatial multiplexing system may come at the sacrifice of degrading link reliability. This tradeoff between antenna multiplexing gain against antenna diversity gain may translate into smaller coverage areas. In this paper, we suggest using the multiuser diversity to replenish the diversity-deficient spatial multiplexing MIMO system. Specifically, we propose a fair scheduling scheme, called the strongest-weakest-normalized-subchannel-first (SWNSF) scheduling, which requires only limited amount of feedback. Our analysis and results indicate that the SWNSF scheduling can significantly increase the coverage of the multiuser MIMO system while further improving the system capacity.
Cumulative power spectrum of EEG-a predictor of awake and sleep state

Anaesthesia administration during surgery is a demanding task. It requires complete knowledge about the exact sleep state of the patient. However, accurate judgement of the patient’s exact state has been a problem faced by anaesthesiologists for years. Till date several electroencephalogram (EEG) parameters have been computed which form the index to indicate the unconsciousness of a patient. Efforts are still continuing to find out parameters with better accuracy in prediction, high repeatability & reproducibility of results and drug independence. A recently discovered parameter from EEG Signal, the cumulative power spectrum (CPS) promises a significant improvement over the existing parameters with higher correlation with awake and sleep state of the patient. This paper focuses on the comparison of the CPS of the awake and sleep states and analyses it with respect to the frequency components in the signal.After analysis, results show that when the patient is in the awake state, the lower frequency components have high power whereas in the sleep state the lower frequency components have comparatively low power. Thus from the cumulative power spectrum plots, one is able to judge the degree of unconsciousness of the patient accurately. The judgement is confirmed by comparing it with a characteristic EEG index which has unique values for the awake and sleep states
A New Evaluation Protocol for Image Pan-sharpening Methods

Pan-sharpening consists in fusing the spatial and spectral characteristics of panchromatic and multispectral (MS) images to get synthesized MS images. When such a fusion technique is proposed, it is delicate and important to evaluate its results. Generally, to evaluate the pan-sharpening methods both spectrally and spatially, a variety of quality indexes are available. Although, spectral indexes play a more important role than spatial ones to assess the fusion methods, spatial quality is important too. In this paper, a new protocol is proposed to evaluate pan-sharpening methods. This evaluation, by considering both spectral and spatial indexes, facilitates, reduces and even avoids any visual analyses, and allows automatic classification when comparing fusion methods.
Ontology to Classify Learning Material in Software Engineering Knowledge Domain

This paper proposes an ontology to automatic classification of learning materials to the Software Engineering knowledge domain. The Software Engineering Body of Knowledge (SWEBOK) was used to define the hierarchical structure of the knowledge area. The Rational Unified Process (RUP) was used to add the axioms to represent the relationships between concepts and to enable the reasoning to SWEBOK knowledge areas. Two testing scenarios were designed and experiments were performed. The results show that the ontology is able to classify and locate learning materials from the Software Engineering area, according to the desired area, role, artifact or task.
Price Transmission in the German Sugar Market

The German sugar market is governed by the European Union’s common market organization (CMO). In 2006, the CMO was subject to its first major reform. Among others, the administered price for sugar was reduced by 36%. We use a data set with monthly prices for sugar and sugar containing products to perform a cointegration analysis. Results show that the reduction of the institutional price has led to a reduction of wholesale prices and of retail prices for table sugar. Prices for sugar containing products are barely integrated with the sugar price, though. Some are found to be integrated with the CPI for food and soft drinks. In none of the cases where linear cointegration could not be detected, threshold cointegration could be found.
Interactive machine learning for incorporating user emotions in automatic music harmonization

Harmonization enriches piano melodies by adding variations such as mood, sound enhancements and beats that are the key building blocks of piano music. However, not all piano players and song writers are gifted with the musical talent of harmonizing piano melodies effectively since it requires keeping track of an extensive set of western music rules and concepts, years of training and practice and also musicality within them to harmonize a melody accurately. This paper discusses a solution for the tedious task of harmonization by introducing ‘ChordATune’, an interactive tool for harmonizing melodies and generating chord progressions according to user emotions. Further, ChordATune provides a mechanism to arrange chords according to different genres, drum beats and tempi based on user preference. A machine learning approach with Hidden Markov Model (HMM), along with dynamic programming is used to generate the chord progression for a given melody and embed the emotional factor of the user. The melody is taken in as an audio file to the system, where a pitch class profile is created at run time representing the pitch content of the file over time. In order to embed the emotional factor, the Hidden Markov Model is dynamically created, and HMM properties are generated at run time according to the selected emotional factor and the input pitch classes (melody). Around 250 lead sheets were used to train the system using data driven and heuristic approaches, and the evaluation results represented 80% user satisfaction of the prototype. This research further opens a path for research concerning chord progression generation for vocals, taking into account the extraction of words, emotional factor and the tune extracted from the actual voice of the user.
PWLCM based image encryption through compressive sensing

In this paper, compressive sensing is combined with a chaotic key based generation of measurement matrix to provide an effective encryption algorithm for multimedia security. Block-based compressive sensing provides a better way in the field of image and video transmission by reducing the memory requirements and complexity, where as multiple hypothesis prediction provides a competent way in improving PSNR during reconstruction of block based compressive sensed images and videos. The measurement matrix Φ place a crucial role in this compressive sensing and as well as in the reconstruction process. A possibility to generate secure measurement matrix using piecewise linear chaotic map (PWLCM) as the seed and then hiding initial condition, system parameter, number of iterations of PWLCM as the key enable the sender to incorporate room for encryption along with the compression in a single step. The above mentioned scheme provides high level of data security, reduced complexity, compression with a good reconstruction quality and beside all it reduce the burden of sending the measurement matrix along with the data which further reduces the complexity in over all compressive sensing framework.
Refining Grammars for Parsing with Hierarchical Semantic Knowledge

This paper proposes a novel method to refine the grammars in parsing by utilizing semantic knowledge from HowNet. Based on the hierarchical state-split approach, which can refine grammars automatically in a data-driven manner, this study introduces semantic knowledge into the splitting process at two steps. Firstly, each part-of-speech node will be annotated with a semantic tag of its terminal word. These new tags generated in this step are semantic-related, which can provide a good start for splitting. Secondly, a knowledge-based criterion is used to supervise the hierarchical splitting of these semantic-related tags, which can alleviate overfitting. The experiments are carried out on both Chinese and English Penn Treebank show that the refined grammars with semantic knowledge can improve parsing performance significantly. Especially with respect to Chinese, our parser achieves an F1 score of 87.5%, which is the best published result we are aware of.
Semantic Annotation of Texts with RDF Graph Contexts

The basic principle of the Semantic Web carried by the RDF data model is that many RDF statements coexist all together and are universally true. However, some case studies imply contextual relevancy and truth this is well known in the Conceptual Graph community and handled through the notion of contexts. In this paper, we present an approach and a tool for semantic annotation of textual data using graph contexts. We rely on both Natural Language Processing and Semantic Web technologies and propose a model of RDF contexts inspired by the nested Conceptual Graphs. Sentences are primarily analysed and their grammatical constituents (subject, verb, object) are extracted and mapped to RDF triples. Links between these triples are then established within a semantic scope (i.e., context). The context definition allows us to validate the generated annotations by disambiguating the misleading RDF triples. We show how far our approach is applicable to texts in Engineering Design.
Agent based models of social systems and collective intelligence

Agent-based modeling and simulation techniques have now become a suitable and popularly used approach to build useful models of social systems, which not only helps to get better understanding of various social phenomena but also enriches the agent-based computing paradigm in return. Agent-based models allow simulating social units such as individuals, households, organizations or nations and their direct or indirect interactions. These models demonstrate how global order and collective intelligence can emerge from relatively simple local interactions and can explain the dynamics of the emergent behaviour. The Agent-based modeling approach has provided the bridging link between psychological & sociological analysis of individual and social behaviours respectively, which was otherwise missing. This generative, proof-by-construction approach has also complemented the individual-centered research in cognitive science by showing that individual alone is not the crucial unit of cognition but is affected by environment and society besides affecting them as well. In this paper, we have given an analytical account of Agent-based modeling of emergent collective social behaviours, on these lines, along with relevant theoretical & experimental outcomes and their implications for multi-agent systems.
Early Hepatic Dysfunction Is Associated with a Worse Outcome in Patients Presenting with Acute Respiratory Distress Syndrome: A Post-Hoc Analysis of the ACURASYS and PROSEVA Studies

Introduction Bilirubin is well-recognized marker of hepatic dysfunction in intensive care unit (ICU) patients. Multiple organ failure often complicates acute respiratory distress syndrome (ARDS) evolution and is associated with high mortality. The effect of early hepatic dysfunction on ARDS mortality has been poorly investigated. We evaluated the incidence and the prognostic significance of increased serum bilirubin levels in the initial phase of ARDS. Methods The data of 805 patients with ARDS were retrospectively analysed. This population was extracted from two recent multicenter, prospective and randomised trials. Patients presenting with ARDS with a ratio of the partial pressure of arterial oxygen to the fraction of inspired oxygen < 150 mmHg measured with a PEEP ≥ 5 cm of water were included. The total serum bilirubin was measured at inclusion and at days 2, 4, 7 and 14. The primary objective was to analyse the bilirubin at inclusion according to the 90-day mortality rate. Results The 90-day mortality rate was 33.8% (n = 272). The non-survivors were older, had higher Sepsis-related Organ Failure Assessment (SOFA) score and were more likely to have a medical diagnosis on admission than the survivors. At inclusion, the SOFA score without the liver score (10.3±2.9 vs. 9.0±3.0, p<0.0001) and the serum bilirubin levels (36.1±57.0 vs. 20.5±31.5 μmol/L, p<0.0001) were significantly higher in the non-survivors than in the survivors. Age, the hepatic SOFA score, the coagulation SOFA score, the arterial pH level, and the plateau pressure were independently associated with 90-day mortality in patients with ARDS. Conclusion Bilirubin used as a surrogate marker of hepatic dysfunction and measured early in the course of ARDS was associated with the 90-day mortality rate.
Semantic Representation and Ontology Construction in the Question Answering System

To build an automatic assistance for learning and provide a self-paced learning mechanism are the objectives in the e-learning environment. The paper improves the previous proposed Semantic Question Answering System which applies Link Grammar and WordNet to form a Semantic Tree to represent the meaning of question and further find the relevant answer based on the selected Data Structure Ontology. The paper addresses the improved functions including the flexible answering method which can refine the Semantic Tree to find more relevant answers, the Ontology Extension Module is designed to acquire data from Internet to raise the ontology and the Feedback for System module is designed for the instructors to provide more sufficient knowledge.
Literature review on CBR approches for matching composite sketches to facial photographs

Facial Sketches are most widely used in law enforcement agencies for identification and apprehension of suspect which may involve in several criminal activities. If the photographs of the suspect is store in the mug shot data base then the identity of the suspect is possible but in most of the cases that it is a possibility that the photographs of the suspect is not available in the mug shot then two methods are used first is forensic sketch and second is composite sketch software. The sketch used in investigations are either drawn by expert artist (forensic sketch) or created with the help of computer software (composite software sketches), the forensic sketch is drawn by expert artist by following the verbal description provided by an eyewitness. Previous research on sketch recognition in which the (LAP) i.e lightning aware preprocessing method, for face recognition was used as a preprocessing method. Another method was based on extended (ASM) i.e Active shape model for locating a facial feature and previous work for matching sketches to mug shot database was based on (LFDA) i.e local feature based discriminant analysis method was used. And also using PCA algorithm for matching hand drawn face sketch in forensic application. After that the (EUCLBP) i.e enhanced uniform circular local binary pattern for matching sketch in forensic application. But the above process are very slow for identifying the suspect so, for this purpose we propose a (CBR) i.e component based representation approach that is used for matching composite sketches to face photo and with the help of this method we can successfully recognize the face based on sketch.
In vivo near-infrared imaging for the tracking of systemically delivered mesenchymal stem cells: tropism for brain tumors and biodistribution

Mesenchymal stem cell (MSC)-based gene therapy is a promising tool for the treatment of various neurological diseases, including brain tumors. However, the tracking of in vivo stem cell migration, distribution, and survival need to be defined for their clinical application. The systemic routes of stem cell delivery must be determined because direct intracerebral injection as a cure for brain tumors is an invasive method. In this study, we show for the first time that near-infrared (NIR) imaging can reveal the distribution and tumor tropism of intravenously injected MSCs in an intracranial xenograft glioma model. MSCs were labeled with NIR fluorescent nanoparticles, and the effects of the NIR dye on cell proliferation and migratory capacity were evaluated in vitro. We investigated the tumor-targeting properties and tissue distribution of labeled MSCs introduced by intravenous injection and followed by in vivo imaging analysis, histological analysis, and real-time quantitative polymerase chain reaction. We observed no cytotoxicity or change in the overall growth rate and characteristics of labeled MSCs compared with control MSCs. NIR fluorescent imaging showed the organ distribution and targeted tumor tropism of systemically injected human MSCs. A significant number of MSCs accumulated specifically at the tumor site in the mouse brain. These results suggest that NIR-based cell tracking is a potentially useful imaging technique to visualize cell survival, migration, and distribution for the application of MSC-mediated therapies in the treatment of malignant gliomas.
Influence of soil fumigation by methyl bromide and methyl iodide on rhizosphere and phyllosphere microbial community structure

Rhizosphere and phyllosphere microbial communities were evaluated on roots and leaves of growth chamber-grown lettuce (Lactuca sativa (L.) cv. Green Forest) plants by culture-dependent and -independent methods after soil fumigation. Denaturing gradient gel electrophoresis (DGGE) with 16S rRNA primers followed by cloning and sequencing was used to identify major rRNA bands from the rhizosphere and phyllosphere. Three weeks after fumigation, there were no differences (P = 0.16) in rhizosphere microbial communities between the fumigated treatments and the control. The same effect was observed during week seven after fumigation (P=0.49). Also, no significant differences (P=0.49) were found in the phyllosphere microbial communities between the fumigated treatments and the control during the growth period of the plant. A majority of the bands in the rhizosphere were related to known bacterial sequences with a 96 to 100 % sequence similarity. Some of the derived sequences were related to Pseudomonas syringae pv. tomato DC300 and Bradyrhizobium japonicum USDA 110. A total of 23 isolates were identified from leaf surface by both culture-dependent and independent methods, and only Photorhabdus luminescens was found on leaf surface using both techniques. All the Biolog isolates from phyllosphere were from the Proteobacteria phylum compared to the culture-independent bands from the leaves that were from different bacterial phyla. Based on our data, methyl bromide (MeBr) and methyl iodide (MeI) did not have any significant negative effects on rhizosphere and phyllosphere microbial communities throughout the growing period of lettuce.