Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of the institutional theory for implementation information technology to enhance safety management in shipping companies

This paper investigates the role of institutional theory for integrating information technology (IT) within the ship safety management system for enhancing compliance with the International Safety Management (ISM) Code. The importance of this integration is based on the needs of ship management companies for proper management of safety information between the head office ashore and their ships at sea. This paper contributes to the theory and practice of e-management and IT adoption research through providing empirical evidence from different business activities, by highlighting how an e-system for maritime ship safety management can be accepted and institutionalized. Thematically, the empirics of the paper has focused on exploring the role of the regulator in institutionalizing the IT integration process; as the institutional theory and the structurational technology adoption model has reflected a positive impact on investigation and explanation organizational change caused by IT adoption, and the role of resourcing in enhancing this process in the maritime safety. In this regard, this paper is the first to utilize institutional theory and legitimization strategies to explain needs of the IT integration process in shipping companies exclusively. The findings of the research show that external institutions such as governments, regulators and international organizations can play a pivotal role in institutionalizing organizational practices.
Tangible Information: Gestures for a Portable e-Nursing touch screen interface

Elderly persons make use of significantly more emergency medical services than do younger adults. The elderly are more prone to medical emergencies such as cardiac arrests, falls or diabetic seizures. Our prototype called Portable Medical Monitoring Computer (PMMC) was developed to provide a possible solution to efficient elderly care, especially in such emergency situations. It is an addition to our ReMoteCare system which uses a wireless sensor network (WSN) to monitor the elderly in aged care facilities. PMMC is classified as a touchable Medical e-Nursing Information System to support registered nurses in nursing homes with vital sign parameters and other medical information about residents who are suffering a medical emergency. The ‘user centred design’ application was predicated on the concept of a portable computer with a touch screen, which allows users to access information next to the casualty at the point-of-treatment. The paper reports on the results of usability issues found during testing, specifically with our gesture enabled interface. We suggest improvements to make the interface more intuitive and suitable for humans' interaction.
Protective Behavioral Strategies and Alcohol Use Outcomes Among College Students: The Moderating Effects of Negative Affect

The current study examined negative affect as a moderator of the relationships between protective behavioral strategies (PBS) used to reduce alcohol-related risk and alcohol use outcomes. Data were obtained from 267 college students using a web-based survey. The use of PBS (i.e., setting a limit on number of drinks, alternating drinks with water, avoiding drinking games) was associated with alcohol use and negative alcohol-related consequences. Negative affect symptoms moderated these relationships such that these relationships were stronger for those students who reported a greater number of negative affect symptoms. The findings indicate that the use of PBS by college students who consume alcohol are more protective for those with poorer mental health.
A Novel Wireless Heterogeneous Data Mining (WHDM) Environment Based on Mobile Computing Environments

Today the concept of Data Mining services is not alone sufficient. Data mining services play an important role in the field of Communication industry. Data mining is also called knowledge discovery in several database including mobile databases and for heterogeneous environment. In this paper, we propose a novel algorithm named Wireless Heterogeneous Data Mining (WHDM). The entire system architecture consists of three phases: 1) Reading the Database. 2) Stores the value in Tbuf with different patterns. 3) Add the superset in the list and remove the related subset from the list. Finally we find the frequent pattern patterns or knowledge from huge amount of data. We also analyze the better method or rule of data mining services which is more suitable for mobile devices. Our simulation result shows better performance and efficiency on different heterogeneous environment.
PERSON LOCALIZATION IN A WEARABLE CAMERA PLATFORM TOWARDS ASSISTIVE TECHNOLOGY FOR SOCIAL INTERACTIONS

Social interactions are a vital aspect of everyone’s daily living. Individuals with visual impairments are at a loss when it comes to social interactions as majority (nearly 65%) of these interactions happen through visual non-verbal cues. Recently, efforts have been made towards the development of an assistive technology, called the Social Interaction Assistant (SIA)[1], which enables access to non-verbal cues for individuals who are blind or visually impaired. Along with self report feedback about their own social interactions, behavioral psychology studies indicate that individuals with visual impairment will benefit in their social learning and social feedback by gaining access to non-verbal cues of their interaction partners. As part of this larger SIA project, in this paper, we discuss the importance of person localization while building a human-centric assistive technology which addresses the essential needs of the visually impaired users. We describe the challenges that arise when a wearable camera platform is used as a sensor for picking up non-verbal social cues, especially the problem of person localization in a real-world application. Finally, we present a computer vision based algorithm adapted to handle the various challenges associated with the problem of person localization in videos and demonstrate its performance on three examplar video sequences.
A retrieval model for cross-media objects based on semantic consistency

The Internet has become the main information resource, but the retrieval capacity of traditional search engines is poorly text-based or single-modal. In this paper, based on the category structure and conjunction reference analysis of the cross-media objects and their primary features, we intent to solve the problem of the heterogeneous and incompatibility among various categories of media features, in order to accomplish the task of cross-media similarity measurement, which is fundamental for both theoretical and practical methods of cross-media information retrieval.
Level Annotation and Test by Autonomous Exploration

Abstract : This paper proposes the use of an autonomous exploring agent to generate and annotate the waypoint graph as an offline process during level development. The explorer incrementally generates the waypoint graph as it explores the level via the same motion model used for player movement, and then revisits the waypoints to annotate them using image-based techniques. Points where the explorer becomes stuck or falls off of the level are flagged for later investigation by a level designer.
Socioeconomic Status and Incident Type 2 Diabetes Mellitus: Data from the Women's Health Study

Objectives We prospectively examined whether socioeconomic status (SES) predicts incident type II diabetes (diabetes), a cardiovascular risk equivalent and burgeoning public health epidemic among women. Methods Participants include 23,992 women with HbA1c levels <6% and no CVD or diabetes at baseline followed from February 1993 to March 2007. SES was measured by education and income while diabetes was self-reported. Results Over 12.3 years of follow-up, 1,262 women developed diabetes. In age and race adjusted models, the relative risk of diabetes decreased with increasing education (<2 years of nursing, 2 to <4 years of nursing, bachelor's degree, master's degree, and doctorate: 1.0, 0.7 [95% Confidence Interval (CI), 0.6–0.8], 0.6 (95% CI, 0.5–0.7), 0.5 (95% CI, 0.4–0.6), 0.4 (95% CI, 0.3–0.5); ptrend<0.001). Adjustment for traditional and non-traditional cardiovascular risk factors attenuated this relationship (education: ptrend = 0.96). Similar associations were observed between income categories and diabetes. Conclusion Advanced education and increasing income were both inversely associated with incident diabetes even in this relatively well-educated cohort. This relationship was largely explained by behavioral factors, particularly body mass index.
The Winograd Schema Challenge

In this paper, we present an alternative to the Turing Test that has some conceptual and practical advantages. Like the original, it involves responding to typed English sentences, and English-speaking adults will have no difficulty with it. Unlike the original, the subject is not required to engage in a conversation and fool an interrogator into believing she is dealing with a person. Moreover, the test is arranged in such a way that having full access to a large corpus of English text might not help much. Finally, the interrogator or a third party will be able to decide unambiguously after a few minutes whether or not a subject has passed the test.
The "Value" of Reputation in Peer-to-Peer Networks

Peer-to-peer networks need to have self-organization properties because of the lack of a centralized authority. This implies that nodes should self-manage and cooperate to sustain the availability of the resources in the system. In this context reputation management schemes have been proven in the past to be a useful tool to foster cooperation. In this paper we discuss the importance for a node to build and use its reputation value. We propose a game-theoretical framework, based on the generalized form of the Iterated prisoner's dilemma, to model the interactions of rational and selfish nodes in distributed systems. We study how a node takes into account the change of its reputation when deciding its behaviour in a transaction and discuss the Nash equilibrium in the system. Then, we also simulate nodes' interactions under different strategies to analyze how cooperation and reputation evolve in the system.
Automatic evaluation of world wide web search services

Users of the World-Wide Web are not only confronted by an immense overabundance of information, but also by a plethora of tools for searching for the web pages that suit their information needs. Web search engines differ widely in interface, features, coverage of the web, ranking methods, delivery of advertising, and more. In this paper, we present a method for comparing search engines automatically based on how they rank known item search results. Because the engines perform their search on overlapping (but different) subsets of the web collected at different points in time, evaluation of search engines poses significant challenges to the traditional information retrieval methodology. Our method uses known item searching; comparing the relative ranks of the items in the search engines' rankings. Our approach automatically constructs known item queries using query log analysis and automatically constructs the result via analysis of editor comments from the ODP (Open Directory Project). Additionally, we present our comparison on five (Lycos, Netscape, Fast, Google, HotBot) well-known search services and find that some services perform known item searches better than others, but the majority are statistically equivalent.
Robust Subspace Approaches to Visual Learning and recognition

In the real world, visual learning is supposed to be a robust and continuous process. All available visual data is not equally important; in the case of occlusions or other undesirable intrusions in the field of view some visual data can even be misleading. Human visual system treats visual data selectively and builds efficient representations of observed objects and scenes even in non-ideal conditions. Furthermore, these representations can afterwards be updated with newly acquired information, thus adapting to the changing world. In this dissertation we study these premises and propose several methods, which introduce similar principles in the machine visual learning and recognition as well. We approach visual learning by the appearance-based modeling of objects and scenes. Models are built using principal component analysis (PCA), which has several shortcomings with respect to the premises mentioned above. In order to overcome these shortcomings, we propose several extensions of the standard PCA. PCA-based learning is traditionally performed in a batch mode, thus requiring all training images to be given in advance. Since this is not admissible in the framework of continuous learning, we propose an incremental method, which processes images sequentially one by one and updates the representation at each step accordingly. Each image can be discarded immediately after the model is updated, which makes the method perfectly well suited for real on-line scenarios. In addition, in the standard PCA approach all pixels of an image receive equal treatment. Also, all training images have equal influence on the estimation of principal subspace. In this dissertation, we present a generalized PCA approach, which estimates principal axes and principal components considering weighted pixels and images. We further extend this weighted approach into a method for learning from incomplete data, which builds the model of an object even when the part of input data is missing. Images of objects and scenes are not always ideal and as such they may contain various deceptive additions like reflections or occlusions. PCA in its standard form is intrinsically non-robust to such non-gaussian noise. Several methods for robust recognition have already been proposed, however robust learning has been tackled very rarely. In the dissertation we introduce a novel approach to the robust subspace learning. The proposed batch and incremental methods detect inconsistencies in the training images and build the representations from consistent data only. As a result, the obtained models are more robust and efficient enabling more reliable visual learning and recognition even when the learning conditions are not ideal. In the dissertation we derive all the methods mentioned above and present suitable algorithms. We also experimentally evaluate all the proposed algorithms on different image domains and determine the applicability of the methods in different scenarios.
A Proportional Study on Feature Extraction Method in Automatic Speech Recognition System

3 Abstract: Automatic speech recognition (ASR) has been the focus of many researchers for several years. In speech recognition system is for a computer be able to "hear," understand," and "act upon" spoken information. The speaker recognition system viewed as working in a Analysis , Feature extraction , Modeling , Testing/Matching techniques . speech processing is to convey information about words, speaker identity, tone of voice, expression, style of speech, emotion and the state of health of the speaker. Speech features can be extracted by the parameter as Linear Predictive Codes (LPC), Perceptual Linear Prediction (PLP), Mel Frequency Cepstral Coefficients (MFCC), PLP-Relative Spectra . Neural network training like Vector Quantization (VQ), Dynamic Time Warping (DTW), Support Vector Machine (SVM), and Hidden Markov Model (HMM),Gaussian Mixture Module (GMM) can be used for classification and recognition technique, We have to illustrate some of most important method in neural network like LPC, MFCC, and PLP.
Kalman Filter and Joint Tracking and Classification in the TBM framework

The paper presents an approach to joint tracking and classification based on belief functions as understood in the transferable belief model (TBM). For the tracking phase, a Kalman filter in the TBM framework is derived. This filter is essentially the same as the classical Kalman filter with a diffuse prior, although it is derived in a more general context. For the classification phase, the TBM solution provides more reasonable results than the corresponding Bayesian classifier in situations where no one-toone mapping between target behaviours and classes can be established.
Severity of chronic experimental Chagas' heart disease parallels tumour necrosis factor and nitric oxide levels in the serum: models of mild and severe disease

Heart tissue inflammation, progressive fibrosis and electrocardiographic alterations occur in approximately 30% of patients infected by Trypanosoma cruzi, 10-30 years after infection. Further, plasma levels of tumour necrosis factor (TNF) and nitric oxide (NO) are associated with the degree of heart dysfunction in chronic chagasic cardiomyopathy (CCC). Thus, our aim was to establish experimental models that mimic a range of parasitological, pathological and cardiac alterations described in patients with chronic Chagas’ heart disease and evaluate whether heart disease severity was associated with increased TNF and NO levels in the serum. Our results show that C3H/He mice chronically infected with the Colombian T. cruzi strain have more severe cardiac parasitism and inflammation than C57BL/6 mice. In addition, connexin 43 disorganisation and fibronectin deposition in the heart tissue, increased levels of creatine kinase cardiac MB isoenzyme activity in the serum and more severe electrical abnormalities were observed in T. cruzi-infected C3H/He mice compared to C57BL/6 mice. Therefore, T. cruzi-infected C3H/He and C57BL/6 mice represent severe and mild models of CCC, respectively. Moreover, the CCC severity paralleled the TNF and NO levels in the serum. Therefore, these models are appropriate for studying the pathophysiology and biomarkers of CCC progression, as well as for testing therapeutic agents for patients with Chagas’ heart disease.
Selective Antibiofilm Effects of Lucilia sericata Larvae Secretions/Excretions against Wound Pathogens

Background. Maggot debridement therapy (MDT), using Lucilia sericata larvae, represents efficient, simple, and low-cost therapy for the treatment of chronic wounds. Aim. The aim was to investigate the antibiofilm activity of maggot excretions/secretions (ES) against biofilm of wound isolates Staphylococcus aureus (S. aureus), Enterobacter cloacae (E. cloacae), and Proteus mirabilis (P. mirabilis). Methods. Quantification of biofilm formation, was carried out using a microtiter plate assay. Proteolytic activity of maggot ES was performed using skim milk agar plates. A solid phase extraction and reverse phase HPLC C18 chromatography were employed to the isolate of maggot ES antibiofilm compounds. Results. Maggot ES at 100 mg/mL concentration significantly reduced biofilm formation thus disrupting established biofilm of E. cloacae. Heat-treated ES did not show any antibiofilm activity towards E. cloacae. Similar results were obtained in the case of S. aureus; however, the heat-treatment of maggot ES did not affect its antibiofilm activity. Moreover, a compound with molecular weight of 25 kDa exhibiting antibiofilm activity was identified in maggot ES. On the other hand, maggot ES protected and even stimulated P. mirabilis biofilm formation. Conclusions. Our results suggest that maggot ES may act selectively against different bacterial strain.
Capturing Common Knowledge about Tasks: Intelligent Assistance for To-Do Lists

Although to-do lists are a ubiquitous form of personal task management, there has been no work on intelligent assistance to automate, elaborate, or coordinate a user’s to-dos. Our research focuses on three aspects of intelligent assistance for to-dos. We investigated the use of intelligent agents to automate to-dos in an office setting. We collected a large corpus from users and developed a paraphrase-based approach to matching agent capabilities with to-dos. We also investigated to-dos for personal tasks and the kinds of assistance that can be offered to users by elaborating on them on the basis of substep knowledge extracted from the Web. Finally, we explored coordination of user tasks with other users through a to-do management application deployed in a popular social networking site. We discuss the emergence of Social Task Networks, which link users‘ tasks to their social network as well as to relevant resources on the Web. We show the benefits of using common sense knowledge to interpret and elaborate to-dos. Conversely, we also show that to-do lists are a valuable way to create repositories of common sense knowledge about tasks.
Differences between an Accelerometer and a Heart Rate Monitor in Monitoring Non-Training-Related Load in Adolescents: An Opportunity to Distinguish between the Physical and Mental Load

Apart from the knowledge of training load, managing the training process also requires information on non-training related load, together with distinguishing between the physical and mental load. However, verified objective measurement methods are lacking. The objective was to assess whether the differences between an accelerometer and heart-rate monitor in non-training related load measurement could be interpreted as a proportion of the physical load or mental load to the total load in the meaningful segments of a school day in adolescents. The sample comprised of 573 girls (mean age of 16.47 ± 1.06 years) form Czech and Polish secondary schools. The ActiTrainer device (including the accelerometer and heart-rate monitor) was used to measure the percentage of selected day time segments in which the load intensity was classified at a given level. Statistically significant differences between the accelerometer and heart-rate monitor derived data were observed, which ranged from -22.8% to 12.9% in the physical education lesson and -6.3% to 8.1% in the remaining segments. The observed differences are in relation to the nature of the segments. The study indicates that some of specific differences between accelerometer and heart-rate monitor derived data can be utilized to distinguish between the non-training related physical and mental load in adolescents.
From safe screening rules to working sets for faster Lasso-type solvers

Convex sparsity-promoting regularizations are ubiquitous in modern statistical learning. By construction, they yield solutions with few non-zero coefficients, which correspond to saturated constraints in the dual optimization formulation. Working set (WS) strategies are generic optimization techniques that consist in solving simpler problems that only consider a subset of constraints, whose indices form the WS. Working set methods therefore involve two nested iterations: the outer loop corresponds to the definition of the WS and the inner loop calls a solver for the subproblems. For the Lasso estimator a WS is a set of features, while for a Group Lasso it refers to a set of groups. In practice, WS are generally small in this context so the associated feature Gram matrix can fit in memory. Here we show that the Gauss-Southwell rule (a greedy strategy for block coordinate descent techniques) leads to fast solvers in this case. Combined with a working set strategy based on an aggressive use of so-called Gap Safe screening rules, we propose a solver achieving state-of-the-art performance on sparse learning problems. Results are presented on Lasso and multi-task Lasso estimators.