Miracle-dz/UniFAR-FTUs huggingface dataset:
Small Business Growth and Internal Transparency: The Role of Information Systems

While many large businesses start out as a small enterprise, remarkably little is known about how an organization actually changes internally during the periods of growth. Small business growth is known to strain internal communication processes, for example, which likely limits growth opportunities. Information systems are often called upon to remedy such deficiencies. Through a participatory action research project, we investigated the ways in which a small business management team developed an IS-enabled solution to address their growth needs. During the progression of the project, a new outcome of organizational effectiveness, internal transparency, was identified and developed. Adopting a punctuated equilibrium perspective, a theoretical process model is proposed that sheds light on a relationship between internal transparency, small business growth, and IS. The paper concludes with observations that internal transparency may well be a concept that offers significant potential for MIS research as well as a discussion about the applicability and credibility of participatory action research for this project.
Towards a passive acoustic underwater system for protecting harbours against intruders

The international rules for the protection of harbors against threats and intruders typically apply to threats emanating from the land side. Protection against actions and threats from the water side is much less regulated, apart from some individual cases. Potential threats from the water side may consist of intruders including divers with and without delivery vehicles, swimmers, small submarines, small vessels or unmanned underwater vehicles. To increase the level of readiness against underwater threats in harbors, the Netherlands Organization for Applied Scientific Research (TNO) and the Stevens Institute of Technology have recently combined their long-standing expertise on observation systems and sonar technology and are developing an integrated system that can be used to protect harbors from both surface and underwater intruders. In the fall of 2010, TNO and Stevens will initiate joint experiments in a harbour in The Netherlands, where passive acoustic systems from both parties will be combined. In this paper, potential performance of the experimental system is illustrated via simulations. Initial experimental results will be presented at the conference.
Language technology tools and resources for the analysis of multimodal communication

In this paper we describe how the complexity of human communication can be analysed with the help of language technology. We present the HuComTech corpus, a multimodal corpus containing 50 hours of videotaped interviews containing a rich annotation of about 2 million items annotated on 33 levels. The corpus serves as a general resource for a wide range of re-search addressing natural conversation between humans in their full complexity. It can benefit particularly digital humanities researchers working in the field of pragmatics, conversational analysis and discourse analysis. We will present a number of tools and automated methods that can help such enquiries. In particular, we will highlight the tool Theme, which is designed to uncover hidden temporal patterns (called T-patterns) in human interaction, and will show how it can applied to the study of multimodal communication.
Identification of quasi-periodically varying systems using the combined nonparametric/parametric approach

The problem of identification of quasi-periodically varying finite impulse response systems is considered. Neither the number of system frequency modes nor the initial frequency values are assumed to be known a priori. The proposed solution is a blend of the parametric (model based) and nonparametric (discrete Fourier transform based) approach to system identification. It is shown that the results of nonparametric analysis can be used to identify the number of frequency modes and to determine initial conditions needed to smoothly start (or restart) the model-based tracking algorithm. Such a combined nonparametric/parametric approach allows one to preserve advantages of both frameworks, leading to an estimation procedure which guarantees global frequency search, high-frequency resolution, fast initial convergence, and good steady-state tracking capabilities.
Engaging Families to Prevent Chronic Truancy

Engaging Families to Prevent Chronic Truancy By Christine Zins, BSW, LSW Research Chair: Ande Nesmith, Ph.D., MSW Committee Members: Avis Fink, MSW, LICSW; Eric Jayne, BSW, LSW Truancy continues to be a nation-wide problem that is destroying the opportunity for educational success and future employment for countless adolescents. This complex social issue has been found to have many factors that contribute to it, while at the same time, this issue contributes to many other risk factors in a youth’s life. The purpose of this project was to explore the relationship between the professional school staff and families that have students experiencing chronic truancy and to distinguish effective interventions and the contributing factors to this social issue. Using a qualitative design, six volunteer middle school professional staff were interviewed regarding their perceptions about the effective way to engage a family in the intervention process of conquering the student’s absenteeism, while also looking at effectiveness in family engagement. The data was analyzed using grounded theory to analyze the data from the transcriptions. The interview responses were then linked to the literature review. The findings indicated that all of the professionals valued the importance of building a strong relationship with the families and engaging them in the process to eliminate the unexcused absences. In addition it was discovered that the school personnel found it beneficial to have some rigid systems in place, but at times these rigid systems could be detrimental to the engagement process with the school and family. These findings accentuate the importance of a positive school and family relationship to prevent chronic truancy. Running Head: ENGAGING FAMILIES TO PREVENT CHRONIC TRUANCY 3
Decrease in the Antioxidant Capacity in Beverages Containing Tea Extracts during Storage

The aim of this work was to determine antioxidant capacity of beverages containing black, white, and green tea extracts using the photochemiluminescence method, and to monitor its changes based on the storage temperature and time. Samples were stored at two different temperatures (refrigerated at 4°C and laboratory temperature 22°C), analyzed after opening of the original package, and consequently after 4 and 7 days. Results of the antioxidant capacity are expressed as the standard equivalents, that is, ascorbic acid in mmol/L. The highest mean value of the antioxidant capacity was found after opening of the original package in fruit-juice-enriched samples and totaled 9.793 mmol/L. This group revealed significant dependence (P < 0.05) not only on the storage time, but also temperature. In samples without added fruit juices containing preservatives the value was 0.428 mmol/L. This group showed significant dependence (P < 0.05) on the decrease of antioxidant capacity only when based on the storage time. Samples without fruit juices or preservatives showed significant decrease in the antioxidant capacity (P < 0.05) after 4 days of storage based on the storage time. The dependence on temperature was revealed only after 7 days of storage.
Challenges and opportunities for system biology standards and tools in medical research

Kinetic models are increasingly relevant in medical research. In systems biology, more than 10 years of experience with the development of standards and tools to construct and analyse kinetic models exists. This has supported the sharing of kinetic models, increased their reuse, and thereby has helped to reproduce and validate scientific results. Given this expertise, it seems natural to consider the application and development of standards and tools to meet the requirements of medical scientists. In this paper, we discuss challenges and opportunities for standards and tools from systems biology in medical research, and we suggest criteria for the safe use of simulations. We conclude that standards, tools and infrastructure need to be extended to ensure the quality, reliability and safety required when working with medical and patient data. This will foster the adaptation of modelling in the clinic, providing tools for improved diagnosis, prognosis and therapy.
Controlling information flows in SaaS cloud services

Information flow control prevents information leakage during software execution. This paper presents the model CloudIFC to control information flows in the SaaS level of cloud services. It uses groups to prevent exchanging incomparable information and uses security level numbers to prevent information leakage. The model strictly controls output statements because only output may leak information. As to other statements, CloudIFC uses join operations to adjust security levels. To reduce runtime overhead, CloudIFC applied MapReduce computing paradigm.
Testing the influence of the Central American Seaway on orbitally forced Northern Hemisphere glaciation

Utilizing a climate model of intermediate complexity, we test the hypothesis that the Pliocene closure of the Central American Seaway was a necessary precondition for orbitally triggered Northern Hemisphere glaciation. We conduct a series of sensitivity experiments in order to analyze the isolated and combined effects of Panama closure and orbital forcing on Northern Hemisphere perennial snow cover. While orbital forcing efficiently controls the extension of perennial snow cover, the model results suggest that the closure of the Central American Seaway did not intensify orbitally forced glaciation in high northern latitudes.
ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering

We propose a novel attention based deep learning architecture for visual question answering task (VQA). Given an image and an image related natural language question, VQA generates the natural language answer for the question. Generating the correct answers requires the model's attention to focus on the regions corresponding to the question, because different questions inquire about the attributes of different image regions. We introduce an attention based configurable convolutional neural network (ABC-CNN) to learn such question-guided attention. ABC-CNN determines an attention map for an image-question pair by convolving the image feature map with configurable convolutional kernels derived from the question's semantics. We evaluate the ABC-CNN architecture on three benchmark VQA datasets: Toronto COCO-QA, DAQUAR, and VQA dataset. ABC-CNN model achieves significant improvements over state-of-the-art methods on these datasets. The question-guided attention generated by ABC-CNN is also shown to reflect the regions that are highly relevant to the questions.
Exploration of automatic optimization for CUDA programming

Graphic processing Units (GPUs) are gaining ground in high-performance computing. CUDA (an extension to C) is most widely used parallel programming framework for general purpose GPU computations. However, the task of writing optimized CUDA program is complex even for experts. We present a method for restructuring loops into an optimized CUDA kernels based on a 3-step algorithm which are loop tiling, coalesced memory access, and resource optimization. We also establish the relationships between the influencing parameters and propose a method for finding possible tiling solutions with coalesced memory access that best meets the identified constraints. We also present a simplified algorithm for restructuring loops and rewrite them as an efficient CUDA Kernel. The execution model of synthesized kernel consists of uniformly distributing the kernel threads to keep all cores busy while transferring a tailored data locality which is accessed using coalesced pattern to amortize the long latency of the secondary memory. In the evaluation, we implement some simple applications using the proposed restructuring strategy and evaluate the performance in terms of execution time and GPU throughput.
Tumor necrosis is an important hallmark of aggressive endometrial cancer and associates with hypoxia, angiogenesis and inflammation responses

Aims Tumor necrosis is associated with aggressive features of endometrial cancer and poor prognosis. Here, we investigated gene expression patterns and potential treatment targets related to presence of tumor necrosis in primary endometrial cancer lesions. Methods and Results By DNA microarray analysis, expression of genes related to tumor necrosis reflected multiple tumor-microenvironment interactions like tissue hypoxia, angiogenesis and inflammation pathways. A tumor necrosis signature of 38 genes and a related patient cluster (Cluster I, 67% of the cases) were associated with features of aggressive tumors such as type II cancers, estrogen receptor negative tumors and vascular invasion. Further, the tumor necrosis signature was increased in tumor cells grown in hypoxic conditions in vitro. Multiple genes with increased expression are known to be activated by HIF1A and NF-kB. Conclusions Our findings indicate that the presence of tumor necrosis within primary tumors is associated with hypoxia, angiogenesis and inflammation responses. HIF1A, NF-kB and PI3K/mTOR might be potential treatment targets in aggressive endometrial cancers with presence of tumor necrosis.
Acetate and Bicarbonate Assimilation and Metabolite Formation in Chlamydomonas reinhardtii: A 13C-NMR Study

Cellular metabolite analyses by 13C-NMR showed that C. reinhardtii cells assimilate acetate at a faster rate in heterotrophy than in mixotrophy. While heterotrophic cells produced bicarbonate and CO2 aq, mixotrophy cells produced bicarbonate alone as predominant metabolite. Experiments with singly 13C-labelled acetate (13CH3-COOH or CH3-13COOH) supported that both the 13C nuclei give rise to bicarbonate and CO2 aq. The observed metabolite(s) upon further incubation led to the production of starch and triacylglycerol (TAG) in mixotrophy, whereas in heterotrophy the TAG production was minimal with substantial accumulation of glycerol and starch. Prolonged incubation up to eight days, without the addition of fresh acetate, led to an increased TAG production at the expense of bicarbonate, akin to that of nitrogen-starvation. However, such TAG production was substantially high in mixotrophy as compared to that in heterotrophy. Addition of mitochondrial un-coupler blocked the formation of bicarbonate and CO2 aq in heterotrophic cells, even though acetate uptake ensued. Addition of PSII-inhibitor to mixotrophic cells resulted in partial conversion of bicarbonate into CO2 aq, which were found to be in equilibrium. In an independent experiment, we have monitored assimilation of bicarbonate via photoautotrophy and found that the cells indeed produce starch and TAG at a much faster rate as compared to that in mixotrophy and heterotrophy. Further, we noticed that the accumulation of starch is relatively more as compared to TAG. Based on these observations, we suggest that acetate assimilation in C. reinhardtii does not directly lead to TAG formation but via bicarbonate/CO2 aq pathways. Photoautotrophic mode is found to be the best growth condition for the production of starch and TAG and starch in C. reinhardtii.
N-triad link prediction model based on structural statistics in social network

Almost all social networks provide a mechanism for user to evaluate a subject or another user which they trust or distrust (like or dislike). These link data is an efficacious tool to help system understand user's interests and other useful information. Therefore, a lot of research on social networks has focused on it. In this paper, we investigate how the positive and negative link reflect the emotion of node and how the basic triad structures influence the link prediction between any two nodes in the social network. And then we develop a model of link prediction based on structural statistics. Finally, discuss our prediction results in Epinions dataset.
Aspects of Training That Meet Indigenous Australians' Aspirations: A Systematic Review of Research.

This systematic review of research provides evidence to enable vocational education and training (VET) policy-makers and practitioners to move forward in developing training that meets the aspirations and needs of Indigenous Australians. This study was intended as a stocktake of research on good practice in culturally appropriate VET and adult and community education (ACE) for Indigenous Australians. New research was not commissioned because it was agreed that a significant existing body of research was available. This required careful and systematic evaluation to draw out the necessary factors which would enable Indigenous Australians to achieve positive outcomes from VET. The following question was developed, to be answered through a systematic review: For Indigenous Australians, what are the key features required in the planning, design and delivery of VET and ACE learning programs to ensure positive educational, employment and social outcomes? The review found that the outcomes Indigenous people aspire to through training include, from the foundations upwards, the development of personal skills, educational achievements, employment and the development of their communities. It found that there are seven critical factors required to ensure that positive outcomes are achieved in any context: (1) community ownership and involvement; (2) affirmation of Indigenous identities, cultures, knowledge and values; (3) true partnerships between Indigenous people, Indigenous communities, industry, VET providers and government; (4) flexibility in course design, content and delivery; (5) quality staff and committed advocacy; (6) student support services; and (7) funding and sustainability. The review identified 12 studies as core studies supported by evidence from 22 supporting studies. These studies are analysed in the appendices.
Robust deep linguistic processing

This dissertation deals with the robustness problem of deep linguistic processing. Hand-crafted deep linguistic grammars provide precise modeling of human languages, but are deficient in their capability of handling ill-formed or extra-grammatical inputs. In this dissertation, we argue that with a series of robust processing techniques, improved coverage can be achieved without sacrificing efficiency or specificity of deep linguistic processing. An overview of the robustness problem in state-of-the-art deep linguistic processing systems reveals that insufficient lexicon and overrestricted constructions are the major sources for the lack of robustness. Targeting both, several robust processing techniques are proposed as add-on modules to the existing deep processing systems. For the lexicon, we propose a deep lexical acquisition model to achieve automatic online detection and acquisition of missing lexical entries. The model is further extended for acquiring multiword expressions which are syntactically and/or semantically idiosyncratic. The evaluation shows that our lexical acquisition results significantly improved grammar coverage without noticeable degradation in accuracy. For the constructions, we propose the partial parsing strategy to maximally recover the intermediate results when the full analysis is not available. Partial parse selection models are proposed and evaluated. Experiment results show that the fragment semantic outputs recovered from the partial parses are of good quality and high value for practical usage. Also, the efficiency issues are carefully addressed with new extensions to the existing efficient processing algorithms.
A scalable peer-to-peer-overlay for real-time massively multiplayer online games

We propose a design for massively multiplayer games with focus on fast-paced action. To reflect the high demands on responsiveness and scalability, we use a fully distributed peer-to-peer network with a dynamic connection scheme. While the capacity of current client/server systems limits the number of players in a game, our system supports a huge number of users by exploiting local interests in the virtual world. By using a publish/subscribe system for message dissemination user interests can be handled efficiently. A Geocast algorithm allows information distribution to arbitrary regions of the virtual world. Simulations show that our design scales well by limiting the number of connections per user even in crowded regions. Moreover, different movement strategies are evaluated for their impact on network load and connection dynamics.
Charge injection and multiplication in SF6 and vacuum under DC electric fields

In order to evaluate the impact of accumulated surface charges on dielectric surfaces in gaseous insulation, the source of such charges was determined experimentally. Injected charges were measured in a coaxial electrode setup having two different surface roughnesses. Measurements were performed at low electric DC fields (<; 2 kV/mm) in both vacuum and pressurized SF6.This allowed to separate gas-dominated from electrode-dominated effects. Measurement results show that charge emission at negative polarity starts at rather low values of the electric field, i.e. 0.3 to 0.7 kV/mm. With the proposed set-up, it is possible to resolve natural ionization currents in SF6.
The UCREL Semantic Analysis System

The UCREL semantic analysis system (USAS) is a software tool for undertaking the automatic semantic analysis of English spoken and written data. This paper describes the software system, and the hierarchical semantic tag set containing 21 major discourse fields and 232 fine-grained semantic field tags. We discuss the manually constructed lexical resources on which the system relies, and the seven disambiguation methods including part-of-speech tagging, general likelihood ranking, multi-word-expression extraction, domain of discourse identification, and contextual rules. We report an evaluation of the accuracy of the system compared to a manually tagged test corpus on which the USAS software obtained a precision value of 91%. Finally, we make reference to the applications of the system in corpus linguistics, content analysis, software engineering, and electronic dictionaries
Psychovisual evaluation of the effect of color spaces and color quantification in JPEG2000 image compression

JPEG2000 is an emerging standard for still image compression. It is not only intended to provide rate-distortion and subjective image quality performance superior to existing standards, but also to provide features and additional functionalities that current standards can not address sufficiently such as lossless and lossy compression, progressive transmission by pixel accuracy and by resolution, etc. Currently the JPEG2000 standard is set up for use with the sRGB three-component color space.the aim of this research is to determine thanks to psychovisual experiences whether or not the color space selected will significantly improve the image compression. The RGB, XYZ, CIELAB, CIELUV, YIQ, YCrCb and YUV color spaces were examined and compared. In addition, we started a psychovisual evaluation on the effect of color quantification on JPEG2000 image compression. the final results indicate that the YCrCb color space is very suitable for this standard as mentioned in the amendement2.