Miracle-dz/UniFAR-FTUs huggingface dataset:
TV advertisement detection for news channels using Local Success Weighted SVM Ensemble

Research in advertisement detection (in news broadcast videos) has mainly focused on development of efficient audio-visual features. These features are used with standard machine learning algorithms to automatically segregate the advertisements. However, the discriminative abilities of features are local and may not have uniform performance through out the input space. Thus, using a fixed set of features for entire input space limits the performance of classifiers. In this paper, we use a Local Success Weighted Ensemble of SVMs (LSWE-SVM) for advertisement detection. The LSWE-SVM ensures the diversity in errors of component SVMs by training them on individual features with different similarity measures (kernels). The weight of each SVM is determined by an instance dependent “success prediction function”. The success prediction functions predict high values for a particular exemplar, if the corresponding base SVMs have high likelihood of predicting the correct label of the exemplar. During training, the success prediction functions are estimated using support vector regression (SVR) trained on exemplars from cross validation sets of respective SVMs. The target for SVRs is set to 1.0 for the successfully classified exemplars and 0.0 otherwise. Given a test pattern, SVMs having high likelihood of predicting the correct label for the pattern are only allowed to contribute in the ensemble decision, thus suppressing the false decisions. Experimentations on over 150 hours of TV Broadcast news dataset have shown the superiority of LSWE-SVM over other baseline methods in terms of balanced F-score and generalization capability.
Culture as an engine for start-up networks in a cultural and creative product-based small service

This research aims to explore the role of culture as a driver for starting up a cultural and creative product-based small service, particularly in discussing the embroidery shoe business in Taiwan. The antecedent studies have heavily explored the driver of innovation in different product-based business. However, the exploration about using culture as a driver for innovation and start-up network building remains undeveloped, and there is still little examination on the development pattern of culture and creative start-up and what role the culture really plays at early stage. As a result, this study conducts a qualitative approach using participant observational studies and self-immerse approach through a single case study of embroidery shoe business. The findings reveal that the role of culture in start-up process will follow three phases including “culture as content”, “culture to be preserved”, and “culture to be regenerated”. Meanwhile, instead of business partners in a network, there are also cultural partners in the start-up networks of cultural and creative business, thereby creating a cultural network for cultural events, workshop, exhibition, educational activity, and sales channel. Finally, the results also show that the role of incubator in the start-up network should more emphasize the service at demand side for cultural preservation and regeneration, instead of the conventional incubation service at supply side.
Are Donors Afraid of Core Costs? Economies of Scale and Contestability in Charity Markets*

We study contestability in charity markets where non-commercial, not-for-profit providers supply a homogeneous collective good through increasing-returns-to-scale technologies. Unlike in the case of for-profit competition, the absence of price-based sales contracts for charities means that fixed costs can translate into entry barriers, protecting the position of an inefficient incumbent; or, conversely, they can make it possible for inefficient newcomers to contest the position of a more efficient incumbent. Evidence from laboratory experiments show that fixed-cost driven tradeoffs between efficiency and perceived risk can lead to inefficient technology adoption.
Concept Based Sentence Modeling for Extractive Speech Summarization

-With large volumes of multimedia data and speech recordings available over internet,there is need to efficiently process these data so that users can quickly review important information. So the research is mainly focused on automatic processing of transcripts.In past decades,lot of methods have been proposed for summarization of text. The solution for speech summarization is to transliterate the spoken documents to texts, and apply some well-defined text summarization methods. When these methodologies are applied to spoken documents, they doesn’t work good for text processing. Shih-Hung Liu et al. performed speech summarization by combiningClarity Measure andRelevance Language Modelling(RLM). Clarity measure is used for important sentence selection, which helps to identify the individual sentences which reflect the main theme of the document. The experimental evidence of this model indicated that the various formulations instantiated are better than few existing methods for extractive speech summarization. The sentence-level clarity measure in combination with RLM indeed benefits speech summarization significantly. The limitations observed in this model are that, it is purely term based and doesn’t consider concept relevance. So this project aims at proposing the use ofCorpus or Knowledge base for Extractive speech summarization,where a subset of sentences will be selected to cover as many important concepts as possible.
Pengaruh Firm Size, DER, ROA dan Current Asset terhadap Price Value pada Perusahaan Manufaktur Sub Sektor Tekstil di Indonesia

The purpose of this study was to examine the effect of size firm, debt to equity ratio, retrun on assets, and current ratio to price book value in textile sector manufacturing companies listed on the IDX. The data used in this study is secondary data consisting of 12 textile companies in the 2012-2016 periods with 60 observations (balance panel data). Data analysis method in this study is panel data regression analysis. The results of the study found that DER had a positive and significant effect on PBV while the size of the company, ROA and CR had no significant effect on the value of textile sector manufacturing companies listed on the IDX.
OPERATIONAL RISK ANALYSIS OF NETWORK OPERATION CENTER DIVISION PT. IO

This research is try to identify the operational risks in Network Operation Center division of PT. IO; measure and evaluate the risks, as well as make control and response measures to operational risks. The research method was a survey and Focus Group Discussion, by using a questionnaire as a research tool. The sample selection is done by Quota sampling and Convenience sampling methods to the employees in Division Network Operating Center PT.IO, which has had experience of at least 5 years. The results showed that as many as 40 out of 49 identified risks have been handled properly. Against these risks, the company have 35 standard operating procedure documents (SOPs) as a mitigation of those risks. Nonetheless, efforts are needed to improve and update mitigation strategies because of the possibility of new risks and increased risk levels. It becomes a suggestion for further research as well as suggestion to undertake further research on mitigation at the division and director level.
JPMorgan Chase, Bank of America, Wells Fargo, and the Financial Crisis of 2008

Following the financial crisis of 2008, major banks such as JPMorgan Chase, Bank of America, and Wells Fargo attempted to rebuild stakeholder and shareholder trust in the American financial system. Through a discourse analysis of JPMorgan Chase, Bank of America, and Wells Fargo’s legitimation efforts, this study provides additional research on the practice of strategic financial communication. Specifically, this article found how JPMorgan Chase, Bank of America, and Wells Fargo responded to questions about their actional and institutional legitimacy in their practice of issue management was echoed within each bank’s press coverage and organizational discourse. Yet this study also found that banks that were better able to directly respond to media critique more effectively navigated the financial crisis. This study underscores the importance of careful communication in managing shareholder and stakeholder concerns and rebuilding public trust in their corporations.
A quantitative reconstruction software suite for SPECT imaging

Quantitative Single Photon Emission Tomography (SPECT) imaging allows for measurement of activity concentrations of a given radiotracer in vivo. Although SPECT has usually been perceived as non-quantitative by the medical community, the introduction of accurate CT based attenuation correction and scatter correction from hybrid SPECT/CT scanners has enabled SPECT systems to be as quantitative as Positron Emission Tomography (PET) systems. We implemented a software suite to reconstruct quantitative SPECT images from hybrid or dedicated SPECT systems with a separate CT scanner. Attenuation, scatter and collimator response corrections were included in an Ordered Subset Expectation Maximization (OSEM) algorithm. A novel scatter fraction estimation technique was introduced. The SPECT/CT system was calibrated with a cylindrical phantom and quantitative accuracy was assessed with an anthropomorphic phantom and a NEMA/IEC image quality phantom. Accurate activity measurements were achieved at an organ level. This software suite helps increasing quantitative accuracy of SPECT scanners.
The bone marrow as production site of the IgA deposited in the kidneys of patients with IgA nephropathy.

Patients with primary IgA nephropathy have increased plasma levels of polymeric IgA1 and deposits of IgA1 in their kidneys. The origin of this material is unknown. The production of IgA and its subclasses was investigated in the bone marrow of 14 patients and 19 controls using two colour immunofluorescence and tissue culture. Patients had an increase in the percentage of plasma cells containing IgA (45.8 +/- 7.2 mean +/- s.d.) compared to controls (40.1 +/- 10.5) (P = 0.08). IgA plasma cells containing subclass IgA1 were significantly (P less than 0.01) increased in patients (89.9 +/- 2.7%) compared to controls (84.1 +/- 6.7%). Correspondingly IgA plasma cells containing subclass IgA2 were significantly decreased (P less than 0.01) in patients (7.4 +/- 3.0%) compared to controls (13.5 +/- 5.9%). Production of IgA in bone marrow culture in patients was increased (1,684 +/- 1,151 ng/culture) compared to controls (1,087 +/- 937), but this difference was not significant (P = 0.2). However, in patients the IgA1 subclass contributed significantly (P less than 0.01) more to the IgA synthesis in culture (ratio of IgA1 over IgA: 0.96 +/- 0.02) than in controls (ratio 0.90 +/- 0.06). These findings suggest that the bone marrow may well be the site of long-term overproduction of the IgA1 found in the circulation and mesangial deposits in IgA nephropathy.
Mental Depreciation and Marginal Decision Making

We propose that individuals practice "mental depreciation," that is, they implicitly spread the fixed costs of their expenses over time or use. Two studies explore how people spread fixed costs on durable goods. A third study shows that depreciation can lead to two distinct errors in marginal decisions: First, people sometimes invest too much effort to get their money's worth from an expense (e.g., they may use a product a lot to spread the fixed expense across more uses). Second, people sometimes invest too little effort to get their money's worth: When people add a portion of the fixed cost to the current costs, their perceived marginal (i.e., incremental) costs exceed their true marginal costs. In response, they may stop investing because their perceived costs surpass the marginal benefits they are receiving. The latter effect is supported by two field studies that explore real board plan decisions by university students.
The Interface of IT Capabilities and Disruptive Innovations

The occurrence of disruptive innovations tends to challenge the fabric, structure and capability that define firms facing their threat. Prior research indicates that the capacity of a firm to restructure and reconfigure its resources to face/leverage such turbulent situations is dependent on how well it can orchestrate its capabilities. Although IT capability has been identified as one of the essential capability of today’s organisations, managers as well as researchers are yet to uncover the dynamics through which an organisation’s IT capability can be leveraged in disruptive innovation situations. This paper contributes to our understanding in this direction by conceptually exploring the different roles of an organisation's IT capabilitiy in disruptive innovation scenarios. The paper provides a synthesis of the current state of knowledge about both concepts and extends this to highlight the different dimensions through which prior IT capability research could contribute to our knowledge of disruptive innovations. We advance the theoretical concepts of “disrupt-ability and disruptability” as dual dimensions of IT capability in disruptive innovation scenarios, that underscore the capacity of an organization's IT capability to serve as an enabler, a sustainer or a barrier.
Fitting for smoothing: A methodology for continuous-time target track estimation

A preliminary framework for inferring continuous-time target trajectory (namely “track”) is given for a class of target tracking problems in which the target is subject to a rather smooth evolving process in time series, such as tracking passenger aircrafts or ships that have scheduled routes. As the core idea, the distant estimates given over time by a recursive estimator are `fitted' by using a function of continuous-time, which can be then used to infer the state for any time instants in the effective fitting period, either the past (like conventional smoothing, but curried out online) or the future (including long-term prediction). This regression analysis methodology, referred to as fitting for smoothing (F4S), also facilitates combating misdetection and outliers from which most existing tracking systems suffer. Simulations are provided to illustrate how it works and benefits in either cluttered or non-cluttered environments, with either a single target or multiple targets.
Toll‐Like Receptor 3: A Link between Toll‐Like Receptor, Interferon and Viruses

Production of type I interferon (IFN‐α/β) by virus‐infected cells is the central event in their antiviral immune responses. In mammalian cells, IFN‐α/β gene transcription is induced through distinct signaling pathways by viral infection or by treatment with double‐stranded (ds) RNA, which is an intermediate of virus replication. Toll‐like receptor 3 (TLR3) was found to recognize dsRNA and transmit signals to activate NF‐κB and the IFN‐β promoter. Recent identification of the TLR3‐adaptor protein and its downstream signaling molecules, which are involved in IFN‐α/β production, revealed a novel IFN‐inducing pathway for an anti‐viral immune response. Here, we summarize the current knowledge of TLR3‐mediated immune responses.
Storage Support for Data-Intensive Applications on Large Scale High-Performance Computing Systems

Many believe that the state-of-the-art yet decades old high-performance computing (HPC) storage would not meet the I/O requirement of the emerging exascale mainly due to the segregation of compute and storage resources. Indeed, our simulation predicts, quantitatively, that the efficiency and availability would go towards zero as the system scales approach exascale. This work proposes a new architecture with nodelocal persistent storage. Although collocating compute and storage has been widely leveraged in cloud computing, such a system never exists in HPC. We implement a system prototype, called FusionFS, with two major design principles: maximal metadata concurrency and optimal file write, both of which are crucial to HPC applications. We also discuss FusionFSs other integral features such as hybrid and cooperative caching, efficient data access to compressed files, space-economic data redundancy, lightweight provenance tracking, and integration with data management systems.
CoopSC: A Cooperative Database Caching Architecture

Semantic caching is a technique used for optimizing the evaluation of database queries by caching results of old queries and using them when answering new queries. CoopSC is a cooperative database caching architecture, which extends the classic semantic caching approach by allowing clients to share their local caches in a cooperative matter. Cache entries of all clients are indexed in a distributed data structure constructed on top of a Peer-to-Peer (P2P) overlay network. This distributed index is used for determining those cache entries that can be used for answering a specific query. Thus, this approach decreases the response time of database queries and the amount of data sent by database server, because the server only answers those parts of queries that are not available in the cooperative cache.
Cognitive neuropsychology has been, is, and will be significant to aphasiology

Background: In recent years some critical voices have been raised in regard to the significance of cognitive neuropsychology (CNP) to the study of brain and mind. Given the central role of language disorders in CNP research, it is time to consider the relevance of this research approach in aphasiology. Aims: We analyse the main points of criticism raised against the CNP research approach, evaluate the significance of this approach to the study of acquired language disorders, and make some suggestions concerning further development of the field. Main Contribution: The main points of criticism against CNP (reliance on single-case studies; single-minded hunt for dissociations; emptiness of theorising) have been important long-term concerns but do not take into account the fact that, during its history of circa four decades, the CNP approach has diversified. There are thus CNP studies that rely on case series analyses, focus on error analyses rather than mere dissociations, or employ computational modelling rather than the “boxes-and-arrows” models of the mental architecture. The CNP approach to cognition and its disorders is thus applicable to different research questions and theoretical stances, providing experimental rigour to single-case patient studies. With regard to clinical applications in aphasia diagnostics and treatment, the CNP approach provides a richer view on the strengths and weaknesses of a patient's cognitive-linguistic abilities. Conclusions: We believe that CNP case studies continue to be an important source of information for generating hypotheses and providing converging evidence for research on the mind and on the brain. However, there is a need for further research development especially in computational modelling of language processes, their impairments, and recovery. This research is expected to provide further benefit to clinical diagnostics and treatment of aphasia.
Reform ID-Achieving Proximate Node Anonymity in MOSNs

Mobile Opportunistic Social Networks(MOSNs) are the networks where the mobile devices interact or communicate with each other when they encounter each other for carrying out MOSN services such as file sharing in a proximate environment i.e., for communication when the devices(nodes) encounter in proximity. At present such services are provided but they come at cost of zero privacy because in already present methods when nodes encounter communication is done only when their Real Identities (RID) are shared which leads to privacy and security concerns. Anonymizing RIDs of proximate nodes(devices) solves such concerns. But for MOSN services to work, requires sharing of RIDs. Hereby we propose Reform ID which supports both anonymizing RIDs among proximate nodes and also collects RIDs based on encountering information (EnIn). For node anonymity, two confronting nodes communicate anonymously. After the nodes terminate the anonymous communication, encountering evidence(EV) is then encrypted and forwarded by them for collection of En-In.
Picture segmentation with introducing an anisotropic preliminary step to an MRF model with cellular neural networks

Due to the large computation power needed for Markovian random field (MRF) based image processing, new variations of the basic MRF model are implemented. The transportation of the model to the very fast cellular neural networks (CNN) gave new tasks and opportunities to improve the technique, since the CNN has a special local architecture. This CNN architecture can be implemented in real VLSI circuits of superior speed in image processing. A type of MRF image segmentation with modified metropolis dynamics (MMD) can be well implemented in the CNN architecture. In this paper we address the improvement of this existing CNN method by introducing anisotropic diffusion as the smoothing process in the model. We suggest that this new feature with the MRF representation will give a new approach to solving early vision problems in the future.
Adaptive modulation with imperfect channel information in OFDM

Adaptive modulation in OFDM is a means by which the OFDM transmitter adapts the subchannel bit and power allocation to the amplitude response of the frequency selective channel for improved performance in high data rate wireless communications. Previous studies demonstrating the performance gains of adaptive modulation have assumed the availability of perfect channel state information. This paper examines the impact on performance of an adaptive OFDM system due to imperfect channel information arising from realistic channel estimators and time-varying channels. The latter is of particular relevance to mobile wireless applications, for which OFDM has received much attention. The performance of a typical channel estimator is found to be sufficient to justify the use of adaptive modulation in wireless OFDM systems, but in mobile situations the effect of outdated channel information in the presence of quickly time-varying channels can be significant. The use of channel prediction is shown to mitigate the impact of outdated channel information and improve performance when the Doppler spread, or mobile velocity, is high.
Osteo-Odonto - patients with corneal blindness: A review

Anatomically, the cornea is the outermost layer of the eye and is primarily responsible for light refraction which allows for central and peripheral vision. Corneal diseases are among the major causes of global blindness, secondary to cataracts. This paper Keratoprosthesis (OOKP), which is a two stage procedure whereby dental and buccal tissue is auto transplanted into eye to serve as a synthetic cornea. Our purpose is to inform readers about the relevant anatomy, two-stage pr contraindications, complications, long of OOKP. The present manuscript art icles and case reports and the data was collected from 32 review articles and case reports.
Picking a human fall detection algorithm for wrist-worn electronic device

Nation aging raises a problem of elderly health care and this problem will only grow stronger next decades. Preventive and remote care for seniors come to the fore as the solution to lower the number of potential patients in clinics and to reduce the cost of medication in case of accidents by providing first aid in time. Human fall is a source of danger in any age and, especially for older people. It can become a cause of injury itself and it can also be a result of dangerous event in human organism. That's why human fall detection is significant. This paper describes a first phase of research dedicated to developing a fall detection algorithm for a wrist-worn wearable device. Comparison and analysis of existing algorithms and solutions on market is made. Hardware setup for human fall detection is built and tested. Live tests on volunteers are performed to evaluate accuracy of chosen type of algorithm. Also algorithm is tested during daily activity to determine most fall-similar actions, and work out a way to distinguish those from true fall.