Trivial Information Research Articles

Critical Analysis of Baidu's Business Strategy

People's life is inseparable from the Internet. However, network information is a huge collection of various information resources. Its quantity is growing rapidly, disorderly, quality varies, and there is no certain organization and control. This brings great inconvenience to people's search and acquisition of information. It is very necessary and important for all network users to accurately and efficiently obtain relevant and applicable information to their needs from massive amounts of information. Search engines can help users find information about themselves in trivial information. Baidu.com has become the leader of China's Internet competition in the world with its relatively conservative specialization strategy. Baidu's brilliance is innovative profit models, tailor-made development strategies, and clear marketing models. At the same time, there are also disadvantages that restrict its development. Thus, this article analyzes the impact of Baidu’s profit model, competitive advantage, shortcomings, shortcomings, and advertising models on business strategies by combing the previous literature.

Cognitive and metacognitive strategy use in poor comprehenders: an exploratory study

ABSTRACT Empirical research has shown that poor comprehenders use reading strategies less frequently than good comprehenders. Moreover, poor comprehenders tend to use simple strategies (e.g. slowing down reading when the text is hard to understand) instead of using more complex strategies (e.g. choosing between main and trivial information). The present study aimed to analyse the reading strategies used by poor (n = 37) and good comprehenders n = 141) in a sample of fifth grade Portuguese students. Results from multivariate analysis of covariance showed statistically significant differences in the use of reading strategies between the two subgroups of students, after controlling for the vocabulary. Specifically, good comprehenders reported to use significantly more strategies such as selecting relevant ideas, reviewing the text, evaluating comprehension after reading, forming pictures in mind and rereading parts of the text that are difficult to understand. Thus, the results suggested that good and poor comprehenders differed mainly in the use of more sophisticated cognitive and metacognitive strategies.

ВИРІШЕННЯ РЕГРЕСІЙНОЇ ПРОБЛЕМИ ДЕМОДУЛЯЦІЇ СИГНАЛІВ З АМПЛІТУДНОЮ МОДУЛЯЦІЄЮ БАГАТЬОХ СКЛАДОВИХ

The article is devoted to the consideration of AMBS features, highlighting the methodology of AMBS signal calculation for presentation in the form of a signal constellation and time graphs, and the use of calculated signals as input data for training a neural network that performs the task of signal demodulation. To represent sets of random values of different symbols of AMBS signals, a method was proposed, the essence of which is the use of Voronoi cells as a way of dividing the space between the points of the signal constellation, which is more efficient from a geometric point of view, compared to how signals are represented in trivial information transmission systems. The theoretical increase in the efficiency of the proposed method was calculated in comparison with the trivial approach assuming a higher efficiency of Voronoi cells as a way to divide the space between points. The described methods and techniques were embodied in the algorithm of the software product, which performs the task of forming the AMBS constellation, creating noisy variations of the signal around the points, recording these variations in a file, which is later used in the training of the neural network. The principle of operation of the software product based on previously formed algorithms is described, the algorithms themselves are described, their effectiveness is evaluated, the design decisions of the software product structure are explained, in particular, attention is paid to flexibility and the possibility of adjustment for specific cases. It is described with what data and in what form the created system operates. The efficiency of the created system was evaluated using relatively high values of added noise in the analyzed signal. Conclusions are drawn regarding ways to maximize system efficiency, and the dependence of accuracy on various model parameters is depicted. The algorithm for assessing the accuracy of the prediction of the neural network was formed, implemented in the form of a subroutine of the software product, the accuracy of the proposed system was evaluated, and conclusions were drawn about the work done.

PROVENANCE RESEARCH AND THE CONSORTIUM OF EUROPEAN RESEARCH LIBRARIES

The Consortium of European Research Libraries (CERL) is a membership organization which started in 1994 after the initiative of research libraries in many European countries. It is formed primarily by Library and Information professionals, which work to provide relevant data and offer meaning and context from their distributed collections. The focus of CERL is on manuscripts and printed books produced before the middle of the 19th century; it is committed on curating data sustainably, creating connections where possible and providing convenient access to data. Among its main resources are The Heritage of the Printed Book Database, the CERL Thesaurus, the Incunabula Short Title Catalogue, Material Evidence in Incunabula, the CERL Provenance Digital Archive, and much more. CERL creates digital resources of its own and offers a structured framework within which member institutions can deliver digital services, which they may not be able to deliver or sustain themselves. All these digital databases provide methods which can help the provenance research flourish, by bringing together collections, institutions, and other trivial information concerning early printed books and documents. Throughout this shared experience and collaboration, CERL can establish how collections reflect an illustration on how European history is inextricably interconnected intellectually, commercially, and politically.

Website Development to Display Crypto Coin Prices with Application Programming Interface

Cryptocurrency is one of the investment instruments and trading assets chosen by old and new traders today. Lots of people claim that the information from the platforms they use is just numbers or trivial information. However, many ordinary people who really need it are still confused about this information. In this study, an application programming interface was developed to help new traders using the instrument easier.

Enhancing Explainable Machine Learning by Reconsidering Initially Unselected Items in Feature Selection for Classification

Feature selection is a common step in data preprocessing that precedes machine learning to reduce data space and the computational cost of processing or obtaining the data. Filtering out uninformative variables is also important for knowledge discovery. By reducing the data space to only those components that are informative to the class structure, feature selection can simplify models so that they can be more easily interpreted by researchers in the field, reminiscent of explainable artificial intelligence. Knowledge discovery in complex data thus benefits from feature selection that aims to understand feature sets in the thematic context from which the data set originates. However, a single variable selected from a very small number of variables that are technically sufficient for AI training may make little immediate thematic sense, whereas the additional consideration of a variable discarded during feature selection could make scientific discovery very explicit. In this report, we propose an approach to explainable feature selection (XFS) based on a systematic reconsideration of unselected features. The difference between the respective classifications when training the algorithms with the selected features or with the unselected features provides a valid estimate of whether the relevant features in a data set have been selected and uninformative or trivial information was filtered out. It is shown that revisiting originally unselected variables in multivariate data sets allows for the detection of pathologies and errors in the feature selection that occasionally resulted in the failure to identify the most appropriate variables.

Uni-CARE: Universal Interface via Cloud Archive Repository Express

Uni-CARE—Universal interface has emerged from Cloud Archive Repository Express that utilizes algorithmic machine learning as a “fastlane” without explicitly coding required to bridge the gap between DATA and wiseCIO. Uni-CARE incorporates DATA of digital archiving & trans-analytics with wiseCIO of web-based intelligent service into a triad for content management and delivery (CMD) that orchestrates Anything as a Service (XaaS) by using mathematical and computational solutions to cloud-based distributed problems. This article presents automated universal interface design through cloud archival repository express in CARE for “DNA-like” ingredients with trivial information eliminated through deep learning. Conceptually, Uni-CARE innovates with algorithmic machine learning and introduces express tokens for information interchange (eTokin) to promote seamless intercommunications among the CMD triad and semantic enrichment of digital archiving via online analytics for XaaS. Specifically, Uni-CARE collaborative with DATA and wiseCIO empowers ordinary users to be UNIQ professionals: such as ubiquitous manager on content management and delivery, novel designer on universal interface and user-centric experience, intelligent expert for business intelligence, and quinary liaison with Anything orchestrated as a Service. The novel designer enabled by Uni-CARE automates layouts of control items, containers and/or folders for hierarchical “in-&-out” interactivity, and multi-aspects through bulletins and/or tabs for contextual “self-paced” spontaneity with individual entities (as bodies) extendable/shrinkable. Furthermore, the CMD triad collaborative with DATA and wiseCIO as a whole harnesses rapid prototyping for human-computer interfacing and propels cohesive assembly from Anything orchestrated as a Service. More importantly, Uni-CARE enables instant typing online publishing over DATA in eTokin, efficient presentation to end-users with diligent intelligence delivered for business, education, and entertainment (iBEE) on wiseCIO through highly robotic process automation.

Online Neighborhood Networks: The Relationship Between Online Communication Practices and Neighborhood Dynamics

This article builds upon communication infrastructure theory and investigates how communication practices on online neighborhood networks (ONNs) relate to the social cohesion of neighborhood communities. Specifically, we study the hyperlocal social media platform Hoplr, which provides ad-free ONNs in which neighbors can communicate with one another. Local governments can subscribe to Hoplr to communicate with their residents and engage them for community and public participation purposes. This study is based on an online survey of Hoplr members (N = 3,055) from 150 randomly selected ONNs. Social cohesion is disentangled as a combination of social support, a sense of community, reciprocal exchange, and social trust. We investigated social cohesion differences at the neighborhood level in relation to self-reported types of ONN communication practices (shared interest, supportive communication, and both tangible and informational support mobilization). The results reveal the limited value of quantified behavioral data to explain differences in neighborhood social cohesion. However, interesting patterns are revealed between different communication practices and neighborhood social cohesion, such as the importance of trivial storytelling and information exchange practices for enhancing trust, reciprocal support, and a sense of community. At the same time, a reversed relation appears when ONNs are considered explicit information exchange platforms. With these insights, we enhance the theoretical understanding of ONNs in relation to neighborhood social cohesion and within a broader repertoire of neighborhood communication infrastructures.

Molecular and immunological characterization of cysteine protease from Phaseolus vulgaris and evolutionary cross-reactivity.

A commonly consumed legume in India, the kidney bean (Phaseolus vulgaris) is associated with allergy. We report molecular and immunological characterization of cysteine protease allergen and its cross-reactivity. In silico allergenicity assessment and phylogenetic analysis of kidney bean cysteine protease showed significant sequence homology (upto 67%) with allergens from kiwi, papaya, soybean, ragweed pollen and mites. Physicochemical properties and motif-analysis depicted cysteine protease as probable allergen. Multiple sequence alignment and phylogenetic analysis indicated structural conservation between kidney bean and homologous cysteine protease sequences. The gene was cloned, expressed and affinity purified. Cysteine protease was resolved at 42 kDa and exhibited high IgE binding (up to 89%) with hypersensitive sera. Cysteine protease showed functional property on cross-linking IgE receptors and upregulated expression of CD203c on activated basophils. In inhibition studies, 8.4ng of cysteine protease was required for 50% self-inhibition, whereas significant inhibition was also observed with kidney bean (52 ng), black gram (155 ng), chick pea (437 ng), mesquite pollen (36 ng), house dust mite (64.85 ng), Alternaria alternata (78.8ng) and Curvularia lunata (73.6ng) extracts. ConSurf analysis indicated conserved active site and catalytic residues in mature domain among proteases from legumes, fruits, pollens, mites and fungus. In summary, P. vulgaris cysteine protease was molecularly characterized having functional activity. This study demonstrated, cross-reactivity between food and aeroallergens based on evolutionary conservancy that showed its clinical importance as cross-reactive allergen. PRACTICAL APPLICATIONS: Adaptation of sustainable lifestyle has led to a surge in consumption of plant-based foods especially legumes. Their high nutritional content lowers the risk of developing cardiovascular diseases, diabetes, obesity, and stroke. Kidney beans, a commonly consumed legume in Indian subcontinent, have a potential to be used as nutraceutical and functional food. Despite its alimentary nature, it elicits allergic reactions. Being a major sensitizer, trivial information regarding its allergic components has led to an urgent need for exploring its allergen repertoire. Our study reported biochemical and immunological characterization of its major cysteine protease allergen. Cysteine proteases are major cross-reactive allergens from insects, fruits and fungal sources. Identification and molecular characterization of such immunodominant allergens by RDT offers the prospect of using recombinant proteins for accurate diagnosis and therapeutic purposes. This study suggests that a potential major cross-reactive allergen may aid in developing allergy management interventions for a wide range of allergenic sources.

Joint projection learning and structured analysis-synthesis dictionary pair learning for pattern classification

Analysis-synthesis dictionary pair learning has attracted much attention in the field of pattern classification. To reduce the negative effect of trivial information contained in raw training samples and improve the computation efficiency, most existing dictionary pair learning methods first learn a projection matrix to project raw training samples into a low-dimensional subspace and then use the dimensionality-reduced samples for dictionary pair learning. However, the separation of projection learning (PL) and dictionary pair learning may make the dimensionality-reduced training samples not fit well for dictionary pair learning. To address this issue, we proposed a joint PL and structured analysis-synthesis dictionary pair learning (PLSDPL) method for pattern classification. Specifically, PLSDPL integrates PL and dictionary pair learning into a unified framework, so the dimensionality-reduced training samples are more suitable for the structured analysis-synthesis dictionary pair learning, thus making the learned analysis-synthesis dictionary pair have powerful discrimination capability. In addition, PLSDPL also imposes a low-rank constraint on each synthesis sub-dictionary to reduce the negative effect of noise contained in training samples. Experimental results on several image datasets show that PLSDPL is effective for pattern classification tasks.

CARE: Cloud Archival Repository Express via Algorithmic Machine Learning

CARE—Cloud Archive Repository Express has emerged from algorithmic machine learning, and acts like a “fastlane” to bridge between DATA and wiseCIO where DATA stands for digital archiving & trans-analytics, and wiseCIO for web-based intelligent service. CARE incorporates DATA and wiseCIO into a triad for content management and delivery (CMD) to orchestrate Anything as a Service (XaaS) by using mathematical and computational solutions to cloud-based problems. This article presents algorithmic machine learning in CARE for “DNA-like” ingredients with trivial information eliminated through deep learning to support integral content management over DATA and informative delivery on wiseCIO. In particular with algorithmic machine learning, CARE creatively incorporates express tokens for information interchange (eTokin) to promote seamless intercommunications among the CMD triad that enables Anything as a Service and empowers ordinary users to be UNIQ professionals: such as ubiquitous manager on content management and delivery, novel designer on universal interface and user-centric experience, intelligent expert for business intelligence, and quinary liaison with XaaS without explicitly coding required. Furthermore, CMD triad harnesses rapid prototyping for user interface design and propels cohesive assembly from Anything orchestrated as a Service. More importantly, CARE collaboratively as a whole promotes instant publishing over DATA, efficient presentation to end-users via wiseCIO, and diligent intelligence for business, education, and entertainment (iBEE) through highly robotic process automation.

Towards real-time privacy-preserving video surveillance

Video surveillance on a massive scale can be a vital tool for law enforcement agencies. To mitigate the serious privacy concerns of wide-scale video surveillance, researchers have designed secure and privacy-preserving protocols that obliviously match live feeds against a suspects’ database. However, existing approaches are very expensive in terms of computation and communication costs and, as a result, they do not scale well for ubiquitous deployment. To this end, we propose a general framework for privacy-preserving identification that operates by storing an encrypted version of the suspects’ database at the video cameras. We show that this approach (i) reduces the protocol to a single round of communication between the camera and the server and (ii) speeds up the computation times significantly through the use of input-independent precomputations. We apply our framework to two practical use-cases, namely, face and license plate number recognition. In addition to the identification result, our face recognition protocol discloses some trivial information to the database server; however, this information is not sufficient for the server to infer any meaningful characteristics about the underlying individuals. On the other hand, the license plate recognition protocol is provably secure and can also handle minor character recognition errors that often occur in such systems. We implemented working prototypes of both surveillance systems and our experimental results are very promising. In the case of face recognition, and for a database of 100 suspects, the online computation time at the camera and the server is 155 ms and 34 ms, respectively, while the online communication cost is only 12 KB. Similarly, for a database of 3000 entries, license plate recognition requires only 232 ms and 75 ms at the camera and the server, respectively, while the online communication cost is 375 KB.

ANMCO Position paper: Prognostic and therapeutic relevance of non-obstructive coronary atherosclerosis

Atherosclerosis often affects the coronary arterial tree. Very often the disease does not translate in significant narrowing of the vessels, thus determining only a non-obstructive disease. This condition that is described as non-obstructive coronary artery disease (NobsCAD) should be distinguished from the absence of disease (i.e. smooth coronary arteries) as it carries a specific prognostic value. The detection and reporting of NobsCAD should prompt preventive measures that can be individualized upon the degree of the underlying burden of disease. The accompanying clinical condition, the other cardiovascular risk factors present, and the description of the severity and extent of NobsCAD should provide the framework for an individualized treatment that should also consider the best available evidences and scientific guidelines. The description of NobsCAD represents an important information to be collected whenever a coronary angiogram (both invasive and non-invasive) is performed. Treating the patient according to the presence and extent of NobsCAD offers prognostic benefits well beyond those offered by considering only the traditional cardiovascular risk factors. In order to reach this goal, NobsCAD should not be confused with the absence of coronary atherosclerosis or even ignored when detected as if it was a trivial information.

ANMCO POSITION PAPER: Prognostic and therapeutic relevance of non-obstructive coronary atherosclerosis.

Atherosclerosis often affects the coronary arterial tree. Frequently the disease does not translate in significant narrowing of the vessels, thus determining only a non-obstructive disease. This condition that is described as non-obstructive coronary artery disease (NobsCAD) should be distinguished from the absence of disease (i.e. smooth coronary arteries) as it carries a specific prognostic value. The detection and reporting of NobsCAD should prompt preventive measures that can be individualized upon the degree of the underlying burden of disease. The accompanying clinical condition, the other cardiovascular risk factors present, and the description of the severity and extent of NobsCAD should provide the framework for an individualized treatment that should also consider the best available scientific evidence and guidelines. The description of NobsCAD represents important information to be collected whenever a coronary angiogram (both invasive and non-invasive) is performed. Treating the patient according to the presence and extent of NobsCAD offers prognostic benefits well beyond those offered by considering only the traditional cardiovascular risk factors. In order to reach this goal, NobsCAD should not be confused with the absence of coronary atherosclerosis or even ignored when detected as if it was a trivial information to provide.

Viral News on the Coronavirus: Does it Contribute to Health Communication?

From a century to a decade ago, the news media played a crucial role in providing the public with valuable in­formation, especially during a crisis. However, the advent of social media has brought about a change in ac­cess and distribution of the news and this may have resulted in less effec­tive health communication during this global coronavirus pandemic. These days, social media can have a great­er public reach and therefore, be the best tool to disseminate information. At the same time, there is the ques­tion of whether the important or trivial information is being shared. The aim of this paper is to explore the role of social media in providing the public with important information during the height of the coronavirus pandemic. Using Great Britain as a case study, the research analysed the kind of content on the coronavirus that had gone vi­ral in online news sources in the Unit­ed Kingdom to determine whether the information that was being shared contributed or not to effective health communication. Keywords: news, viral news, online media, journalism, crisis communica­tion, coronavirus.

Learning long-term temporal contexts using skip RNN for continuous emotion recognition

BackgroundContinuous emotion recognition as a function of time assigns emotional values to every frame in a sequence. Incorporating long-term temporal context information is essential for continuous emotion recognition tasks. MethodsFor this purpose, we employ a window of feature frames in place of a single frame as inputs to strengthen the temporal modeling at the feature level. The ideas of frame skipping and temporal pooling are utilized to alleviate the resulting redundancy. At the model level, we leverage the skip recurrent neural network to model the long-term temporal variability by skipping trivial information for continuous emotion recognition. ResultsThe experimental results using the AVEC 2017 database demonstrate that our proposed methods are beneficial to a performance improvement. Further, the skip long short-term memory (LSTM) model can focus on the critical emotional state when training the models, thereby achieving a better performance than the LSTM model and other methods.

Domain-Oriented Semantic Embedding for Zero-Shot Learning

Zero-Shot Learning (ZSL) targets to recognize images from new classes. Existing methods focus on learning a projection function to associate the visual features and category descriptions in the seen domain, which is directly transferred to the unseen domain. However, due to the inherent domain shift, a single shared projection cannot fully capture the domain difference and similarity, thereby making the unseen samples tend to be recognized as seen categories. In this paper, we propose a novel Domain-Oriented Semantic Embedding (DOSE) network that learns specific projections for different domains to better capture the domain characteristics for unbiased ZSL. Besides a domain-shared projection, DOSE learns two auxiliary domain-specific sub-projections to model the semantic-visual association in respective seen and unseen domains. Specifically, the domain-specific projections are learned in a cycle consistency way to capture domain characteristics, and a domain division constraint is developed to penalize the margin between two domain embeddings. Furthermore, to boost semantic-visual association, a semantic-visual dual attention module is designed to automatically remove trivial information in both visual and semantic embeddings under a co-guidance learning manner. Experiments on four public benchmarks prove that the proposed DOSE is robust to the domain shift problem in ZSL and obtains an averaged 5.6% improvement in terms of harmonic mean.

Interpretation of the category of “complex” in terms of dialectical positivism

The interpretation of the category of complex is given on the basis of dialectical positivism, in the framework of which the principle of dialectic symmetry is formulated. It, in turn, is based on the consideration of matter and information as paired dialectic categories. In accordance with this principle, information objects form a hierarchy similar to that which corresponds to different levels of matter organization (mechanical, chemical, biological, social). As “complex”, one should interpret the system that is the medium for the information object at least one level higher than the level related to trivial information (that is, one that is inextricably linked with the object itself). This level is the level corresponding to estranged information (simplifying information about a certain object recorded on another medium).

Semantic Image Segmentation with Improved Position Attention and Feature Fusion

Encoder–decoder structure is an universal method for semantic image segmentation. However, some important information of images will lost with the increasing depth of convolutional neural network (CNN), and the correlation between arbitrary pixels will get worse. This paper designs a novel image segmentation model to obtain dense feature maps and promote segmentation effects. In encoder stage, we employ ResNet-50 to extract features, and then add a spatial pooling pyramid (SPP) to achieve multi-scale feature fusion. In decoder stage, we provide an improved position attention module to integrate contextual information effectively and remove the trivial information through changing the construction way of attention matrix. Furthermore, we also propose the feature fusion structure to generate dense feature maps by preforming element–wise sum operation on the upsampling features and corresponding encoder features. The simulation results illustrate that the average accuracy and mIOU on CamVid dataset can reach 90.7% and 63.1% respectively. It verifies the effectiveness and reliability of the proposed method.

How decisions and the desire for coherency shape subjective preferences over time

Recent findings suggest a bidirectional relationship between preferences and choices such that what is chosen can become preferred. Yet, it is still commonly held that preferences for individual items are maintained, such as caching a separate value estimate for each experienced option. Instead, we propose that all possible choice options and preferences are represented in a shared, continuous, multidimensional space that supports generalization. Decision making is cast as a learning process that seeks to align choices and preferences to maintain coherency. We formalized an error-driven learning model that updates preferences to align with past choices, which makes repeating those and related choices more likely in the future. The model correctly predicts that making a free choice increases preferences along related attributes. For example, after choosing a political candidate based on trivial information (e.g., they like cats), voters' views on abortion, immigration, and trade subsequently shifted to match their chosen candidate.