Different Kinds Of Information Research Articles

Criminal Investigation System

The Criminal Investigation System with represents a significant advancement in the field of law enforcement technology. By leveraging the power of data and predictive analytics, it empowers investigators to solve cases faster, identify suspects more accurately, and ultimately, enhance public safe. Based on the historical crime data suggest that Hashing algorithm. The digital system functions by gathering and classifying different kinds of information, such as crime scene reports, witness statements, forensic evidence, and suspect profiles. It facilitates the work of an officer by allowing him to compile new, vital case details on demand and to monitor the case's progress online. Keywords: Criminal Investigation, System Law, enforcement technology ,Data Predictive, analytics, Solve cases, faster, Identify suspects public safety, Historical crime , data Hashing algorithm , Gathering information Classifying , information Crime ,scene reports, Witness statements ,Forensic evidence ,Suspect profiles , Officer.

MIRROR MAGIC: REFLECTING INNOVATION FOR EVERYDAY CONVENIENCE USING RASPBERRY PI

Smart is not an adjective anymore, Now it is an used for anything that improves functioning in multi-device environment, by simplifying our daily tasks by speeding up the information flow and allowing us to do incredible tasks in the twinkling of an eye. A smart mirror is a two-way with an electronic display behind the glass. The display can show the viewer different kinds of information in the form of widgets, such as weather, time and date and news updates. This project would be useful for busy individuals that want to multitask and stay informed while on the go.The basic design of smart mirror starts with two-way glass is the recommended types as it lets the graphics on the display come through clearer. Upon uploading the code to the device from android studio, all the prototypes and design that we have installed in the design module will be displayed. The electrical component to our project was a simple 19mm Computer monitor mounted to the back of the see through glass. In addition to, installation of PIR on and off sensor to different motion atmospheres is added. And an LED strip around the smart mirror comes with multiple luminosity levels from cool to warm light. The Google voice assistant feature has been implemented to control device with spoken commands. All of this works with Raspberry pi 3 A+ model which is a single board computer. To install the raspbian OS a micro SD card is needed prepared with an OS image all that will be running on the mirror will be coded on the same device at least a keyboard, mouse are required. One mode of interaction with smart is through the microphones. All the coding are written in python which most popular in the Raspberry pi community and it has all the libraries Mirror OS is the software we created for the smart mirror’s interface and it runs on the Raspbian (LINUX) ontop if the electron. The device must be connected to the Wi-fi in order to gather the data through online services.When every device such as phone, watch, TV, speaker has their smart versions, it doesn’t come as a surprise that mirrors they become smart too. KEYWORDS: IOT, PIR, RASPBERRY-PI, RASPBIAN OS, LINUX

Exploring explainable AI: category theory insights into machine learning algorithms

Explainable artificial intelligence (XAI) is a growing field that aims to increase the transparency and interpretability of machine learning (ML) models. The aim of this work is to use the categorical properties of learning algorithms in conjunction with the categorical perspective of the information in the datasets to give a framework for explainability. In order to achieve this, we are going to define the enriched categories, with decorated morphisms, LearnLearn , ParaPara and MNetMNet of learners, parameterized functions, and neural networks over metric spaces respectively. The main idea is to encode information from the dataset via categorical methods, see how it propagates, and lastly, interpret the results thanks again to categorical (metric) information. This means that we can attach numerical (computable) information via enrichment to the structural information of the category. With this, we can translate theoretical information into parameters that are easily understandable. We will make use of different categories of enrichment to keep track of different kinds of information. That is, to see how differences in attributes of the data are modified by the algorithm to result in differences in the output to achieve better separation. In that way, the categorical framework gives us an algorithm to interpret what the learning algorithm is doing. Furthermore, since it is designed with generality in mind, it should be applicable in various different contexts. There are three main properties of category theory that help with the interpretability of ML models: formality, the existence of universal properties, and compositionality. The last property offers a way to combine smaller, simpler models that are easily understood to build larger ones. This is achieved by formally representing the structure of ML algorithms and information contained in the model. Finally, universal properties are a cornerstone of category theory. They help us characterize an object, not by its attributes, but by how it interacts with other objects. Thus, we can formally characterize an algorithm by how it interacts with the data. The main advantage of the framework is that it can unify under the same language different techniques used in XAI. Thus, using the same language and concepts we can describe a myriad of techniques and properties of ML algorithms, streamlining their explanation and making them easier to generalize and extrapolate.

Seeing With Your Tongue? How The Brain Uses Multiple Senses At Once

Humans have seven main senses, each of which has its own unique sensory organs. These organs relay different kinds of information about the outside world (sights, sounds, etc.) to the brain. The information the sensory organs relay is then combined together throughout the brain. This combining of information from multiple senses is known as multisensory integration, and this is how healthy brains operate. However, for some people with a condition called synesthesia, activating a single sense (for instance, sight alone) can trigger multiple sensations such as sight and sound. Researchers are investigating how to use multisensory integration to help patients who have lost the ability to use one of their senses. For example, in blind patients, information from a healthy sense like the sense of touch could be used to make up for decreased vision. This is an exciting area of current and future research.

Less vigilant in the mobile era? A comparison of information processing on mobile phones and personal computers

Do people process information differently on mobile phones compared to computers? We investigate this question by conducting two online field experiments. We randomly assigned participants to use their mobile phones or personal computers (PCs) to process different kinds of information. Study 1 ( N = 116) discovered that participants using mobile phones process emails more efficiently (i.e., spend less time) than those using PCs. Study 2 ( N = 241) extended this to online deceptive content and found that individuals using mobile phones, especially habitual users, are more efficient, but engage in less information processing, are less attentive and less vigilant about misinformation, compared to those using PCs. However, the latter are more likely to succumb to phishing emails by clicking on malicious links. We discuss theoretical implications for information processing across media devices and practical implications for combating misinformation and cybersecurity attacks.

The Term "Regulatory Guillotine" in the Legal Discourse

The article studies the peculiarities of coining a new term "regulatory guillotine" in the modern Russian language. It describes the semantic, syntagmatic and pragmatic features of this term. It has been established that at the initial stage of its entry into the legal discourse, the term "regulatory guillotine" is characterized by high variation. Here are revealed its semantic, grammatical and graphic variants. Based on comparative analysis of various definitions of the term "regulatory guillotine", as well as on its syntagmatics in legal discourse, the author concludes it necessary to amend the definition of this term. A new term can be prospectively developed in close connection with the field knowledge and traditional use of the Russian language in texts of official communication. When describing the syntagmatics of the term in the academic legal discourse, the author points out a violation of the norms of lexical compatibility of the modern Russian literary language. The syntagmatics of the term "regulatory guillotine" in the Russian legal discourse reflects at the linguistic level the peculiarities of the correlation of the general and special consciousness of lawyers. It is also established that in the processes of functioning of the term "regulatory guillotine", quotation marks convey a complex mix of different kinds of information. The use of the term "regulatory guillotine" in the legal text with quotation marks and without quotation marks expresses a certain pragmatic meaning of the attitude of the subject of speech to the linguistic sign. Quotation marks implement both figurative and metalinguistic functions, and are also a means of adapting a metaphor-term to legal discourse.

Person Re-Identification Using Local Relation-Aware Graph Convolutional Network.

Local feature extractions have been verified to be effective for person re-identification (re-ID) in recent literature. However, existing methods usually rely on extracting local features from single part of a pedestrian while neglecting the relationship of local features among different pedestrian images. As a result, local features contain limited information from one pedestrian image, and cannot benefit from other pedestrian images. In this paper, we propose a novel approach named Local Relation-Aware Graph Convolutional Network (LRGCN) to learn the relationship of local features among different pedestrian images. In order to completely describe the relationship of local features among different pedestrian images, we propose overlap graph and similarity graph. The overlap graph formulates the edge weight as the overlap node number in the node's neighborhoods so as to learn robust local features, and the similarity graph defines the edge weight as the similarity between the nodes to learn discriminative local features. To propagate the information for different kinds of nodes effectively, we propose the Structural Graph Convolution (SGConv) operation. Different from traditional graph convolution operations where all nodes share the same parameter matrix, SGConv learns different parameter matrices for the node itself and its neighbor nodes to improve the expressive power. We conduct comprehensive experiments to verify our method on four large-scale person re-ID databases, and the overall results show LRGCN exceeds the state-of-the-art methods.

Media Literacy Formation of Non-journalism Students

This article is devoted to the issue of choosing a methodology for the formation of media literacy among students of non-core areas of training. In the world of post-truth, where you can’t trust anyone, especially your own feelings, feelings and emotions, it is necessary to develop the competence of media literacy. The purpose of this study is to describe the methods of developing media literacy among students of non-core specialties. Research tasks set by the author: 1) to review the literature on the topic; 2) to characterize the methods of forming media literacy among students of non-core specialties; 3) give as an example task from the author’s methods. Is it possible to form critical thinking as the basis of media literacy, to learn to distinguish real news from fake news, to check information, to resist the manipulative influence of the media? The author of the article answers in the affirmative. This is a long and painstaking job. Media literacy is not just a skill that we acquire in order to pass an exam or get a credit. Media literacy is as much a skill as being able to read and write. He is with us forever, he is necessary and vital. To become media literate, it is necessary to learn to distinguish between different kinds of information, to understand media, to know their typology, to be able to create and interpret media text, to use information and communication devices. For this purpose, the author chose a problem-based approach and creative tasks that contribute to the formation of media literacy among students of non-core areas of study.

Fecal Impairment Framework, A New Conceptual Framework for Assessing Fecal Contamination in Recreational Waters.

Fecal pollution of surface water is a pervasive problem that negatively affects waterbodies concerning both public health and ecological functions. Current assessment methods monitor fecal indicator bacteria (FIB) to identify pollution sources using culture-based quantification and microbial source tracking (MST). These types of information assist stakeholders in identifying likely sources of fecal pollution, prioritizing them for remediation, and choosing appropriate best management practices. While both culture-based quantification and MST are useful, they yield different kinds of information, potentially increasing uncertainty in prioritizing sources for management. This study presents a conceptual framework that takes separate human health risk estimates based on measured MST and E. coli concentrations as inputs and produces an estimate of the overall fecal impairment risk as its output. The proposed framework is intended to serve as a supplemental screening tool for existing monitoring programs to aid in identifying and prioritizing sites for remediation. In this study, we evaluated the framework by applying it to two primarily agricultural watersheds and several freshwater recreational beaches using existing routine monitoring data. Based on a combination of E. coli and MST results, the proposed fecal impairment framework identified four sites in the watersheds as candidates for remediation and identified temporal trends in the beach application. As these case studies demonstrate, the proposed fecal impairment framework is an easy-to-use and cost-effective supplemental screening tool that provides actionable information to managers using existing routine monitoring data, without requiring specialized expertize.

Beyond Logography


 
 
 Zhuānzì 專字 are often defined as graphs representing a “special meaning” of a word, manifest in the choice (i.e., addition or alteration) of a semantic component. This article approaches the zhuānzì phenomenon in the broader context of the logographic nature of the Chinese writing system. It first identifies and discusses several thematic categories of zhuānzì, including both historical examples from unearthed and transmitted corpora and instances from the Chinese script as used in the linguistic environment of modern Mandarin. The article then seeks to compare and describe different kinds of information conveyed by different zhuānzì and suggests a terminological shift toward defining zhuānzì as “representing a subset of the word’s denotation,” a phrasing more suited to the complex reality of the phenomenon in question.
 
 

Identifying Plants as a Process of Cultural Cognition: Comparing Knowledge Production and Communities of Practice in Modern Botanical Science and Nuaulu Ethnobotany

We all seek to identify plants in our ordinary lives, or as professionals, yet what we mean by ‘identifications’ and our intentions in seeking them are not always the same. Moreover, the ‘identifications’ we achieve are often subject to disagreement. This paper compares the practices of contemporary professional taxonomists in producing herbarium reference collections, and plant naming among Nuaulu subsistence cultivators in eastern Indonesia. I examine how these communities of practice differ as groups and among themselves in the identifications they make of plants. I argue that the differences between them arise from the way material presents itself in radically different socio-cultural contexts, and the purposes for which the identifications are made. Differences between the groups arise from the ways individuals prioritise different kinds of information as it becomes available. Ethnobotanists often seek to translate between different worlds of identification by seeking one-to-one correspondences between scientific and local categories that we describe as taxa, but sometimes fail because the material used to identify plants, and the purposes of identification, are so different. I conclude by asking how intra-cultural and cross-cultural translation might operate in in-between hybrid spaces, such as para-taxonomy, where different assumptions and practices overlap or collide.

Searching for ESG Information: Heterogeneous Preferences and Information Acquisition

We study how environmental, social, and governance (ESG) investing reshapes investors’ information-searching decisions. We develop a rational expectations equilibrium (REE) with endogenous information-searching decisions, in which traditional and green investors have distinct preferences for monetary and non-monetary risks. These investor groups seek different information from a security’s price. The heterogeneity in price interpretation makes investors with heterogeneous preferences focus more on the same risk component that is less represented in the price. Furthermore, trading intensities respond asymmetrically to different kinds of information. Traditional and green investors trade similar information in opposite ways. As more traditional investors switch to green trading, the price is more (less) informative about monetary (non-monetary) risks.

Study on Tech Pack of Different Foreign Buyers to Facilitate Different Sectional Garments Merchandizer

The research team has made it easier for the industry merchandiser to understand the buyers after they change jobs in this study. Different kinds of tech pack have been gathered for this study directly from the business. Tech Pack has gathered two different kinds of information: primary and secondary. Data that has been entered for analysis after reviewing the tech pack is called secondary data. In the first segment, 30 foreign consumers were examined, along with information about their home countries, designers, and product categories. Size charts, including allowances, size codes, product sizes, and the rationale for designating this kind of code in the second segment, have all been examined in this sector. The final stages of this study have included an analysis of the major, minor, critical, and positioning defects points used by various buyers. Finally, the research team has provided recommendations for buyers, factory owners, and future productivity of the industry's merchandising team.

Inferring the decision rules that drive co-foraging affiliations in wild mixed-species parrot groups.

Animals gathered around a specific location or resource may represent mixed-species aggregations or mixed-species groups. Patterns of individuals choosing to join these groups can provide insight into the information processing underlying these decisions. However, we still have little understanding of how much information these decisions are based upon. We used data on 12 parrot species to test what kind of information each species may use about others to make decisions about which mixed-species aggregations to participate in. We used co-presence and joining patterns with categorization and model fitting methods to test how these species could be making grouping decisions. Species generally used a simpler lower-category method to choose which other individuals to associate with, rather than basing these decisions on species-level information. We also found that the best-fit models for decision-making differed across the 12 species and included different kinds of information. We found that not only does this approach provide a framework to test hypotheses about why individuals join or leave mixed-species aggregations, it also provides insight into what features each parrot could have been using to make their decisions. While not exhaustive, this approach provides a novel examination of the potential features that species could use to make grouping decisions and could provide a link to the perceptive and cognitive abilities of the animals making these minute-by-minute decisions. This article is part of the theme issue 'Mixed-species groups and aggregations: shaping ecological and behavioural patterns and processes'.

Synthetic polymers as bone engineering scaffold

AbstractDamage or loss of the bone tissue leads to mobility decline contributing to one of the major issues of human well‐being. Tissue engineering is used to recover fractures and damaged parts of the bone tissue. Various biomaterials and scaffolds have been evaluated for regeneration of hard tissues, however, polymers are the most frequently required biomaterials for the improvement of synthetic bone scaffolds due to their suitable mechanical properties and similar degradation rates to the proteins in hard and soft tissues. Synthetic polymeric materials in bone replacement have several advantages because their physical characteristics can be designed according to their application and their composition can be changed easily. On the other hand, the high adaptability, tenability, and biocompatibility of synthetic materials have received considerable acceptance in the field of tissue engineering. This article presents an update on materials for the fabrication of scaffolds in bone tissue engineering; in addition, we provide information of different kinds of scaffolds and their application usage for hard tissue regeneration.

CURATING VIRALITY: EXPLORING CURATED LOGICS WITHIN #BLACKLIVESMATTER ON TWITTER

This study investigates the information sharing behavior of different levels of Twitter influencers within the context of the #BlackLivesMatter social movement and its related discussions #AlllivesMatter and #BlueLivesMatter during the 2-week period around Derek Chauvin’s trial. Using qualitative content analysis and quantitative machine learning methods, we analyzed over one million retweets to test if different levels of influencers tend to spread different kinds of information in the discussions around #All/Black/BlueLivesMatter on Twitter. We found out that different levels of influencers tend to spread different information within and between the #All/Black/BlueLivesMatter, and we offer some explanations through the lens of curation logics. We suggest that different levels of influencers may be exposed to different incentives, and be facing different social norms, which leads to different information behaviors. This research contributes to updating the theory of curated logics, virality, and influencers, as well as provides empirical data for the discussions of the #BlackLivesMatter social movement and its related discussions of #AllLivesMatter and #BlueLivesMatter.

Assessing long-term climate change impact on spatiotemporal changes of groundwater level using autoregressive-based and ensemble machine learning models

To evaluate the long-term climate change impacts on groundwater fluctuations of the Ardabil plain, Iran, a groundwater level (GWL) modeling was proposed in this study. Accordingly, the outputs of Global Climate Models (GCMs) under the sixth report of Coupled Model Intercomparison Project (CMIP6) and future scenario of the Shared Socioeconomic Pathway 5-8.5 (SSP5-8.5), were used as climate change forcing to the Machine learning (ML) models. The GCM data were first downscaled and projected for the future via Artificial Neural Networks (ANNs). Based on the results, compared to 2014 (the last year of the base period), the mean annual temperature may increase by 0.8 °C per decade until 2100. On the other hand, the mean precipitation may decrease by about 8% compared to the base period. Then, the centroid wells of clusters were modeled by Feedforward Neural Network (FFNN), examining different input combination sets to simulate both autoregressive and non-autoregressive models. Since each of the ML models can extract different kinds of information from a dataset, after finding the dominant input set via FFNN, GWL time series were modeled via various ML methods. The modeling results indicated that the ensemble of shallow ML models could lead to a 6% more accurate outcome than the individual shallow ML models, and 4% than the deep learning models. Also, the simulation results for future GWLs illustrated that temperature can impact groundwater oscillations directly, whereas precipitation may not have uniform impacts on the GWLs. The uncertainty evolving in the modeling process was quantified and observed to be in acceptable range. Modeling results showed that the main reason for the declining GWL in the Ardabil plain could be primarily linked to the excessive exploitation of the water table, while climate change impact could be also notable.

Orientation determination of nitrogen-vacancy center in diamond using a static magnetic field

Nitrogen-vacancy (NV) centers in a bulk diamond are often employed to realize measurement of multiple physical quantities, which depends on orientation information of NV axis. We report a fast and effective method to determine the orientation of NV axis with the aid of a static magnetic field. By measuring the optically detected magnetic resonance spectra, we can precisely extract the polar angle information between the NV axis and the known magnetic field. Combining with the polar angle information of different kinds of NV centers, we employ the Nelder–Mead algorithm to get the optimal solution of the orientation of NV axis. This method is simple and efficient, and is easily applied in NV-based quantum sensing.

Modality-DTA: Multimodality Fusion Strategy for Drug-Target Affinity Prediction.

Prediction of the drug-target affinity (DTA) plays an important role in drug discovery. Existing deep learning methods for DTA prediction typically leverage a single modality, namely simplified molecular input line entry specification (SMILES) or amino acid sequence to learn representations. SMILES or amino acid sequences can be encoded into different modalities. Multimodality data provide different kinds of information, with complementary roles for DTA prediction. We propose Modality-DTA, a novel deep learning method for DTA prediction that leverages the multimodality of drugs and targets. A group of backward propagation neural networks is applied to ensure the completeness of the reconstruction process from the latent feature representation to original multimodality data. The tag between the drug and target is used to reduce the noise information in the latent representation from multimodality data. Experiments on three benchmark datasets show that our Modality-DTA outperforms existing methods in all metrics. Modality-DTA reduces the mean square error by 15.7% and improves the area under the precisionrecall curve by 12.74% in the Davis dataset. We further find that the drug modality Morgan fingerprint and the target modality generated by one-hot-encoding play the most significant roles. To the best of our knowledge, Modality-DTA is the first method to explore multimodality for DTA prediction.

Children intend to teach conventional but not moral norms selectively to ingroup members.

Several studies have investigated factors guiding children's decisions when learning from others, although less is known about factors that govern children's decisions when they transfer knowledge to others. Here we asked whether children would privilege ingroup members when teaching and, if so, whether this tendency would persist when transferring different kinds of information (conventional norms vs. moral norms). In Experiment 1 (N = 24), we first replicated ingroup preference based on minimal group membership with 5- and 6-year-old Turkish children. In Experiment 2 (N = 64), we examined whether children would consider group membership and the type of knowledge to be transferred in their teaching intentions. Children were introduced to two ignorant targets differing in their group membership and were asked to choose one or both of these targets to teach conventional or moral norms. Children were more likely to choose ingroup members for teaching conventional norms and both members when teaching moral norms. Further, this trend was particularly evident among girls. These results suggest that children make flexible teaching decisions considering the social attributes of the learners and raise interesting questions regarding the mechanisms underlying children's information transfer. (PsycInfo Database Record (c) 2022 APA, all rights reserved).