Information Content Research Articles

Deepfake Detection System

Abstract: Because deep fake technology allows for the creation of incredibly realistically modified material that has the potential to mislead viewers and possibly cause instability across a range of businesses, it poses a severe threat to modern society. These days, detecting such modified content is crucial to maintaining the trustworthiness and integrity of digital media. In response, this research proposes a robust deep learning-based technique for detecting deep fakes in videos. Our method uses the Deep fake Detection Challenge dataset, which contains both real and deep fake films, to train and assess our deep learning model. We want to provide a dependable solution that can differentiate between artificially generated information and actual content using stateof-the-art neural networks. Artificial intelligence-generated phony videos are more likely to spread thanks to deep learning algorithms, which raises serious concerns about official blackmail, terrorist propaganda, revenge pornography, and political manipulation. To alleviate these concerns, our approach is designed to automatically identify several forms of deep forgery, including replacement and re-enactment techniques. By means of extensive testing and analysis, we demonstrate the effectiveness of our deep fake detection system in precisely distinguishing bogus films from authentic ones. Our system, which combines cutting-edge deep learning methods with a large dataset, offers a promising way to lessen the hazards related to the spread of deep fake technologies

ALVoyage – An AI Travel Planner

Abstract: The document discusses the ALVoyage AI travel planner, which integrates with Gemini and Weather APIs and utilizes Firebase authentication. ALVoyage offers personalized travel planning by leveraging AI technology to curate itineraries based on user preferences. It seamlessly integrates with the Gemini API to generate informative content and incorporates real-time weather data from the OpenWeatherMap API. Firebase authentication ensures user security and privacy. The document also highlights the benefits and key features of Gemini and OpenWeatherMap APIs. The methodology section outlines the system architecture and components of ALVoyage, including user authentication, UI/UX design, Gemini API integration, Weather API integration, and user reviews. The results and discussion section emphasizes the system's performance, user feedback, and the future scope of the project. The conclusion highlights the potential of AI-powered travel planning apps and the need for further research and development in the field.

Wave-Function Network Description and Kolmogorov Complexity of Quantum Many-Body Systems

Programmable quantum devices are now able to probe wave functions at unprecedented levels. This is based on the ability to project the many-body state of atom and qubit arrays onto a measurement basis which produces snapshots of the system wave function. Extracting and processing information from such observations remains, however, an open quest. One often resorts to analyzing low-order correlation functions—that is, discarding most of the available information content. Here, we introduce wave-function networks—a mathematical framework to describe wave-function snapshots based on network theory. For many-body systems, these networks can become scale-free—a mathematical structure that has found tremendous success and applications in a broad set of fields, ranging from biology to epidemics to Internet science. We demonstrate the potential of applying these techniques to quantum science by introducing protocols to extract the Kolmogorov complexity corresponding to the output of a quantum simulator and implementing tools for fully scalable cross-platform certification based on similarity tests between networks. We demonstrate the emergence of scale-free networks analyzing experimental data obtained with a Rydberg quantum simulator manipulating up to 100 atoms. Our approach illustrates how, upon crossing a phase transition, the simulator complexity decreases while correlation length increases—a direct signature of buildup of universal behavior in data space. Comparing experiments with numerical simulations, we achieve cross-certification at the wave-function level up to timescales of 4 μs with a confidence level of 90% and determine experimental calibration intervals with unprecedented accuracy. Our framework is generically applicable to the output of quantum computers and simulators with access to the system wave function and requires probing accuracy and repetition rates accessible to most currently available platforms. Published by the American Physical Society 2024

Movement trajectories as a window into the dynamics of emerging neural representations

The rapid transformation of sensory inputs into meaningful neural representations is critical to adaptive human behaviour. While non-invasive neuroimaging methods are the de-facto method for investigating neural representations, they remain expensive, not widely available, time-consuming, and restrictive. Here we show that movement trajectories can be used to measure emerging neural representations with fine temporal resolution. By combining online computer mouse-tracking and publicly available neuroimaging data via representational similarity analysis (RSA), we show that movement trajectories track the unfolding of stimulus- and category-wise neural representations along key dimensions of the human visual system. We demonstrate that time-resolved representational structures derived from movement trajectories overlap with those derived from M/EEG (albeit delayed) and those derived from fMRI in functionally-relevant brain areas. Our findings highlight the richness of movement trajectories and the power of the RSA framework to reveal and compare their information content, opening new avenues to better understand human perception.

Rockfall susceptibility assessment in Kuandian County (Northeast China) by combining information content method and analytic hierarchy process

Rockfall susceptibility assessment in Kuandian County (Northeast China) by combining information content method and analytic hierarchy process

Sampling and misspecification errors in the estimation of observation‐error covariance matrices using observation‐minus‐background and observation‐minus‐analysis statistics

AbstractSpecification of the observation‐error covariance matrix for data assimilation systems affects the observation information content retained by the analysis, particularly for observations known to have correlated observation errors (e.g., geostationary satellite and Doppler radar data). A widely adopted approach for estimating observation‐error covariance matrices uses observation‐minus‐background and observation‐minus‐analysis residuals, which are routinely produced by most data assimilation systems. Although this approach is known to produce biased and noisy estimates, due to sampling and misspecification errors, there has been no systematic study of sampling errors with this approach to date. Furthermore, the eigenspectrum of the estimated observation‐error covariance matrix is known to influence the analysis information content and numerical convergence of variational assimilation schemes. In this work, we provide new theorems for the sampling error and eigenvalues of the estimated observation‐error covariance matrices with this approach. We also conduct numerical experiments to illustrate our theoretical results. We find that this method produces large sampling errors if the true observation‐error standard deviation is large, while the other error characteristics, including the true background‐error standard deviation and observation‐ and background‐error correlation length‐scales, have a relatively small effect. We also find that the smallest eigenvalues of the estimated matrices may be smaller or larger than the true eigenvalues, depending on the assumed and true observation‐ and background‐error statistics. These results may provide insights for practical applications: for example, in deciding on appropriate sample sizes and choosing parameters for matrix reconditioning techniques.

Analisis Literasi Digital Siswa Kelas X Mata Pelajaran Dasar-dasar Kuliner di SMKN 9 Padang

Digital literacy in culinary lessons for class X students at SMKN 9 Padang is still not optimal. This can be seen from the learning results of students in the Culinary Basics lesson in class X, many of whom have not yet reached the KKM. This research aims to analyze digital literacy in Class X Culinary Basics lessons at SMK Negeri 9 Padang. This type of research is quantitative. The population of this study was all class X students at SMKN 9 Padang totaling 210 people. Sampling in the study used the Slovin formula so that a sample of 138 was obtained using the proportional random sampling technique. The data collection instrument uses a questionnaire using a Likert Scale, data analysis techniques include descriptive. The results of the research found that digital literacy in culinary basics subjects at SMKN 9 Padang was considered good. If we look at each of the internet search indicators, hupertect directions, evaluation of information content and knowledge compiler, they are in the good category.

On the evolution of chromosomal regions with high gene strand bias in bacteria.

On circular bacterial chromosomes, the majority of genes are coded on the leading strand. This gene strand bias (GSB) ranges from up to 85% in some Bacillota to a little more than 50% in other phyla. The factors determining the extent of the strand bias remain to be found. Here, we report that species in the phylum Gemmatimonadota share a unique chromosome architecture, distinct from neighboring phyla: in a conserved 600-kb region around the terminus of replication, almost all genes were located on the leading strands, while on the remaining part of the chromosome, the strand preference was more balanced. The high strand bias (HSB) region harbors the rRNA clusters, core, and highly expressed genes. Selective pressure for reduction of collisions with DNA replication to minimize detrimental mutations can explain the conservation of essential genes in this region. Repetitive and mobile elements are underrepresented, suggesting reduced recombination frequency by structural isolation from other parts of the chromosome. We propose that the HSB region forms a distinct chromosomal domain. Gemmatimonadota chromosomes evolved mainly by expansion through horizontal gene transfer and duplications outside of the ancient high strand bias region. In support of our hypothesis, we could further identify two Spiroplasma strains on a similar evolutionary path.IMPORTANCEOn bacterial chromosomes, a preferred location of genes on the leading strand has evolved to reduce conflicts between replication and transcription. Despite a vast body of research, the question why bacteria show large differences in their gene strand bias is still not solved. The discovery of "hybrid" chromosomes in different phyla, including Gemmatimonadota, in which a conserved high strand bias is found exclusively in a region at ter, points toward a role of nucleoid structure, additional to replication, in the evolution of strand preferences. A fine-grained structural analysis of the ever-increasing number of available bacterial genomes could help to better understand the forces that shape the sequential and spatial organization of the cell's information content.

The Emergence of Consciousness in a Physical Universe

Consciousness appears so mysterious and hard to formulate within physical sciences because the present day scientific thinking excludes an element of reality and a general mechanics of its processing from its consideration. The primary missing element is the reality of information in the physical universe as an intrinsic causal correlate of observable physical states. Moreover, there exists a general formalism of information processing that is universally applicable to the processing resulting from each physical interaction. As shown, the formalism further enables a general mechanism to construct arbitrary structured and abstract semantics or object description in modular hierarchy as well as a powerful mechanism of population coding to represent arbitrary precision and variation in object description resolving the combinatorial problem. Here, a semantic value, or simply semantics, is equivalent (\(\equiv\)) to the content of information of causal correlation, and treated as a value to enable its formal processing. The primary motive here is to lay down a formal account of information (semantic) processing that leads to bridging the conceptual gap between the objectively observable elements in nature and the subjective consciousness. It is shown that the qualities we associate with consciousness are causally correlated semantics of relation that a represented agency holds with other objects within a dynamically evolving semantic structure, where the state of the population of physical systems (neurons) correlating with the structure holds causal powers to effect appropriate behavior. Since the information (semantic value) arises from natural causal dependence, the correlation based consciousness forms an undeniable reality of existence. It is derived here how a semantic value equivalent to ‘a self as an observer of objects and controller of actions’ is constructed. If the semantic components of a conscious experience, such as the self, the objects of experience, and the relation of experience attributing the self as the owner or experiencer, causally correlate with a system’s state having causal influence in action, then it suffices to bridge the gap between the objective reality and the subjective consciousness. That is, the semantic value corresponding to the thoughts and senses is the reality of nature the semantics of self relates to as the owner. Moreover, the semantics of ‘self as an observer and controller of action’ is itself shown to form a part of observed objects giving rise to self awareness.

Mediatised Participation: Citizen Journalism and the Decline in User-Generated Content in Online News Media

The second generation of web tools shook the journalist profession approximately two decades ago with the proactive incorporation of audiences into the media. Citizen journalism and user-generated content arose as an object of interest due to the democratising value of participation attributed to them, with empowered citizens who could emulate the professional and institutional practises of journalists. However, difficulties soon came to the surface, and audience participation in news media began to be limited. Within this context, this article conducts a critical review of studies on audience participation in news media based on a systematic literature review. The results indicate that, in general, audiences showed low interest in the creation of informative content and that their participation has grown increasingly problematic. In addition, journalists are reticent as they defend their professional role above all else, while company strategies have prioritised making participation profitable. For this reason, the idea of citizen journalism that offers user-created content through the media appears to be a thing of the past, with many characteristics that could define it as a failed innovation. Therefore, the text concludes that audience participation in the media could be defined as mediatised participation.

Proposal for a Flipped Classroom Program with Massive Open Online Courses to Improve Access to Information and Information Literacy in Primary School Teachers

The objective of this study was to propose a teacher training program based on the flipped classroom model with MOOCs to strengthen access to information and information literacy among primary education teachers in the Lambayeque region of Peru. The non-experimental design was assumed with a quantitative approach and a propositional, descriptive type. A diagnosis was made using a questionnaire given to 917 primary school teachers. It was discovered that nearly all of the items in the questionnaire revealed a deficiency in the ability to navigate, search, and filter information, data, and digital content; the highest percentages were at the Basic level, with the exception of the item expressing information needs in an organized manner, which was at the Advanced C2 level. The lowest percentage was at the Advanced C1 level, and the majority of the lower percentages were at the Advanced level. In addition, there are competency deficiencies in the evaluation of information, data, and digital content of nearly all the items: the highest percentages were at the Basic level, with the exception of the item involving the processing of information, data, and digital content, where 26.4% were at the Intermediate B1 level and just 2.8% managed to be at the highest level, which is Advanced C2. Furthermore, when it came to storage and retrieval of information, data, and digital content competency, all the high percentages were at the Basic level, and all the low percentages were at the highest level, that is, Advanced. These findings helped us to understand that teachers have only a basic knowledge of information literacy and information competency. As a result, it is necessary to advocate for a teacher training program based on the flipped classroom model with MOOCs. This idea was supported by the opinions of five experts, who stated that its implementation would enable primary teachers of Regular Basic Education in the region of Lambayeque (Peru) to develop their access to information and information literacy competency area.

Patient Perspectives on a Decision Aid for Lupus: Insights and Future Considerations.

Systemic lupus erythematosus, is a chronic autoimmune disease with a wide spectrum of clinical manifestations. A decision aid (DA) for lupus was developed and implemented in 15 rheumatology clinics throughout the United States. This study explored the experiences of patients who viewed the DA to understand how patients engage with and respond to the lupus DA. We conducted a qualitative descriptive study using semi-structured interviews with a convenience sample of 24 patients during May-July 2022. Patients recognized the value of the lupus DA, providing general knowledge about lupus and different treatment options. However, patients expressed a desire for more comprehensive lifestyle information to better manage their condition. Another theme was the importance of having multiple formats available to cater to their different needs, as well as tailoring the DA to different stages of lupus. This study contributes to a broader understanding of how to provide patient-centered care for lupus patients by offering practical insights that can inform the development of more effective, patient-centric health-information technologies for managing chronic diseases, ultimately improving patient outcomes. Overall, this study underscores the significance of optimizing both the information content and determining the appropriate delivery of the tool for its future sustainability.

Molecular Identification of Genetic Diversity and Population Structure in Moroccan Male Date Palm (Phoenix dactylifera L.) Using Inter-Simple Sequence Repeat, Direct Amplification of Minisatellite DNA, and Simple Sequence Repeat Markers

Understanding genetic diversity and population structure plays a vital role in the efficient use of available material in plant-breeding programs and in germplasm conservation strategies. In the present study, we aim to evaluate the genetic variations and population structure of male date palms from Morocco. The genetic diversity of 100 date palm (Phoenix dactylifera L.) genotypes was investigated using the performance of three types of molecular markers: inter-simple sequence repeats (ISSRs), direct amplification of minisatellite DNA (DAMD), and simple sequence repeats (SSRs). On the basis of their polymorphic information content (PIC) (ISSRs = 0.38; DAMD = 0.4; SSRs = 0.33), effective multiplex ratio (EMR) (ISSRs = 27.34; DAMD = 52.31; SSRs = 22.20), Resolving power Rp (ISSR = 13.81; DAMD = 28.73; SSR = 14.6), and marker index (MI) (ISSRs = 9.22; DAMD = 20.23; SSRs = 7.54) values, all markers used in our study are considered informative markers. Among them, DAMD markers demonstrated slightly higher informativeness compared to ISSR and SSR markers. A total of 216, 438, and 248 bands were, respectively, detected using ISSRs, DAMD, and SSRs, with 95%, 98% and 94% of polymorphism, respectively. The AMOVA results revealed considerable diversity within date palms. The PCOa results showed that males of Tinghir and Errachidia were regrouped into the same cluster, while males of Goulmima were separated into another group. A cluster and structure analysis separated the studied genotypes into three groups. One group comprises genotypes of males from Zagora with some female varieties scattered in this group. The second group includes male genotypes from Goulmima along with accessions of female and male varieties. The third group contains males of Errachidia, Tata and Tinghir populations. The cluster and structure analysis separated the studied genotypes according to their origin.

Enhanced Wild Horse Optimizer with Cauchy Mutation and Dynamic Random Search for Hyperspectral Image Band Selection

The high dimensionality of hyperspectral images (HSIs) brings significant redundancy to data processing. Band selection (BS) is one of the most commonly used dimensionality reduction (DR) techniques, which eliminates redundant information between bands while retaining a subset of bands with a high information content and low noise. The wild horse optimizer (WHO) is a novel metaheuristic algorithm widely used for its efficient search performance, yet it tends to become trapped in local optima during later iterations. To address these issues, an enhanced wild horse optimizer (IBSWHO) is proposed for HSI band selection in this paper. IBSWHO utilizes Sobol sequences to initialize the population, thereby increasing population diversity. It incorporates Cauchy mutation to perturb the population with a certain probability, enhancing the global search capability and avoiding local optima. Additionally, dynamic random search techniques are introduced to improve the algorithm search efficiency and expand the search space. The convergence of IBSWHO is verified on commonly used nonlinear test functions and compared with state-of-the-art optimization algorithms. Finally, experiments on three classic HSI datasets are conducted for HSI classification. The experimental results demonstrate that the band subset selected by IBSWHO achieves the best classification accuracy compared to conventional and state-of-the-art band selection methods, confirming the superiority of the proposed BS method.

Superobbing and Thinning Scales for All-sky Humidity Sounder Assimilation

Abstract Humidity sounder radiances are currently thinned to 110 km spacing prior to assimilation at ECMWF and used with no averaging applied. In this paper, the thinning scale and possible averaging of all-sky humidity sounder observations into “superobs” are considered. The short- and medium-range forecast impacts of changing the thinning and averaging scales of humidity sounder radiances prior to the data assimilation are investigated separately and then together. Superobbing acts as a low-pass filter and provides smoother images of departures, decreasing the effective sensor noise and thus the standard deviation of background departures, marginally for 183GHz channels (5-15%) and significantly for 118GHz channels (5-55%). Observations are thus more representative of the model effective resolution, with a better utilisation of total information content than thinning native-resolution radiances, as less information is discarded. Whether changed in isolation or combination, the additions of data via superobbing and finer thinning are both shown to markedly improve background fits to independent observations, indicative of better short-range forecasts of humidity and improved winds via the 4D-Var tracer effect. Wind forecasts in the Southern Hemisphere are slightly improved in the medium-range. A final configuration is tested at the resolution of the current operational model, with humidity sounder radiances averaged into 50 km superobs with 70 km spacing. This provides about 140% more radiances for assimilation and more finely detailed maps to analyse mesoscale features. The forecast impact of this change is larger in testing with higher model and data assimilation resolutions, showing the scale-dependence of such decisions and the expected performance in an operational configuration.

An entropy-based study on the mutational landscape of SARS-CoV-2 in USA: Comparing different variants and revealing co-mutational behavior of proteins

COVID-19 emergency has pushed the international scientific community to use every resource to combat the spread of the virus, to understand its biology and predict its possible evolution in terms of new variants. Since the first SARS-CoV-2 virus nucleotide and amino acid sequences were made available, information theory was used to study how viral information content was changing over time and then trace the evolution of its mutational landscape. In this work we analyzed SARS-CoV-2 sequences collected mainly in the USA in a period from March 2020 until December 2022 and computed mutation profiles of viral proteins over time through an entropy-based approach using Shannon Entropy and Hellinger distance. This representation allows an at-a-glance view of the mutational landscape of viral proteins over time and can provide new insights on the evolution of the virus from different points of view. Non-structural proteins typically showed flat mutation profiles, characterized by a very low Average mutation Entropy, while accessory and structural proteins showed mostly non uniform and high mutation profiles, often coupled with the predominance of variants. Interestingly NSP2 protein, whose function is currently still debated, falls in the same branch of NSP14 and NSP10 in the phylogenetic tree of mutations constructed through correlations of mutation profiles, suggesting a co-evolution of those proteins and a possible functional link with each other. To the best of our knowledge this is the first study based on a massive amount of data (n = 107,939,973) that analyzes from an entropy point of view the mutational landscape of SARS-CoV-2 over time and depicts a mutational temporal profile of each protein of the virus.

Integrated Reporting and the Informativeness of Financial Analysts’ Stock Recommendations

Synopsis The research problem The purpose of this study was to examine the market reaction to sell-side analyst recommendation revisions issued under an integrated reporting (IR) approach. Motivation Advocates of this corporate reporting approach argue that IR enhances capital markets’ information environment by rendering investors better able to assess the value creation process of a firm. Recent empirical studies corroborate this argument. Considering the central role of financial intermediaries, we investigated whether the informativeness of analyst recommendation revisions is associated with the adoption of IR and the quality of integrated reports. The test hypotheses We tested whether the informativeness of analyst recommendation revisions decreases or increases after the mandatory adoption of an IR approach. Furthermore, we tested whether the informativeness is negatively or positively related to the quality of the released integrated report after the mandatory adoption of an IR approach. Target population We focused on the South African capital market, which is the only setting where IR is mandated. We utilized a sample of 3,201 recommendation revisions made within a 3-year window around the mandatory IR adoption. Adopted methodology This study used ordinary least square (OLS) regressions and applied difference-in-differences as well as instrumental variable approaches. Analyses We modeled the market reaction to the recommendation revisions as a function of the period in which the recommendations are announced (i.e., pre- or post-adoption), along with other factors affecting market reaction. In subsequent tests, we also modeled the market reaction to the recommendation revisions as a function of the quality of a firm’s integrated report. Findings We found strong evidence that analysts’ revisions exhibited economically and statistically significantly lower information content under IR. Moreover, we found that upgrades and downgrades issued in the post-adoption period were less informative when issued for firms with high-quality integrated reports. Overall, results showed that the benefit of acquiring advice from analysts became more marginal under an IR approach.

Market supervisor monetary penalties for non‐compliance with informational requirements: Do investors care?

AbstractThis study measures and differentiates investors' responses to market supervisors' monetary penalties for non‐compliance with informational requirements for the capital market in Poland, broken down by the type of distorted information and the form of a breach. An event study was conducted to measure the information content of monetary penalties. We used a market model and tested the significance of abnormal daily returns using both parametric and non‐parametric tests. Cross‐sectional analyses were conducted to measure the determinants of market reactions. We employed two novel classifications of non‐compliance: by the type of distorted information (financial reporting information and other information) and by the form of a breach (failing to provide information and providing erroneous information). We contribute to the literature by finding that investors react negatively to monetary penalties imposed on companies for non‐compliance with financial reporting information requirements, whereas they do not react to such penalties for non‐compliance with other information requirements. We incorporate original variables that explain the magnitude of the market reaction and find that (i) the longer the distance between the breach and penalty imposition, the weaker the market reaction, and (ii) the greater the monetary penalty, the stronger the market reaction. We also find that both forms of breach lead to similar negative market reactions.

Examining the Content of Disaster-Related Information Disseminated to a Migrant Population

Many local governments, especially in Japan, disseminate multilingual disaster-related information to migrants as part of their disaster education programs to make their disaster risk reduction (DRR) policies more inclusive. However, little is known about the specific content of the information disseminated through such programs and how or why that content is selected. Using an example case of Okayama Prefecture in Japan and its principal city, Okayama City, the current study identifies the content of multilingual disaster-related information, as well as the assumptions behind its selection. To this end, the content of multilingual disaster-related information disseminated by these local governments was inductively analyzed, focusing on (1) what is and is not communicated, (2) how the government views the migrant population, and (3) their goals of information dissemination targeting migrants. As a result of conducting a qualitative content analysis on information disseminated by the Okayama local governments via information booklets and a YouTube video, it was found that the content was largely concerned with calling migrants to be proactive in taking preventive and harm reduction measures, as well as acquiring prerequisite knowledge (e.g., about disasters and their management). The underlying assumptions and expectations behind this content (e.g., how a behavioral change occurs and self-sufficiency as an expectation in disaster preparedness) are discussed, as well as issues associated with these assumptions.

Influence of Severity and Susceptibility Information Content on Users’ Psychological Response in Emergencies—The Moderating Effect of Regulatory Focus

Information content is crucial for public opinion governance, yet its psychological mechanisms remain unclear. This paper endeavors to bridge this knowledge gap by examining the influence of information content on users’ psychological responses. Drawing insights from the heuristic-systematic model (HSM) and regulatory focus theory, we constructed a novel cognitive and emotional response model. Regression analysis reveals that susceptibility strongly influences primary cognitive responses, while severity affects secondary ones. Susceptibility has a stronger impact on emotional responses than severity. Promotion-focus users are less influenced by severity and susceptibility than prevention-focus users. These results offer valuable insights for emergency management strategies and enhance individuals’ resilience in coping with the psychological toll of emergencies.