Fake Reports Research Articles

A Study on the Role of Constructive Journalism During Covid-19 Pandemic

An expansive proliferation of misinformation and fake news followed with the outbreak of the Covid-19 pandemic, a devastating event with lasting scars that changed the world forever. While social media may have originally helped in connecting people and sharing information, it may have also triggered a heightened accessibility in spreading misinformation and fake reports during the pandemic. The Covid-19 outbreak cannot be held as the sole reason for the mounting scepticism among people over the mainstream news reporting or the rising news avoidance observed in recent years. The role of news media organisations during pandemic situations towards disseminating scientific and verified information is indispensable. However, a shift from the normative role of mass media as a mere informant and mute spectator to one that reflexively gathers and presents socially useful news for its audience could have quelled the misinformation-induced hysteria to a certain extent during the peak of the pandemic—a constructive approach. This study critically analyses the various discourses surrounding constructive journalism and its role during the Covid-19 pandemic. It was found that most discourses vouched for the inclusion of constructive approaches in news sharing during crises and in future. However, regional, geopolitical and socioeconomic differences may allude to different understandings and contextual applications of constructive journalism that would require greater academic research.

PELATIHAN MEMINIMALISIR PEMBERITAAN HOAX MEDIA SOSIAL DAN PENERAPAN INTERNET SEHAT

At this time, the progress of science and technology is very rapid, making it easier for people to access everything, one of which is the information needed. Along with its development, technological advances not only have a positive impact but also have a negative impact. In accessing information at this time, the delivery of information will be very easy and fast. Where someone very easily produces information and shares it via social media such as Facebook, Twitter, Instagram, Google, Youtube or handheld messages such as WhatsApp, LINE, BBM (Blackberry Messenger) and so on which cannot be filtered properly. Information that has been read can affect emotions, feelings, thoughts and even actions of both individuals and groups. It is very unfortunate if social media is used to obtain and provide inaccurate information, especially to the point of making social media a tool for spreading fake news (hoaxes) using very provocative titles to direct readers to negative public opinion. A fake report is different from, for example, a magic show, in that it is fake news, the listener/audience is not aware that they are being lied to, whereas in a magic show, the audience actually expects to be deceived. Fake news (hoaxes) need to get serious attention at this time because hoaxes themselves are "an act of communication carried out by an individual or group in the form of provocation, incitement, or insult to other individuals or groups to make things disorderly and trigger fights, debates and divisions for a person, group or nation and state. This is done by irresponsible people to benefit both psychologically and financially.

Analyzing the misleading information on Covid-19 using MBCFWS4

The COVID-19 pandemic had a major effect on almost every area of human lives in the majority of the world's countries. Misinformation spreads quickly in the initial phases of the COVID-19 pandemic in different forms, such as fear, distortion, contraindication assumption, and others. False and misleading advertisements harm millions of individuals. In recent research, there are more advanced techniques have been used to address the misinformation about the COVID-19 pandemic. But they are self-reported and probabilistic data during the lockdown period. So, it was difficult to find the respondents' perceptions based on sharing the COVID-19 misinformation. This proposed work analyzed and filtered some optimized factors to analyze the misinformation such as fake reports, fake remedies, conspiracy, susceptibility, vaccine rumors, social media, vitamin D prevents corona, political corona, socio-Economic, and more side effects after getting vaccinated. Collaborative filtering (CF) is the most efficient recommender system, and it is extensively utilized by a broad range of research institutions and enterprises, as well as being used in practice. It consists of two types of CF namely Memory-based CF and Model-based CF. In this work, Memory-based CF recommendation algorithm is combined with a similarity measure called MBCFWS4 to analyze the similarity measure between the factors to conclude the most impact factor. The Primary and secondary dataset helps to identify the respondents' perception based on the COVID-19 misinformation. The efficiency comparison of the proposed work is measured in terms of Precision, Recall & F-measure and found that this analysis using MBCFWS4 is outperforming well than the others as MBCFWS4 predicted accurately and revealed the conclusion based on the COVID-19 misinformation.

SARS-CoV-2 consequences for mental health: Neuroinflammatory pathways linking COVID-19 to anxiety and depression.

The coronavirus disease 2019 (COVID-19) pandemic has been linked to an increased prevalence of mental health disorders, particularly anxiety and depression. Moreover, the COVID-19 pandemic has caused stress in people worldwide due to several factors, including fear of infection; social isolation; difficulty in adapting to new routines; lack of coping methods; high exposure to social media, misinformation, and fake reports; economic impact of the measures implemented to slow the contagion and concerns regarding the disease pathogenesis. COVID-19 patients have elevated levels of pro-inflammatory cytokines, such as interleukin (IL)-1β, IL-6, and tumor necrosis factor-α, and other inflammation-related factors. Furthermore, invasion of the central nervous system by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may potentially contribute to neuroinflammatory alterations in infected individuals. Neuroinflammation, a consequence of psychological stress due to the COVID-19 pandemic, may also play a role in the development of anxiety and depressive symptoms in the general population. Considering that neuroinflammation plays a significant role in the pathophysiology of depression and anxiety, this study investigated the effects of SARS-CoV-2 on mental health and focused on the impact of the COVID-19 pandemic on the neuroinflammatory pathways.

Process of Investigation onCriminal Actions of Fake Reports by the Reserse & Criminal Unit Investigators to Prosecutor

The aim of this research is to find out and analyze the process of investigating criminal acts of false reports by investigators from the Criminal Investigation Unit to the prosecutor's office. To find out and analyze the obstacles to the process of investigating criminal acts of false reports by investigators from the Criminal Investigation Unit to the prosecutor's office. To find out and analyze efforts to overcome obstacles to the process of investigating criminal acts of false reports by investigators from the Criminal Investigation Unit to the prosecutor's office. This study uses a sociological juridical approach, with descriptive analytical research specifications. The data used in this research is secondary data obtained through literature study which is then analyzed qualitatively. The results of this study are the Process of Investigating False Reports by Criminal Investigation Unit Investigators to the Prosecutor's Office is an investigator who submits a case file or the result of the report turns out to be false to the prosecutor's office, the prosecutor can return the case file or the results of the report to the investigator with instructions or directions from the prosecutor. Its office with coordination or cooperation between the police and the prosecutor's office. The resistance is the investigation of a criminal act of a false report takes a long time: in handling a criminal act of a false report by a defendant, it cannot be processed further if it only relies on information/confessions from the suspect, and if the case related to the crime of a false report has not been revealed or proven. Proving is difficult, it is difficult to prove the case in the investigation because in fact this case does not exist so that investigators must be more observant and patient in every collection of evidence. The solution is intensify every report or input from everyone about everything related to the problem of the crime of the false report. Increased awareness of new modes of crime.

A Novel Road Maintenance Prioritisation System Based on Computer Vision and Crowdsourced Reporting

The maintenance of critical infrastructure is a costly necessity that developing countries often struggle to deliver timely repairs. The transport system acts as the arteries of any economy in development, and the formation of potholes on roads can lead to injuries and the loss of lives. Recently, several countries have enabled pothole reporting platforms for their citizens, so that repair work data can be centralised and visible for everyone. Nevertheless, many of these platforms have been interrupted because of the rapid growth of requests made by users. Not only have these platforms failed to filter duplicate or fake reports, but they have also failed to classify their severity, albeit that this information would be key in prioritising repair work and improving the safety of roads. In this work, we aimed to develop a prioritisation system that combines deep learning models and traditional computer vision techniques to automate the analysis of road irregularities reported by citizens. The system consists of three main components. First, we propose a processing pipeline that segments road sections of repair requests with a UNet-based model that integrates a pretrained Resnet34 as the encoder. Second, we assessed the performance of two object detection architectures—EfficientDet and YOLOv5—in the task of road damage localisation and classification. Two public datasets, the Indian Driving Dataset (IDD) and the Road Damage Detection Dataset (RDD2020), were preprocessed and augmented to train and evaluate our segmentation and damage detection models. Third, we applied feature extraction and feature matching to find possible duplicated reports. The combination of these three approaches allowed us to cluster reports according to their location and severity using clustering techniques. The results showed that this approach is a promising direction for authorities to leverage limited road maintenance resources in an impactful and effective way.

A Decentralized Electronic Reporting Scheme with Privacy Protection Based on Proxy Signature and Blockchain

The electronic reporting system can alleviate the problems in terms of efficiency, content confidentiality, and reporter privacy imposed in the traditional reporting system. Relying on anonymity, the privacy of reporters can be protected, but the authentication of reporters with fake names should also be maintained. If authenticated anonymity is guaranteed, the reporters may still conduct misbehaviors such as submitting fake reports after the authentication. To address the above dilemma, we propose to apply a proxy signature to achieve authenticated anonymity and employ blockchain to maintain anonymity yet guarantee traceability for reporters’ misbehaviors. We also propose a new proxy signature scheme in this paper by module lattice for postquantum security. The extensive analysis justified our proposed scheme is secure and manageable.

FAKES IDENTIFICATION IN THE CONTEXT OF SHAPING A CRITICAL THINKING OF FUTURE EDUCATORS

Today medias have substantial influence on development of young personality. All without an exception mass media, and especially on-line-space, offering the recipients unbelievable amount of information content, come forward as effective factors of formation of one’s own opinion, development of conception of collective activity, strategy of public policy on local and global scales. Therefore, formation of critical immunity for teachers and students is a necessity. In the context of modern mass-informative manipulations fake news – untruthful reports which are purposefully widespread for the sake of misleading an audience – had become very popular.
 The aim of this publication is to pay attention to problem of fake reports in mediaspace, to define the criteria of fake news authentication, the most obvious threats of distribution of untruthful news and ways of counteraction of misinformation for the sake of formation of critical immunity of future teachers in the conditions of global information conflicts. The authors of the article considered the concept of fake news, outlined the reasons of active fake news pandemic, paid attention to cases of purposeful distribution of untruthful media reports for the sake of misleading an audience. The complexity of authentication of fake information, that is distributed through traditional mass-media and internet-medias, especially social networks has been marked. There have been presented different fake news classification approaches according to methods of transmission, forms, maintenance, markers. Importance of awareness of audience has been highlighted along with the possibilities of verification of the perceived and intended for distribution information, as well as usage of the results of activity of faktchecking projects.
 The characteristic markers of purposeful untruthful information messages have been defined: shocking character of news, powerful emotionality of report; absence of reference to the source; erroneous argumentation; manipulative title; versions or theory of conspiracy; fully invented news or absolute fake news. The examples of fake information and possibilities of its recognition have been proved. The authors state the most obvious threats of distribution of unverified news, role of teachers in the process of formation of the critical thinking for the bread-winners of education as effective weapon of fight against manipulations, the necessity of medialiteracy, conscious consumption and distribution of information for all participants of educational environment in the context of new calls of contemporaneity, that is predefined by establishment and development of informative society.

Geo Landmark Recognition and Detection

The widespread use of smartphones and mobile data in the present-day society has exponentially led to the interaction with the physical world. The increase in the amount of image data in web and mobile applications makes image search slow and inaccurate. Landmark recognition, an image retrieval task, faces its challenges due to the uncommon structure it possesses, such as, buildings, cathedrals, castles or museums. These are shot from various angles which are often different from each other, for instance, the exterior and interior of a landmark. This paper makes use of a Convolutional Neural Networks (CNN) based efficient recognition system that serves in navigation, to organize photo collections, identify fake reports and unlabeled landmarks from historical data. It identifies landmarks correctly from a variety of images taken at different viewpoints as well as distances. An appropriate CNN architecture helps to provide the best solution for the currently selected dataset.

ОСОБЛИВОСТІ НЕПРАВДИВИХ МЕДІАПОВІДОМЛЕНЬ (НА ПРИКЛАДІ РОСІЙСЬКИХ ФЕЙКІВ ПРО УКРАЇНУ)

Information wars have long been used as a full-fledged weapon against the enemy, using both manipulation and completely fake messages. The methods used to disseminate false media messages by their authors are completely different, but the goal is almost always the same - to make the target audience that consumes information, believe and be influenced. Scientists have identified three main elements of the media chain: the author of the message - the transmission channel - the recipient of the message. According to M. McLuhan, media channels of information transmission are a technical continuation of natural channels: radio (auditory), printed periodicals (visual), television (combination of vocal and visual), and Internet MMC (combination of auditory, optic and visual). Therefore, it is worth considering the ways in which fakes are distributed - through photos, videos or just texts. Very often two or sometimes three distribution channels are used, because video can accompany text and attached to it photos, so this division should be considered conditional. Having analysed fake reports from the Russian-language media segment, it became possible to draw conclusions about the use of basic evaluation criteria according to professional journalistic standards of publications. The emotionality of the texts, which was conditionally divided into two groups - "excessive emotionality" and "moderate neutrality" was taken into account. As for the excessive emotionality of the texts, it has been determined that it is most common in the video, a little less in the photo, and very little in the texts. As to the studies concerning the topic of fakes in the Russian-language media segment, a vast majority of studies concerned Ukraine, and military issues namely. Usually, the authors of fake media reports aim to destabilize the situation, and to make the target audience believe in nonsense and behave predictably, to divert attention from their own problems.

Collaborative and efficient privacy-preserving critical incident management system

When a critical incident occurs, timely location-based status messages (known as alerts) conveyed by a witness present at the incident site to the competent authority constitute a key part of an effective approach to handle the situation. Details provided by witnesses are of extreme significance, but their collaboration with the authority may make them susceptible to various threats such as loss of anonymity, false implication, unwarranted surveillance, etc. As a solution, an anonymous reporting mechanism can be provided to encourage the witnesses to report the incident to the concerned authority. However, there is a possibility of system collapse in case the anonymous witnesses inundate the authority with multiple fake reports, resulting in delayed response to the critical incident. In this paper, we present a critical incident reporting system that provides privacy to honest witnesses but can disclose the identity and punish the malicious ones. The proposed system facilitates the indistinguishability of the witness among a group of users such that reports are linked to groups of k (or more) users instead of a single user. We use a game-theoretic approach based on the co-utility principle to encourage the users of the system to engage in mutually beneficial collaboration, which leads to a “rewards and punishment” mechanism to encourage legitimate information and discourage false information. The incident management authority rewards honest witnesses with a blockchain-based cryptocurrency, which can be redeemed anonymously by the awardees from the city council. We have analyzed the security and privacy properties of the system, and carried out real-device testing to evaluate the system performance and its feasibility.

Security of Stored Data using AES Algorithm for Hospital Management System

Securing data of patient in hospital is need of hour since hackers can misuse this data for doing some scam. There is increase in number of cyber attacks on healthcare organizations and the cybercriminals developing more sophisticated tools to attack healthcare industry, therefore security of healthcare has become more important. Many incidents have been reported, where patient comes to hospital routine blood check up, but pathologist hack old data of patient and generate a fake report by putting figure based on old data of last report and fool patient. There are scandals wherein certain organization hack the system of well reputed hospital to get private information about patient history and health record and misuse this information which lead to lot of agony and harassment to patient. Cyber attacks cause major disruption in healthcare services even in the best of time. Our project will ensure that that patient's data remain secure and does not goes into hand of wrong people. In this paper we have discussed method to store healthcare data in data in cloud and use AES encryption algorithm to encrypt data, so it does not go into wrong hand.

A Misbehaving-Proof Game Theoretical Selection Approach for Mobile Crowd Sourcing

With the tremendous advances in ubiquitous computing, mobile crowd sourcing (MCS) has become an appealing part of the Internet of Things (IoT). In MCS systems, workers collect data with a certain quality, and get incentivized in return. However, MCS systems are vulnerable to misbehaving acts such as workers submitting multiple false or fake reports using multiple devices to affect the majority vote of the task. In addition, workers may try to maximize their profit by submitting multiple truthful data, a behavior that may prevent other potential workers to participate. The selection of such workers for a task has a negative impact on the decision making or the payoff of the task. Most of the current approaches aim to maximize the completion of tasks based on the reputation or the credibility of workers, but without consideration of tasks' payoff and the threat of misbehavior. In literature, a misbehaving act, where workers impersonate multiple identities using multiple devices to maliciously change the majority votes or selfishly increase their payment, has not been addressed during MCS recruitment. In this paper, a two-layer selection approach is proposed based on game-theory in which the payoff of the tasks is maximized based on the individual contributions of the workers. In addition, the proposed model detects and eliminates the misbehaving act where workers submit multiple reports using multiple devices, during the recruitment phase. Simulations using real-life datasets show that the proposed approach succeeds in detecting and eliminating misbehaving devices, and outperforms the benchmarks in terms of the payoff of the tasks.

South African censorship: the production & liberation of Waiting for the barbarians, by J. M Coetzee

During Apartheid, South African censorship became the main legal instrument for the control and cultural tutelage of society. Censors decided which literary works could be read. Thousands of books were withdrawn from circulation, but the censorship did not achieve total success in curtailing the circulation of ideas. One novel, in particular, was released despite being not only very political, but also representing events that were – as J. M Coetzee puts it – in the minds and hearts of people of conscience during Apartheid: torture and fake reports issued by the South African police. Through documental and critical analysis, this article aims to answer one question: why Waiting for the barbarians, novel by J. M Coetzee published in 1980, was never banned? We will bring information and reflections on how the use of literary strategies, a political strategy and the exercise of censorship by a peculiar censor were crucial for the system’s circumvention.

Malicious Cluster Head Detection Mechanism in Wireless Sensor Networks

In wireless ad hoc network, all nodes participate for the transmission of data within the network and responsible for designing network topology. Suspicious and malicious activities can be detected by different techniques like IDS that is dynamic in nature. In clustering environment all the communication is carried out through cluster heads, we are having two cluster heads a primary cluster head (PCH) and secondary cluster heads (SCH) where SCHs communicate via PCH and if one of the secondary cluster head is compromised the entire network will be affected so malfunctioning of cluster head must be detected and identified. To handle this issue, we proposed a secure mechanism that provides security by minimum utilization of the resources after detection and identification the malicious Cluster Head. The proposed mechanism based on two level thresholds for the detection and identification of malicious cluster head that is dropping packets because of some attack. We used watchdog technique for initial monitoring. An agent is launched for detection and identification of the packets dropping problem. It is specially designed for secure UDP traffic transmission and fake report detection done by any of the malicious SCH to PCH.

Accurate detection of selective forwarding attack in wireless sensor networks

Selective forwarding attack in wireless sensor networks shows great impact on network performance and consumes limited energy resource. In previous countermeasures, it is assumed that all nodes in the communication range can detect misbehaviors of the attacker. However, as wireless devices require certain signal-to-noise ratio to receive frames correctly, and interference among nodes is inevitable in densely deployed wireless sensor networks, it is very difficult for previous approaches to detect misbehaviors accurately. In this article, a scheme named E-watchdog is proposed to improve accuracy of selective forwarding attack detection. Detection agents that are closer to the attacker are used to detect misbehaviors, which can improve the detection accuracy and reduce the false alarm rate effectively. Moreover, to prevent collaborative selective forwarding attack, E-watchdog uses reports from more than one detection agents. Fake reports from attackers are filtered out through an election algorithm. Simulation results show that the E-watchdog reduces the false detection rate by 25% and improves the detection accuracy by 10% on the premise of increasing negligible energy consumption.

The Optimum Design of Location-Dependent Key Management Protocol for a Multiple Sink WSN Using a Random Selected Cell Reporter

The use of wireless sensor networks for the purposes of border surveillance is a very hot topic at the moment. Such a network comprises large numbers of sensor nodes, sparsely located over a wide terrain, where the generated data are collected by multiple sinks located in secure head quarters. Due to its importance and criticality, data disseminated through such a network is vulnerable to outsider attacks. Therefore, a location-dependent key management scheme could be employed in order to provide security in terms of confidentiality and authenticity. This paper presents a novel location-dependent key management protocol for multiple sinks (LKMP-MS) whose design is based on a cell reporters scheme. In this protocol, the generated report regarding any event contains three security levels. In particular, the third level is implemented as an endorsement of the set of cell reporters allocated inside each cell. Accordingly, the generated event report is accepted by the sink. As a result, the adversary would have to compromise all endorsement nodes and all cell reporters selected by all sinks in order to generate a fake report inside a particular spot. This paper introduces a novel algorithm to implement the required revocation for both compromised nodes and cells in order to overcome the possible consequences of these entities being compromised. In order to evaluate and compare the LKMP-MS with recent schemes, a mathematical analysis is implemented and a simulation environment is built based on Contiki and MATLAB to validate the mathematical analyses. Finally, an extensive mathematical analysis is presented in order to calculate the optimum number of cell reporters that gives the highest level of security.

A Dynamic Panel Analysis of Drivers of Output Growth in the Nigerian Manufacturing Firms

Regardless of the efforts of government to revamp the manufacturing sector in Nigeria, the sub-sector has remained ineffective with dwindling output and there have been consistent fluctuations in the share of the manufacturing sub-sector to Gross Domestic Product (GDP) in Nigeria. This study therefore examines the determinants of output growth in the Nigerian formal manufacturing sub-sector. The study made use of fifty (50) formal manufacturing firms listed in the Nigerian Stock Exchange Data for the formal manufacturing firms were sourced from the Nigeria Stock Exchange (NSE) Fact Book and the Central Bank of Nigeria Statistical Bulletin 2014. The estimated models in the study were specified following the works of Sangosanya (2011). The study employed the dynamic panel data analysis (the dynamic models of the Generalized Method of Moments (GMM) and the Systemic Generalized Method of Moments (SYSGMM)) for the Nigerian formal manufacturing sub-sector. The study showed that the coefficient of operating efficiency in the GMM&SYSGMM estimate, i.e. -0.0349214 and -0.0199787 respectively showed a negative relationship between OPREF and firms’ growth. This implied that information supplied by firms about their growth indicators is at variance with their performance. This further speaks volume of the weakness of regulatory agencies to effectively monitor the performance of manufacturing firms in Nigeria. Also, the study showed that exchange rate, bank efficiency and managerial efficiency have significant positive relationship with output growth of firms. Also variables such as degree of financial development, energy infrastructural facilities and government regulations and policy have significant negative impact with output growth of firms in Nigeria. Findings revealed that all the explanatory variables identified in the study are strong determinants of firm growth in the Nigerian manufacturing sub-sector. The study recommended among others that government should formulate and implement policies that would hinder formal manufacturing firms from publishing fake report of their growth. Also, government should formulate and implement policy measures that would make imported goods more expensive and appropriate monetary policies that would make the cost of borrowing from banks (interest rate) affordable should be priotised in Nigeria.

En-Route Filtering Techniques in Wireless Sensor Networks: A Survey

Majority of wireless sensor networks (WSNs) are deployed in unattended environments and thus sensor nodes can be compromised easily. A compromised sensor node can be used to send fake sensing reports to the sink. If undetected these reports can raise false alarms. To deal with the problem of fake report generation, a number of en-route filtering schemes have been proposed. Each of these schemes uses different cryptographic methods to check the authenticity of reports while they are being forwarded hop by hop toward base station. However, majority of these techniques can handle only limited compromised nodes or they either need node localization or statically configured routes for sending reports. Furthermore, majority of en-route filtering techniques are vulnerable to various denial of service attacks. Our main aims in this survey are: (a) to describe the major en-route filtering techniques, (b) to analyze these techniques on various parameters including security and (c) to outline main unresolved research challenges in en-route filtering in WSNs.

A Proposed Solution for Crime Reporting and Crime Updates on Maps in Android Mobile Application

purpose of this research paper is to propose and develop an android mobile application for the general public awareness of the crime situation of their area and to provide them crime locations on the map. This application will also help general public to report a crime to law enforcement agency. This android mobile application will help public to see the locations on map which will help them to track the current situation of their surroundings. Some part of this android mobile applications is being developed in SEGi University with sample databases. In this research we will suggest some methods for future recommendations that how can law enforcement agencies avoid fake reports which are reported from mobile?