1,721,025 research outputs found
A Cluster-based Approach of Smartphone Camera Fingerprint for User Profiles Resolution within Social Network
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Distributed Ledger and Text Watermarking for Fine-Grain Provenance Checking of Textual Content
Full text linkInformation disorder has become a major societal challenge, impacting public discourse and democracy. This phenomenon has been exacerbated by the spread of social media platforms, affecting various areas, ranging from national elections to public health. Addressing fake news through a manual approach (e.g., human fact-checking) is unfeasible due to the rapid production of textual content. At the same time, applying automatic tools is equally challenging, primarily due to the ambiguity of natural language. In this paper, we addressed online information disorder from a different perspective by proposing a platform that supports trustworthy and reputable news producers and enhances awareness among readers across various social media. Specifically, the proposed platform enables news producers to automatically embed a unique watermark in the text they create, ensuring that the news cannot be manipulated or misattributed. The watermarking is embedded in a fine-grained way, allowing even small extracts of the news to be shared while preserving traceability. Additionally, the association between the watermark and the news item is recorded in a distributed ledger, preventing further manipulation that could arise from centralised management. The aim is to enable readers to make more informed decisions about the content they encounter, even when engaging with excerpts of the original document, minimising reliance on external fact-checking organisations
Balancing Calories with Smartphone
No full textBackground: People gain weight when assume more calories than their body can consume. Instead, they lose weight when they consume more than what they eat. Comparing the body to a closed system, we can call calorie balance the difference between the input and output calories. However, keep a balanced diet regime is never a rewarding activity and often people give up.
Objectives: The goal is to help subjects with overweight problems: educate these people reduces the number of people that might migrate in the obese class. We propose an application to encourage healthier lifestyles, whose innovative feature is an automatic adaptive monitoring of the daily calorie balance. The system uses a familiar device and motivates the users to reach best result with the diet.
Methods: The energy consumption is related to the oxygen consumption, obviously also if combined with physical activity. The heart rate is directly related to the supply of oxygen. Using this simple relation, the heartbeats are bound to the calorie consumption.
Results: People achieve a greater awareness about food dosage and its calories weight. Moreover the application allows a more careful choice in food selection in order to not vanish the efforts made to change the lifestyles.
Conclusions: We have obtained good result with off-theshelf hardware and user friendly software solutions. The users consider the system like a game where they have to keep higher the burned calories level. A motivational application has been found to be a winning card to promote healthier lifestyles, without intimidating the user
Balancing Calories with Smartphone
No full textBackground: People gain weight when assume more calories than their body can consume. Instead, they lose weight when they consume more than what they eat. Comparing the body to a closed system, we can call calorie balance the difference between the input and output calories. However, keep a balanced diet regime is never a rewarding activity and often people give up.
Objectives: The goal is to help subjects with overweight problems: educate these people reduces the number of people that might migrate in the obese class. We propose an application to encourage healthier lifestyles, whose innovative feature is an automatic adaptive monitoring of the daily calorie balance. The system uses a familiar device and motivates the users to reach best result with the diet.
Methods: The energy consumption is related to the oxygen consumption, obviously also if combined with physical activity. The heart rate is directly related to the supply of oxygen. Using this simple relation, the heartbeats are bound to the calorie consumption.
Results: People achieve a greater awareness about food dosage and its calories weight. Moreover the application allows a more careful choice in food selection in order to not vanish the efforts made to change the lifestyles.
Conclusions: We have obtained good result with off-theshelf hardware and user friendly software solutions. The users consider the system like a game where they have to keep higher the burned calories level. A motivational application has been found to be a winning card to promote healthier lifestyles, without intimidating the user
User profiles’ image clustering for digital investigations
Full text linkSharing images on Social Network (SN) platforms is one of the most widespread behaviors which may cause privacy-intrusive and illegal content to be widely distributed. Clustering the images shared through SN platforms according to the acquisition cameras embedded in smartphones is regarded as a significant task in forensic investigations of cybercrimes. The Sensor Pattern Noise (SPN) caused by camera sensor imperfections due to the manufacturing process has been proved to be an effective and robust camera fingerprint that can be used for several tasks, such as digital evidence analysis, smartphone fingerprinting and user profile linking as well. Clustering the images uploaded by users on their profiles is a way of fingerprinting the camera sources and it is considered a challenging task since users may upload different types of images, i.e., the images taken by users’ smartphones (taken images) and single images from different sources, cropped images, or generic images from the Web (shared images). The shared images make a perturbation in the clustering task, as they do not usually present sufficient characteristics of SPN of their related sources. Moreover, they are not directly referable to the user’s device so they have to be detected and removed from the clustering process. In this paper, we propose a user profiles’ image clustering method without prior knowledge about the type and number of the camera sources. The hierarchical graph-based method clusters both types of images, taken images and shared images. The strengths of our method include overcoming large-scale image datasets, the presence of shared images that perturb the clustering process and the loss of image details caused by the process of content compression on SN platforms. The method is evaluated on the VISION dataset, which is a public benchmark including images from 35 smartphones. The dataset is perturbed by 3000 images, simulating the shared images from different sources except for users’ smartphones. Experimental results confirm the robustness of the proposed method against perturbed datasets and its effectiveness in the image clustering
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