Cyberbullying is one of the serious threats on social networks particularly toward children and teenagers. Cyberbullying can cause many harmful consequences towards victims such as anxiety, depression and even suicide. This research studies cyberbullying detection in Arabic language on online social networks using deep learning techniques to automatically detect and quarantine the cyberbullying messages to safe children from getting exposed to harmful cyberbullying content. In this paper, real Arabic dataset is collected from YouTube and Twitter that is annotated manually to improve the quality of the data. All experiments went through three rounds of trials to make sure that the results are consistent. Many evaluation metrics were used to evaluate the performance of the classifiers such as macro F1 score and AUC. The best model achieves 84.58% and 85.94% in F1 score and AUC respectively.

Artificial intelligence (AI) has become increasingly popular in recent years, due to advancements in computing power and data availability, as well as its broad range of applications in various industries. ChatGPT is a state-of-the-art language model developed by OpenAI, capable of generating human-like responses to text-based inputs. Its advanced natural language processing abilities have made it a valuable tool for a variety of applications, from customer service chatbots to language translation services. In general, similar to many other language models, ChatGPT uses an autoregression process to generate text. After training on massive data, ChatGPT predicts the likelihood of the next word in a sentence based on the previous words in the sentence. However, this autoregression working strategy of ChatGPT has caused some linguistic limitations. The first limitation is its tendency to generate text that exhibits a machine-patterned language. The second limitation is the generation of wrong invalid references. This paper conducts an experimental analysis of the linguistic limitations of ChatGPT caused by the word-by-word autoregression working strategy of ChatGPT. Three experiments will be conducted in this paper for proper evaluation: 1) Detecting texts written by ChatGPT. 2) Detecting text paraphrased by ChatGPT. 3) Detecting code written by ChatGPT. Extensive experiments involving a total number of 89,902 words and 552,381 characters have yielded compelling evidence that ChatGPT-generated text can be detected with a high percentage using AI techniques. Moreover, it was observed that AI-based detection methods did not consistently identify text paraphrased by ChatGPT due to the preservation of the original human structure within the paraphrased text. This also proves that there is a detectable difference between the human-based text structure and ChatGPT-based text structure.

It has recently been shown that there are no multimedia fingerprinting codes that can find all malicious users when they use arbitrary linear attacks plus adversarial noise. It is shown that such codes exist if the complete recovery property is limited to the IPP property, i.e., the property to find at least one malicious user. Moreover, we extend this property to a property that allows us to detect all users whose contribution to the forgery is large enough. Efficient decoding (tracing traitors) algorithms are developed for these codes.

Linear measurements of the maxillary sinus are useful for gender prediction. This variation could be used for forensic identification purposes in mass disasters. The study aims to evaluate the accuracy of the artificial intelligence-based technique in gender prediction using linear measurements of maxillary sinus from Cone Beam Computed Tomography CBCT scans.

Utilizing data redundancy has been widely proposed as a practical solution to hide a substantial amount of secret data. For digital images, the distribution of pixels in a 2D matrix has increased the possibility of having redundancy since each pixel has a relation with the eight neighboring pixels. However, the 3D nature of video segments has introduced an additional source of data redundancy, namely temporal redundancy. Several recent video steganography schemes have suggested exploiting temporal redundancy to hide the secret data. This paper aims to fully capture the redundancy available in a video segment by incorporating both spatial and temporal redundancy using an Octree segmentation method. The input video signal is segmented into homogeneous variable-sized 3D cubes to maximize the redundancy in each region and thus, stronger energy compaction is expected in the 3D Discrete Cosine Transform (3D-DCT) domain. In other words, the hiding capacity is maximized since most of the signal will be concentrated within a few significant 3D-DCT coefficients leaving a large area with insignificant 3DDCT coefficients, which can be safely replaced by the secret data without noticeable degradations in the quality of the stego signal.

Recently, cyberbullying has grown significantly on social platforms, impacting users especially teenagers and young adults. The effects of this threat are so severe and damaging that could lead to suicide. Lately, this threat has become a significant issue in Arab countries, especially with the wide adoption of social media by the young generation. Most of existing research proposed solutions for detecting cyberbullying, mainly in English language. However, only few papers studied cyberbullying detection in Arabic Social Media Communications. This paper used machine learning for automatic detection of cyberbullying in Arabic. The proposed scheme detects cyberbullying using Naive Bayes(NB) classifier algorithm by training and testing the classifier with real data set which was collected from Youtube and Twitter.

Database outsourcing is a common cloud computing paradigm that allows data owners to take advantage of its on-demand storage and computational resources. The main challenge is maintaining data confidentiality with respect to untrusted parties i.e., cloud service provider, as well as providing relevant query results in real-time to authenticated users. Existing approaches either compromise confidentiality of the data or suffer from high communication cost between the server and the user. To overcome this problem, we propose a dual transformation and encryption scheme for spatial data, where encrypted queries are executed entirely at the service provider on the encrypted database and encrypted results are returned to the user. The user issues encrypted spatial range queries to the service provider and then uses the encryption key to decrypt the query response returned. This allows a balance between the security of data and efficient query response as the queries are processed on encrypted data at the cloud server. Moreover, we compare with existing approaches on large datasets and show that this approach reduces the average query communication cost between the authorized user and service provider, as only a single round of communication is required by the proposed approach.

Nowadays, social network sites; such as Facebook and Twitter, have tremendous number of users in their repositories. Having this huge amount of data requires analyzing them to get statistics about the users and their interests. In this paper, we propose a new algorithm that clusters the nodes in social networks into communities based on their geodesic location and the similarity between their interests. The algorithm is examined thoroughly to test its performance. The experiments show that the algorithm achieves a high community detection accuracy.

The increase of spatial data has led organizations to upload their data onto third-party service providers. Cloud computing allows data owners to outsource their databases, eliminating the need for costly storage and computational resources. The main challenge is maintaining data confidentiality with respect to untrusted parties as well as providing efficient and accurate query results to the authenticated users. We propose a dual transformation scheme on the spatial database to overcome this problem, while the service provider executes queries and returns results to the users. First, our approach utilizes the space-filling Hilbert curve to map each spatial point in the multidimensional space to a one-dimensional space. This space transformation method is easy to compute and preserves the spatial proximity. Next, the order-preserving encryption algorithm is applied to the clustered data. The user issues spatial range queries to the service provider on the encrypted Hilbert index and then uses a secret key to decrypt the query response returned. This allows data protection and reduces the query communication cost between the user and service provider.

Data streams and their applications appear in several fields such as physics, finance, medicine, environmental science, etc. As sensor technology improves, sensor data rates continue to increase. Consequently, analyzing data streams becomes ever more

A phenomenon appears in a sensor network when a group of sensors persist to generate similar behavior over a period of time. PhenomenaBases (or databases of phenomena) are equipped with phenomena detection and tracking (PDT) techniques that continuously run in the background of a sensor database system to detect new phenomena and to track already existing phenomena. The process of phenomena detection and tracking is initiated by a multi-way join operator that comes at the core of PDT techniques to report similar sensor readings. With the increase in the sensor network size, the join operator (and, consequently, query processing in the PhenomenaBase) face several scalability challenges. In this paper, we present a join operator for PhenomenaBases (the SNJoin operator) that is specially-designed for dynamically-configured large-scale sensor networks with distributed processing capabilities. Experimental studies illustrate the scalability of the proposed join operator in PhenomenaBases with respect to the number of detected phenomena and the output delay

Multicast delivery is an efficient approach to the provision of a video-on-demand (VoD) service. Interacting with the video stream is a desirable feature for users. However, it is a challenging task to provide the functionality in the multicast environment because a lot of users share multicast delivery channels. In this paper, we propose an active buffer management technique to provide interactive functions in broadcast VoD systems. In our scheme, the client can selectively prefetch segments from broadcast channels based on the observation of the play point in its local buffer. The content of the buffer is adjusted in such a way that the relative position of the play point is kept in the middle part of the buffer. Our simulations show that the active buffer management scheme can implement interactive actions through buffering with a high probability in a wide range of user interaction levels.

A new multimedia disk-scheduling algorithm, termed Cascaded-SFC, is presented. The Cascaded-SFC multimedia disk scheduler is applicable in environments where multimedia data requests arrive with different quality of service (QoS) requirements such as real-time deadline and user priority. Previous work on disk scheduling has focused on optimizing the seek times and/or meeting the real-time deadlines. The Cascaded-SFC disk scheduler provides a unified framework for multimedia disk scheduling that scales with the number of scheduling parameters. The general idea is based on modeling the multimedia disk requests as points in multiple multidimensional subspaces, where each of the dimensions represents one of the parameters (e.g., one dimension represents the request deadline, another represents the disk cylinder number, and a third dimension represents the priority of the request, etc.). Each multidimensional subspace represents a subset of the QoS parameters that share some common scheduling characteristics. Then the multimedia disk scheduling problem reduces to the problem of finding a linear order to traverse the multidimensional points in each subspace. Multiple space-filling curves are selected to fit the scheduling needs of the QoS parameters in each subspace. The orders in each subspace are integrated in a cascaded way to provide a total order for the whole space. Comprehensive experiments demonstrate the efficiency and scalability of the Cascaded-SFC disk scheduling algorithm over other disk schedulers.

This paper describes a novel cost-driven disk scheduling algorithm for environments consisting of multipriority requests. An example application is a video-on-demand (VOD) system that provides high and low quality services, termed priority 2 and 1, respectively. Customers ordering a high quality (priority 2) service pay a higher fee and are assigned a higher priority by the underlying system. Our proposed algorithm minimizes costs by maintaining one-queue and managing requests intelligently in order to meet the deadline of as many priority 1 requests as possible while maximizing the number of priority 2 requests that meet their deadline. Our algorithm is general enough to accommodate an arbitrary number of priority levels. Prior schemes, collectively termed "multiqueue" schemes maintain a separate queue for each priority level in order to optimize the performance of the high priority requests only. When compared with our proposed scheme, in certain cases, our technique provides more than one order of magnitude improvement in total cost.

A new quality of service (QoS) aware disk scheduling algorithm is presented. It is applicable in environments where data requests arrive with different QoS requirements such as real-time deadline, and user priority. Previous work on disk scheduling has focused on optimizing the seek times and/or meeting the real-time deadlines. A unified framework for QoS disk scheduling is presented that scales with the number of scheduling parameters. The general idea is based on modeling the disk scheduler requests as points in the multi-dimensional space, where each of the dimensions represents one of the parameters (e.g., one dimension represents the request deadline, another represents the disk cylinder number and a third dimension represents the priority of the request, etc.). Then the disk scheduling problem reduces to the problem of finding a linear order to traverse these multi-dimensional points. Space-filling curves are adopted to define a linear order for sorting and scheduling objects that lie in the multi-dimensional space. This generalizes the one-dimensional disk scheduling algorithms (e.g., EDF SATF, FIFO). Several techniques are presented to show how a QoS-aware disk scheduler deals with the progressive arrival of requests over time. Simulation experiments are presented to show a comparison of the alternative techniques and to demonstrate the scalability of the proposed QoS-aware disk scheduling algorithm over other traditional approaches.

This paper addresses the problem of providing different levels of performance guarantee for disk I/O. In typical applications, disk requests are classified into different categories based on the required quality of service (QoS), which is usually characterized by a priority and a deadline for each request. Traditional algorithms usually service all high priority requests before low priority requests, and this may result in potential starvation for the low priority requests. In this paper, a disk-scheduling algorithm is introduced to provide such QoS guarantee and avoid starvation. Our target applications for this algorithm are non-linear editing systems for continuous data, where the block size is large enough to ignore the seek time. The proposed algorithm tries to service a request with lower priority and strict deadline only if servicing this request will not violate the deadline constraints of a higher priority request. Simulation experiments are presented to show the superiority of the proposed algorithm over the traditional ones.

Modern video servers support both video-on-demand and nonlinear editing applications. Video-on-demand servers enable the user to view video clips or movies from a video database, while nonlinear editing systems enable the user to manipulate the content of the video database. Applications such as video and news editing systems require that the underlying storage server be able to concurrently record live broadcast information, modify prerecorded data, and broadcast an authored presentation. A multimedia storage server that efficiently supports such a diverse group of activities constitutes the focus of this study. A novel real-time disk scheduling algorithm is presented that treats both read and write requests in a homogeneous manner in order to ensure that their deadlines are met. Due to real-time demands of movie viewing, read requests have to be fulfilled within certain deadlines; otherwise, they are considered lost. Since the data to be written into disk is stored in main memory buffers, write requests can be postponed until critical read requests are processed. However, write requests still have to be processed within reasonable delays and without the possibility of indefinite postponement. This is due to the physical constraint of the limited size of the main memory write buffers. The new algorithm schedules both read and write requests appropriately, to minimize the amount of disk reads that do not meet their presentation deadlines, and to avoid indefinite postponement and large buffer sizes in the case of disk writes. Simulation results demonstrate that the proposed algorithm offers low violations of read deadlines, reduces waiting time for lower priority disk requests, and improves the throughput of the storage server by enhancing the utilization of available disk bandwidth.

We introduce a new deadline driven disk scheduling algorithm designed for multimedia servers. The proposed algorithm supports real time requests with multiple priorities, e.g., those for different object classes in digital library applications. The proposed algorithm enhances utilization of disk bandwidth by: maintaining one queue for all requests; and optimizing the seek time. Prior schemes, collectively termed "multi-queue schemes", maintain a separate queue for each priority group and optimize the performance of the high priority requests only. When compared with our proposed scheme, our technique provides approximately two order of magnitude improvement in meeting the deadline of low priority requests. In addition, this algorithm provides both a better disk utilization and a better average response time. Under certain conditions, our algorithm violates the deadline of a few high priority requests (less than 5 out of a million requests).

This paper presents an indexing method that can be used to search a large collection of cursive handwriting. The basic idea is to segment each cursive string into a set of strokes. Each of these strokes can be described with a set of features and, thus, can be stored as points in the feature space. The Karhuraen-Lor%e transform is then used to minimize the number of features used (data dimensionality) and thus the index size. Feature vectors are stored in an R-tree. Similarity search can be performed by executing a few range queries and then applying a simple voting algorithm to the output to select the strings that are most similar to the query. The proposed index can support similarity queries as well as substring mat thing. It is resilient to the kind of errors that result from the segmentation process, namely, stroke insertion/deletion and m-n substitution.

Article Free Access Share on The time index+: an incremental access structure for temporal databases Authors: Vram Kouramajian Department of Computer Science, The Wichita State University, Wichita, Kansas Department of Computer Science, The Wichita State University, Wichita, KansasView Profile , Ibrahim Kamel Department of Computer Science, University of Maryland, College Park, Maryland Department of Computer Science, University of Maryland, College Park, MarylandView Profile , Ramez Elmasri Computer Science Engineering Department, The University of Texas at Arlington, Arlington, Texas Computer Science Engineering Department, The University of Texas at Arlington, Arlington, TexasView Profile , Syed Waheed Computer Science Engineering Department, The University of Texas at Arlington, Arlington, Texas Computer Science Engineering Department, The University of Texas at Arlington, Arlington, TexasView Profile Authors Info & Claims CIKM '94: Proceedings of the third international conference on Information and knowledge managementNovember 1994 Pages 296–303https://doi.org/10.1145/191246.191298Online:29 November 1994Publication History 17citation423DownloadsMetricsTotal Citations17Total Downloads423Last 12 Months6Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

We propose the concept of fractal dimension of a set of points, in order to quantify the deviation from the uniformity distribution. Using measurements on real data sets (road intersections of U.S. counties, star coordinates from NASA's Infrared-Ultraviolet Explorer etc.) we provide evidence that real data indeed are skewed, and, moreover, we show that they behave as mathematical fractals, with a measurable, non-integer fractal dimension.

We propose new R-tree packing techniques for static databases.Given a collection of rectangles, we sort them and build the R-tree bottom-up.There are several ways time of a range query as a function of the geometric characteristics of the R-tree.

We consider the problem of exploiting parallelism to accelerate the performance of spacial access methods and specifically, R-trees [11]. Our goal is to design a server for spatial data, so that to maximize the throughput of range queries. This can be achieved by (a) maximizing parallelism for large range queries, and (b) by engaging as few disks as possible on point queries [22].