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Content-based video search engines (CBVSE) are broadly needed in many mainstream video search engines retrieving videos from public video streaming services over the Internet such as YouTube. As they are mostly text-based search engines that index and retrieve videos depending on the surrounding text around the video web page that contains information representing this video file. This paper is an attempt to improve the performance of a previously developed technique for a content-based video search engine designed to firstly index videos on YouTube and search and retrieve videos using a non-semantic video query. Moreover, a large-scale dataset was indexed containing more than 1088 YouTube video records. Each record contains a feature vector of the temporal set, key-objects sets, and keyframes representing video shots in each video file in addition to the URLs and other information gathered from each video file web page.

Most health care systems use various physiological signals to provide an accurate diagnosis performance. The main common signals functional in health care applications are the electrocardiogram (ECG) and photoplethysmogram (PPG). ECG signal represents the electrical cardiac activity of the heart, while the PPG signal measures the changes in the blood volume. There are several applications in which the ECG combined with PPG can be used in the field of medical health care. This survey illustrates the various applications that combine features from the ECG and PPG signals. The review manifests the techniques, methodologies used in the data acquisition, pre-processing of the signals. The feature extraction and classification phases for both ECG and PPG are explained. The limitations, challenges, and future directions for the combined application of ECG and PPG are clarified to solve the medical problems that existed, presented, and feasible. This study aims to increase the interest in applying the combination between ECG and PPG signals in more applications and to obtain optimal measurements related to cardiac activity.

Arabic language has a set of sound letters called diacritics, these diacritics play an essential role in the meaning of words and their articulations. The change in some diacritics leads to a change in the context of the sentence. However, the existence of these letters in the corpus transcription affects the accuracy of speech recognition. In this paper, we investigate the effect of diactrics on the Arabic speech recognition based end-to-end deep learning. The applied end-to-end approach includes CNN-LSTM and attention-based technique presented in the state-of-the-art framework namely, Espresso using Pytorch. In addition, and to the best of our knowledge, the approach of CNN-LSTM with attention-based has not been used in the task of Arabic Automatic speech recognition (ASR). To fill this gap, this paper proposes a new approach based on CNN-LSTM with attention based method for Arabic ASR. The language model in this approach is trained using RNN-LM and LSTM-LM and based on nondiacritized transcription of the speech corpus. The Standard Arabic Single Speaker Corpus (SASSC), after omitting the diacritics, is used to train and test the deep learning model. Experimental results show that the removal of diacritics decreased out-of-vocabulary and perplexity of the language model. In addition, the word error rate (WER) is significantly improved when compared to diacritized data. The achieved average reduction in WER is 13.52%.

End-to-end deep learning approach has greatly enhanced the performance of speech recognition systems. With deep learning techniques, the overfitting stills the main problem with a little data. Data augmentation is a suitable solution for the overfitting problem, which is adopted to improve the quantity of training data and enhance robustness of the models. In this paper, we investigate data augmentation method for enhancing Arabic automatic speech recognition (ASR) based on end-to-end deep learning. Data augmentation is applied on original corpus for increasing training data by applying noise adaptation, pitch-shifting, and speed transformation. An CNN-LSTM and attention-based encoder-decoder method are included in building the acoustic model and decoding phase. This method is considered as state-of-art in end-to-end deep learning, and to the best of our knowledge, there is no prior research employed data augmentation for CNN-LSTM and attention-based model in Arabic ASR systems. In addition, the language model is built using RNN-LM and LSTM-LM methods. The Standard Arabic Single Speaker Corpus (SASSC) without diacritics is used as an original corpus. Experimental results show that applying data augmentation improved word error rate (WER) when compared with the same approach without data augmentation. The achieved average reduction in WER is 4.55%.

Numerous researches on content-based video indexing and retrieval besides video search engines are tied to a large-scaled video dataset. Unfortunately, reduction in open-sourced datasets resulted in complications for novel approaches exploration. Although, video datasets that index video files located on public video streaming services have other purposes, such as annotation, learning, classification, and other computer vision areas, with little interest in indexing public video links for purpose of searching and retrieval. This paper introduces a novel large-scaled dataset based on YouTube video links to evaluate the proposed content-based video search engine, gathered 1088 videos, that represent more than 65 hours of video, 11,000 video shots, and 66,000 unmarked and marked keyframes, 80 different object names used for marking. Moreover, a state-of-the-art features vector, and combinational-based matching, beneficial to the accuracy, speed, and precision of the video retrieval process. Any video record in the dataset is represented by three features: temporal combination vector, object combination vector with shot annotations, and 6 keyframes, sideways with other metadata. Video classification for the dataset was also imposed to expand the efficiency of retrieval of video-based queries. A two-phased approach has been used based on object and event classification, storing video records in aggregations related to feature vectors extracted. While object aggregation stores video records with the maximal occurrence of extracted object/concept from all shots, event aggregation classify based on groups according to the number of shots per video. This study indexed 58 out of 80 different object/concept categories, each has 9 shot number groups.

With the vast growth and progress of multimedia on the Internet, particularly videos, which caused a growing demand for video retrieval, organizing, and automate systems, as many users mandate content-based retrieval systems over text-based retrieval systems. The process of extracting and selecting features, plays a very important role in content-based video retrieval regardless the significance of video characteristics. The reduction of time and space costs of the retrieval process is dependent on effective features selection. In this paper, we will discuss content-based video indexing and retrieval, in the application of video search engines CBVSE, which is the problem of searching for digital videos over the Internet. Addressing diverse approaches regarding video indexing and retrieval with brief outline and classification. Furthermore, we will propose a framework for a CBVSE that will offer a less complex solution for content-based video retrieval for video search engines over the web using reduced features vector with better accuracy to decrease the complexity trade-off. These features include temporal boundaries patterns combination and key-object concept features, as well as classifying the index list with a multi key-object concepts arrangement built on the statistical redundancy of the detected key-object concepts for each video shot in the sequence.

Concrete structures are increasing every day, to facilitate people's lives. With this expansion, the traditional manual maintenance method becomes unpractical, costly and time-wasting. The fast detection and maintenance of concrete surfaces defects is necessary to save people's lives, reducing maintenance cost, and increase the lifetime of concrete structures. Thus, the researches came up over the last twenty years to find an automatic way in order to maintain, apply regularly check-ups over concrete structures and assist engineers to take fast decisions. The most researches came up with high precision algorithms to allocate cracks and defects over the concrete surfaces with no human intervention. Nowadays, the computer programs can be dependable to capture large data sets of concrete structure, and then give precise locations of cracks. However, there exists a lack of researches that work on crack interpretation and automatic decision-making, which is considered as a critical part of those systems. Therefore, there exists a need for methods that describe the crack characteristics in terms of width, length, and other morphological attributes. In this paper, a crack interpretation algorithm is proposed to extract crack geometrical attributes and support the decision maker.

Live Migration is the backbone of dynamic VM consolidation in modern data centers. However, live migration process comes with a cost, including VM performance degradation, downtime and energy cost. In this work, we present and evaluate the research attempts to quantify the energy cost that accompanies live migration process. In order to measure the energy overhead, the amount of power drawn during migration and the total migration time are needed to be quantified using mathematical models. We find that the network bandwidth and the page dirtying rate varying according to the type of workload are the major factors influencing the amount of power drawn by a physical server. While the VM size and the network bandwidth are the factors affecting the total migration time.

Intelligent agents - as a modern artificial intelligence concept - are now widely deployed in various software systems. The agent can be defined as a software entity which functions continuously and autonomously in a particular environment, able to carry out activities in a flexible and intelligent manner that is responsive to changes in the environment, and able to learn from its experience. An intrusion detection system can be decomposed into steps where an agent can perform a single or more step. But performance evaluation techniques of their use are still in the early stage. This infancy status holds especially true for agents used in intrusion detection systems (IDS) since they are new to this field. We believe that the two most important factors to measure for a given IDS are the risk time and the detection time. The risk time is the time at which the computer could be under risk of attack since the intrusion is not discovered yet. The smaller the detection time is the smaller the risk time. Thus, as a step towards a complete evaluation strategy, a simple IDS with agents was tested.

This article describes a framework for intrusion detection using agent-based technology. Agents are ideally qualified due to their reactivity, interactivity, autonomy and intelligence. The system discussed is implemented in a TCP/IP LAN (local area network) environment. It represents a step towards a complete multi-agent based system for networking security.

The main objective of this study was to understand the residue and persistence behaviour of new insecticide chlorantraniliprole in tomato fruit and soil samples. Its residue was analyzed by HPLC and it dissipated in tomato fruit and soil following first order kinetics. The results showed half life (t(1/2)) value of 3.30 and 3.66 days for chlorantraniliprole in tomato fruit and soil, respectively. According to maximum residue limit (MRL) the pre-harvest interval (PHI) of chlorantraniliprole on tomato was 8-days after the treatment.

Networking security demands have been considerably increased during the last few years. One of the critical networking security applications is the intrusion detection system. Intrusion detection systems should be faster enough to catch different types of intruders. This paper describes a framework for multiagent-based system for intrusion detection using the agent-based technology. Agents are ideally qualified to play an important role in intrusion detection systems due to their reactivity, interactivity, autonomy, and intelligence. The system is implemented in a real TCP/IP LAN environment. It is considered a step towards a complete multiagent based system for networking security.