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This research is based on the practical facts related to the human resource department of any organization for the recruitment of personnel. As it is a challenging and crucial aspect for any organization to select the right talent for the right place. This paper helps in expertise finding in the different fields of Computer Science. Employers receive a bunch of resumes upon job openings. The primary purpose of this research study is to exploit the class NLP techniques to perform the information retrieval task for resume ranking based on job description similarity. In this proposed methodology, we compared document vectors with word embedding. Experiments show that word embedding method is more effective than the document vector.2, {"references":["[1]\tR. Kessler, J. M. Torres-Moreno, and M. El-Bèze, \"E-gen: Automatic job offer processing system for human resources,\" in MICAI 2007: Advances in Artificial Intelligence, Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. https://doi.org/10.1007/978-3-540-76631-5_94 [2]\tD. Looser, H. Ma, and K.-D. Schewe, \"Using formal concept analysis for ontology maintenance in human resource recruitment,\" in Proceedings of the Ninth Asia-Pacific Conference on Conceptual Modelling - Volume 143, 2013. [3]\tBojārs, U., & Breslin, J. G, . ResumeRDF: Expressing skill information on the Semantic Web. In 1 st International ExpertFinder Workshop. [4] Shibata, N., Kajikawa, Y., & Sakata, \"How to measure the semantic similarities between scientific papers and patents to discover uncommercialized research fronts: A case study of solar cells\". In PICMET 2010 technology management for global economic growth . IEEE. [5] Zhang, S., Zheng, X., & Hu, C. (2015, October). 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El Helou, and D. Gillet, \"A recommender system for job seeking and recruiting website,\" in Proceedings of the 22nd International Conference on World Wide Web - WWW '13 Companion, 2013. https://doi.org/10.1145/2487788.2488092 [14]\tC. Bizer, R. Heese, M. Mochol, R. Oldakowski, R. Tolksdorf, and R. Eckstein, \"The impact of semantic web technologies on job recruitment processes,\" in Wirtschaftsinformatik 2005, Heidelberg: Physica-Verlag HD, 2005, pp. 1367–1381. https://doi.org/10.1007/3-7908-1624-8_72 [15]\tM. Mochol, A. Jentzsch, M. Mochol, and A. Jentzsch, \"Applying an analytic method for matching approach selection ome Euzenat To cite this version : Applying an Analytic Method for Matching Approach Selection.\" 2013. [16]\tD. Celik et al., \"Towards an information extraction system based on ontology to match resumes and jobs,\" in 2013 IEEE 37th Annual Computer Software and Applications Conference Workshops, 2013. https://doi.org/10.1109/COMPSACW.2013.60 [17]\t\"An integrated e-recruitment system for CV ranking based on ahp,\" in Proceedings of the 7th International Conference on Web Information Systems and Technologies, 2011. https://doi.org/ 10.5220/0003337901470150. [18]\tP. Turney and M. L. Littman, \"Unsupervised learning of semantic orientation from a hundred-billion-word corpus,\" National Research Council of Canada, 2002. [19]\tU. Marjit, \"Discovering Resume Information using linked data,\" Int. j. web semant. technol., vol. 3, no. 2, pp. 51–62, 2012. https://doi.org/ 10.5121/ijwest.2012.3204 [20] Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J.\" Distributed representations of words and phrases and their compositionality\",2013. [21] Pennington, J., Socher, R., & Manning, C. D,. Glove \"Global vectors for word representation\" In Proceedings of the conference on empirical methods in natural language processing, 2014. [22] Manning, C. D., Raghavan, P., & Schütze, H,\" Xml retrieval. In Introduction to Information Retrieval\" Cambridze University Press 2008. https://doi.org/ 10.1109/INFRKM.2016.7806343 [23] Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (pp. 3111-3119). [24] Rehurek, R., & Sojka, P. (2010). Software framework for topic modelling with large corpora. In In Proceedings of the LREC 2010 workshop on new challenges for NLP frameworks. [25] Speer, R., Chin, J., & Havasi, C. (2017, February). Conceptnet 5.5: An open multilingual graph of general knowledge. In Thirty-first AAAI conference on artificial intelligence. [26] Ye, X., Shen, H., Ma, X., Bunescu, R., & Liu, C. (2016, May). From word embeddings to document similarities for improved information retrieval in software engineering. In Proceedings of the 38th international conference on software engineering (pp. 404-415). https://doi.org/10.1145/2884781.2884862 [27] Xia, X., Lo, D., Wang, X., & Zhou, B. (2013, May). Tag recommendation in software information sites. In 2013 10th Working Conference on Mining Software Repositories (MSR) (pp. 287-296). IEEE. https://doi.org/10.1109/MSR.2013.6624040"]}

Face detection is an important problem in computer vision research and applications are getting trending due to the advancement in the file of machine learning and computer vision. This research proposed a face detection method based on an enhanced Multi-Task Convolution Neural Network (MTCNN) and improves the network of MTCNN, creates a neural network model based on MTCNN using Python, and cascades to increase the accuracy of face location in difficult scenarios. In this research paper, we evaluated the performance of three famous face detector models on CPU-based machines. MTCNN and DLIB based detectors are designed for GPU-based machines, {"references":["P. Viola and M. Jones, \"Robust real-time face detection,\" in Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, 2005. [2]\tA. Benzaoui, H. Bourouba, and A. 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Scalability and ease of implementation make Peer-to-Peer (P2P) infrastructure an attractive option for live video streaming. Peer end-users or peers in these networks have extremely complex features and exhibit unpredictable behavior, i.e. any peer may join or exit the network without prior notice. Peers' dynamics is considered one of the key problems impacting the Quality of Service (QoS) of the P2P based IPTV services. Since, peer dynamics results in video disruption to consumer peers, for smooth video distribution, stable peer identification and selection is essential. Many research works have been conducted on stable peer identification using classical statistical methods. In this paper, a model based on machine learning is proposed in order to predict the length of a user session on entering the network. This prediction can be utilized in topology management such as offloading the departing peer before its exit. Consequently, this will help peers to select stable provider peers, which are the ones with longer session duration. Furthermore, it will also enable service providers to identify stable peers in a live video streaming network. Results indicate that the SVR based model performance is superior to an existing Bayesian network model.

In this paper, we propose simple and effective compression of CSLBP (Center Symmetric Local Binary Pattern) descriptors, which is a textured based operator and mostly used as key point descriptor. With default parameters for computation, it is 256-length descriptor for each keypoint or affine patch. CSLBP is an extended form of LBP (Local Binary Patterns). The calculation of CSLBP descriptor is effective, robust, and straightforward for different image transformations for instance; image blurring and illumination alteration. However, an improvement in time and space consumption of CSLBP can be attained by means of simple compression. For this reason, CSLBP is a smart choice for smart phones as well as large databases. We reduce the descriptor length (dimensions) upto 50% without applying any techniques of dimensionality reduction like PCA (Principle Component Analysis) or LDA (Linear Discriminant Analysis). The compressed CSLBP descriptor is denoted as C-CSLBP. The performance of C-CSLBP is evaluated on state-of-the-art datasets using standard metrics. It is quantitatively shown by experiments that C-CSLBP is equivalently effective compared to CSLBP despite of reduced dimensions

In this paper, the performance of Facial Expression Recognition (FER) using Deep Convolutional Neural Network (DCNN) model is evaluated. The expressions include Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral. In addition to performance evaluation, the analysis on Interclass false positives are also discussed which helps to analyze the underlying challenges to improve the model. All classifiers give low performance on Fer2013 datasets. DCNN gives 54.46% accuracy on test and 89.52% on training set, whereas, in case of different kernels of Support Vector Machines (SVM), the highest accuracy is 45% using cubic kernel on training set. Experiments show that certain facial expressions have more false positives and few of them are very dominant. In case of Disgust expression, it has Angry as dominant false positive. Based on false positives analysis, binary classifiers can be trained to improve the accuracy of the expressions with dominant false positives. Experiments on Fer2013 dataset confirms that the accuracy of expression Disgust is improved by piping the binary classifiers, Disgust vs. Angry, to existing DCNN of multi-class.

The usefulness and importance of sentiment analysis task is a widely discussed and effective technique in e-commerce. E-commerce is a very convenient way to buy things online. It saves a lot of time that is usually spent traveling and buying by visiting the shops. E-commerce provides an efficient and effective way to shop sitting right in front of one’s computer/mobile at home. For a given product, sentiment analysis captures the users views; their feelings and opinion related to that product. The reviews are categorized into three basic classes i.e. negative, positive, and neutral. This paper focuses on Urdu Roman reviews that are obtained by one of the most famous and accessed e-commerce website of Pakistan–Daraz.pk. There are total 20.286 K reviews which are annotated into three classes by three different experts. Vector space model, a.k.a bag of word model is applied for feature extraction which are later passed to Support Vector Machines (SVM) for sentiment classification. Experiments are conducted on MATLAB Linux server. The dataset is kept public for future use and experiments.

Keypoint based descriptors are widely used for various computer vision applications. During this process, key-points are initially detected from the given images which are later represented by some robust and distinctive descriptors like scale-invariant feature transform (SIFT). Keypoint based image-to-image matching has gained significant accuracy for image retrieval type of applications like image copy detection, similar image retrieval and near duplicate detection. Local keypoint descriptors are quantized into visual words to reduce the feature space which makes image-to-image matching possible for large scale applications. Bag of visual word quantization makes it efficient at the cost of accuracy. In this paper, the bag of visual word model is extended to detect frequent pair of visual words which is known as frequent item-set in text processing, also called visual phrases. Visual phrases increase the accuracy of image retrieval without increasing the vocabulary size. Experiments are carried out on benchmark datasets that depict the effectiveness of proposed scheme.

Support Vector Machines (SVMs) have shown bet-ter generalization and classification capabilities in different appli-cations of computer vision; SVM classifies underlying data by a hyperplane that can separate the two classes by maintaining the maximum margin between the support vectors of the respective classes. An empirical analysis of SVMs on the facial expression recognition task is reported with high intra and low inter class variations by conducting an extensive set of experiments on a large-scale Fer 2013 dataset. Three different kernel functions of SVM are used; linear kernel, quadratic kernel and cubic kernel, whereas, Histogram of Oriented Gradient (HoG) is used as a feature descriptor. Cubic Kernel achieves highest accuracy on Fer 2013 dataset using HoG.

Searching the information from the web has attracted the attention of industries and researchers, relaying mainly on text based queries. Text based queries are foundation of visual or text based information for retrieval. However, query as visual content also getting popularity. The main problem for visual query is the computational cost for searching. A number of text based inspiring frameworks were proposed for said objective. Bag of word model is one of the prominent and state-of-the art method for text retrieval, also widely used in visual search known as bag of visual word. In this paper, we proposed bag of visual word model based framework for image retrieval that encodes the geometric information of given point in the image. Our bag of visual word representation is relatively compact and effective. Experiments on benchmark dataset, Oxford 5-K, show the effectiveness of proposed framework.

Image segmentation helps computer to understand visual information. A number of techniques are proposed for image segmentation that are either interactive or automatic. Interactive techniques provide satisfactory result but user interaction is the biggest limitation when large number of images are considered. On the other hand automatic image segmentation is difficult to acquire satisfactory results. We propose an unsupervised approach for automatic image segmentation that employ image color information to segment an image into foreground and background. The results confirm the effectiveness of the approach.

Convolutional Neural Network (NN) has gained a lot of attention of the researchers due to its high accuracy in classification and feature learning. In this paper, we evaluated the performance of CNN used as feature for image retrieval with the gold standard feature, aka SIFT. Experiments are conducted on famous Oxford 5k data-set. The mAP of SIFT and CNN is 0.6279 and 0.5284, respectively. The performance of CNN is also compared with bag of visual word (BoVW) model. CNN achieves better accuracy than BoVW.

Images have become main sources for the informa-tion, learning, and entertainment, but due to the advancement and progress in multimedia technologies, millions of images are shared on Internet daily which can be easily duplicated and redistributed. Distribution of these duplicated and transformed images cause a lot of problems and challenges such as piracy, redundancy, and content-based image indexing and retrieval. To address these problems, copy detection system based on local features are widely used. Initially, keypoints are detected and represented by some robust descriptors. The descriptors are computed over the affine patches around the keypoints, these patches should be repeatable under photometric and geometric transformations. However, there exist two main challenges with patch based descriptors, (1) the affine patch over the keypoint can produce similar descriptors under entirely different scene or the context which causes “ambiguity”, and (2) the descriptors are not enough “distinctive” under image noise. Due to these limitations, the copy detection systems suffer in performance. We present a framework that makes descriptor more distinguishable and robust by influencing them with the texture and gradients in vicinity. The experimental evaluation on keypoints matching and image copy detection under severe transformations shows the effectiveness of the proposed framework.

Image segmentation is one of the most significant tasks in computer vision. Since automatic techniques are hard for this purpose, a number of interactive techniques are used for image segmentation. The result of these techniques largely depends on user feedback. It is difficult to get good interactions for large databases. On the other hand, automatic image segmentation is becoming a significant objective in computer vision and image analysis. We propose an automatic framework to detect foreground. We are applying Maximal Similarity Based Region Merging (MSRM) technique for region merging and using image boundary to identify foreground regions. The results confirm the effectiveness of the proposed framework. The proposed framework reveals its effectiveness especially to extract multiple objects from background.

Images have become one of the main sources for the information, learning and entertainment; but due to the advancement and progress in multimedia technologies, millions of images are shared daily on Internet which can be easily duplicated and redistributed. Distribution of these duplicated and transformed images causes a lot of problems and challenges such as piracy, redundancy, and content-based image indexing and retrieval. To address these problems, copy detection systems based on local features are widely used. Initially, keypoints are detected and represented by some robust descriptors. The descriptors are computed over the affine patches around the keypoints, these patches should be repeatable under photometric and geometric transformations. However, there exists two main challenges with patch based descriptors, (1) the affine patch over the keypoint can produce similar descriptors under entirely different scene or context which causes ``ambiguity'' (in-distinctiveness), and (2) the descriptors are not enough ``robust'' under image noise. In this paper, we present a framework that makes descriptor more distinguishable and robust by influencing them with the texture or gradients in vicinity by computing them on different and multiple scales. To evaluate the robustness of descriptors, an experiment on keypoint matching under severe transformations is conducted. On average the robustness of SIFT descriptor is increased up-to 12.5\%, and robustness of CSLBP descriptor is increased up-to 31\%. The distinctiveness is evaluated on image copy retrieval experiment where copies of images are retrieved under challenging transformations. On average, the performance of SIFT to retrieve all copies is increased up-to 27.27\%, and the performance of CSLBP to retrieve all copies is increased up-to 27.02\%.