Reviewerly/exHarmony huggingface dataset:
Yet Another Framework for Tweet Entity Linking (YAFTEL)

Social media data has become an important source of information. Linking a named entity mentioned in tweets with a knowledge base is called Named Entity Linking. By linking a tweet with a knowledge base, it clarifies the implication of the named entity. The drawback of tweet entity linking is that Twitter limits a status to 280 characters in a tweet and this fact leaves a tweet with less semantics compared with a lengthy document. The proposed framework 'Yet Another Framework for Tweet Entity Linking (YAFTEL)' incorporates the degree of direct references between candidate entities into the traditional approach adopted by the KAURI system. In our approach, two candidate entities get a higher score for entity mapping when at least one of them references the other. Our experiment shows that YAFTEL maps entity mentions to Wikipedia entities more accurately than KAURI when candidate entities reference mutually or in one-way.
Measuring Semantic Relatedness with Knowledge Association Network

Measuring semantic relatedness between two words is a fundamental task for many applications in both databases and natural language processing domains. Conventional methods mainly utilize the latent semantic information hidden in lexical databases (WordNet) or text corpus (Wikipedia). They have made great achievements based on the distance computation in lexical tree or co-occurrence principle in Wikipedia. However these methods suffer from low coverage and low precision because (1) lexical database contains abundant lexical information but lacks semantic information; (2) in Wikipedia, two related words (e.g. synonyms) may not appear in a window size or a sentence, and unrelated ones may be mentioned together by chance. To compute semantic relatedness more accurately, some other approaches have made great efforts based on free association network and achieved a significant improvement on relatedness measurement. Nevertheless, they need complex preprocessing in Wikipedia. Besides, the fixed score functions they adopt cause the lack of flexibility and expressiveness of model. In this paper, we leverage DBPedia and Wikipedia to construct a Knowledge Association Network (KAN) which avoids the information extraction of Wikipedia. We propose a flexible and expressive model to represent entities behind the words, in which attribute and topological structure information of entities are embedded in vector space simultaneously. The experiment results based on standard datasets show the better effectiveness of our model compared to previous models.
Classifying and Grouping Mammography Images into Communities Using Fisher Information Networks to Assist the Diagnosis of Breast Cancer

The aim of this paper is to build a computer based clinical decision support tool using a semi-supervised framework, the Fisher Information Network (FIN), for visualization of a set of mammographic images. The FIN organizes the images into a similarity network from which, for any new image, reference images that are closely related can be identified. This enables clinicians to review not just the reference images but also ancillary information e.g. about response to therapy. The Fisher information metric defines a Riemannian space where distances reflect similarity with respect to a given probability distribution. This metric is informed about generative properties of data, and hence assesses the importance of directions in space of parameters. It automatically performs feature relevance detection. This approach focusses on the interpretability of the model from the standpoint of the clinical user. Model predictions were validated using the prevalence of classes in each of the clusters identified by the FIN.
A Hybrid Progressive Image Compression, Transmission, and Reconstruction Architecture

Image compression and progressive transmission (PIT) is a technique used as an alternative solution to the communication problem, where there is a need to transmit huge data such as in medical image transmission. The technique divides image that is to be transmitted into several phases and effectively provides a fairly accurate reconstruction of the original image in every phase. This paper proposes a novel architecture that combines bit-plane slicing, DD-dual-tree DWT, and SPIHT. The bit-plane slicing separates the image into eight planes; each bit plane is then decomposed with wavelet. The wavelet coefficients in different sub-bands are then encoded using SPIHT and transmitted progressively and reconstructed by combining the data received in each phase with the previous phase data. Experimental results have shown that the proposed technique has better efficiency. Even at the lowest SPIHT rate of 0.1 bits/pixel for a 512 × 512 gray scale medical image, the reconstructed image shows higher quality at third phase itself, which makes it an efficient choice for transmission of medical images even at low speed communication channel.
Empirical Investigation of Stale Value Tolerance on Parallel RNN Training

The objective of this paper is to provide a detailed understanding of stale value tolerance of parallel training. During parallel training, multiple workers read-and-modify shared model parameters multiple times, incurring multiple data transactions between workers, most of which are redundant due to the stale value tolerant characteristic of training. While considerable effort has tried to reduce the excessive data communication by utilizing stale value tolerance, there is a lack of detailed understanding of stale value tolerance and its dependence on multiple design choices in training of neural networks. This ambiguity has prevented domain experts from designing systems that take full advantage of the performance potential by leveraging stale value tolerance. This paper investigates how communication reduction affects the progress of parallel training for recurrent neural networks (RNN). We investigate stale value tolerance of RNN training by varying the update density, activation functions, and learning rate.
Tailoring an Interpretable Neural Language Model

Neural networks have shown great potential in language modeling. Currently, the dominant approach to language modeling is based on recurrent neural networks (RNNs) and convolutional neural networks (CNNs). Nonetheless, it is not clear why RNNs and CNNs are suitable for the language modeling task since these neural models are lack of interpretability. The goal of this paper is to tailor an interpretable neural model as an alternative to RNNs and CNNs for the language modeling task. This paper proposes a unified framework for language modeling, which can partly interpret the rationales behind existing language models (LMs). Based on the proposed framework, an interpretable neural language model (INLM) is proposed, including a tailored architectural structure and a tailored learning method for the language modeling task. The proposed INLM can be approximated as a parameterized auto-regressive moving average model and provides interpretability in two aspects: component interpretability and prediction interpretability. Experiments demonstrate that the proposed INLM outperforms some typical neural LMs on several language modeling datasets and on the switchboard speech recognition task. Further experiments also show that the proposed INLM is competitive with the state-of-the-art long short-term memory LMs on the Penn Treebank and WikiText-2 datasets.
Towards Robust Arbitrarily Oriented Subspace Clustering

Clustering high-dimensional data is challenging since meaningful clusters usually hide in the arbitrarily oriented subspaces, and classical clustering algorithms like k-means tend to fail in such case. Subspace clustering has thus attracted growing attention in the last decade and many algorithms have been proposed such as ORCLUS and 4C. However, existing approaches are usually sensitive to global and/or local noisy points, and the overlapping subspace clusters are little explored. Beyond, these approaches usually involve the exhaustive local search for correlated points or subspaces, which is infeasible in some cases. To deal with these problems, in this paper, we introduce a new subspace clustering algorithm called RAOSC, which formulates the Robust Arbitrarily Oriented Subspace Clustering as a group structure low-rank optimization problem. RAOSC is able to recover subspace clusters from a sea of noise while noise and overlapping points can be naturally identified during the optimization process. Unlike existing low-rank based subspace clustering methods, RAOSC can explicitly produce the subspaces of clusters without any prior knowledge of subspace dimensionality. Furthermore, RAOSC does not need a post-processing procedure to obtain the clustering result. Extensive experiments on both synthetic and real-world data sets have demonstrated that RAOSC allows yielding high-quality clusterings and outperforms many state-of-the-art algorithms.
Object Detection and Recognition

In the previous chapter, you learned about image segmentation and contours. You also learned how to detect lines and circles using Hough lines and circles in OpenCV. In this chapter, you will learn how to detect objects and label them. Object detection is one of the most widely used capabilities of computer vision in multiple domains. In Chapter 1 , you saw some real-world use cases. In this chapter, you will start with object detection and then move on to object recognition, landmark identification, and finally handwriting recognition.
Model Driven Architectural Design of Information Security System

The main objective of the paper is to present model-driven approach to development of Information Security System. We use data centric models, in which the main focus is on the data and we define a conceptual model of Information Security System architecture using the main information security concepts. Its construction is based on the domain analysis organized around the viewpoints "Information Security" and "Information Processing". The meta-models based on these viewpoints concern different aspects of the data and data protection. They are based on the summary of our practical experience in information security activities. Then the conceptual model is transformed to system design model with the help of UML – class, activity and deployment diagrams that transform the conceptual model of system architecture into actual solution or physical system.
A Spatial Filtering Model in Recommender Systems using Fuzzy Approach

Recommender systems analyze conditions and user behaviors to recommend proportional services to users. Since the aim of such systems is to provide the most appropriate services, it appears essential to use filtering techniques to limit recommender items. In this study, spatial criteria such as distance, movement direction, visibility, and topological relationships were employed as filtering tools to provide the right items. Our model creates appropriate items for better recommendation based on spatial relationships between users and the surrounding service sites. This method demonstrates that the number of recommended items can be limited by considering the shortest distance from the service centers intended by users and taking user direction into account. Moreover, appropriate service centers can be proposed with respect to user visibility. In this study, topological relationships between user location and near places were used as spatial filters, too. Further, if these filters can interact with the environment in the same way as humans, it can be expected the recommendation process to improve. Thus, our model uses the fuzzy approach to help the system to perceive the uncertainty of the spatial linguistic terms. To evaluate the performance and effectiveness of our proposed spatial filtering model, we conduct several experiments on real datasets that were obtained from tracking the users’ location through GPS. Considering the actual conditions, this system solved the cold start problem using spatial filtering model. Experimental results show that 68% of test users considered our recommendations as relevant in new item cold start problem. Moreover, results reveal that compared with an LA-LDA model, using spatial filtering in cold start item problem is more robust.
Advantages of Augmented Reality, Virtual Reality, QR Code, Near Field Communication, Geo-Tagging, Geo-Fencing, and Geo-Targeting for Marketing Tourism

The developing number of applications available through smart devices provides an increasing diversity of tourism marketing, destination and city marketing, tourism business marketing, and advertising. In the last decade, marketers have developed more creative practices to attract consumers. These new marketing tools are augmented reality, virtual reality, QR code, near field communication (NFC), geo-tagging, geo-fencing, and geo-targeting applications with the smart technologies, which are considered as a new marketing communication instruments these increase awareness and help get information about tourist and give information to the tourist. This chapter presents the profile of smart marketing applications and their marketing benefits in tourism industry. More specifically, this chapter aimed to achieve the following research objectives: to determine the potentials of the augmented reality, virtual reality, QR code, near field communication (NFC), geo-tagging, geo-fencing, and geo-targeting applications in the tourism industry.
Bar Induction is Compatible with Constructive Type Theory

Powerful yet effective induction principles play an important role in computing, being a paramount component of programming languages, automated reasoning, and program verification systems. The Bar Induction (BI) principle is a fundamental concept of intuitionism, which is equivalent to the standard principle of transfinite induction. In this work, we investigate the compatibility of several variants of BI with Constructive Type Theory (CTT), a dependent type theory in the spirit of Martin-Löf’s extensional theory. We first show that CTT is compatible with a BI principle for sequences of numbers. Then, we establish the compatibility of CTT with a more general BI principle for sequences of name-free closed terms. The formalization of the latter principle within the theory involved enriching CTT’s term syntax with a limit constructor and showing that consistency is preserved. Furthermore, we provide novel insights regarding BI, such as the non-truncated version of BI on monotone bars being intuitionistically false. These enhancements are carried out formally using the Nuprl proof assistant that implements CTT and the formalization of CTT within the Coq proof assistant presented in previous works.
Smart Home Automation using Computer Vision and Segmented Image Processing

The objective of this paper is to develop a smart IoT based light control system. Due to negligence and forgetfulness, many instances occur in establishments where the electrical appliances are left turned on even if there no human presence in a room. This is one of the most prominent cases of electricity wastage prevalent in society. Hence there is a need for an intelligent system that can ensure both efficiency and effectiveness. This project combines IoT, Artificial Intelligence and Image Processing which are powerful modern technologies. In this system, object detection methods are used which enable us to control appliances in a specific spatial region. It also uses image processing methods which are more efficient than conventional IR blaster-based home automation systems. conventional IR blasters inevitably come with a fallacy where any and all objects that obstruct the infrared ray trigger whatever response the system was programmed to achieve. These actions that are only meant for actual human beings can now be activated by any object. This produces an undesired result. This paper proposes a system which can efficiently utilize the lighting output and minimize the wastage of electricity by controlling the electrical appliances by detecting changes in the position of the humans in the room.
Jargon of Hadoop MapReduce scheduling techniques: a scientific categorization

Abstract Recently, valuable knowledge that can be retrieved from a huge volume of datasets (called Big Data) set in motion the development of frameworks to process data based on parallel and distributed computing, including Apache Hadoop, Facebook Corona, and Microsoft Dryad. Apache Hadoop is an open source implementation of Google MapReduce that attracted strong attention from the research community both in academia and industry. Hadoop MapReduce scheduling algorithms play a critical role in the management of large commodity clusters, controlling QoS requirements by supervising users, jobs, and tasks execution. Hadoop MapReduce comprises three schedulers: FIFO, Fair, and Capacity. However, the research community has developed new optimizations to consider advances and dynamic changes in hardware and operating environments. Numerous efforts have been made in the literature to address issues of network congestion, straggling, data locality, heterogeneity, resource under-utilization, and skew mitigation in Hadoop scheduling. Recently, the volume of research published in journals and conferences about Hadoop scheduling has consistently increased, which makes it difficult for researchers to grasp the overall view of research and areas that require further investigation. A scientific literature review has been conducted in this study to assess preceding research contributions to the Apache Hadoop scheduling mechanism. We classify and quantify the main issues addressed in the literature based on their jargon and areas addressed. Moreover, we explain and discuss the various challenges and open issue aspects in Hadoop scheduling optimizations.
Using PESTELMO to Frame HCI Contextual Development in Developing Countries

HCI4D is context-oriented. Literature highlights that successful implementation of HCI solutions in developing countries needs deeper understanding of the context. Despite numerous literatures pointing the importance of context-oriented HCI4D, there is no holistic method that guides researchers and implementers of HCI solutions in analysing different contexts of countries. To bridge this gap, this paper introduces the PESTELMO (Political, Economic, Socio-cultural, Technological, Environmental, Managerial and Organisational) analysis method for holistic analysis of contexts in which HCI4D is researched or implemented. PESTELMO analysis method is proposed in this research as an integrated approach to analyse the external and internal environment surround HCI4D research or deployment, as a suitable method for analysis of dynamic political, economic, socio-cultural, technological, environmental and legal parameters in long-term planning and finally as a holistic approach to assess relations, interactions and interdependences among the PESTELMO factors and sub-factors.
Investigating Visualisation Techniques for Rapid Triage of Digital Forensic Evidence

This study investigates the feasibility of a tool that allows digital forensics (DF) investigators to efficiently triage device datasets during the collection phase of an investigation. This tool utilises data visualisation techniques to display images found in near real-time to the end user. Findings indicate that participants were able to accurately identify contraband material whilst using this tool, however, classification accuracy dropped slightly with larger datasets. Combined with participant feedback, the results show that the proposed triage method is indeed feasible, and this tool provides a solid foundation for the continuation of further work.
Value of Information: Quantification and Application to Coalition Machine Learning

The creation of good machine learning models relies on the availability of good training data. In coalition settings, this training data may be obtained from many different coalition partners. However, due to the difference in the trust level of the coalition partners, the value of the information provided by the coalition partners could be questionable. In this paper, we examine the concept of Value of Information, provide a quantitative measure for it, and show how this can be used to determine the policies for information fusion in the training of machine learning models.
Foreground Feature Enhancement for Object Detection

Deep convolutional neural networks have shown great success in object detection. Most object detection methods focus on improving network architecture and introducing additional objective functions to improve the discrimination of object detectors, while the informative annotations of the training data obtained from enormous human effort are mainly used in the last stage of the network for producing supervisions, thus being under-explored. In this paper, we propose to take further advantage of bounding box annotations to highlight the feature map of foreground objects by erasing background noise with a novel Mask loss, in which process L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm is further incorporated to avoid degenerated features. The extensive experiments on PASCAL VOC 2007, VOC 2012, and COCO 2017 will demonstrate the proposed method can greatly improve detection performance compared with baseline models, thus achieving competitive results.
Proof-theoretic pluralism

Starting from a proof-theoretic perspective, where meaning is determined by the inference rules governing logical operators, in this paper we primarily aim at developing a proof-theoretic alternative to the model-theoretic meaning-invariant logical pluralism discussed in Beall and Restall (Logical pluralism, Oxford University Press, Oxford, 2006). We will also outline how this framework can be easily extended to include a form of meaning-variant logical pluralism. In this respect, the framework developed in this paper—which we label two-level proof-theoretic pluralism—is much broader in scope than the one discussed in Beall and Restall’s book.
Deep Learning

Deep learning is a subset of machine learning. As the name suggests, deep learning means more and more layers. Deep leaning basically works on the principle of neurons. With the increase in big data or large quantities of data, deep learning methods and techniques have been widely used to extract the useful information. Deep learning can be applied to computer vision, bioinformatics, and speech recognition or on natural language processing. This chapter covers the basics of deep learning, different architectures of deep learning like artificial neural network, feed forward neural network, CNN, recurrent neural network, deep Boltzmann machine, and their comparison. This chapter also summarizes the applications of deep learning in different areas.
RACC: An efficient and revocable fine grained access control model for cloud storage

The requirement of cloud data sharing is becoming increasingly important to achieve flexible, scalable, reliable and fine grained access control. Currently decentralized access control model is widely used for data sharing in cloud environment. The existing multi authority decentralized data sharin g model is discouraged for practical use due to its inefficiency in decryption and attribute revocation. Attribute revocation method is not supported by most of existing decentralized data sharing model. In this paper we propose a new data sharing model which supports decryption outsourcing as well as attribute revocation. In our proposed model most of the computational operations of decryption is transfered to cloud server which was earlier carried out by user. The use of linear secret sharing scheme strengthen the access control policy. The performance of our model outperforms LWABE in comparison with communication cost, secret key update ciphertext update and decryption cost. The cost incurred during encryption process of our model is almost same as LWABE.