Reviewerly/exHarmony huggingface dataset:
Target-Directed MixUp for Labeling Tangut Characters

Deep learning largely improves the performance in computer vision and image understanding tasks depending on large training datasets of labeled images. However, it is usually expensive and time-consuming to label data although unlabeled data are much easier to get. It is practical to build the training dataset iteratively from a small set of manually labeled data because of the limited budget or emerging new categories. The labeled data could not only be used for training the model but also some knowledge could be mined from them for finding examples of the classes not included in the training dataset. Mixup [1] improves the model's accuracy and generalization by augmenting the training dataset with the "virtual examples" that are generated by mixing pairs of randomly selected examples from the training dataset. Motivated by Mixup, we propose the Target-Directed Mixup (TDM) method for building the training dataset of the deep learning-based Tangut character recognition system. The virtual examples are generated by mixing two or more similar examples in the training dataset, together with the target examples of unseen classes that need to be labeled, which is a kind of generative few-shot learning. This method can help expand the training dataset by finding real examples of unseen Tangut characters and provide virtual examples that could represent the rare characters that are used very limited in historical documents. According to our experiments, TDM can help recognize the unseen examples at the accuracy of 80% with only 4 to 5 real target examples, which largely reduces human labor in data annotation.
A Dual Memory Structure for Efficient Use of Replay Memory in Deep Reinforcement Learning

In this paper, we propose a dual memory structure for reinforcement learning algorithms with replay memory. The dual memory consists of a main memory that stores various data and a cache memory that manages the data and trains the reinforcement learning agent efficiently. Experimental results show that the dual memory structure achieves higher training and test scores than the conventional single memory structure in three selected environments of OpenAI Gym. This implies that the dual memory structure enables better and more efficient training than the single memory structure.
3SGAN: 3D Shape Embedded Generative Adversarial Networks

Despite recent advances in Generative Adversarial Models(GAN) for image generation, significant gaps remain concerning the generation of boundary and spatial structure. In this paper, we propose a new approach to generate edge and depth information combined with an RGB image to solve this problem. More specifically, we propose two new regularization models. Our first model enforces image-depth-edge alignments by controlling the second-order derivative of depth and the first-order derivative of RGB maps, enforcing smoothness and consistency. The second model leverages multiview synthesis to regularize RGB and depth by computing the difference between an expected rotated object compared to a conditionally generated view of the object; enforcing projection consistency enables the model to directly learn spatial structures and depths. To evaluate our approach, we generated an RGB-D dataset with edge contours from ShapeNet models. Furthermore, we utilized an existing RGB-D dataset, NYU Depth V2 with edges learned by the Holistically-nested Edge Detection model.
Affinity Aware-Scheduling of Live Migration of Virtual Machines Under Maintenance Scenarios

During maintenance and disaster recovery scenarios, Virtual Machine (VM) inter-site migrations usually take place over limited bandwidth-typically Wide Area Network (WAN)-which is highly affected by the amount of inter-VM traffic that becomes separated during the migration process. This causes both a degradation of the Quality of Service (QoS) of inter-communicating VMs and an increase in the total migration time due to congestion of the migration link. We consider the problem of scheduling VM migration in those scenarios. In the first stage, we resort to graph partitioning theory in order to partition the VMs into groups with high intra-group communication. In the second stage, we devise an affinity-based scheduling algorithm for controlling the order of the migration groups by considering their inter-group traffic. Comprehensive real-life experimental results and simulations show that our approach in some cases is able to decrease the volume of separated traffic by a factor larger than 60%.
Enhanced Heterogeneous Cloud: Transparent Acceleration and Elasticity

This paper presents ORIAN, a fully-managed Platform-as-a-Service (PaaS) for deploying high-level applications onto large-scale heterogeneous cloud infrastructures. We aim to make specialised, accelerator resources in the cloud accessible to software developers by extending the traditional homogeneous PaaS execution model to support automatic runtime management of heterogeneous compute resources such as CPUs and FPGAs. In particular, we focus on two mechanisms: transparent acceleration, which automatically maps jobs to the most suitable resource configuration, and heterogeneous elasticity, which performs automatic vertical (type) and horizontal (quantity) scaling of provisioned resources to guarantee QoS (Quality of Service) objectives while minimising cost. We develop a prototype to validate our approach, targeting a hardware platform with combined computational capacity of 28 FPGAs and 36 CPU cores, and evaluate it using case studies in three application domains: machine learning, bioinformatics, and physics. Our transparent acceleration decisions achieve on average 96% of the maximum manually identified static configuration throughput for large workloads, while removing the burden of determining configuration from the user; an elastic ORIAN resource group provides a 2.3 times cost reduction compared to an over-provisioned group for non-uniform, peaked job sequences while guaranteeing QoS objectives; and our malleable architecture extends to support a new, more suitable resource type, automatically reducing the cost by half while maintaining throughput, and achieving a 23% throughput increase while fulfilling resource constraints.
Vallum: Privacy, Confidentiality and Access Controlfor Sensitive Data in Cloud Environments

Managing sensitive data in shared environments such as public clouds is an enduring challenge. While several approaches exist to protect data at rest such as end-to-end encryption, there exist only a few solutions such as homomorphic encryption that offer secure data processing. Unfortunately, these solutions cannot be used in practice as they incur non-negligible run-time overheads and security risks. Moreover, as the majority of data management systems were designed to operate in private cloud environments, which are under the control of the data owner, they often lack appropriate mechanisms for access control as well as privacy assurance. In this paper we propose Vallum, a data access and protection layer that closes these gaps while enabling users to operate data management systems in shared environments such securely as public clouds. Vallum utilizes Intel SGX and remote attestation to ensure confidentiality and integrity of the data being stored and processed. Furthermore, it provides access protection and privacy assurance through a extensible architecture. Our performance evaluation indicates that the overhead introduced by Vallum makes it viable to be deployed in cloud infrastructures.
Classification Techniques Used in Sentiment Analysis &amp; Prediction of Heart Disease using Data Mining Techniques: Review

Sentiment analysis uses data mining methods to extract information and data from the web through natural language processing. This consists of emotion artificial intelligent and text analysis. It basically helps in finding out the polarity of word data which is categorized into negative, positive and neutral. Sentiment extraction from data sources is a difficult task because some data sources may have unstructured format of data. In this review paper, we tried to summarize a number of classification techniques used in sentiment analysis stating some of their advantages and disadvantages, performance and their accuracy.In this paper, the various data mining techniques used for the prediction of the heart disease are discussed. With the help of data mining, it is very easy task to make expert system where this plays an important role in the prediction of the health related problems. This helps in solving threat of heart related issues also. Data mining is the extraction of hidden predictive information from large databases which creates enhanced knowledge in the field of pharmaceutical science which helps to predict heart disease. Various data mining techniques are applied here. It produces fast, straightforward assessment of the distinct prediction prototype with the help of Artificial Intelligent techniques.
Real Time Sentiment Analysis and Opinion Mining on Refugee Crisis

It is acknowledged that Twitter is a micro blogging social site and millions of people share their thoughts, views and reactions, as well as commenting on particular subject as his opinion or fact for seeking attention from different categorical person. In the current analysis and experimentation, we investigate the public opinions, facts and sentiments on Refugee Crisis which is widely discussed topic in social media platform today. To analyze the public sentiment on this crisis, around 35,000 relevant Twitter data have been collected in five different languages such as English, Bangla, Turkish, Chinese and Urdu, and use them for sentiment analysis and decision mining in the way of data mining and data science for Refugee Crisis. For that, a new model of real time sentiment analysis on Refugee Crisis is presented in this paper to provide some prediction on political improvements. This paper will also be able to give end level decision of how much people are commenting for supporting Refugee and how much comments are posting against Refugee by binomial classification of positive and negative using supervised machine learning algorithms such as DT, RF, NB and KNN. Result shown that KNN algorithm perform the best polarity accuracy of 95% compared to DT, RF and NB classifier. Respective and responsible person for Refugee can get a better knowledge by having our analysis.
Detecting Named Entities in Unstructured Bengali Manuscript Images

In this paper, we undertake a task to find named entities directly from unstructured handwritten document images without any intermediate text/character recognition. Here, we do not receive any assistance from natural language processing. Therefore, it becomes more challenging to detect the named entities. We work on Bengali script which brings some additional hurdles due to its own unique script characteristics. Here, we propose a new deep neural network-based architecture to extract the latent features from a text image. The embedding is then fed to a BLSTM (Bidirectional Long Short-Term Memory) layer. After that, the attention mechanism is adapted to an approach for named entity detection. We perform experimentation on two publicly-available offline handwriting repositories containing 420 Bengali handwritten pages in total. The experimental outcome of our system is quite impressive as it attains 95.43% balanced accuracy on overall named entity detection.
Decision Tree Classifier Based Pedestrian Detection for Autonomous Land Vehicle Development

Pedestrian detection and accident avoidance system plays very important role in development of autonomous vehicle. A system which can detect pedestrian accurately and take action accordingly can avoid happening the misfortune. This paper proposed pedestrian detection system based on combined information from the decomposition of body part. Three different cascade classifiers trained with different features of body part. Aggregated channel features consumed to train the full body of pedestrian detector while Haar like features utilized to train upper body and face detector. Decision tree algorithm evaluated the output of different body part detector and formulated final decision about the existence of pedestrian accordingly. Body part decomposition and different features utilization by the system makes system more accurate detector under different hazardous condition. Achieved results show that proposed pedestrian detection system works effectively.
Nonclassicality of the two-photon laser with Kerr nonlinearity

We have studied the photon statistics of the non-degenerate two-photon laser composed of many noninteracting atoms with Kerr nonlinearity. Nonlinearity of the gain is accounted for by the Lamb–Scully approach. Under certain conditions, we obtain an approximate photon distribution function. In the region above lasing threshold, the first few moments of the photon numbers are calculated analytically, and the threshold condition for stable lasing that depends on the Kerr parameter and detuning are obtained. We analyze the properties of the cavity field through the Mandel <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>Q</mml:mi> </mml:math> parameter, second-order correlation functions, Cauchy–Schwarz inequality for a range of Kerr parameters, atomic injection rate, and cavity detuning. Nonclassicality of the field is found through sub-Poissonian statistics, particularly at a low injection rate and high Kerr coupling with negative detuning. These nonclassicality conditions correspond to low photon numbers and depend on the sign of the detuning. The violation of the Cauchy–Schwarz inequality also occurs at low photon numbers but over a wide range of parameters, especially at short transient times. Our results show that the two-photon laser with Kerr nonlinearity is a promising quantum nonlinear optical system for developing controllable nonclassical light sources.
Discovering Patterns Using Feature Selection Techniques and Correlation

Term Frequency and inverse document frequency is reported to have a significant contribution for various text categorization, document clustering and many other text mining related tasks. A collection of the applications and the enhancements of the Term Frequency and Inverse Document Frequency based document representation technique is examined in this work. The document representation algorithm is essential in the field of text - script mining. In this algorithm, unstructured data is converted into a vector space model where each related document is considered as a point in the vector space. Related documents come in proximity to the other related documents while the documents that are very far away from being coherent remain different from each other. In this paper, four feature selection techniques are implemented to discover the patterns from a repository of unstructured data by using correlation similarity measure. Analysis and comparison with other existing technique is also included. The validation of the patterns formed is performed by using silhouette values. Experiments are conducted to compare performance. Results indicate that TDMp1 performance is poor compared to others.
Progressive learning for weakly supervised fine-grained classification

Despite fine-grained image classification has made considerable progress, it still remains a challenging task due to the difficulty of finding subtle distinctions. Most existing methods solve this problem by selecting the top-N highest scores’ discriminative patches from candidate patches at one time. However, since the classification network often highlights small and sparse regions, the selected patches with the lower rank may contain noise information. To address this problem and ensure the diversity of fine-grained features, we propose a progressive patch localization module (PPL) to find the discriminative patches more accurately. Specifically, this work employs the classification model to find first most discriminative patch, then removes the most salient region to help the localization of the next most discriminative patch, and the top-K discriminative patches can be found by repeating this procedure. In addition, in order to further improve the representational power of patch-level features, we propose a feature calibration module (FCM). This module employs the global information to selectively emphasize discriminative features and suppress useless information, which can obtain more robust and discriminative local feature representations and then help classification network achieve better performance. Extensive experiments are conducted to show the substantial improvements of our method on three benchmark datasets.
Organization of Information System for Semantic Search Based on Associative Vector Space

Text arrays, created by the online community, contain specific cognitive capabilities. Analyses cover the following: mass media materials, social networks, forums, blogs, political materials, biographies and diaries, scientific publications, belles-letters and other. As practice shows, standard search systems are not always able to find the required data out of this huge volume of the data file. A difficult task for the automated computer text processing is the semantic analysis, which is interpretation of meaning of the text, its content and semantics. Performance of this task requires knowledge of meaning of words and sentences; the way to describe these values formally, and to carry out operations with them, even their storage in computer memory, cause difficulties. That is why in automated text processing computer is not able to search texts of a certain subject, without explicitly specified keywords or phrases, as well as to find texts with the similar meaning, which is quite difficult for the search procedure. Modern information retrieval systems (IRS) are mainly based on key words. The major features for this approach are frequency of word occurrence in document collection, its uniqueness, morphological and syntactic properties. The problem is that the full-text information retrieval systems initially do not imply any semantic connection between the documents and the information they contain, and do not take into account the context and many other issues of importance for semantic interpretation, which makes full-text information retrieval systems unsuitable solution for contextual search. Semantic information retrieval systems should settle the issue of full-text IRS and assist the computer in formal description of semantic meaning of the documents and the data about it. This paper examines possible organization of semantic information-retrieval system based on associations, and uses associative vector spaces as the basic semantic structures.
MODDALS methodology for designing layered ontology structures

Global ontologies include common vocabularies to provide interoperability among different applications. These ontologies require a balance of reusability-usability to minimise the ontology reuse effort in different applications. To achieve such a balance, reusable and usable ontology design methodo logies provide guidelines to design and develop layered ontology networks. Layered ontology networks classify into different abstraction layers the domain knowledge relevant to many applications (common domain knowledge) and the domain knowledge relevant only to certain application types (variant domain knowledge). This knowledge classification is performed from scratch by domain experts and ontology engineers. This process is a heavy workload, making it difficult to design the layered structures of reusable and usable global ontologies. Considering how common and variant software features are classified when designing Software Product Lines (SPLs), we argue that SPL engineering techniques can facilitate the domain knowledge classification taking as reference existing ontologies. This paper presents a methodology that provides guidelines to design the layered structure of reusable and usable ontology networks called MODDALS. In contrast to previous methods, MODDALS applies SPL engineering techniques to systematically (1) identify the ontology common and variant domain knowledge and (2) classify it into different abstraction layers taking as reference existing ontologies. This approach complements domain experts’ and ontology engineers’ expertise, preventing them from classifying the domain knowledge from scratch facilitating the design of the layered ontology structure. MODDALS methodology is evaluated in the design of the layered structure of a reusable and usable global ontology for the energy domain. The results show that MODDALS enables to classify the domain knowledge taking as reference existing ontologies.
Evolution-based Refinement of Cross-language Ontology Alignments

Ontology alignment plays a key role for information interconnectivity between computational systems relying on ontologies described in different natural languages. Existing approaches for ontology matching usually provide equivalent type of relation in the generated mappings. In this article, we propose a refinement technique to enable the update of the semantic type of the mapping such as “is-a”, “part-of”, etc. Our approach relies on information from the ontology evolution to apply refinement actions. We formalize the refinement actions and procedures, as well as apply the proposal in application scenarios.
The Sprawlter Graph Readability Metric: Combining Sprawl and Area-Aware Clutter

Graph drawing readability metrics are routinely used to assess and create node-link layouts of network data. Existing readability metrics fall short in three ways. The many count-based metrics such as edge-edge or node-edge crossings simply provide integer counts, missing the opportunity to quantify the amount of overlap between items, which may vary in size, at a more fine-grained level. Current metrics focus solely on single-level topological structure, ignoring the possibility of multi-level structure such as large and thus highly salient metanodes. Most current metrics focus on the measurement of clutter in the form of crossings and overlaps, and do not take into account the trade-off between the clutter and the information sparsity of the drawing, which we refer to as sprawl. We propose an area-aware approach to clutter metrics that tracks the extent of geometric overlaps between node-node, node-edge, and edge-edge pairs in detail. It handles variable-size nodes and explicitly treats metanodes and leaf nodes uniformly. We call the combination of a sprawl metric and an area-aware clutter metric a sprawlter metric. We present an instantiation of the sprawlter metrics featuring a formal and thorough discussion of the crucial component, the penalty mapping function. We implement and validate our proposed metrics with extensive computational analysis of graph layouts, considering four layout algorithms and 56 layouts encompassing both real-world data and synthetic examples illustrating specific configurations of interest.