Reviewerly/exHarmony huggingface dataset:
Improved wolf algorithm on document images detection using optimum mean technique

Detection text from handwriting in historical documents provides high-level features for the challenging problem of handwriting recognition. Such handwriting often contains noise, faint or incomplete strokes, strokes with gaps, and competing lines when embedded in a table or form, making it unsuitable for local line following algorithms or associated binarization schemes. In this paper, a proposed method based on the optimum threshold value and namely as the Optimum Mean method was presented. Besides, Wolf method unsuccessful in order to detect the thin text in the non-uniform input image. However, the proposed method was suggested to overcome the Wolf method problem by suggesting a maximum threshold value using optimum mean. Based on the calculation, the proposed method obtained a higher F-measure (74.53), PSNR (14.77) and lowest NRM (0.11) compared to the Wolf method. In conclusion, the proposed method successful and effective to solve the wolf problem by producing a high-quality output image.
Hyper-Scalable JSQ with Sparse Feedback

Load balancing algorithms play a vital role in enhancing performance in data centers and cloud networks. Due to the massive size of these systems, scalability challenges, and especially the communication overhead associated with load balancing mechanisms, have emerged as major concerns. Motivated by these issues, we introduce and analyze a novel class of load balancing schemes where the various servers provide occasional queue updates to guide the load assignment.
Matching Entities Across Different Knowledge Graphs with Graph Embeddings

This paper explores the problem of matching entities across different knowledge graphs. Given a query entity in one knowledge graph, we wish to find the corresponding real-world entity in another knowledge graph. We formalize this problem and present two large-scale datasets for this task based on exiting cross-ontology links between DBpedia and Wikidata, focused on several hundred thousand ambiguous entities. Using a classification-based approach, we find that a simple multi-layered perceptron based on representations derived from RDF2Vec graph embeddings of entities in each knowledge graph is sufficient to achieve high accuracy, with only small amounts of training data. The contributions of our work are datasets for examining this problem and strong baselines on which future work can be based.
A Context-Aware Citation Recommendation Model with BERT and Graph Convolutional Networks

With the tremendous growth in the number of scientific papers being published, searching for references while writing a scientific paper is a time-consuming process. A technique that could add a reference citation at the appropriate place in a sentence will be beneficial. In this perspective, context-aware citation recommendation has been researched upon for around two decades. Many researchers have utilized the text data called the context sentence, which surrounds the citation tag, and the metadata of the target paper to find the appropriate cited research. However, the lack of well-organized benchmarking datasets and no model that can attain high performance has made the research difficult. In this paper, we propose a deep learning based model and well-organized dataset for context-aware paper citation recommendation. Our model comprises a document encoder and a context encoder, which uses Graph Convolutional Networks (GCN) layer and Bidirectional Encoder Representations from Transformers (BERT), which is a pre-trained model of textual data. By modifying the related PeerRead dataset, we propose a new dataset called FullTextPeerRead containing context sentences to cited references and paper metadata. To the best of our knowledge, This dataset is the first well-organized dataset for context-aware paper recommendation. The results indicate that the proposed model with the proposed datasets can attain state-of-the-art performance and achieve a more than 28% improvement in mean average precision (MAP) and recall@k.
Spatially-Adaptive Residual Networks for Efficient Image and Video Deblurring

In this paper, we address the problem of dynamic scene deblurring in the presence of motion blur. Restoration of images affected by severe blur necessitates a network design with a large receptive field, which existing networks attempt to achieve through a simple increment in the number of generic convolution layers, kernel-size, or the scales at which the image is processed. However, increasing the network capacity in this manner comes at the expense of an increase in model size and inference time, and ignoring the non-uniform nature of blur. We present a new architecture composed of spatially adaptive residual learning modules that implicitly discover the spatially varying shifts responsible for non-uniform blur in the input image and learn to modulate the filters. This capability is complemented by a self-attentive module which captures non-local relationships among the intermediate features and enhances the receptive field. We then incorporate a spatiotemporal recurrent module in the design to also facilitate efficient video deblurring. Our networks can implicitly model the spatially-varying deblurring process while dispensing with multi-scale processing and large filters entirely. Extensive qualitative and quantitative comparisons with prior art on benchmark dynamic scene deblurring datasets clearly demonstrate the superiority of the proposed networks via a reduction in model-size and significant improvements in accuracy and speed, enabling almost real-time deblurring.
Building a novel classifier based on teaching learning based optimization and radial basis function neural networks for non-imputed database with irrelevant features

This work presents a novel approach by considering teaching learning based optimization (TLBO) and radial basis function neural networks (RBFNs) for building a classifier for the databases with missing values and irrelevant features. The least square estimator and relief algorithm have been used for imputing the database and evaluating the relevance of features, respectively. The preprocessed dataset is used for developing a classifier based on TLBO trained RBFNs for generating a concise and meaningful description for each class that can be used to classify subsequent instances with no known class label. The method is evaluated extensively through a few bench-mark datasets obtained from UCI repository. The experimental results confirm that our approach can be a promising tool towards constructing a classifier from the databases with missing values and irrelevant attributes.
Contextual Compositionality Detection with External Knowledge Bases and Word Embeddings

When the meaning of a phrase cannot be inferred from the individual meanings of its words (e.g., hot dog), that phrase is said to be non-compositional. Automatic compositionality detection in multi-word phrases is critical in any application of semantic processing, such as search engines [9]; failing to detect non-compositional phrases can hurt system effectiveness notably. Existing research treats phrases as either compositional or non-compositional in a deterministic manner. In this paper, we operationalize the viewpoint that compositionality is contextual rather than deterministic, i.e., that whether a phrase is compositional or non-compositional depends on its context. For example, the phrase “green card” is compositional when referring to a green colored card, whereas it is non-compositional when meaning permanent residence authorization. We address the challenge of detecting this type of contextual compositionality as follows: given a multi-word phrase, we enrich the word embedding representing its semantics with evidence about its global context (terms it often collocates with) as well as its local context (narratives where that phrase is used, which we call usage scenarios). We further extend this representation with information extracted from external knowledge bases. The resulting representation incorporates both localized context and more general usage of the phrase and allows to detect its compositionality in a non-deterministic and contextual way. Empirical evaluation of our model on a dataset of phrase compositionality1, manually collected by crowdsourcing contextual compositionality assessments, shows that our model outperforms state-of-the-art baselines notably on detecting phrase compositionality.
Efficient Attribute-Based Access Control With Authorized Search in Cloud Storage

Attribute-based encryption has been widely employed to achieve data confidentiality and fine-grained access control in cloud storage. To enable users to identify accessible data in numerous dataset, clear attributes should be appended to the ciphertext, which results in the exposure of attribute privacy. In this paper, we propose an efficient attribute-based access control with authorized search scheme (EACAS) in cloud storage by extending the anonymous key-policy attribute-based encryption (AKP-ABE) to support fine-grained data retrieval with attribute privacy preservation. Specifically, by integrating the key delegation technique into AKP-ABE, EACAS enables data users to customize search policies based on their access policies, and generate the corresponding trapdoor using the secret key granted by the data owner to retrieve their interesting data. In addition, a virtual attribute with no semantic meaning is utilized in data encryption and trapdoor generation to empower the cloud to perform an attribute-based search on the outsourced ciphertext without knowing the underlying attributes or outsourced data. The data owners can achieve fine-grained access control on their outsourced data, and the data users are flexible to search their interesting data based on protected attributes through customizing the search policies. Finally, we demonstrate that EACAS is more efficient than existing solutions on computation and storage overheads.
Greedy Multi-Cloud Selection Approach to Deploy an Application Based on Microservices

The multi-cloud delivery model allows the deployment of a distributed application in a manner in which the software architect chooses the services that better meet the application and the software architect's requirements. However, choosing cloud services is a complex task for a software architect, as there are several cloud providers that offer many services, which makes the selection process from the user perspective a relevant research problem. Therefore, in this paper, we propose a multiple providers selection approach to host a distributed application based on microservices to facilitate the software architect's decision-making. Our approach differs from others because each microservice is deployed in a single provider that better meets requirements from the software architect's perspective. Hence, our approach selects various services of a single provider for a microservice, and a distinct provider for each microservice, which makes this a complex task. We also use multi-criteria decision-making to rank the cloud services, and we map the selection process to multi-choice knapsack problem, which we develop using a greedy algorithm. Furthermore, we implement a tool to evaluate our approach performance, and the results show our approach feasibility, varying the number of cloud providers and services, microservices and requirements.
Learning to learn with active adaptive perception

Increasingly, autonomous agents will be required to operate on long-term missions. This will create a demand for general intelligence because feedback from a human operator may be sparse and delayed, and because not all behaviours can be prescribed. Deep neural networks and reinforcement learning methods can be applied in such environments but their fixed updating routines imply an inductive bias in learning spatio-temporal patterns, meaning some environments will be unsolvable. To address this problem, this paper proposes active adaptive perception, the ability of an architecture to learn when and how to modify and selectively utilise its perception module. To achieve this, a generic architecture based on a self-modifying policy (SMP) is proposed, and implemented using Incremental Self-improvement with the Success Story Algorithm. The architecture contrasts to deep reinforcement learning systems which follow fixed training strategies and earlier SMP studies which for perception relied either entirely on the working memory or on untrainable active perception instructions. One computationally cheap and one more expensive implementation are presented and compared to DRQN, an off-policy deep reinforcement learner using experience replay and Incremental Self-improvement, an SMP, on various non-episodic partially observable mazes. The results show that the simple instruction set leads to emergent strategies to avoid detracting corridors and rooms, and that the expensive implementation allows selectively ignoring perception where it is inaccurate.
On recovery of discrete time signals from their periodic subsequences

The paper investigates recoverability of discrete time signals (sequences) from their subsequences sampled at m-periodic points. It is shown that this recoverability is associated with arbitrarily small spectrum degeneracy of subsequences amended via insertion of dummy elements and bundled into a system. It appears that, for a given m, the signals of a general kind can be approximated by signals with this feature. A recovery algorithm is suggested, and some robustness of this recovery is established.
A Naturalness‐Preserved Low‐Light Enhancement Algorithm for Intelligent Analysis

Images/videos captured in low-light conditions often present low luminance and contrast. Although the existing low-light enhancement algorithms can improve the subjective perception, color distortion and over-enhancement are extremely obvious, which will disturb the subsequent intelligent analysis. Therefore, a naturalness-preserved low-light enhancement algorithm for intelligent analysis is proposed in this paper. An enhancement model is established in RGB color space. Images of ColorChecker color chart are captured under a series of light conditions. To preserve the naturalness, the factors of the proposed enhancement model are estimated by the images captured in practical illumination environment. Experimental results demonstrate that the proposed algorithm can produce natural enhanced results and improve the performance of vehicle license plate localization and skin color detection compared to the existing algorithms. Furthermore, the proposed algorithm can process the 720p videos at the speed of 28.3 fps on average.
Improving undersampling-based ensemble with rotation forest for imbalanced problem

As one of the most challenging and attractive issues in pattern recognition and machine learning, the imbalanced problem has attracted increasing attention. For two-class data, imbalanced data are characterized by the size of one class (majority class) being much larger than that of the other class (minority class), which makes the constructed models focus more on the majority class and ignore or even misclassify the examples of the minority class. The undersampling-based ensemble, which learns individual classifiers from undersampled balanced data, is an effective method to cope with the class-imbalance data. The problem in this method is that the size of the dataset to train each classifier is notably small; thus, how to generate individual classifiers with high performance from the limited data is a key to the success of the method. In this paper, rotation forest (an ensemble method) is used to improve the performance of the undersampling-based ensemble on the imbalanced problem because rotation forest has higher performance than other ensemble methods such as bagging, boosting, and random forest, particularly for small-sized data. In addition, rotation forest is more sensitive to the sampling technique than some robust methods including SVM and neural networks; thus, it is easier to create individual classifiers with diversity using rotation forest. Two versions of the improved undersampling-based ensemble methods are implemented: 1) undersampling subsets from the majority class and learning each classifier using the rotation forest on the data obtained by combing each subset with the minority class and 2) similarly to the first method, with the exception of removing the majority class examples that are correctly classified with high confidence after learning each classifier for further consideration. The experimental results show that the proposed methods show significantly better performance on measures of recall, g-mean, f-measure, and AUC than other state-of-the-art methods on 30 datasets with various data distributions and different imbalance ratios.
Mining User-Generated Repair Instructions from Automotive Web Communities

The objective of this research was to automatically extract user-generated repair instructions from large amounts of web data.An artifact has been created that classifies a web post as containing a repair instruction or not.Methods from Natural Language Processing are used to transform the unstructured textual information from a web post into a set of numerical features that can be further processed by different Machine Learning Algorithms.The main contribution of this research lies in the design and prototypical implementation of these features.The evaluation shows that the created artifact can accurately distinguish posts containing repair instructions from other posts e.g. containing problem reports.With such a solution, a company can save a lot of time and money that was previously necessary to perform this classification task manually.
Contrastive Predictive Coding Based Feature for Automatic Speaker Verification

This thesis describes our ongoing work on Contrastive Predictive Coding (CPC) features for speaker verification. CPC is a recently proposed representation learning framework based on predictive coding and noise contrastive estimation. We focus on incorporating CPC features into the standard automatic speaker verification systems, and we present our methods, experiments, and analysis. This thesis also details necessary background knowledge in past and recent work on automatic speaker verification systems, conventional speech features, and the motivation and techniques behind CPC.
Ensuring Fuel Economy Performance of Commercial Vehicle Fleets Using Blockchain Technology

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;In the past, research on blockchain technology has addressed security and privacy concerns within intelligent transportation systems for critical V2I and V2V communications that form the backbone of Internet of Vehicles. Within trucking industry, a recent trend has been observed towards the use of blockchain technology for operations. Industry stakeholders are particularly looking forward to refining status quo contract management and vehicle maintenance processes through blockchains. However, the use of blockchain technology for enhancing vehicle performance in fleets, especially while considering the fact that modern-day intelligent vehicles are prone to cyber security threats, is an area that has attracted less attention. In this paper, we demonstrate a case study that makes use of blockchains to securely optimize the fuel economy of fleets that do package pickup and delivery (P&amp;amp;D) in urban areas. We implement a consortium blockchain infrastructure, as opposed to a fully public blockchain (similar to the blockchain underlying Bitcoin) which is arguably not real-time or well suited for this safety-critical application. By leveraging in real-time a fleet vehicle’s powertrain status, geospatial traffic data, along with driver information, the fleet vehicle acts as a node ready for data transactions in the blockchain network. Each such vehicle in the fleet communicates with its local central hub, and these secure exchanges of data and information act as immutable blockchain transactions. Consensus within the fleet is established when multiple vehicles report data updates for a given subregion in the fleet route map. The proposed infrastructure will support a fleet route planning algorithm running on the local hub, responsible for optimizing fuel economy for the fleet, increasing its reliability and credibility.&lt;/div&gt;&lt;/div&gt;
Theoretical and Experimental Analysis of Cryptographic Hash Functions

The paper presents a theoretical introduction to the cryptographic hash function theory and a statistical experimental analysis of selected hash functions. The definition of hash functions, diﬀerences between them, their strengths and weaknesses are explained as well. Diﬀerent hash function types, classes and parameters are described. The features of hash functions are analyzed by performing statistical analysis. Experimental analysis is performed for three certiﬁed hash functions: SHA1-160, SHA2-512 and SHA3-512. Such an analysis helps understand the behavior of cryptographic hash functions and may be very helpful for comparing the security level of the hashing method selected. The tests may serve as a basis for examination of each newly proposed hash function. Additionally, the analysis may be harness as a method for comparing future proposals with the existing functions.
ExCL: Extractive Clip Localization Using Natural Language Descriptions

The task of retrieving clips within videos based on a given natural language query requires cross-modal reasoning over multiple frames. Prior approaches such as sliding window classifiers are inefficient, while text-clip similarity driven ranking-based approaches such as segment proposal networks are far more complicated. In order to select the most relevant video clip corresponding to the given text description, we propose a novel extractive approach that predicts the start and end frames by leveraging cross-modal interactions between the text and video - this removes the need to retrieve and re-rank multiple proposal segments. Using recurrent networks we encode the two modalities into a joint representation which is then used in different variants of start-end frame predictor networks. Through extensive experimentation and ablative analysis, we demonstrate that our simple and elegant approach significantly outperforms state of the art on two datasets and has comparable performance on a third.
Zero-Knowledge Elementary Databases with More Expressive Queries

Zero-knowledge elementary databases (ZK-EDBs) are cryptographic schemes that allow a prover to commit to a set $$\mathsf {D}$$ of key-value pairs so as to be able to prove statements such as “x belongs to the support of $$\mathsf {D}$$ and $$\mathsf {D}(x)=y$$ ” or “x is not in the support of $$\mathsf {D}$$ ”. Importantly, proofs should leak no information beyond the proven statement and even the size of $$\mathsf {D}$$ should remain private. Chase et al. (Eurocrypt’05) showed that ZK-EDBs are implied by a special flavor of non-interactive commitment, called mercurial commitment, which enables efficient instantiations based on standard number theoretic assumptions. On the other hand, the resulting ZK-EDBs are only known to support proofs for simple statements like (non-)membership and value assignments. In this paper, we show that mercurial commitments actually enable significantly richer queries. We show that, modulo an additional security property met by all known efficient constructions, they actually enable range queries over keys and values – even for ranges of super-polynomial size – as well as membership/non-membership queries over the space of values. Beyond that, we exploit the range queries to realize richer queries such as $$k$$ -nearest neighbors and revealing the $$k$$ smallest or largest records within a given range. In addition, we provide a new realization of trapdoor mercurial commitment from standard lattice assumptions, thus obtaining the most expressive quantum-safe ZK-EDB construction so far.