Reviewerly/exHarmony huggingface dataset:
Expanding the Web of Knowledge

Textbooks are educational documents created, structured and formatted in a way that facilitates understanding. Most digital textbooks are released as mere digital copies of their printed counterparts. We present a mechanism that extracts knowledge models from textbooks and enriches their content with additional links (both internal and external). The textbooks essentially become hypertext documents where individual pages are annotated with important concepts in the domain. We also show that extracted models can be automatically connected to the Linked Open Data cloud, which helps further facilitate access, discovery, enrichment, and adaptation of textbook content. Integrating multiple textbooks from the same domain increases the coverage of the composite model while keeping its accuracy relatively high. The overall results of the evaluation show that the proposed approach can generate models of good quality and is applicable across multiple domains.
Progressively Refined Face Detection Through Semantics-Enriched Representation Learning

Feature pyramids aim to learn multi-scale representations for detecting faces over various scales. However, they often lack adequate context over different scales, especially when there are many tiny faces in the wild. In this paper, we propose an attention-guided semantically enriched feature aggregation framework to learn a feature pyramid with rich semantics at all scales for face detection. Specifically, high-level abstract features are directly integrated into low-level representations by skip connections to retain as much semantic as possible. In addition, an attention mechanism is employed as a gate to emphasize relevant features and suppress useless features during feature fusion. Inspired by human visual perception of tiny faces, we specially design a deep progressive refined loss (DPRL) to effectively facilitate feature learning. According to the above principles, we design and investigate various feature pyramid frameworks through extensive experiments. Finally, two typical structures named Centralized Attention Feature (CAF) and Distributed Attention Feature (DAF) are proposed for face detection, which are in-place and end-to-end trainable. Extensive experiments across different aggregation architectures on four challenging face detection benchmarks demonstrate the superiority of our framework over state-of-the-art methods.
Public key digital contents confidentiality scheme based on quantum spin and finite state automation

Numerous encryption plans are legitimately founded on the transformation of frameworks or by characterizing the strict guidelines. Most of the security systems are based on mathematical structures and their applications in diverse applied sciences. We propose here an advanced digital contents confidentiality scheme to simulate the phenomenon rather than creating rigid rules. We operate digital data trailed by quantum spin states for specific phase and finite state machine for limited number of rounds. The extent of the presented article revolves around the development and deployment of public key cryptosystem basis on the concepts of quantum spin states and finite state machine effectively. Both states (spin and finite state machine) provide the high degree of naturalness contrasted with ordinary cryptosystem.
An Approach for Behavioral Drift Detection in a Smart Home

With the global population growing older and more vulnerable, healthcare systems are considering new approaches to maintain people autonomous in their own homes. Recent advances in pervasive computing technologies have opened up new opportunities to unobtrusively monitor human behavior at home. Sensors data related to the activities of daily living (ADLs) performed by the inhabitant can be collected and labeled manually or by using activity recognition algorithms. The purpose of this work is to propose an approach for detecting changes (drift) in the inhabitant behavior in order to detect potential changes in the health-state of the inhabitant. Based on unsupervised clustering, our approach use activity starting time and duration as key features to detect changes between time periods. Variations from one behavior to another one can be identified for subsequent review or intervention by a healthcare professional. The relevance and the nature of the change are asserted using the clustering validation metric called Silhouette. Case study experiments on real life and simulated datasets suggest that some user's behavior can go through smooth or abrupt changes and that these changes can be highlighted using our approach.
Characterizing Developer Use of Automatically Generated Patches

We present a study that characterizes the way developers use automatically generated patches when fixing software defects. Our study tasked two groups of developers with repairing defects in C programs. Both groups were provided with the defective line of code. One was also provided with five automatically generated and validated patches, all of which modified the defective line of code, and one of which was correct. Contrary to our initial expectations, the group with access to the generated patches did not produce more correct patches and did not produce patches in less time. We characterize the main behaviors observed in experimental subjects: a focus on understanding the defect and the relationship of the patches to the original source code. Based on this characterization, we highlight various potentially productive directions for future developer-centric automatic patch generation systems.
Database Security with AES Encryption, Elliptic Curve Encryption and Signature

A database is an organized collection of data. Though a number of techniques, such as encryption and electronic signatures, are currently available for the protection of data when transmitted across sites. Database security refers to the collective measures used to protect and secure a database or database management software from illegitimate use and malicious threats and attacks. In this paper, we create 6 types of method for more secure ways to store and retrieve database information that is both convenient and efficient. Confidentiality, integrity, and availability, also known as the CIA triad, is a model designed to guide policies for information security within the database. There are many cryptography techniques available among them, ECC is one of the most powerful techniques. A user wants to the data stores or request, the user needs to authenticate. When a user who is authenticated, he will get key from a key generator and then he must be data encrypt or decrypt within the database. Every keys store in a key generator and retrieve from the key generator. We use 256 bits of AES encryption for rows level encryption, columns level encryption, and elements level encryption for the database. Next two method is encrypted AES 256 bits random key by using 521 bits of ECC encryption and signature for rows level encryption and column level encryption. Last method is most secure method in this paper, which method is element level encryption with AES and ECC encryption for confidentiality and ECC signature use for every element within the database for integrity. As well as encrypting data at rest, it's also important to ensure confidential data are encrypted in motion over our network to protect against database signature security. The advantages of elements level are difficult for attack because the attacker gets a key that is lose only one element. The disadvantages need to thousands or millions of keys to manage.
TexFusionNet: An Ensemble of Deep CNN Feature for Texture Classification

The textureRoy, Swalpa Kumar classification from images is one of the important problemsDubey, Shiv Ram in pattern recognitionChanda, Bhabatosh. Several hand-designedChaudhuri, Bidyut B. methods have been proposed in last few decades for this problemGhosh, Dipak Kumar. Nowadays, it is observed that the convolutional neural networks (CNN) perform extremely well for the classification task mainly over object and scene images. This improved performance of CNN is caused by the availability of huge amount of images in object and scene databases such as ImageNet. Still, the focus of CNN in texture classification is limited due to non-availability of large texture image data sets. Thus, the trained CNN over Imagenet database is used for texture classification by fine-tuning the last few layers of the network. In this paper, a fused CNN (TexFusionNet) is proposed for texture classification by fusing the last representation layer of widely adapted AlexNet and VGG16. On the top of the fused layer, a fully connected layer is used to generate the class score. The categorical cross-entropy loss is used to generate the error during training, which is used to train the added layer after the fusion layer. The results are computed over several well-known Brodatz, CUReT, and KTH-TIPS texture data sets and compared with the state-of-the-art texture classification methods. The experimental results confirm outstanding performance of the proposed TexFusionNet architecture for texture classification.
Lossless image hierarchical recovery based on POB number system

This paper proposes an effective method for image tampering localization and recovery. In the proposed scheme, the host image is firstly encrypted by a chaotic system, and then bit-plane separation is conducted on the encrypted image. Afterwards, the certificate watermark and the recovery watermark are calculated respectively on the MSB-plane and LSB-plane. Right after, watermarks are embedded into the two bit-planes with the help of chaotic mapping and permutation ordered binary number system (POB number system), thus, the two shares are generated. The recovery of images contains a two-level comparison, neighbor-based refinement and watermark-based refinement. For the tampered share, the comparison can localize all the tampers, two refinements can eliminate the error judgments generated by two-level comparison and recover the image losslessly. Experimental results demonstrate that the proposed method can recover the tampered image losslessly with one of two shares tampered; some analysis results and comparisons with some existing schemes are presented to show that the proposed scheme achieves the better performance in some criteria.
Noise-Robust Iterative Back-Projection

Noisy image super-resolution (SR) is a significant challenging process due to the smoothness caused by denoising. Iterative back-projection (IBP) can be helpful in further enhancing the reconstructed SR image, but there is no clean reference image available. This paper proposes a novel back-projection algorithm for noisy image SR. Its main goal is to pursuit the consistency between LR and SR images. We aim to estimate the clean reconstruction error to be back-projected, using the noisy and denoised reconstruction errors. We formulate a new cost function on the principal component analysis (PCA) transform domain to estimate the clean reconstruction error. In the data term of the cost function, noisy and denoised reconstruction errors are combined in a region-adaptive manner using texture probability. In addition, the sparsity constraint is incorporated into the regularization term, based on the Laplacian characteristics of the reconstruction error. Finally, we propose an eigenvector estimation method to minimize the effect of noise. The experimental results demonstrate that the proposed method can perform back-projection in a more noise-robust manner than the conventional IBP, and harmoniously work with any other SR methods as a post-processing.
An Overview of Deep Reinforcement Learning

As a new machine learning method, deep reinforcement learning has made important progress in various fields of people's production and life since it was proposed. However, there are still many difficulties in function design and other aspects. Therefore, further research on deep reinforcement learning is of great significance for promoting the progress of the whole science and society. Based on the basic theory of deep learning, this paper introduces the basic theory, research method, main network model and successful application in various fields of deep reinforcement learning.
Phishing: message appraisal and the exploration of fear and self-confidence

Phishing attacks have threatened the security of both home users and organisations in recent years. Phishing uses social engineering to fraudulently obtain information that is confidential or sensitive. Individuals are targeted to take action by clicking on a link and providing information. This research explores fear arousal and self-confidence in subjects confronted by phishing attacks. The study collected data from multiple sources (including an attempted phishing attack). The survey results indicated that when individuals had a high level of fear arousal related to providing login credentials they had a decreased intention to respond to a phishing attack. Self-confidence did not significantly moderate the relationship between fear arousal and intention to respond to a phishing attack but it did have a significant direct positive influence on intention. The results from the experiment indicated that 18% of individuals overall clicked on the link. The combined data indicated that higher level of fear arousal resulted in a decreased intention to respond to a phishing attack and a decreased actual click behaviour. The research explores how fear of providing login credentials influences both intention to respond and actual response to a phishing attack. When fear arousal is high, individuals are less likely to respond.
Framing hate: Moral foundations, party cues, and (in)tolerance of offensive speech

One of the most controversial elements of political tolerance concerns support for hate speech. We argue that there are two factors that can reduce tolerance for hate speech: 1) moral foundations and 2) party cues. U.S. citizens’ tolerance of hate speech will be reduced when it is framed as a violation of a specific moral foundation, opposed by a political party, or when the morality violation is utilized by party elites. Using two survey experiments, we manipulated the target of hate speech (i.e. Muslims or the American flag), whether the speech violated a moral foundation (i.e. harm or loyalty), and which political party supported or opposed the hate speech in question. For flag burning, moral frames and party cues on their own reduced U.S. citizens’ tolerance relative to a non-political control, while moral frames and party cues were successful in reducing tolerance of anti-Muslim speech compared to a free speech appeal. Partisans were generally responsive to cues from the in-party. We also found instances of moral repackaging, where morally incongruent appeals from the in-party reduced tolerance of flag burning among Democrats. Among Republicans, harm morality decreased tolerance of anti-Muslim speech when invoked by the in-party, but increased tolerance when used by the out-party – an indication of the power of party cues to repackage moral arguments and to trigger backlash. These results provide a better understanding of what factors can affect tolerance for hate speech, providing political leaders and social justice advocates with a roadmap to alleviate this problem.
The Internet of Things, Artificial Intelligence, and Blockchain: Implementation Perspectives

Blockchain technology, Artificial Intelligence (AI), and Internet of Things (IoT) will be used as the infrastructure of modern applications in the near future. Therefore, we need to know some information about the implementation of them. For this purpose, many tools and applications have been reported in the literature. In this chapter, we show how an application can be implemented using blockchain, AI, and IoT. In addition, we will introduce an approach for designing this type of applications using object-oriented techniques. At first, we summarize popular implementation technologies. Then, an implementation perspective based on object-oriented concepts for cognitive IoT based on blockchain is given. Finally, two case studies are analyzed.
Technical Analysis of an Initial Coin Offering

Initial Coin Offering (ICO) is a process similar to crowdfunding, in which companies raise funds from investors, who in return receive tokens or digital assets. With conventional methods of crowdfunding, the level of transparency depends on a centralized platform used in campaigns. With the emergence of peer-to-peer digital currency systems, also known as cryptocurrencies, it became possible to implement crowdfunding campaigns in a fully transparent way for investors. Public sales of blockchain digital assets are known as crowdsales which are implemented using blockchain smart contracts. The paper presents a solution for automating ICO processes implemented using the Ethereum blockchain and explain the basics of crowdsale token sale flow. Next, we analyze features vital for transparent ICO execution as well as the benefits and costs of running an ICO on cryptocurrency application platforms.
otsad: A package for online time-series anomaly detectors

This paper presents otsad, the first R package which implements a set of novel online detection algorithms for univariate time-series. Moreover, this package also provides advanced functionalities and contents such as new false positive reduction algorithm and the novel NAB detectors measurement technique which is specifically designed to measure online time-series anomaly detectors.
Red Cloud and Aristotle

Campus cloud resources represent significant resources for research computing tasks, with the caveat that transitioning to cloud contexts and scaling analyses is not always as simple as it might seem. We detail Red Cloud, Cornell's campus research cloud, and some of the work undertaken by the Center for Advanced Computing (CAC) to help researchers make use of cloud computing technologies. In 2015, Cornell CAC joined with two other universities to develop the Aristotle Cloud Federation, composed of separate campus cloud resources and data sources, supporting a range of science use cases. We discuss the lessons learned from helping researchers leverage both of these science cloud resources as well as leveraging other research cloud infrastructure and transitioning to public cloud.
Complex object detection using deep proposal mechanism

Complex object detection is one of the most important and challenging problems in computer vision tasks. To provide a high-performance complex object detection method, a deep proposal mechanism (DPM) is proposed. (1) A region proposal strategy is used to localize potential objects in most top methods; however, this process also represents an intractable computational bottleneck. Therefore, to localize potential complex effectively, a region proposal mechanism (RPM) is proposed. The mechanism shares common features with ResNet and achieves excellent performance. (2) To solve the problem of insufficient amount of labeled training data, an efficient semisupervised pretraining method, instead of traditional unsupervised pretraining, is carried out. (3) To further improve the computational speed, a novel joint learning strategy is introduced. Extensive experiments are performed, and the results show that DPM achieves much better performance than state-of-the-art methods.
Using Training Technology Probes in Bodystorming for Physical Training

A promising domain technology design for physical training is as assistive tangible and wearable tools to help people understand, access, and act on their movement senses. To design these tools in close relation to a targeted training practice, we propose an embodied design activity using Training Technology Probes (TTPs). These are pieces of technology with a simple interactivity augmenting and exteriorizing cues from those senses. Here, we explain how we used existing TTPs as design material to spur creativity in an embodied co-creation design workshop that happened as part of an ongoing technology-supported circus training course for children with motor difficulties. Our contribution is two-fold: We present a novel form of embodied ideation method using TTPs as design materials. We also present and characterize novel design concepts, elaborating on three that were implemented and deployed in the context of our circus training class. We discuss their relevance in several physical training domains.
On the use of the modified power series family of distributions in a cure rate model context

In this paper, we propose a generalization of the power series cure rate model for the number of competing causes related to the occurrence of the event of interest. The model includes distributions not yet used in the cure rate models context, such as the Borel, Haight and Restricted Generalized Poisson distributions. The model is conveniently parameterized in terms of the cure rate. Maximum likelihood estimation based on the Expectation Maximization algorithm is discussed. A simulation study designed to assess some properties of the estimators is carried out, showing the good performance of the proposed estimation procedure in finite samples. Finally, an application to a bone marrow transplant data set is presented.
Learning Scene Attribute for Scene Recognition

Scene recognition has been a challenging task in the field of computer vision and multimedia for a long time. The current scene recognition works often extract object features and scene features through CNN, and combine these two types of features to obtain complementary and discriminative scene representations. However, when the scene categories are visually similar, the object features might lack of discriminations. Therefore, it may be debatable to consider only object features. In contrast to the existing works, in this paper, we discuss the discrimination of scene attributes in local regions and utilize scene attributes as the complementary features of object and scene features. We extract these visual features from two individual CNN branches, one extracting the global features of the image while the other extracting the features of local regions. Through contextual modeling framework, we aggregate these features and generate more discriminative scene representations, which achieve better performance than the feature aggregation of object and scene. Moreover, we achieve the new state-of-the-art performance on both standard scene recognition benchmarks by aggregating more complementary visual features: MIT67 (88.06%) and SUN397 (74.12%).
The Method of Single Errors Correction in the Residue Class

In this paper the correcting capabilities of arithmetic codes are investigated with the use of transformations in the residue class. The new method of single errors correction is proposed, examples of concrete realization of errors correction procedures that show practical realizability of the offered method are given.
Dual triplet network for image zero-shot learning

As a cross-modal task, zero-shot learning (ZSL) is generally achieved by aligning the semantic relationships between different modalities. It is a key issue in the alignment to accurately measure the multi-modal data distances. Although metric learning has been employed in many image ZSL approaches, few of them make full use of the data information. To address this issue, we propose a novel deep metric learning framework called Dual-Triplet Network (DTNet) for image ZSL. The DTNet first projects the semantic information into the visual space with a mapping network and then employs two triplet networks for learning the visual-semantic mapping. Specifically, one triplet network focuses on negative attribute features, and the other pays special attention to negative visual features, which guarantees the sufficient discovery and utilization of data information. Extensive experiments on three benchmark datasets demonstrate that our proposed DTNet achieves the state-of-the-art results on both traditional and generalized image ZSL tasks. Especially, on the H measurement of generalized image ZSL, DTNet has improvements of 18% on AwA, 1.9% on CUB, and 12.9% on aPY, respectively.