Reviewerly/exHarmony huggingface dataset:
Deep Underwater Image Enhancement Through Integration of Red Color Correction Based on Blue Color Channel and Global Contrast Stretching

Deep underwater images experience some complicated problems, such as low contrast and blue-green illumination effect due to light attenuation in water medium. These problems reduce the extraction rate of valuable information from the image. This paper proposes a new method of enhancing underwater image. The proposed method consists of two major steps. The first step is explicitly designed to minimize the effect of blue-green illumination. This technique operates by correcting the red color channel by taking into account the differences between the red color with blue color in term of total pixel value. The more significant the difference of total pixel value between these colors, the higher the pixel value will be added to improve the red color and vice versa. Then, the overall image contrast is improved through global contrast stretching technique that is applied to all color channels. Qualitative and quantitative evaluations prove the effectiveness of the proposed method.
Image Compression and Encryption Using DCT and Gaussian Map

The use of the image in daily life keeps increasing as information technology is constantly developing. Consequently, the demand for a form of security in sending images through the internet is unavoidable. The encryption algorithm is one of the solutions. Encryption will make any image become only readable by those authorized to access. A chaos-based encryption algorithm is the most commonly used image encryption method because of its sensitivity to the parameter value. In addition to security, image transmission speed is also on demand. Images with bigger size than texts require longer transmission time. For this issue, image compression is the answer. There are many kinds of algorithm that can be used for image size compression. One of which is DCT. DCT algorithm is known to transform spatial domain to frequency. Image encryption and compression are two different processes. Nevertheless, this study combined both processes into one scheme so that they become inseparable. This study has two findings. First, in the area of compression, the scheme produced cipher image with considerable compression ratio. Second, in the area of encryption, the produced cipher image's histogram became completely different from the original histogram, indicating that the encryption algorithm used was immune to histogram analysis.
Multivariate mixed membership modeling: Inferring domain-specific risk profiles

Characterizing the shared memberships of individuals in a classification scheme poses severe interpretability issues, even when using a moderate number of classes (say 4). Mixed membership models quantify this phenomenon, but they typically focus on goodness-of-fit more than on interpretable inference. To achieve a good numerical fit, these models may in fact require many extreme profiles, making the results difficult to interpret. We introduce a new class of multivariate mixed membership models that, when variables can be partitioned into subject-matter based domains, can provide a good fit to the data using fewer profiles than standard formulations. The proposed model explicitly accounts for the blocks of variables corresponding to the distinct domains along with a cross-domain correlation structure, which provides new information about shared membership of individuals in a complex classification scheme. We specify a multivariate logistic normal distribution for the membership vectors, which allows easy introduction of auxiliary information leveraging a latent multivariate logistic regression. A Bayesian approach to inference, relying on Pólya gamma data augmentation, facilitates efficient posterior computation via Markov Chain Monte Carlo. We apply this methodology to a spatially explicit study of malaria risk over time on the Brazilian Amazon frontier.
Entitling concurrency to smart contracts using optimistic transactional memory

It is commonly believed that blockchain is a revolutionary technology for doing business on the Internet. Blockchain is a decentralized, distributed database or ledger of records. It ensures that the records are tamper-proof but publicly readable. Blockchain platforms such as Ethereum [3] and several others execute complex transactions in blocks through user-defined scripts known as smart contracts. Normally, a block of the chain consists of multiple transactions of smart contracts which are added by a miner. To append a correct block into the blockchain, miners execute these transactions of smart contracts sequentially. Later the validators serially re-execute the smart contract transactions of the block. If the validators agree with final state of the blocks as recorded by the miner, then the block is said to be valid and added to the blockchain using a consensus protocol.
Keyword extraction from social media via AHP

In the context of natural-language processing, keyword extraction has been studied widely. In promoting business-enterprise goods and services, however, a major challenge remains to extracting keywords effectively and efficiently from social-media user-generated data, wherein employed are tradition al, language-dependent and supervised keyword-extraction techniques. This study contributes a keyword extraction analytic hierarchy process (KEAHP), as a language-independent and unsupervised keyword-extraction technique. By using four user-generated data attributes, KEAHP identifies keywords from the word co-occurrence in linguistic networks, based on a multiple-attribute decision-making approach. The proposed technique has been validated via a publically-available standard dataset, and the experimental results show the effectiveness and efficiency of the algorithm in KEAHP. Despite its limitations, the study contends that KEAHP can drastically improve performance in promoting business-enterprise goods and services, while also discussed are implications for future research and practice in keyword-extraction techniques.
Injective Encodings to Binary Ordinary Elliptic Curves

Representing points of elliptic curves in a way that no pattern can be detected by sensors in the transmitted data is a crucial problem in elliptic curve cryptography. One of the methods that we can represent points of the elliptic curves in a way to be indistinguishable from random bit strings is using injective encoding function. So far, several injective encodings to elliptic curves have been presented, but the previous encoding functions have not supported the binary elliptic curves. More precisely, the only injective encoding to binary elliptic curves was given for Hessian curves, the family of elliptic curves with a point of order 3. In this paper, we propose approaches for constructing injective encoding algorithms to the ordinary binary elliptic curves $$y^2+xy=x^3+ax^2+b$$ with $$\mathrm {Tr}(a)=1$$ as well as those with $$\mathrm {Tr}(a+1)=0$$ .
Evolution of ArchiMate and ArchiMate Models

Models are a fundamental aspect of enterprise architecture, as they capture the concepts and relationships that describe the essentials of the different enterprise domains. These models are tightly coupled to an enterprise architecture modeling language that defines the rules for creating and updating such models. In the model-driven engineering field, these languages are formalized as meta-models. Over time, to keep up with the need to capture a more complex reality in their enterprise architecture models, organizations need to enrich the meta-model and, consequently, migrate the existing models. Model migration poses a strenuous modeling effort with the gathering of enterprise data and model redesign, leading to an error-prone and time-consuming task. In this chapter, the authors present a catalog of co-evolution operations for enabling automation of ArchiMate model migration based on a set of meta-model changes.
User Controllable Content Retargeting and Depth Adaptation for Stereoscopic Display

This paper presents a content retargeting and depth adaptation method for the stereoscopic image. Our method allows the user to specify the retargeting scenarios to enhance the user's visual experience. Toward this end, we proposed a warping framework that takes content retargeting, depth adaptation, and interactive editing into account simultaneously for stereoscopic images. From the viewpoint of accuracy, comfortability, and controllability, we exploit the complementary relationship among image quality, depth quality, and important content and propose a grid optimization framework to fuse the three indictors. The experimental results demonstrate that our method achieves a preferable tradeoff among accuracy, comfortability, and controllability of information presentation in retargeting, obtaining satisfactory visual experience for users.
BreastNet: Entropy-Regularized Transferable Multi-task Learning for Classification with Limited Breast Data

We describe a framework to automatically separate malignant from benign breast lesions using limited breast ultrasound data.The main uniqueness of this framework includes: (1) in terms of the unique shape features of breast lesions, two types of image patches are designed to fine-tune pre-trained models, aiming to characterize the overall appearance and heterogeneity in shapes of breast lesions.(2) taking the BI-RADS regression task as an auxiliary task, a multi-task architecture is proposed to improve the accuracy of classification.(3) instead of prevalent cross-entropy loss, we introduce training with confusion by means of regularizing prediction entropy to prevent overfitting.Extensive experimental results on small-scale breast ultrasound dataset corroborate that the proposed framework is superior to the state-of-the-art approaches in breast lesions classification with limited data.Besides, we provide detailed analysis of the choice of regularizing parameter and visual evidence that introduction of confusion leads to increase in feature generalization.
Design and Implementation of Monitoring System for Extracurricular Physical Exercise Based on Energy Consumption Measurement

Although the present attendance management system, adopted by universities, determines students’ physical presence. It does not determine whether they perform physical activities. It is important to monitor students’ extracurricular physical exercise scientifically and effectively to solve the actual effect of extracurricular physical exercise attendance and exercise. Calorie management is one solution to this problem. Additionally, an extracurricular physical exercise monitoring and management system is developed to record the energy consumption of students during their physical activities. To realize the demand for the management of calories and the monitoring and analysis of the energy consumption of students through the two development of the energy consumption instrument. This plan has certain significance for solving the actual effect of extracurricular physical training.
Constructions of polyphase ZCZ sequence sets with low cross‐correlation property

Zero correlation zone (ZCZ) sequences have received much research attention in many practical applications, such as quasi-synchronous code-division multiple-access systems and MIMO systems. In this study, three constructions of multiple polyphase ZCZ sequence sets with low inter-set cross-correlation property are proposed. The resultant periodic ZCZ sequence sets are optimal with respect to the theoretical bound and have new parameters uncovered in the literature. Consequently, a great deal of flexibility may be provided in the choice of periodic optimal ZCZ sequence sets with good inter-set cross-correlation property for their applications.
Water to the Thirsty Reflections on the Ethical Mission of Libraries and Open Access

The shift to digital information determines a parallel shift in access modes, and digital libraries are called to action by the ethical foundations of their mission. Open Access makes information potentially available not just to researchers, but to everyone, yet there are still barriers to be overcome in terms of technical infrastructures, points of access, digital and cultural divide. The mission of libraries, as stated by IFLA Manifesto for Digital Libraries and IFLA/FAIFE Code of Ethics for Librarians and other Information Workers, converges with the mission and ethics of the BBB declarations on Open Access: it is about delivering information to everyone, from scholars to the “curious minds”, and librarians can be mediators in the wide diffusion, at all levels of society, of scientific, scholarly knowledge, to foster “active” and “scientific” citizenship.
Optimized CL-PKE with lightweight encryption for resource constrained devices

Resource constrained devices such as sensors and RFIDs are utilized in many application areas to sense, store and transmit the sensitive data. This data must be encrypted to achieve confidentiality. The implementation of traditional public key encryption (PKE) techniques by these devices is always challenging as they possess very limited computational resources. Various encryption schemes based on identity-based encryption (IBE) and certificate-less public key encryption (CL-PKE) have been proposed to overcome limitations of PKI. However, many of these schemes involve the computationally expensive exponentiation and bilinear pairing operations on elliptic curve group to encrypt the messages. In this context, we propose a lightweight optimized CL-PKE scheme in which exponentiation and pairing operations are completely eliminated during encryption and only involves computation of cheaper addition and multiplication operations on elliptic curve. Implementation of the proposed scheme confirms its lightweight nature as compared to original CL-PKE scheme.
Security and fault tolerance evaluation of TMR–QDI circuits

The authors report the results of a study on the impact of resistive bridging faults on triple modular redundancy (TMR)-based quasi-delay-insensitive (QDI) asynchronous countermeasures, which is used to provide a secure circuit against power analyses. They have carried out the present study on CADENCE using a resistive bridges fault model. They show that the resistive bridge faults can have serious impacts on the security of integrated circuits and it is possible to discover the secret data. Based on the bridges resistance value, there are three operating intervals of secure circuits, in which TMR-based QDI may or may not function correctly in the presence of two resistive bridge faults depending on the interval of the resistance value.
Figure Retrieval From Biomedical Literature: An Overview of Techniques, Tools, and Challenges

Publications in biomedical research are increasing in number so fast that it is nearly impossible for scholars and professionals to keep track of new developments or even search the literature easily. Moreover, reading full text is time consuming and it often suffices experts to access salient points like experimental setups and results as captured in the illustrations, photographs, tables, and graphs (here, collectively called figures) within a research article. Indeed, a biomedical paper, in contradistinction to papers in other technical fields, typically includes a number of figures of various types, which provide a wealth of information through their visual content, captions, and references from the text. Hence, multimodal search is gaining prominence in digital libraries where users search for not only relevant papers or monographs but also figures directly. Researchers have designed several techniques and tools to allow figure search over biomedical literature. In this chapter, we survey these techniques—including methods for figure extraction from papers, figure classification, and figure indexing—that enable figure search. We then review some of the publicly available web applications that allow search for figures in biomedical papers. The chapter concludes with a discussion of future challenges in the field.
Spatially varying blur estimation from a single image

Detection of single image spatially varying blur is a challenging task. Here, the authors propose a powerful spatially varying blur detection method. Blur is detected from a single image without requiring any prior knowledge about the parametric blur kernel, the camera settings, or information about the input image. The proposed approach detects both motion and defocus blurred regions in partially blurred images. First, blur-type classification is performed using Hough transform and the structured learning edge-detection method. Second, the initial blur kernel is estimated with a kernel direction that maximises the likelihood of a local window being blurred by incorporating either vertical or positive diagonal kernels. Blur detection is implemented using a kernel-specific feature. Third, initial blur image regions are refined with the support of the reduced edges image segmentation (CCP) method. Finally, neighbouring information is utilised to refine detected blur results. Comparative results reveal accuracy improvements in the image blur detection results at a reasonable execution time.
Data-driven discovery of causal interactions

Causal discovery is a primary focus in many fields. Various methods have been developed to mine causal relationships from observational data. Most of the methods are only capable of identifying individual causes without considering their interactions. However, in real life, many effects are due to multiple factors that interact with each other. Therefore, detecting the interactions between those causal factors is essential for understanding the real causal mechanisms. So far, there are no efficient data-driven approaches to discovering causal interactions from data, especially large data sets. In this paper, we propose a general data-driven framework and develop four algorithms instantiated from the framework to detect causal interactions, directly from data. Extensive experiments on both synthetic and real-world data have shown that the proposed framework and the algorithms can achieve high effectiveness and efficiency for causal interaction discovery.
Report on GLARE 2018

This is a report on the first edition of the International Workshop on Generalization in Information Retrieval (GLARE 2018), co-located with the 27th ACM International Conference on Information and Knowledge Management (CIKM 2018) held in Turin, Italy, on October 22, 2018.
Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm

A state-of-the-art entity recognition system relies on deep learning under data-driven conditions. In this paper, we combine deep learning with linguistic features and propose the long short-term memory-conditional random field model (LSTM-CRF model) with the integrity algorithm. This approach is primarily based on the use of part-of-speech (POS) syntactic rules to correct the boundaries of LSTM-CRF model annotations and improve its performance by raising the integrity of the elements. The method incorporates the advantages of the data-driven method and dependency syntax, and improves the precision rate of the elements without losing recall rate. Experiments show that the integrity algorithm is not only easy to combine with the other neural network model, but the overall effect is better than several advanced methods. In addition, we conducted cross-domain experiments based on a multi-industry corpus in the financial field. The results indicate that the method can be applied to other industries.
An Information Theoretically Secure E-Lottery Scheme Based on Symmetric Bivariate Polynomials

E-lottery schemes have attracted much interest from both industry and academia recently, because they are not only useful to raise funds for charity institutions, but also can be used as the major building blocks to design micro-payment systems. In the literature, a number of e-lottery schemes have been introduced over the last two decades. However, most of these schemes rely on some computational assumptions. In this paper, we introduce a novel e-lottery scheme that achieves information theoretical security. Our proposed scheme is designed using symmetric bivariate polynomials, and it satisfies the required security properties, such as correctness, unpredictability, verifiability, and robustness. Moreover, the winning number is generated in a distributed fashion, so that no trusted third party needs to be involved and the danger of a single point of failure is minimized.
Root Rank

A popular approach to hosting Keyword Search Systems (KWS) on relational DBMS platforms is to employ the Candidate Network framework. The quality of a Candidate Network-based search is critically dependent on the scoring function used to rank the relevant answers. In this paper, we first demonstrate, through a detailed empirical study, that the Labrador scoring function provides the best user relevance among contemporary Candidate Network scoring functions.
Clustering-Based Transductive Semi-Supervised Learning for Learning-to-Rank

Learning-to-rank (LTR) is a very hot topic of research for information retrieval (IR). LTR framework usually learns the ranking function using available training data that are very cost-effective, time-consuming and biased. When sufficient amount of training data is not available, semi-supervised learning is one of the machine learning paradigms that can be applied to get pseudo label from unlabeled data. Cluster and label is a basic approach for semi-supervised learning to identify the high-density region in data space which is mainly used to support the supervised learning. However, clustering with conventional method may lead to prediction performance which is worse than supervised learning algorithms for application of LTR. Thus, we propose rank preserving clustering (RPC) with PLocalSearch and get pseudo label for unlabeled data. We present semi-supervised learning that adopts clustering-based transductive method and combine it with nonmeasure specific listwise approach to learn the LTR model. Moreover, each cluster follows the multi-task learning to avoid optimization of multiple loss functions. It reduces the training complexity of adopted listwise approach from an exponential order to a polynomial order. Empirical analysis on the standard datasets (LETOR) shows that the proposed model gives better results as compared to other state-of-the-arts.
Inverse Linking, Possessive Weak Definites and Haddock Descriptions: A Unified Dependent Type Account

This paper proposes a unified dependent type analysis of three puzzling phenomena: inversely linked interpretations, weak definite readings in possessives and Haddock-type readings. We argue that the three problematic readings have the same underlying surface structure, and that the surface structure postulated can be interpreted properly and compositionally using dependent types. The dependent type account proposed is the first, to the best of our knowledge, to formally connect the three phenomena. A further advantage of our proposal over previous analyses is that it offers a principled solution to the puzzle of why both inversely linked interpretations and weak definite readings (in contrast to Haddock-type readings) are blocked with certain prepositions.