Reviewerly/exHarmony huggingface dataset:
Parametric Binary Tree Labeling Based RDHEI Technique

The current paper proposes a reversible data hiding technique in an encrypted image (RDHEI) using a parametric binary tree labeling (PBTL) scheme. The PBTL scheme provides a range of prediction errors to be utilized in data embedding. The binary tree is characterized by a tuple parameter, which is configured to provide the highest embedding rate. Yi et. al.'s technique introduces PBTL based RDHEI scheme, but it lacks high embedding capacity and high security. Further, it utilizes a causal predictor, which is not efficient in predicting embeddable errors. The proposed technique suggests a two-layer embedding strategy in encrypted image in which one of the pixel layers is exploited using the non-causal predictor and another pixel layer is exploited using the causal predictor. The advantages of both causal predictor and non-causal predictor are utilized in the proposed technique to increase embedding capacity than the conventional techniques. In addition to this, data security is also improved by employing a standard pixel-based encryption technique. Thus, in comparison to conventional techniques, the proposed technique maximizes the embedding rate and also ensures high data security.
Trustable Risk Scoring for Non-bank Gateways in Blockchain and DLT Financial Networks

We propose a method to assess the counterparty risk of non-banking financial institutions that operate as fiat-crypto gateways in blockchain and distributed ledger technology financial infrastructures. The risk scores are suitable to evaluate both traditional money services businesses and fintech companies (including cryptocurrency payments and blockchain systems operators). The main users are banks, investors, and, businesses that need to assess counterparty risk across jurisdictions and under uncertainty, as non-banks are often less regulated than other financial institutions. We follow an automation-focused, multidisciplinary approach rooted in established techniques from network science and genetic programming, as well as in the emerging fields of machine behavior and trustworthy artificial intelligence. The method and findings pertain to any decentralized financial infrastructure with centralized components such as fiat on/off ramps, where counterparty risk assessment becomes a necessity for regulatory, investment, and, operational purposes. The method is demonstrated on a network of Ripple payment gateways.
Simple Yet Efficient Algorithms for Maximum Inner Product Search via Extreme Order Statistics

We present a novel dimensionality reduction method for the approximate maximum inner product search (MIPS), named CEOs, based on the theory of concomitants of extreme order statistics. Utilizing the asymptotic behavior of these concomitants, we show that a few projections associated with the extreme values of the query signature are enough to estimate inner products. This yields a sublinear approximate MIPS algorithm with search recall guarantee under a mild condition. The indexing space is exponential but optimal for the approximate MIPS on a unit sphere.
Rural road ecological landscape planning system based on interactive genetic algorithm

Traditional vegetation landscape spatial hierarchical planning system is faced with huge and complex real data, because its throughput is too low to get more accurate data, this paper proposes a rural road ecological landscape planning system based on interactive genetic algorithm. In the hardware design of the system, the structure distribution of ZingRDK feature extraction hardware is mainly designed, and the functions and parameters of the chip and HDMI interface in the structure are designed in detail. In the software design of the system, the interactive genetic algorithm is applied to the planning process, and then the visual feature reconstruction method is used to collect the green plant landscape image. The edge contour feature of the collected image is extracted, and the color component fusion method is used to enhance the information of green plant landscape image. The intuitionistic ambiguity set of multi-resolution landscape image is given, and the intelligent landscape planning result is calculated by fuzzy information clustering method. The results of the system test show that the net data throughput of the designed rural road landscape planning system is greater than that of the traditional planning system, which indicates that the plant landscape planning system has stronger ability to acquire and process data.
Robust Video Text Detection Through Parametric Shape Regression, Propagation and Fusion

Scene text in one video carries valuable semantic information for various video applications. The varied and complex text appearance and video context, however, make reliable detection of scene text in the video a challenging task. In this paper, we propose a novel end-to-end video text detection frame-work with an effective proposal-level text information propagation and fusion mechanism for robust detection of video text. Specifically, on the basis of a parametric shape representation and regression model for intra-frame text detection and an integrated cross-frame text region propagation mechanism, we correlate corresponding text candidates in adjacent frames and accordingly propagate a text candidate’s shape parameters and features in the previous frame to the current frame as supplementary text cues, which are then attentively fused with those of the current frame for improved text detection results. Experiment results on standard benchmarks demonstrate the effectiveness of our method for robustly detecting video text with widely varied appearances.
Towards GANs’ Approximation Ability

Generative adversarial networks (GANs) have attracted in-tense interest in the field of generative models. This paper will first theoretically analyze GANs’ approximation property. Similar to the universal approximation property of the fully connected neural networks with one hidden layer, we prove that the generator with the input latent variable in GANs can universally approximate the potential data distribution given the increasing hidden neurons. Furthermore, we propose an approach named stochastic data generation (SDG) to enhance GANs’ approximation ability. Our approach is based on the simple idea of imposing randomness through data generation in GANs by a prior distribution on the conditional probability between the layers. The experimental results on synthetic dataset verify the improved approximation ability obtained by this SDG approach. In the practical dataset, three GANs using SDG can also outperform the corresponding traditional GANs when the model architectures are smaller.
Age Group Prediction on Textual Data using Sentiment Analysis

Social media platforms provide a large amount of textual data covering various topics to explore opinions and emotions, hidden in the content using sentiment analysis. The consumer perspective on the quality and popularity of a product can be deduced from the product reviews, available at social media platforms by performing sentiment analysis. Sentiment analysis tells about the polarity of a sentence whether positive, negative or neutral. It can be used to predict personality, age and gender, based on writing style using feature extraction on the labeled training data sets. Understanding human emotions and opinions from text is a difficult task and to make it easier, sentiment analyzers are used. This paper proposes a method for prediction of age groups namely teenagers, adults and senior citizens from textual data collected from twitter and compares performance of different classifiers such as K-Nearest Neighbor (KNN), Multi-layer Perceptron (MLP), Decision tree, Random forest and Support Vector Machine (SVM), based on certain performance metrics like f-score, precision, recall and accuracy. One of the basic applications of this work can be for web readability analysis of resources, available on Internet.
Stick-Breaking Processes With Exchangeable Length Variables

Our object of study is the general class of stick-breaking processes with exchangeable length variables. These generalize well-known Bayesian nonparametric priors in an unexplored direction. We give conditions to assure the respective species sampling process is proper and the corresponding prior has full support. For a rich subclass we explain how, by tuning a single [0,1]-valued parameter, the stochastic ordering of the weights can be modulated, and Dirichlet and Geometric priors can be recovered. A general formula for the distribution of the latent allocation variables is derived and an MCMC algorithm is proposed for density estimation purposes.
Review on Exploring Similarity between Two Questions Using Machine Learning

Question duplication is the main problem which is based on functionality of allowing users to ask questions . Questions are often answered, and the duplication problem is faced by question and answer sites such as Quora and Reddit, Stack-overflow, and others. Answers are segmented through various iterations of the same question due to question continuity. The aim is to detect the duplicate questions for reducing the redundancy in data. This is a worst experience of users, as the answers get segmented on various versions of the same question, it is bad for writers as well as seekers. Actually this problem also has been noticed on other platforms of Q&amp;A. In this proposed work a simple neural architecture with natural language inference will be used. The approach gathers an attention to pound the problem into sub-problems that helps it to be solved separately, thus making it menially parallelizable. This work is just completely a new pattern, for the solution and it is also possible that it will not provide the complete solution to the problem but may help in increasing the efficiency of the model to predict the duplication's among several question pairs. Question duplication is the serious problem due to the segmentation of answers in various variants of the same question because ofduplication's in these discussion boards. Lastly, As a consequence, there is a lack of a rational search, solution indifference, knowledge separation, and an insufficiency of responses to the questioners. This could be avoided by employing Natural Language Processing as well as Machine Learning, which will help to improve the performance as well.
Lattice-Based Cryptography and NTRU

With the publication of Peter Shor’s seminal paper that factoring and discrete log computations would be entirely feasible on a quantum computer, and with advances in the building of quantum computers, there has been a focus on what is referred to as “post-quantum cryptography”. Among the most viable candidates for post-quantum cryptography are cryptosystems based on the problem of finding short vectors in lattices. In this chapter we outline briefly why quantum computers can make RSA-type cryptosystems obsolete and how lattices can be used in cryptography. We concentrate on perhaps the best-known lattice system, NTRU, and explain how it is used and why attacks on it still seem computationally infeasible.
Machine Learning for Predicting Cancer Disease: Comparative Analysis

As time passes, the data across different areas on the Internet is increasing rapidly. In order to utilize this data in a beneficial manner, the concept of data mining has been introduced. Although there exist numerous data mining techniques among them artificial neural network, support vector machine, and K-nearest neighbor are all considered the most powerful and superior techniques that are able to provide accurate results without needing a large dataset. In this paper, we provide a comparison of the most recent papers that predict cancer diseases using one of the three algorithms artificial neural network, support vector machine, and K-nearest neighbor. Furthermore, one of the studies in the literature obtained 100% accuracy in predicting breast cancer when using an algorithm support vector machine. In this research, we found that breast cancer is more common in the research area than any other type of cancer. Although it is not the first cause of death in recent years, according to the National Cancer Institute. The reason to focus on breast cancer is that it is one of the most prevalent types of cancer and has increased awareness about it. Additionally, the K-nearest neighbor is the most popular algorithm used in cancer prediction.
Password Hashing and Preprocessing

How does the cryptanalytic effort needed to compromise t out of m instances of hashed passwords scale with the number of users when arbitrary preprocessing information on the hash function is available? We provide a formal treatment of this problem in the multi-instance setting with auxiliary information. A central contribution of our work is an (arguably simple) transcript-counting argument that allows us to resolve a fundamental question left open by Bellare, Ristenpart, and Tessaro (BRT; CRYPTO 2012) in multi-instance security. We leverage this proof technique to formally justify unrecoverability of hashed salted passwords in the presence of auxiliary information in the random-oracle model. To this end we utilize the recent pre-sampling techniques for dealing with auxiliary information developed by Coretti et al. (CRYPTO 2018). Our bounds closely match those commonly assumed in practice. Besides hashing of passwords through a monolithic random oracle, we consider the effect of iteration, a technique that is used in classical mechanisms, such as bcrypt and PBKDF2, to slow down the rate of guessing. Building on the work of BRT, we formulate a notion of KDF security, also in the presence of auxiliary information, and prove an appropriate composition theorem for it.
Block Chain and IoT Architecture

From its inception, the Internet of Things (IoT) has consisted of integrated smart devices, which can lead to decentralized equipment and programming platforms using a common foundation. Disadvantages of this foundation include cloud server security costs, powerless point for assisting time-sensitive IoT applications, low interoperability, single points of failure, and insurance issues. Incorporating Blockchain into IoT can help to design an appropriate decentralization of the IoT platform, with advantages including decrease for the measure of realities moved, improved security and privacy, and extra purposeful and self-continuing tasks. In this chapter, we discuss the integration of block chain into IoT using nearby half breed square chain its hybrid IoT design. Block chain engineering can scale a large number of IoT devices. In addition, they can be included in a Blockchain coordinate with a high volume of exchanges. In such hybrid engineering, IoT structures are combined with Blockchain and IoT design. Hybrid engineering uses both proof of work (PoW) Blockchains and Byzantine fault tolerant (BFT) conventions. PoW Blockchains increase compatibility among numerous IoT gadgets on the Blockchain. At that point, hybrid IoT uses a BFT between the connector systems to achieve interoperability among the secondary Blockchains.
Minimally Supported Frequency (MSF) d-Dilation Wavelets

In this paper, we provide a geometric construction of a symmetric 2n-interval minimally supported frequency (MSF) d-dilation wavelet set with d∈(1,∞) and characterize all symmetric d-dilation wavelet sets. We also provide two special kinds of symmetric d-dilation wavelet sets, one of which has 4m-intervals whereas the other has (4m+2)-intervals, for m∈N. In addition, we construct a family of d-dilation wavelet sets that has an infinite number of components.
Applied Switching Bilateral Filter for Color Images under Mixed Noise

In the image, mixed noise color images independently contaminate each component of the color image with varying strength. In this paper, we propose a detection switching bilateral filter (DSBF) to reduce the mixed noise for the color images. We compare the performance of the proposed filter with three well-known filters including SBF, BM3D, and NCSR. We improve the performance of the SBF filter for color images by enhancing the noise separation process based on primary color values. In simulation results, the DSBF maintains high performance for color images than others. On the other hand, the performance filter-based non-iterative scheme, SBF and BM3D, significantly decrease for color images since the quality of output in each component cannot maintain the filter performance.
Privacy heroes need data disguises

Providing privacy in complex, data-rich applications is hard. Deleting accounts, anonymizing an account's contributions, and other privacy-related actions may require the traversal and transformation of interwoven state in a relational database. Finding the affected data is already nontrivial, but privacy actions must additionally balance competing requirements, such as preserving data trails for legal reasons or allowing users to change their mind. We believe a systematic shared framework for specifying and implementing privacy transformations could simplify and empower applications. Our prototype, data disguising, supports fine-grained, nuanced, and useful policies that would be cumbersome to implement manually, including reversible transformations that can compose.
Extraction of Opinion Targets and Words from Reviews Using Collective Parallel Cluster Algorithm

Online customer reviews are considered to be a significant informative resource that is useful for both customers as well as manufacturers. The manual scanning of reviews makes the process difficult and thus the evolution of opinion targets and words arise. In the last decade, several techniques have been proposed for the extraction of opinion targets and words which have certain limitations such as long-span relations and syntactic pattern errors. In addition, the proposed methods consume more time in case of a huge volume of reviews with some erroneous review statements. To reduce the time consumption, this paper proposes a parallel collective clustering algorithm for the better extraction of ordered pairs in a shorter time despite the errors. The proposed parallel collective clustering algorithm utilizes the concept of Parallel Word Alignment Model for the generation of opinion targets and words. Extensive experimentation has been carried out with the larger reviews collected from Amazon and the proposed technique has been evaluated using familiar metrics such as Precision, Recall and F-Measure in addition to execution time. The experimental results show that the proposed approach is flexible and effective for a larger dataset with erroneous review statements.
UltraPIN: Inferring PIN Entries via Ultrasound

While PIN-based user authentication systems such as ATM have long been considered to be secure enough, they are facing new attacks, named UltraPIN, which can be launched from commodity smartphones. As a target user enters a PIN on a PIN-based user authentication system, an attacker may use UltraPIN to infer the PIN from a short distance (50 cm to 100 cm). In this process, UltraPIN leverages smartphone speakers to issue human-inaudible ultrasound signals and uses smartphone microphones to keep recording acoustic signals. It applies a series of signal processing techniques to extract high-quality feature vectors from low-energy and high-noise signals and then applies a combination of machine learning models to classify finger movement patterns during PIN entry and generate a ranked list of highly possible PINs as result. Rigorous experiments show that UltraPIN is highly effective and robust in PIN inference.
Intent Recognition in Smart Homes with ProbLog

Smart homes and assisted living facilities are better able to improve the lives of residents if they can perform intent recognition. If the system understands the goals of residents it can select appropriate assistive actions in advance, for example. The increasing availability of cheap sensors and smart devices provides fine grained data about residents and the ambient environment. Making full use of this data for intent recognition requires both relational and probabilistic reasoning. In this paper we present a framework for performing combined logical and probabilistic reasoning about intentions using data from simple switch sensors. We employ ProbLog, a probabilistic extension to Prolog, to infer the most probable intention given observations of the actions of the agent and sensor readings of important aspects of the environment.
Improving distributed collaboration – methods for identification and developing of improvement potentials

Due to the increasing globalization, collaboration in product development between different locations is gaining more importance. Current circumstances, such as the Covid-19 pandemic, are even forcing the introduction of distributed collaboration in product development. Regarding to the Covid-19 pandemic, in many cases the transition was completed in a short time and even unplanned. The transition to a distributed approach creates new, unexpected problems in the collaboration of distributed product development teams, which have to be solved continuously. Therefore, the aim of this paper is to develop a concept that offers methods for the continuous improvement of distributed collaboration that are appropriate to the specific circumstances. Following a literature search, 135 participants were asked about the need for methodological support in identifying and developing improvement potentials in distributed collaboration. The demand was confirmed by the majority of participants. Consequently, a concept was developed to methodically support the improvement process. The developed concept was designed as an interactive tool, which offers the user methods for methodical approach to improvement potentials in accordance with his needs and resources. The basis is the problem-solving methodology SPALTEN. For a standardized and comprehensive description of the methods, fact sheets were prepared. The tool and the concept were evaluated in the industry in a distributed and agile development simulator. The evaluation confirmed the added benefit of the methodical support for the improvement process in the distributed collaboration.
KVCG

We present KVCG, a novel heterogeneous key-value store whose primary objective is to serve client requests targeting frequently accessed (hot) keys at sub-millisecond latency and requests targeting less frequently accessed (cold) keys with high throughput. To accomplish this goal, KVCG deploys an architecture where requests on hot keys are routed to a software cache operated by CPU threads, while the remainder are offloaded to a data repository optimized for execution on modern GPU devices. Cold/hot partitioning is done at runtime through a model trained with the incoming workload. Against a state-of-the-art competitor, we obtain up to 34x improvement in latency.
Studying Conceptual Modeling Processes: A Modeling Tool, Research Observatory, and Multimodal Observation Setup

Abstract What do (non-)experienced modelers reason while conceptual modeling and how do they arrive at modeling decisions, which modeling and learning difficulties do they face and why, and how do they overcome these difficulties by tailored modeling tool support are questions of relevance and importance to practicing modelers and, likewise, to conceptual modeling research. For the past 7 years, we have been designing, developing, and evaluating a modeling tool integrating a research observatory aimed at studying individual modeling processes online, in the field, and under laboratory conditions—to contribute to a richer understanding of modeler reasoning and decision-making, to identify common modeling and learning difficulties, and, ultimately, to design tool support to mitigate difficulties and to improve assistance for (non-)experienced modelers. We present an overview of the modeling observatory and of a corresponding multimodal observation setup.