Reviewerly/exHarmony huggingface dataset:
A Novel Framework for Implementation of Land Registration and Ownership Management via Blockchain in Bangladesh

Registration process regarding ownership, possession or other rights for properties like land is a tedious method in progressing countries like Bangladesh. This paper highlights issues related to manual land registrations processes such as transparency, centralization, authenticity, reliability, etc and proposes a better method to overcome these problems using Blockchain Technology. The comparison between Blockchain-based digital land record systems in different countries is also explored in this paper. Finally, we have developed a novel framework that uses Blockchain method for executing the process of Land Registration and providing authentic and indisputable rights on ownership for the people in Bangladesh.
Bayesian Variable Selection for Gaussian Copula Regression Models

We develop a novel Bayesian method to select important predictors in regression models with multiple responses of diverse types. A sparse Gaussian copula regression model is used to account for the multivariate dependencies between any combination of discrete and/or continuous responses and their association with a set of predictors. We use the parameter expansion for data augmentation strategy to construct a Markov chain Monte Carlo algorithm for the estimation of the parameters and the latent variables of the model. Based on a centered parameterization of the Gaussian latent variables, we design a fixed-dimensional proposal distribution to update jointly the latent binary vectors of important predictors and the corresponding nonzero regression coefficients. For Gaussian responses and for outcomes that can be modeled as a dependent version of a Gaussian response, this proposal leads to a Metropolis-Hastings step that allows an efficient exploration of the predictors' model space. The proposed strategy is tested on simulated data and applied to real datasets in which the responses consist of low-intensity counts, binary, ordinal and continuous variables.
An Adaptive Priority Allocation for Formation UAVs in Complex Context

A novel passing priority allocation method, based on the collision avoidance urgency, used for a formation of UAVs in a serried obstacle laden environment, is presented in this article. The method employs the collision cone and the prediction of the collision time to evaluate the urgency of imminent collision of each UAV, so as to establish a primary criterion for the priority allocation. And, the priority update policy is also innovative designed to alleviate burdens on the instability of frequent shifty context. This article exploits the potential field in a cooperative way to plan the path for each UAV, steering the UAVs to a given 3-D position, which combines the adaptive priorities to guide the UAVs passing through the barriers in turn. Numerical simulations and semi-physical experiments in designed outdoor condition are carried out to compare the performance of the proposed adaptive priorities with the regular-used default priorities. The total passing time and the energy cost of the semi-physical tests provide the promising validation in terms of the efficiency and superiority of the proposed method.
Tail bounds on hitting times of randomized search heuristics using variable drift analysis

Abstract Drift analysis is one of the state-of-the-art techniques for the runtime analysis of randomized search heuristics (RSHs) such as evolutionary algorithms (EAs), simulated annealing, etc . The vast majority of existing drift theorems yield bounds on the expected value of the hitting time for a target state, for example the set of optimal solutions, without making additional statements on the distribution of this time. We address this lack by providing a general drift theorem that includes bounds on the upper and lower tail of the hitting time distribution. The new tail bounds are applied to prove very precise sharp-concentration results on the running time of a simple EA on standard benchmark problems, including the class of general linear functions. On all these problems, the probability of deviating by an r -factor in lower-order terms of the expected time decreases exponentially with r . The usefulness of the theorem outside the theory of RSHs is demonstrated by deriving tail bounds on the number of cycles in random permutations. All these results handle a position-dependent (variable) drift that was not covered by previous drift theorems with tail bounds. Finally, user-friendly specializations of the general drift theorem are given.
AOBTM: Adaptive Online Biterm Topic Modeling for Version Sensitive Short-texts Analysis

Analysis of mobile app reviews has shown its important role in requirement engineering, software maintenance and evolution of mobile apps. Mobile app developers check their users' reviews frequently to clarify the issues experienced by users or capture the new issues that are introduced due to a recent app update. App reviews have a dynamic nature and their discussed topics change over time. The changes in the topics among collected reviews for different versions of an app can reveal important issues about the app update. A main technique in this analysis is using topic modeling algorithms. However, app reviews are short texts and it is challenging to unveil their latent topics over time. Conventional topic models such as Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (PLSA) suffer from the sparsity of word co-occurrence patterns while inferring topics for short texts. Furthermore, these algorithms cannot capture topics over numerous consecutive time-slices (or versions). Online topic modeling algorithms such as Online LDA (OLDA) and Online Biterm Topic Model (OBTM) speed up the inference of topic models for the texts collected in the latest time-slice by saving a fraction of data from the previous time-slice. But these algorithms do not analyze the statistical-data of all the previous time-slices, which can confer contributions to the topic distribution of the current time-slice.In this paper, we propose Adaptive Online Biterm Topic Model (AOBTM) to model topics in short texts adaptively. AOBTM alleviates the sparsity problem in short-texts and considers the statistical-data for an optimal number of previous time-slices. We also propose parallel algorithms to automatically determine the optimal number of topics and the best number of previous versions that should be considered in topic inference phase. Automatic evaluation on collections of app reviews and real-world short text datasets confirm that AOBTM can find more coherent topics and outperforms the state-of-the-art baselines. For reproducibility of the results, we open source all scripts.
Single-Image Depth Prediction Makes Feature Matching Easier

Good local features improve the robustness of many 3D re-localization and multi-view reconstruction pipelines. The problem is that viewing angle and distance severely impact the recognizability of a local feature. Attempts to improve appearance invariance by choosing better local feature points or by leveraging outside information, have come with pre-requisites that made some of them impractical. In this paper, we propose a surprisingly effective enhancement to local feature extraction, which improves matching. We show that CNN-based depths inferred from single RGB images are quite helpful, despite their flaws. They allow us to pre-warp images and rectify perspective distortions, to significantly enhance SIFT and BRISK features, enabling more good matches, even when cameras are looking at the same scene but in opposite directions.
Minimum Uncertainty Based Detection of Adversaries in Deep Neural Networks

Despite their unprecedented performance in various domains, utilization of Deep Neural Networks (DNNs) in safety-critical environments is severely limited in the presence of even small adversarial perturbations. The present work develops a randomized approach to detecting such perturbations based on minimum uncertainty metrics that rely on sampling at the hidden layers during the DNN inference stage. Inspired by Bayesian approaches to uncertainty estimation, the sampling probabilities are designed for effective detection of the adversarially corrupted inputs. Being modular, the novel detector of adversaries can be conveniently employed by any pre-trained DNN at no extra training overhead. Selecting which units to sample per hidden layer entails quantifying the amount of DNN output uncertainty, where the overall uncertainty is expressed in terms of its layer-wise components - what also promotes scalability. Sampling probabilities are then sought by minimizing uncertainty measures layer-by-layer, leading to a novel convex optimization problem that admits an exact solver with superlinear convergence rate. By simplifying the objective function, low-complexity approximate solvers are also developed. In addition to valuable insights, these approximations link the novel approach with state-of-the-art randomized adversarial detectors. The effectiveness of the novel detectors in the context of competing alternatives is highlighted through extensive tests for various types of adversarial attacks with variable levels of strength.
CSI-RAShi: Distributed Key Generation for CSIDH

We present an honest-majority Distributed Key Generation protocol (DKG) based on Shamir's (k, n)-threshold secret sharing in the setting of Very Hard Homogenous Spaces (VHHS). DKGs in the discrete logarithm setting use Pedersen commitments, for which there is no known analogue in the VHHS setting. As a replacement, we introduce a new primitive called piecewise verifiable proofs, which allow a prover to prove that a list of NP-statements is valid with respect to a common witness, and such that the different statements can be verified individually. Our protocol is robust and actively secure in the Quantum Random Oracle Model. For n participants, the total runtime of our protocol is $$2+\lambda +n(1+4\lambda )$$ group action evaluations, where $$\lambda $$ is the underlying security parameter, and is thus independent of the threshold k. When instantiated with CSIDH-512, this amounts to approximately $$4.5+18n$$ seconds.
Class-Wise Difficulty-Balanced Loss for Solving Class-Imbalance

Class-imbalance is one of the major challenges in real world datasets, where a few classes (called majority classes) constitute much more data samples than the rest (called minority classes). Learning deep neural networks using such datasets leads to performances that are typically biased towards the majority classes. Most of the prior works try to solve class-imbalance by assigning more weights to the minority classes in various manners (e.g., data re-sampling, cost-sensitive learning). However, we argue that the number of available training data may not be always a good clue to determine the weighting strategy because some of the minority classes might be sufficiently represented even by a small number of training data. Overweighting samples of such classes can lead to drop in the model’s overall performance. We claim that the ‘difficulty’ of a class as perceived by the model is more important to determine the weighting. In this light, we propose a novel loss function named Class-wise Difficulty-Balanced loss, or CDB loss, which dynamically distributes weights to each sample according to the difficulty of the class that the sample belongs to. Note that the assigned weights dynamically change as the ‘difficulty’ for the model may change with the learning progress. Extensive experiments are conducted on both image (artificially induced class-imbalanced MNIST, long-tailed CIFAR and ImageNet-LT) and video (EGTEA) datasets. The results show that CDB loss consistently outperforms the recently proposed loss functions on class-imbalanced datasets irrespective of the data type (i.e., video or image).
Fine-grained Feature Alignment with Part Perspective Transformation for Vehicle ReID

Given a query image, vehicle Re-Identification is to search the same vehicle in multi-camera scenarios, which are attracting much attention in recent years. However, vehicle ReID severely suffers from the perspective variation problem. For different vehicles with similar color and type which are taken from different perspectives, all visual patterns are misaligned and warped, which is hard for the model to find out the exact discriminative regions. In this paper, we propose part perspective transformation module (PPT) to map the different parts of vehicle into a unified perspective respectively. The PPT disentangles the vehicle features of different perspectives and then aligns them in a fine-grained level. Further, we propose a dynamically batch hard triplet loss to select the common visible regions of the compared vehicles. Our approach helps the model to generate the perspective invariant features and find out the exact distinguishable regions for vehicle ReID. Extensive experiments on three standard vehicle ReID datasets show the effectiveness of our method.
Digital punctuation as an interactional resource: the message-final period among German adolescents

This paper outlines a multi-level framework for empirical research on punctuation in digitally-mediated interaction and exemplifies it by close qualitative analysis of a corpus of WhatsApp conversations collected among secondary-school students in Northern Germany. We propose a sociolinguistic analysis of digital punctuation that orients to Silverstein's notion of "total linguistic fact" (Silverstein 1985), i.e. the integrative study of linguistic form, language practices, and language ideologies. Applying this to the case of the period, our analysis integrates its frequency of use in digital conversations, its placement in a message, the illocutionary force of messages that contain a period, and their sequential placement in digital interaction. Our findings suggest the period undergoes a process of pragmaticalization, i.e. a gain of pragmatic functions at the expenses of syntactic ones. In particular, its overall quite rare use in message-final position is found to contextualize a terminal point in the on-going interaction. Based on the interplay between digital data, ethnographic insights and interviews with informants, the analysis then shows that the metapragmatic awareness of period use among the adolescents is linked to its on-going enregisterment as an index of communicative distance and a part of adult and professional registers of language. We suggest the pragmaticalized period is a salient feature of a register of informal written language, which our informants identify as part of their everyday out-of-school digital practices. These informal practices co-exist with institutional literacy norms to which the students equally orient to. We juxtapose these insights to the thematising of punctuation in German school textbooks.
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians

Learning the parameters of Gaussian mixture models is a fundamental and widely studied problem with numerous applications. In this work, we give new algorithms for learning the parameters of a high-dimensional, well separated, Gaussian mixture model subject to the strong constraint of differential privacy. In particular, we give a differentially private analogue of the algorithm of Achlioptas and McSherry. Our algorithm has two key properties not achieved by prior work: (1) The algorithm's sample complexity matches that of the corresponding non-private algorithm up to lower order terms in a wide range of parameters. (2) The algorithm does not require strong a priori bounds on the parameters of the mixture components.
Utility of Deep Learning Features for Facial Attributes Manipulation Detection

ML-synthesized face samples, frequently called DeepFakes, is a serious issue menacing the integrity of information on the Internet and face recognition systems. One of the main defenses against face manipulations is DeepFakes detection. In this paper, we first created a new DeepFakes dataset using a publicly available MUCT database, which contains diverse set of facial manipulations. In particular, we employed smartphone FaceApp with eleven different filters (i.e., every filter concurs with a different facial manipulation) such as gender conversion, face swapping, tattoo and hair style changes. Deep learning features have recently demonstrated magnificent performances in various real-world applications. Therefore, with collected dataset, we study the efficiency of deep features for identifying the DeepFakes under different scenarios. We performed a rigorous and comparative analysis of a convolutional neural networks (CNNs) model and immensely utilized deep architectures such as VGG16, SqueezNet, DenseNet, ResaNet, and GoogleNet via transfer learning for face manipulation detection. Empirical results show that deep features based DeepFakes detection systems attain notable accuracies when trained and tested on same kind of manipulation. But their performances drop drastically when they encounter with novel manipulation type that was not used during the training stage, thereby having low generalization capability.
On Predicting and Analyzing Breast Cancer using Data Mining Approach

The highest invading cancer among the women is breast cancer. Early detection of breast cancer is the higher chance of the patient being treated. In this study, we have proposed an ensemble method named stacking classifier which combines multiple classification techniques and efficaciously classifies the benign and malignant tumor. “Wisconsin Diagnosis Breast Cancer” dataset culled from the UC Irvine Machine Learning Repository has been used for our experiment. We applied different classification techniques over the dataset and tuned their parameters to improve accuracy. We chose the three best classifiers for our proposed method. Generally, our proposed Stacking classifier combined the results of those best classifiers using meta classifier and provided 97.20% accuracy for breast cancer prediction. Performance of different data mining approaches have been evaluated rigorously through different evaluation metrics.
Reading Akkadian cuneiform using natural language processing

In this paper we present a new method for automatic transliteration and segmentation of Unicode cuneiform glyphs using Natural Language Processing (NLP) techniques. Cuneiform is one of the earliest known writing system in the world, which documents millennia of human civilizations in the ancient Near East. Hundreds of thousands of cuneiform texts were found in the nineteenth and twentieth centuries CE, most of which are written in Akkadian. However, there are still tens of thousands of texts to be published. We use models based on machine learning algorithms such as recurrent neural networks (RNN) with an accuracy reaching up to 97% for automatically transliterating and segmenting standard Unicode cuneiform glyphs into words. Therefore, our method and results form a major step towards creating a human-machine interface for creating digitized editions. Our code, Akkademia, is made publicly available for use via a web application, a python package, and a github repository.
Analyzing history-related posts in twitter

Abstract Microblogging platforms such as Twitter have been increasingly used nowadays to share information between users. They are also convenient means for propagating content related to history. Hence, from the research viewpoint they can offer opportunities to analyze the way in which users refer to the past, and how as well when such references appear and what purposes they serve. Such study could allow to quantify the interest degree and the mechanisms behind content dissemination. We report the results of a large scale exploratory analysis of history-oriented posts in microblogs based on a 28-month-long snapshot of Twitter data. The results can increase our understanding of the characteristics of history-focused content sharing in Twitter. They can also be used for guiding the design of content recommendation systems as well as time-aware search applications.
Shrec

The success of Bitcoin and Ethereum has attracted many efforts to build high-throughput blockchain systems. This paper focuses on transaction dissemination --- a rather overlooked issue in these systems. We argue that efficient transaction dissemination is the key for a blockchain system to sustain at high-throughput --- usually thousands of transactions per second --- and the existing solutions fell short at doing so.
Saffire: Context-sensitive Function Specialization against Code Reuse Attacks

The sophistication and complexity of recent exploitation techniques, which rely on memory disclosure and whole-function reuse to bypass address space layout randomization and control flow integrity, is indicative of the effect that the combination of exploit mitigations has in challenging the construction of reliable exploits. In addition to software diversification and control flow enforcement, recent efforts have focused on the complementary approach of code and API specialization to restrict further the critical operations that an attacker can perform as part of a code reuse exploit. In this paper we propose <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Saffire</sub> , a compiler-level defense against code reuse attacks. For each calling context of a critical function, Saffire creates a specialized and hardened replica of the function with a restricted interface that can accommodate only that particular invocation. This is achieved by applying <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">static</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">argument</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">binding</sub> , to eliminate arguments with static values and concretize them within the function body, and <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">argument</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">binding</sub> , which applies a narrow-scope form of data flow integrity to restrict the acceptable values of arguments that cannot be statically derived. We have implemented Saffire on top of LLVM, and applied it to a set of 11 applications, including Nginx, Firefox, and Chrome. The results of our experimental evaluation with a set of 17 real-world ROP exploits and three whole-function reuse exploits demonstrate the effectiveness of Saffire in preventing these attacks while incurring a negligible runtime overhead.
Inception residual attention network for remote sensing image super-resolution

How to enhance the spatial resolution for a remote sensing image is an important issue that we face. Many image super-resolution (SR) techniques have been proposed for this purpose and deep convolutional neural network (CNN) is the most effective approach in recent years. However, we observe that most CNN-based SR methods treat low-frequency areas and high-frequency areas equally, hence hindering the recovery of high-frequency information. In this paper, we propose a network named inception residual attention network (IRAN) to address this problem. Specifically, we propose a spatial attention module to make the network adaptively learn the importance of different spatial areas, so as to pay more attention to the areas with high-frequency information. Furthermore, we present an inception module to fuse local multilevel features, so as to provide richer information for reconstructing detailed textures. In order to evaluate the effectiveness of the proposed method, a large number of experiments are performed on UCMerced-LandUse data set and the results show that the proposed method is superior to the current state-of-the-art methods in both visual effects and objective indicators.