Reviewerly/exHarmony huggingface dataset:
Modeling Transferable Topics for Cross-Target Stance Detection

Targeted stance detection aims to classify the attitude of an opinionated text towards a pre-defined target. Previous methods mainly focus on in-target setting that models are trained and tested using data specific to the same target. In practical cases, the target we concern may have few or no labeled data, which restrains us from training a target-specific model. In this paper we study the problem of cross-target stance detection, utilizing labeled data of a source target to learn models that can be adapted to a destination target. To this end, we propose an effective method, the core intuition of which is to leverage shared latent topics between two targets as transferable knowledge to facilitate model adaptation. Our method acquires topic knowledge with neural variational inference, and further adopts adversarial training that encourages the model to learn target-invariant representations. Experimental results verify that our proposed method is superior to the state-of-the-art methods.
On the negative impact of social influence in recommender systems: A study of bribery in collaborative hybrid algorithms

Recommender systems are based on inherent forms of social influence. Indeed, suggestions are provided to the users based on the opinions of peers. Given the relevance that ratings have nowadays to push the sales of an item, sellers might decide to bribe users so that they rate or change the ratings given to items, thus increasing the sellers' reputation. Hence, by exploiting the fact that influential users can lead an item to get recommended, bribing can become an effective way to negatively exploit social influence and introduce a bias in the recommendations. Given that bribing is forbidden but still employed by sellers, we propose a novel matrix completion algorithm that performs hybrid memory-based collaborative filtering using an approximation of Kolmogorov complexity. We also propose a framework to study the bribery effect and the bribery resistance of our approach. Our theoretical analysis, validated through experiments on real-world datasets, shows that our approach is an effective way to counter bribing while, with state-of-the-art algorithms, sellers can bribe a large part of the users.
Natural Software Revisited

Recent works have concluded that software code is more repetitive and predictable, i.e. more natural, than English texts. On re-examination, we find that much of the apparent "naturalness" of source code is due to the presence of language specific syntax, especially separators, such as semi-colons and brackets. For example, separators account for 44% of all tokens in our Java corpus. When we follow the NLP practices of eliminating punctuation (e.g., separators) and stopwords (e.g., keywords), we find that code is still repetitive and predictable, but to a lesser degree than previously thought. We suggest that SyntaxTokens be filtered to reduce noise in code recommenders. Unlike the code written for a particular project, API code usage is similar across projects: a file is opened and closed in the same manner regardless of domain. When we restrict our n-grams to those contained in the Java API, we find that API usages are highly repetitive. Since API calls are common across programs, researchers have made reliable statistical models to recommend sophisticated API call sequences. Sequential n-gram models were developed for natural languages. Code is usually represented by an AST which contains control and data flow, making n-grams models a poor representation of code. Comparing n-grams to statistical graph representations of the same codebase, we find that graphs are more repetitive and contain higherlevel patterns than n-grams. We suggest that future work focus on statistical code graphs models that accurately capture complex coding patterns. Our replication package makes our scripts and data available to future researchers[1].
Robust Visual Servoing Control for Ground Target Tracking of Quadrotors

In this brief, the robust position-based visual servoing control problem is addressed for a quadrotor with a down-facing monocular camera to perform ground target tracking tasks in outdoor environments. The object is to regulate the position of the quadrotor relative to the ground target by using a minimum sensor set, including an inertial measurement unit, an ultrasonic sensor, and a vision sensor. The robust compensators are designed in the visual servoing control system to enhance the robustness against nonlinearities, couplings, and uncertainties, including parameter uncertainties and external disturbances in the translational and rotational quadrotor dynamics. Theoretical analysis and experimental results demonstrate the effectiveness of the proposed robust visual servoing control method.
Deep neuroevolution of recurrent and discrete world models

Neural architectures inspired by our own human cognitive system, such as the recently introduced world models, have been shown to outperform traditional deep reinforcement learning (RL) methods in a variety of different domains. Instead of the relatively simple architectures employed in most RL experiments, world models rely on multiple different neural components that are responsible for visual information processing, memory, and decision-making. However, so far the components of these models have to be trained separately and through a variety of specialized training methods. This paper demonstrates the surprising finding that models with the same precise parts can be instead efficiently trained end-to-end through a genetic algorithm (GA), reaching a comparable performance to the original world model by solving a challenging car racing task. An analysis of the evolved visual and memory system indicates that they include a similar effective representation to the system trained through gradient descent. Additionally, in contrast to gradient descent methods that struggle with discrete variables, GAs also work directly with such representations, opening up opportunities for classical planning in latent space. This paper adds additional evidence on the effectiveness of deep neuroevolution for tasks that require the intricate orchestration of multiple components in complex heterogeneous architectures.
Privacy aware decentralized access control system

IoT security and privacy have proven to be a significant challenge. The traditional access control protocols are not suitable for IoT mainly due to a massive scale, ubiquitous connectivity and distributed nature. Blockchain based access control approaches provide decentralized security but they involve scalability problem, high transaction fees, a significant delay, and computational overhead that is not acceptable for resource-constrained IoT devices. Moreover, data published on the blockchain are public which is not ideal for many scenarios. In this paper, we proposed a new decentralized access control system based on the Tangle which empowers the users to dictate the access to their resource. In our proposed decentralized access control model the policies and access rights are published on the Tangle which guarantees distributed auditability and prevents the user from fraudulently denying the granted access rights. The main contribution of the paper is to provide privacy of the policy by leveraging Masked Authenticated Messaging (MAM) data communication protocol. The proposed work is validated by implementation and is tested with AVISPA tool which confirms security in the presence of the intruder.
An Adaptive Framework for Augmented Reality Instructions Considering Workforce Skill

Human resources have always been a key component in manufacturing systems. The digital transformation of modern shop floor undoubtedly induced the evolution of new requirements in workforce skills. In recent years, Augmented Reality (AR) has arisen as a promising technology for knowledge transfer across the factory. AR applications need to be tailored into the workforce skills, considering their limited experience. This paper presents an approach for AR instruction generation, adaptive to the shop floor operators’ level of experience, to assist them in production machinery operation. The proposed framework is validated in a press brake manufacturer use case.
On domain knowledge and novelty to improve program synthesis performance with grammatical evolution

Programmers solve coding problems with the support of both programming and problem specific knowledge. They integrate this domain knowledge to reason by computational abstraction. Correct and readable code arises from sound abstractions and problem solving. We attempt to transfer insights from such human expertise to genetic programming (GP) for solving automatic program synthesis. We draw upon manual and non-GP Artificial Intelligence methods to extract knowledge from synthesis problem definitions to guide the construction of the grammar that Grammatical Evolution uses and to supplement its fitness function. We examine the impact of using such knowledge on 21 problems from the GP program synthesis benchmark suite. Additionally, we investigate the compounding impact of this knowledge and novelty search. The resulting approaches exhibit improvements in accuracy on a majority of problems in the field's benchmark suite of program synthesis problems.
Ekush: A Multipurpose and Multitype Comprehensive Database for Online Off-Line Bangla Handwritten Characters

Ekush the largest dataset of handwritten Bangla characters for research on handwritten Bangla character recognition. In recent years Machine learning and deep learning application-based researchers have achieved interest and one of the most significant application is handwritten recognition. Because it has the tremendous application such in Bangla OCR. Also, Bangla writing script is one of the most popular in the world. For that reason, we are introducing a multipurpose comprehensive dataset for Bangla Handwritten Characters. The proposed dataset contains Bangla modifiers, vowels, consonants, compound letters and numerical digits that consists of 367,018 isolated handwritten characters written by 3086 unique writers which were collected within Bangladesh. This dataset can be used for other problems i.e.: gender, age, district base handwritten related research, because the samples were collected include verity of the district, age group and the equal number of male and female. It is intended to fabricate acknowledgment technique for hadn written Bangla characters. This dataset is unreservedly accessible for any sort of scholarly research work. The Ekush dataset is trained and validated with EkushNet and indicated attractive acknowledgment precision 97.73% for Ekush dataset, which is up until this point, the best exactness for Bangla character acknowledgment. The Ekush dataset and relevant code can be found at this link: https://github.com/ShahariarRabby/ekush .
An Educational Chatbot for Answering Queries

The fast progress in the development of communication and information has made people very diverse in knowledge improvement, education, and learning methods. In this paper, we show how we can convert documents into the knowledge of a chatter robot. It helps users to make profit out of it by asking and answering queries, using electronic documents which are integrated with the simulated system. Hence, we are motivated to develop an educational chatbot system which provides a virtual assistant. Its main function is to streamline and to automate manual and administrative tasks while supporting other course-related activities. The aim of this research work is to develop a system which is automated and can provide an answer to a question asked by a user on behalf of a person, for educational purposes. We have focused on the local as well as Web databases to make the model user-friendly, interactive, and scalable.
On Lions and Elligators: An Efficient Constant-Time Implementation of CSIDH

The recently proposed CSIDH primitive is a promising candidate for post quantum static-static key exchanges with very small keys. However, until now there is only a variable-time proof-of-concept implementation by Castryck, Lange, Martindale, Panny, and Renes, recently optimized by Meyer and Reith, which can leak various information about the private key. Therefore, we present an efficient constant-time implementation that samples key elements only from intervals of nonnegative numbers and uses dummy isogenies, which prevents certain kinds of side-channel attacks. We apply several optimizations, e.g. Elligator and the newly introduced SIMBA, in order to get a more efficient implementation.
Dynamic attention network for semantic segmentation

Semantic segmentation networks usually utilize special pyramid structure after encoder or combine low-level and high-level feature maps in decoder to capture multi-scale context information, which we term them feature combination and feature connection, respectively. However, both such frameworks are less valid with the fixed geometric structure or unsuitable interim. In this paper, we advocate Dynamic Attention Network (DAN) to solve these problems. First, we design a Deformable Attention Pyramid (DAP) module to perform a self-adjustable descriptor of high-level output, which utilizes deformable function to model geometric transformation. With DAP, semantic information can be captured effectively. Second, we propose a Fusing Attention Interim (FAI) module to guide the back-propagation of long-short range information in each level of decoder. We evaluate DAN on the challenging PASCAL VOC 2012 and Cityscapes segmentation benchmarks and find that it achieves state-of-the-art results without post-processing. Our observation can be concluded that the flexible structure that possesses dynamic attention mechanism is beneficial to learn multi-scale context information.
A Hierarchical Grocery Store Image Dataset With Visual and Semantic Labels

Image classification models built into visual support systems and other assistive devices need to provide accurate predictions about their environment. We focus on an application of assistive technology for people with visual impairments, for daily activities such as shopping or cooking. In this paper, we provide a new benchmark dataset for a challenging task in this application - classification of fruits, vegetables, and refrigerated products, e.g. milk packages and juice cartons, in grocery stores. To enable the learning process to utilize multiple sources of structured information, this dataset not only contains a large volume of natural images but also includes the corresponding information of the product from an online shopping website. Such information encompasses the hierarchical structure of the object classes, as well as an iconic image of each type of object. This dataset can be used to train and evaluate image classification models for helping visually impaired people in natural environments. Additionally, we provide benchmark results evaluated on pretrained convolutional neural networks often used for image understanding purposes, and also a multi-view variational autoencoder, which is capable of utilizing the rich product information in the dataset.
Multi-target tracking in distributed sensor networks using particle PHD filters

Multi-target tracking is an important problem in civilian and military applications. This paper investigates multi-target tracking in distributed sensor networks. Data association, which arises particularly in multi-object scenarios, can be tackled by various solutions. We consider sequential Monte Carlo implementations of the Probability Hypothesis Density (PHD) filter based on random finite sets. This approach circumvents the data association issue by jointly estimating all targets in the region of interest. To this end, we develop the Diffusion Particle PHD Filter (D-PPHDF) as well as a centralized version, called the Multi-Sensor Particle PHD Filter (MS-PPHDF). Their performance is evaluated in terms of the Optimal Subpattern Assignment (OSPA) metric, benchmarked against a distributed extension of the Posterior Cramér-Rao Lower Bound (PCRLB), and compared to the performance of an existing distributed PHD Particle Filter. Furthermore, the robustness of the proposed tracking algorithms against outliers and their performance with respect to different amounts of clutter is investigated.
The decade-long cryptocurrencies and the blockchain rollercoaster: Mapping the intellectual structure and charting future directions

Recent advances in science mapping allowed to analyze the entire intellectual structure of blockchain and cryptocurrencies in business-related disciplines to identify 174 academic articles as well as 1482 practitioner-oriented articles published since the inception of cryptocurrencies in 2008 to highlight key trends of the published outputs. The results demonstrate academic research done by 389 authors in 296 organizations based in 50 countries that only just initiated the conversation on four major streams of the literature—Bitcoin and cryptocurrencies; blockchain adoption; cryptocurrency and blockchain environment; and business model innovations. When comparing academic scholarship to practitioner-oriented literature, the results demonstrate that practitioners discussed investor-related themes, cryptocurrency intrinsic value, political-economic sphere, and the impact of cryptocurrency and blockchain technologies on the wider society in greater detail. As a result, a number of themes are identified and discussed that could align academic and practitioner interests and provide guidance for further research in this important field.
Defending Against Adversarial Attacks by Randomized Diversification

The vulnerability of machine learning systems to adversarial attacks questions their usage in many applications. In this paper, we propose a randomized diversification as a defense strategy. We introduce a multi-channel architecture in a gray-box scenario, which assumes that the architecture of the classifier and the training data set are known to the attacker. The attacker does not only have access to a secret key and to the internal states of the system at the test time. The defender processes an input in multiple channels. Each channel introduces its own randomization in a special transform domain based on a secret key shared between the training and testing stages. Such a transform based randomization with a shared key preserves the gradients in key-defined sub-spaces for the defender but it prevents gradient back propagation and the creation of various bypass systems for the attacker. An additional benefit of multi-channel randomization is the aggregation that fuses soft-outputs from all channels, thus increasing the reliability of the final score. The sharing of a secret key creates an information advantage to the defender. Experimental evaluation demonstrates an increased robustness of the proposed method to a number of known state-of-the-art attacks.
VIDOSAT: High-Dimensional Sparsifying Transform Learning for Online Video Denoising

Techniques exploiting the sparsity of images in a transform domain are effective for various applications in image and video processing. In particular, transform learning methods involve cheap computations and have been demonstrated to perform well in applications, such as image denoising and medical image reconstruction. Recently, we proposed methods for online learning of sparsifying transforms from streaming signals, which enjoy good convergence guarantees and involve lower computational costs than online synthesis dictionary learning. In this paper, we apply online transform learning to video denoising. We present a novel framework for online video denoising based on high-dimensional sparsifying transform learning for spatio-temporal patches. The patches are constructed either from corresponding 2D patches in successive frames or using an online block matching technique. The proposed online video denoising requires little memory and offers efficient processing. Numerical experiments evaluate the performance of the proposed video denoising algorithms on multiple video data sets. The proposed methods outperform several related and recent techniques, including denoising with 3D DCT, prior schemes based on dictionary learning, non-local means, background separation, and deep learning, as well as the popular VBM3D and VBM4D.
Predictive Variables for Agile Development Merging Cloud Computing Services

Cloud Technology is a most challenging modern area in the field of modern technologies in which assets (e.g., CPU and capacity) can be rented and discharged by the clients through internet on-demand basis. The cloud computing has been giving virtual computing services to a little, medium and extensive industries, and services, for example, infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). Cloud computing has a great combination with the agile software development as a research area. Many researchers worked in Agile Cloud field. The software industries are using the agile methodology for efficient software development need some platform to get quick feedback from the client. Therefore, the agile-cloud is a great combination for it but due to security reasons that directly influence the less adoption of cloud in software industries. This paper reports the survey results of software industries. The total of seven IT industries and many professionals was involved in this paper. However, this paper also contributes and reveals how existing issues can affect agile-cloud adoption for efficient software development. Furthermore, we do not find any type of survey conducted in Pakistan's software industries-related to cloud-agile adoption.
Local Attention Networks for Occluded Airplane Detection in Remote Sensing Images

Despite the great progress of deep learning and target detection in recent years, the accurate detection of the occluded targets in remote sensing images still remains a challenge. In this letter, we propose a new detection method called local attention networks to improve the detection of occluded airplanes. Following the idea of “divide and conquer,” the proposed method is designed by first dividing an airplane target into four visual parts: head, left/right wings, body, and tail, and then considering the detection as the prediction of the individual key points in each of the visual parts. We further introduce an additional attention branch in the standard detection pipeline to enhance the features and make the model focus on individual parts of a target even if it is only partially visible in the image. Detection results and ablation studies on three remote sensing target detection data sets (including two publicly available ones) demonstrate the effectiveness of our method, especially for occluded airplane targets. In addition, our method outperforms the other state-of-the-art detection methods on these data sets.