Reviewerly/exHarmony huggingface dataset:
Benchmark Pashto Handwritten Character Dataset and Pashto Object Character Recognition (OCR) Using Deep Neural Network with Rule Activation Function

In the area of machine learning, different techniques are used to train machines and perform different tasks like computer vision, data analysis, natural language processing, and speech recognition. Computer vision is one of the main branches where machine learning and deep learning techniques are being applied. Optical character recognition (OCR) is the ability of a machine to recognize the character of a language. Pashto is one of the most ancient and historical languages of the world, spoken in Afghanistan and Pakistan. OCR application has been developed for various cursive languages like Urdu, Chinese, and Japanese, but very little work is done for the recognition of the Pashto language. When it comes to handwritten character recognition, it becomes more difficult for OCR to recognize the characters as every handwritten character’s shape is influenced by the writer’s hand motion dynamics. The reason for the lack of research in Pashto handwritten character data as compared to other languages is because there is no benchmark dataset available for experimental purposes. This study focuses on the creation of such a dataset, and then for the evaluation purpose, a machine is trained to correctly recognize unseen Pashto handwritten characters. To achieve this objective, a dataset of 43000 images was created. Three Feed Forward Neural Network models with backpropagation algorithm using different Rectified Linear Unit (ReLU) layer configurations (Model 1 with 1-ReLU Layer, Model 2 with 2-ReLU layers, and Model 3 with 3-ReLU Layers) were trained and tested with this dataset. The simulation shows that Model 1 achieved accuracy up to 87.6% on unseen data while Model 2 achieved an accuracy of 81.60% and 3% accuracy, respectively. Similarly, loss (cross-entropy) was the lowest for Model 1 with 0.15 and 3.17 for training and testing, followed by Model 2 with 0.7 and 4.2 for training and testing, while Model 3 was the last with loss values of 6.4 and 3.69. The precision, recall, and f-measure values of Model 1 were better than those of both Model 2 and Model 3. Based on results, Model 1 (with 1 ReLU activation layer) is found to be the most efficient as compared to the other two models in terms of accuracy to recognize Pashto handwritten characters.
Digital transformation of the educational space: new tools and technological solutions

Introduction. The digital transformation of the educational space of a pedagogical university provides great opportunities for improving the quality of education and at the same time requires a radical change in methodological approaches to the organization of training; search, development, and application of new technological solutions and tools. The purpose of this article is to search for and analyze new tools and technologies for the digital transformation of the modern educational space. A fundamentally new approach is laid in the design of the educational and scientific electronic service "Pedagogical map of the world". The electronic scientific and educational service of the new generation "Pedagogical map of the world" makes it possible not only to broadcast but also to generate new knowledge, as well as to form readiness for transferring it to other social and humanitarian subject fields of science. Materials and methods. In the course of the study of the effectiveness of the electronic resource "Pedagogical map of the world" as a tool for the digital transformation of pedagogical education, a survey was conducted among students (1837 students) and scientific and pedagogical workers (211 employees) of higher education institutions in various regions of the Russian Federation. Results. The study among the university students aimed at determining the level of students' satisfaction with the functionality of the electronic service showed that a significant number of respondents (90.7 %) noted a positive trend in their attitude towards the study of the historical and pedagogical space when using the electronic service. The positive dynamics affected not only classroom studies but also the area of students' independent work and their personal development. A survey among the professional community also confirmed the effectiveness of the electronic resource. Discussion and conclusion. The modern electronic service "Pedagogical map of the world" incorporates a database, a "live textbook", an analytical simulator, and social networks. The possibilities of the "Pedagogical map of the world" include "Sets of tasks", developed on the basis of a four-dimensional model of a set of exercises for developing historical and pedagogical space.
Continual learning via inter-task synaptic mapping

Learning from streaming tasks leads a model to catastrophically erase unique experiences it absorbs from previous episodes. While regularization techniques such as LWF, SI, EWC have proven themselves as an effective avenue to overcome this issue by constraining important parameters of old tasks from changing when accepting new concepts, these approaches do not exploit common information of each task which can be shared to existing neurons. As a result, they do not scale well to large-scale problems since the parameter importance variables quickly explode. An Inter-Task Synaptic Mapping (ISYANA) is proposed here to underpin knowledge retention for continual learning. ISYANA combines task-to-neuron relationship as well as concept-to-concept relationship such that it prevents a neuron to embrace distinct concepts while merely accepting relevant concept. Numerical study in the benchmark continual learning problems has been carried out followed by comparison against prominent continual learning algorithms. ISYANA exhibits competitive performance compared to state of the arts. Codes of ISYANA is made available in https://github.com/ContinualAL/ISYANAKBS.
Anti-terrorism laws and data retention: war is over?

The Anti-terrorism, Crime and Security Act 2001 signals a determined response to the attacks of September 11th. One aspect involves the facilitation of the use of electronic surveillance in order to prevent, detect or prosecute the perpetrators of terrorism. The role of Part XI of the 2001 Act is to augment existing surveillance powers in the Regulation of Investigatory Powers Act 2000. This paper plots the relationships between those two statutes and also their relationship to data protection laws. Delays and difficulties in enforcement are noted and are related to a process of return to greater normality after an initial period of panic.
Tales of Favicons and Caches: Persistent Tracking in Modern Browsers

Statement from the NDSS 2021 Program Committee: NDSS is devoted to ethical principles and encourages the research community to ensure its work protects the privacy, security, and safety of users and others involved.While the NDSS 2021 PC appreciated the technical contributions of this paper, it was the subject of a debate in our community regarding the responsible disclosure of vulnerabilities for the Firefox web browser.The PC examined and discussed the ethics concerns raised and the authors' response.Although no harm came to users, the authors' oversight could have made a non-vulnerable
Deep Convolutional Neural Network based Ship Images Classification

Ships are an integral part of maritime traffic where they play both militaries as well as non-combatant roles. This vast maritime traffic needs to be managed and monitored by identifying and recognising vessels to ensure the maritime safety and security. As an approach to find an automated and efficient solution, a deep learning model exploiting convolutional neural network (CNN) as a basic building block, has been proposed in this paper. CNN has been predominantly used in image recognition due to its automatic high-level features extraction capabilities and exceptional performance. We have used transfer learning approach using pre-trained CNNs based on VGG16 architecture to develop an algorithm that performs the different ship types classification. This paper adopts data augmentation and fine-tuning to further improve and optimize the baseline VGG16 model. The proposed model attains an average classification accuracy of 97.08% compared to the average classification accuracy of 88.54% obtained from the baseline model.
A Preliminary Embodiment Short Questionnaire

Consumer virtual reality (VR) technologies have made embodying a virtual avatar during an immersive experience more feasible. The sense of embodiment toward that virtual avatar can be characterized and measured along three factors: self-location, agency, and body ownership. Some measures of embodiment have been previously proposed, but most have not been validated or do not measure the three individual factors of embodiment. In this paper, we present the construction and validation of a preliminary version of a short questionnaire that not only addresses these factors of embodiment but can also be used as an in-VR questionnaire, which we call the pESQ. By using and validating the pESQ, we provide results indicating that foot tracking significantly improves self-location and agency, and that an avatar significantly improves body ownership.
Transforming higher education learning ecosystem: teaching staff perspective

As a result of the COVID-19 crisis, organizing, designing and delivering learning programs in higher educational institutions needed to be revised and transferred to the digital world without destroying the overall cohesion and balance of the educational ecosystem. We present a case study of transformation challenges of the institution during COVID-19 from the perspective of teaching staff. The results of a survey indicate, that the learning ecosystem was able to respond to the sudden external disturbances as it had the basic conditions fulfilled. However, the transformation represents rather a first-order change, being an online extension of previous everyday practices and not bringing forth deep changes in ways of learning and teaching. As a result, the institution’s management needs to invest in additional infrastructure as well as in educational technologists who would support the teaching staff to systematically widen their pedagogical repertoire and raise their digital competencies to the next level.
Cross-Department Secures Data Sharing in Food Industry via Blockchain-Cloud Fusion Scheme

The barriers of food enterprises and departments caused information asymmetry, which is the root cause of food safety incidents. Simultaneously, it is challenging to solve the information asymmetry by the existing cloud-based food supply-chain regulation system. Establishing a secure and reliable data sharing environment is an effective solution to the information island. Blockchain can construct a security network based on mathematical algorithms, eliminating the third party’s potential security risk, and realize transparently share data. In this paper, on the principle of metadata remaining in the food enterprises, we propose a blockchain-cloud fusion scheme based on Decentralized Attribute-Based Signature (DABS) to realize secure data sharing between departments. It constructs a decentralized and trusting environment for data owners to share data and achieves social co-governance of food safety based on the smart contract. It can also preserve the existing system architecture and complement the performance disadvantage of blockchain and cloud storage. The result achieved from security analysis shows that our scheme supports unconditional full anonymity and can resist collusion attacks of N-1 out of N corrupted attribute authorities.
Noise Robustness and Experimental Demonstration of a Quantum Generative Adversarial Network for Continuous Distributions

The potential advantage of machine learning in quantum computers is a topic of intense discussion in the literature. Theoretical, numerical and experimental explorations will most likely be required to understand its power. There has been different algorithms proposed to exploit the probabilistic nature of variational quantum circuits for generative modelling. In this paper, we employ a hybrid architecture for quantum generative adversarial networks (QGANs) and study their robustness in the presence of noise. We devise a simple way of adding different types of noise to the quantum generator circuit, and numerically simulate the noisy hybrid quantum generative adversarial networks (HQGANs) to learn continuous probability distributions, and show that the performance of HQGANs remain unaffected. We also investigate the effect of different parameters on the training time to reduce the computational scaling of the algorithm and simplify its deployment on a quantum computer. We then perform the training on Rigetti's Aspen-4-2Q-A quantum processing unit, and present the results from the training. Our results pave the way for experimental exploration of different quantum machine learning algorithms on noisy intermediate scale quantum devices.
Privacy-Preserving Quantum Two-Party Geometric Intersection

Abstract：Privacy-preserving computational geometry is the research area on the intersection of the domains of secure multi-party computation (SMC) and computational geometry.As an important field, the privacy-preserving geometric intersection (PGI) problem is when each of the multiple parties has a private geometric graph and seeks to determine whether their graphs intersect or not without revealing their private information.In this study, through representing Alice's (Bob's) private geometric graph A G ( B G ) as the set of numbered grids A S ( B S ), an efficient privacy-preserving quantum two-party geometric intersection (PQGI) protocol is proposed.In the protocol, the oracle operation A O ( B O ) is firstly utilized to encode the private elements of 0 1 1 ( , , , )) into the quantum states, and then the oracle operation f O is applied to obtain a new quantum state which includes the XOR results between each element of A S and B S .Finally, the quantum counting is introduced to get the amount ( t ) of the states i j a b  equaling to 0 , and the intersection result can be obtained by judging 0 t  or not.Compared with classical PGI protocols, our proposed protocol not only has higher security, but also holds lower communication complexity.
FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches

The machine learning (ML) life cycle involves a series of iterative steps, from the effective gathering and preparation of the data-including complex feature engineering processes-to the presentation and improvement of results, with various algorithms to choose from in every step. Feature engineering in particular can be very beneficial for ML, leading to numerous improvements such as boosting the predictive results, decreasing computational times, reducing excessive noise, and increasing the transparency behind the decisions taken during the training. Despite that, while several visual analytics tools exist to monitor and control the different stages of the ML life cycle (especially those related to data and algorithms), feature engineering support remains inadequate. In this paper, we present FeatureEnVi, a visual analytics system specifically designed to assist with the feature engineering process. Our proposed system helps users to choose the most important feature, to transform the original features into powerful alternatives, and to experiment with different feature generation combinations. Additionally, data space slicing allows users to explore the impact of features on both local and global scales. FeatureEnVi utilizes multiple automatic feature selection techniques; furthermore, it visually guides users with statistical evidence about the influence of each feature (or subsets of features). The final outcome is the extraction of heavily engineered features, evaluated by multiple validation metrics. The usefulness and applicability of FeatureEnVi are demonstrated with two use cases and a case study. We also report feedback from interviews with two ML experts and a visualization researcher who assessed the effectiveness of our system.
Sentiment Analysis between VADER and EDA for the US Presidential Election 2020 on Twitter Datasets

The 2020 US Election took place on November 3, 2020, the result of the election was that Joe Biden received 51.4% of the votes, Donald Trump 46.9%, and the rest were other candidates. The period before the election was a time when people conveyed who would vote and conveyed the reasons directly or through social media, especially Twitter through keywords or tags such as #JoeBiden & #DonaldTrump. In this paper, we will compare sentiment analysis and explanatory data analysis against US election data on Twitter. The overall objective of the two case studies is to evaluate the similarity between the sentiment of location-based tweets and on-ground public opinion reflected in election results. In this paper, we find that there are more "neutral" sentiments than "negative" and "positive" sentiments.
An Evaluation of Word-Level Confidence Estimation for End-to-End Automatic Speech Recognition

Quantifying the confidence (or conversely the uncertainty) of a prediction is a highly desirable trait of an automatic system, as it improves the robustness and usefulness in downstream tasks. In this paper we investigate confidence estimation for end-to-end automatic speech recognition (ASR). Previous work has addressed confidence measures for lattice-based ASR, while current machine learning research mostly focuses on confidence measures for unstructured deep learning. However, as the ASR systems are increasingly being built upon deep end-to-end methods, there is little work that tries to develop confidence measures in this context. We fill this gap by providing an extensive benchmark of popular confidence methods on four well-known speech datasets. There are two challenges we overcome in adapting existing methods: working on structured data (sequences) and obtaining confidences at a coarser level than the predictions (words instead of tokens). Our results suggest that a strong baseline can be obtained by scaling the logits by a learnt temperature, followed by estimating the confidence as the negative entropy of the predictive distribution and, finally, sum pooling to aggregate at word level.
High-Resolution Depth Maps Imaging via Attention-Based Hierarchical Multi-Modal Fusion

Depth map records distance between the viewpoint and objects in the scene, which plays a critical role in many real-world applications. However, depth map captured by consumer-grade RGB-D cameras suffers from low spatial resolution. Guided depth map super-resolution (DSR) is a popular approach to address this problem, which attempts to restore a high-resolution (HR) depth map from the input low-resolution (LR) depth and its coupled HR RGB image that serves as the guidance. The most challenging issue for guided DSR is how to correctly select consistent structures and propagate them, and properly handle inconsistent ones. In this paper, we propose a novel attention-based hierarchical multi-modal fusion (AHMF) network for guided DSR. Specifically, to effectively extract and combine relevant information from LR depth and HR guidance, we propose a multi-modal attention based fusion (MMAF) strategy for hierarchical convolutional layers, including a feature enhancement block to select valuable features and a feature recalibration block to unify the similarity metrics of modalities with different appearance characteristics. Furthermore, we propose a bi-directional hierarchical feature collaboration (BHFC) module to fully leverage low-level spatial information and high-level structure information among multi-scale features. Experimental results show that our approach outperforms state-of-the-art methods in terms of reconstruction accuracy, running speed and memory efficiency.
Machine Learning-Based Technique to Detect SQL Injection Attack

Lack of secure codes implemented in the web apps will lead to cyber-attack because of vulnerabilities.The statistic shows that highest record on the data theft related cyber-attacks are through the SQL injection technique.Hence, an effective SQL injection detection is needed in any web system to combat this threat.In this research, machine learning technique is used where training is provided to the SQL injection detector using a training data and then is evaluated against a testing data.The research relies on the preparation of the training and testing datasets.Training sets are used by the detector to establish the knowledge base and the test set is used to evaluate the performance of the detector.The result of the detection shows that the proposed technique produces high accuracy in recognizing malicious and benign web requests.
Moving on from OntoNotes: Coreference Resolution Model Transfer

Academic neural models for coreference resolution (coref) are typically trained on a single dataset, OntoNotes, and model improvements are benchmarked on that same dataset.However, real-world applications of coref depend on the annotation guidelines and the domain of the target dataset, which often differ from those of OntoNotes.We aim to quantify transferability of coref models based on the number of annotated documents available in the target dataset.We examine eleven target datasets and find that continued training is consistently effective and especially beneficial when there are few target documents.We establish new benchmarks across several datasets, including state-of-the-art results on PreCo.Dataset Example Comments OntoNotes (general) Judging from the Americana in [[Haruki Murakami's]1 "A Wild Sheep Chase" [Kodansha]2, 320 pages, $18.95]3, baby boomers on both sides of the Pacific have a lot in common.Only coreferring mentions are marked (no singletons).ARRAU (news) Judging from [the Americana in [[Haruki Murakami's]1 "A Wild Sheep Chase" [[Kodansha]2, [320 pages]3, [$18.95]4]5]6]7,[baby boomers on [both sides of [the Pacific]8]9]10 have [a lot in [common]11]12.All mentions are marked, even if they are singletons.
Visibility Restoration: A Systematic Review and Meta-Analysis

Image acquisition is a complex process that is affected by a wide variety of internal and environmental factors. Hence, visibility restoration is crucial for many high-level applications in photography and computer vision. This paper provides a systematic review and meta-analysis of visibility restoration algorithms with a focus on those that are pertinent to poor weather conditions. This paper starts with an introduction to optical image formation and then provides a comprehensive description of existing algorithms as well as a comparative evaluation. Subsequently, there is a thorough discussion on current difficulties that are worthy of a scientific effort. Moreover, this paper proposes a general framework for visibility restoration in hazy weather conditions while using haze-relevant features and maximum likelihood estimates. Finally, a discussion on the findings and future developments concludes this paper.
Extractive Summarization Considering Discourse and Coreference Relations based on Heterogeneous Graph

Modeling the relations between text spans in a document is a crucial yet challenging problem for extractive summarization.Various kinds of relations exist among text spans of different granularity, such as discourse relations between elementary discourse units and coreference relations between phrase mentions.In this paper, we propose a heterogeneous graph based model for extractive summarization that incorporates both discourse and coreference relations.The heterogeneous graph contains three types of nodes, each corresponds to text spans of different granularity.Experimental results on a benchmark summarization dataset verify the effectiveness of our proposed method.