Reviewerly/exHarmony huggingface dataset:
Switchable Glass Enabled Contextualization for a Cyber-Physical Safe and Interactive Spatial Augmented Reality PCBA Manufacturing Inspection System

Augmented reality (AR) has been demonstrated to improve efficiency by up to thrice the level of traditional methods. Specifically, the adoption of visual AR is performed widely using handheld and head-mount technologies. Despite spatial augmented reality (SAR) addressing several shortcomings of wearable AR, its potential is yet to be fully explored. To date, it enhances the cooperation of users with its wide field of view and supports hands-free mobile operation, yet it has remained a challenge to provide references without relying on restrictive static empty surfaces of the same object or nearby objects for projection. Towards this end, we propose a novel approach that contextualizes projected references in real-time and on demand, onto and through the surface across a wireless network. To demonstrate the effectiveness of the approach, we apply the method to the safe inspection of printed circuit board assembly (PCBA) wirelessly networked to a remote automatic optical inspection (AOI) system. A defect detected and localized by the AOI system is wirelessly remitted to the proposed remote inspection system for prompt guidance to the inspector by augmenting a rectangular bracket and a reference image. The rectangular bracket transmitted through the switchable glass aids defect localization over the PCBA, whereas the image is projected over the opaque cells of the switchable glass to provide reference to a user. The developed system is evaluated in a user study for its robustness, precision and performance. Results indicate that the resulting contextualization from variability in occlusion levels not only positively affect inspection performance but also supersedes the state of the art in user preference. Furthermore, it supports a variety of complex visualization needs including varied sizes, contrast, online or offline tracking, with a simple robust integration requiring no additional calibration for registration.
Diving into Email Bomb Attack

We explore Email Bomb - a particularly devastating type of Denial of Service (DOS) attack that recently gained traction. During the attack Email account of a victim is targeted with a flood of Emails. Existing anti-spam defences fail at filtering this Emails' flood, since the Emails are not sent from spoofed addresses, but originate from legitimate web services on the Internet which are exploited as reflectors. We perform a two-year study of the Email bomb attack and the affected actors - the victims and the reflectors. We show that although the attack is rented for one day, the Email flood proceeds over longer time periods often lasting months after the initial attack. We identify the properties that allow the attackers to recruit web sites as potential reflectors and demonstrate how the attackers harvest web reflectors. We show that even popular Alexa web sites, such as booking.com, are exploited to launch Email bomb attacks. The main problem is that such attacks are extremely simple to launch and can be rented for 5USD on darknet. We setup a tool which periodically collects and analyses the Emails received during the attack, the analysis as well as the data is presented online at http://emailbombresearch.xyz. We argue that email bomb attacks do not only pose inconvenience and hinder the ability of victims to function, but also we provide the first demonstration how such attacks can be leveraged for hiding other devastating attacks which take place in parallel. We show that existing countermeasures fall short of preventing email bomb attacks and provide effective mitigation recommendations that are based on our study of this attack.
Semantic-aware label placement for augmented reality in street view

In an augmented reality (AR) application, placing labels in a manner that is clear and readable without occluding the critical information from the real world can be a challenging problem. This paper introduces a label placement technique for AR used in street view scenarios. We propose a semantic-aware task-specific label placement method by identifying potentially important image regions through a novel feature map, which we refer to as guidance map. Given an input image, its saliency information, semantic information and the task-specific importance prior are integrated in the guidance map for our labeling task. To learn the task prior, we created a label placement dataset with the users' labeling preferences, as well as use it for evaluation. Our solution encodes the constraints for placing labels in an optimization problem to obtain the final label layout, and the labels will be placed in appropriate positions to reduce the chances of overlaying important real-world objects in street view AR scenarios. The experimental validation shows clearly the benefits of our method over previous solutions in the AR street view navigation and similar applications.
SCM: Secure and accountable TLS certificate management

Summary In classical public‐key infrastructure (PKI), the certificate authorities (CAs) are fully trusted, and the security of the PKI relies on the trustworthiness of the CAs. However, recent failures and compromises of CAs showed that if a CA is corrupted, fake certificates may be issued, and the security of clients will be at risk. As emerging solutions, blockchain‐ and log‐based PKI proposals potentially solved the shortcomings of the PKI, in particular, eliminating the weakest link security and providing a rapid remedy to CAs' problems. Nevertheless, log‐based PKIs are still exposed to split‐world attacks if the attacker is capable of presenting two distinct signed versions of the log to the targeted victim(s), while the blockchain‐based PKIs have scaling and high‐cost issues to be overcome. To address these problems, this paper presents a secure and accountable transport layer security (TLS) certificate management (SCM), which is a next‐generation PKI framework. It combines the two emerging architectures, introducing novel mechanisms, and makes CAs and log servers accountable to domain owners. In SCM, CA‐signed domain certificates are stored in log servers, while the management of CAs and log servers is handed over to a group of domain owners, which is conducted on the blockchain platform. Different from existing blockchain‐based PKI proposals, SCM decreases the storage cost of blockchain from several hundreds of GB to only hundreds of megabytes. Finally, we analyze the security and performance of SCM and compare SCM with previous blockchain‐ and log‐based PKI schemes.
Integrating Foresight, Artificial Intelligence and Data Science to Develop Dynamic Futures Analysis

The paper discusses the role of integrating Artificial Intelligence (AI) and Data Science (DS) with the strategic foresight. That is, combining the art of conjecture with several algorithms available in the literature. The combination between qualitative and quantitative methods offers a new dynamic adaptation route to face the rapidly changing environment, to make strategic design more flexible in the long term. The proposal uses the ISSM framework as a notion of a Strategic Early Warning System (SEWS). Thus, an ISSM framework could assist organizations during the transition from dealing with discontinuities or strategic surprises to a new normal, using new capacities to creating knowledge of futures. The purpose is help to develop the component named competitive intelligence (CI) architecture to create knowledge about the future, improving continuous learning, and promote the capacity to adaptation. Therefore, anticipation, learning and strategic adaptation, are key attributes for building strategic flexibility that responds to several challenges.
Paper Review On Data Mining, components, And Big Data

Recent progress in software and hardware has allowed different data measurements in a variety of fields to be captured. These measures are produced continuously at very fluctuating data rates. For example, network sensors, web logs and computer network traffic. Computer-intensive activities are the collection, query and removal of these data sets. Mining data sources include the recovery of knowledge systems incorporated into templates and trends on streams of non-stop information. Because of the importance of its applications and the increasing generation of data stream research, information. Information. Information. Analysis applications for data streams can range from critical scientific and astronomical applications to major business and financial applications. Algorithms, processes and structures Streaming challenges have been developed over the last three years. We present the newest in this increasingly important field in this review paper.
Modification of blurred image matching method

The article proposes a modification of the Blurred Image Matching (BIM) method, a key point selection method in images, thus solving a problem of their accurate comparison. A new approach for blobs selection and comparison is proposed. The use of these modifications allows us to achieve an increase in the proportion of correctly matched pairs of images by 30.2% compared to the basic method when working with noisy data.
Vectorising k-Core Decomposition for GPU Acceleration

k-Core decomposition is a well-studied community detection problem in graph analytics in which each k-core of vertices induces a subgraph where all vertices have degree at least k. The decomposition is expensive to compute on large graphs and efforts to apply massive parallelism have had limited success. This paper presents a vectorisation of the problem that reframes it as a composition of vector primitives on flat, 1d arrays. With such a formulation, we can deploy highly optimised Deep Learning GPU and SIMD frameworks. On a moderate GPU, using PyTorch, we obtain up to 8 × improvement over the best parallel state-of-the-art implemented in C++ and running on an expensive 32-core machine. More importantly, our approach represents a novel abstraction showing that redesigning graph operations as a series of vectorised primitives makes highly-parallel analytics both easier and more accessible for developers. We posit that such an approach can vastly accelerate the use of cheap GPU hardware in complex graph analytics.
Array Aware Training/Pruning: Methods for Efficient Forward Propagation on Array-based Neural Network Accelerators

Due to the increase in the use of large-sized Deep Neural Networks (DNNs) over the years, specialized hardware accelerators such as Tensor Processing Unit and Eyeriss have been developed to accelerate the forward pass of the network. The essential component of these devices is an array processor which is composed of multiple individual compute units for efficiently executing Multiplication and Accumulation (MAC) operation. As the size of this array limits the amount of DNN processing of a single layer, the computation is performed in several batches serially leading to extra compute cycles along both the axes. In practice, due to the mismatch between matrix and array sizes, the computation does not map on the array exactly. In this work, we address the issue of minimizing processing cycles on the array by adjusting the DNN model parameters by using a structured hardware array dependent optimization. We introduce two techniques in this paper: Array Aware Training (AAT) for efficient training and Array Aware Pruning (AAP) for efficient inference. Weight pruning is an approach to remove redundant parameters in the network to decrease the size of the network. The key idea behind pruning in this paper is to adjust the model parameters (the weight matrix) so that the array is fully utilized in each computation batch. Our goal is to compress the model based on the size of the array so as to reduce the number of computation cycles. We observe that both the proposed techniques results into similar accuracy as the original network while saving a significant number of processing cycles (75%).
Gradient search in the space of permutations

Gradient search is a classical technique for optimizing differentiable functions that has gained much relevance recently due to its application on Neural Network training. Despite its popularity, the application of gradient search has been limited to the continuous optimization and its usage in the combinatorial case is confined to a few works, all which tackle the binary search space. In this paper, we present a new approach for applying Gradient Search to the space of permutations. The idea consists of optimizing the expected objective value of a random variable defined over permutations. Such a random variable is distributed according to the Plackett-Luce model, and a gradient search over its continuous parameters is performed. Conducted experiments on a benchmark of the linear ordering problem confirm that the Gradient Search performs better than its counterpart Estimation of Distribution Algorithm: the Plackett-Luce EDA. Moreover, results reveal that the scalability of the Gradient Search is better than that of the PL-EDA.
Putting the Passe Into Passwords: How Passwordless Technologies Are Reshaping Digital Identity

Despite significant flaws, passwords persist as the predominant method to authenticate digital identity. Looks at alternatives to password authentication and explores where password use is headed in the future to ensure privacy and security.
What Emotions Make One or Five Stars? Understanding Ratings of Online Product Reviews by Sentiment Analysis and XAI

When people buy products online, they primarily base their decisions on the recommendations of others given in online reviews. The current work analyzed these online reviews by sentiment analysis and used the extracted sentiments as features to predict the product ratings by several machine learning algorithms. These predictions were disentangled by various methods of explainable AI (XAI) to understand whether the model showed any bias during prediction. Study 1 benchmarked these algorithms (knn, support vector machines, random forests, gradient boosting machines, XGBoost) and identified random forests and XGBoost as best algorithms for predicting the product ratings. In Study 2, the analysis of global feature importance identified the sentiment joy and the emotional valence negative as most predictive features. Two XAI visualization methods, local feature attributions and partial dependency plots, revealed several incorrect prediction mechanisms on the instance-level. Performing the benchmarking as classification, Study 3 identified a high no-information rate of 64.4% that indicated high class imbalance as underlying reason for the identified problems. In conclusion, good performance by machine learning algorithms must be taken with caution because the dataset, as encountered in this work, could be biased towards certain predictions. This work demonstrates how XAI methods reveal such prediction bias.
A FPGA-Based Post-Processing and Validation Platform for Random Number Generators

Cryptography and computer security rely heavily on random numbers for key exchange of authentication algorithms. However, current Internet-of-Things (IoT) device security is often based on poor quality pseudo-random number generators (PRNGs). This issue can be overcome using true random number generators (TRNGs) that may offer better quality and higher security. Nonetheless, TRNG often provide slow throughput and require post-processing to correct hardware biases and ensure the desired statistical behavior. In this paper, we present a FPGA-based hardware platform able to validate and post-process multiple TRNG sources. Moreover, we propose a hardware implementation of a provably secure post-processing algorithm called SPRG. Based on the sponge construction and the Keccak-f standard, it improves random number quality while maintaining high data throughput. A full platform providing hardware acceleration has been implemented on a Xilinx Kintex- 7 FPGA board to test the validity of the generated numbers through χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and SP800-90B online statistical tests, and to improve the randomness using AIS-31 or SPRG post-processing hardware cores. The proposed platform is modular and targets both IoT edge devices and back-end servers.
Two-Stream Mid-Level Fusion Network for Human Activity Detection

Automatic recognition of human activity,Chakraborty, Mainak although a challenging task, it is particularly useful in case of defence security applications, content-based video analysis, city crime prevention, and ambient assistive living. The purposePramanick, Alik of this study is to determine whether two-stream approaches are better than single-stream approaches in case of deep learning based human activity detection. Initially, we implemented single-stream models based on both spatial and motion features. During theSunita Dhavale, Sanjay experiment, we found that DenseNet201 CNN performs better in the case of spatial feature extraction while MobileNetV2 CNN performs better in the case of motion feature extraction. Experimental results confirm that the efficient extraction of spatial and motion features representation and learning how representation changes over time enhance the accuracy of two-stream models. Furthermore, we investigated that layer merging by averaging is more effective than layer concatenation. Also, we observed that changes in the learning rate affect model performance. The experimental results of our proposed model outperform the existing state-of-the-art methods and achieved $$85.21\%$$ Top-1 and $$98.02\%$$ Top-5 accuracy on the UCF-101 dataset.
A Brief Understanding of Blockchain-Based Healthcare Service Model Over a Remotely Cloud-Connected Environment

Blockchain technology is in the limelight due to its immutable and anonymous data recording without a centralized authority. In this paper, we present a blockchain-based healthcare model to demonstrate a large-scale blockchain. Blockchain-based model covers numerous sectors like financial, IoT, health care, etc. The blockchain technology has proved its authenticity as a virtually incorruptible cryptographic data storage which provides to the end user in a secure way. The blockchain model is composed of a series of machines that can give on-demand service over the safety net. The access point of heterogeneous medical records over the network is maintained by a hub of computers which operates as a pseudo-anonymous system. The requisite of medical sensitive data is a value from all aspects that range from patient symptoms information, medical certificate, transaction detail, etc. The beauty of the blockchain model is its decentralization by which it can fight against a single point attack. The requisite of sensitive data is growing stronger day by day which needs more sophisticated service for devices collecting personal information through web-based applications or cloud. This paper highlights a comprehensive insight on accessing structured medical data through blockchain. By prototyping, this model will identify, extract and analyze the real-life data metric point in further implementation. A medical certificate was counterfeited by a certain group of people; by using this they are creating a headache for the government and different services for a physically challenged person are misused by them inappropriately. Electronically medical records are using age-old technology for storing; they need modification and strict secure regulation to showcase our technological up-gradation. In this paper blockchain, the pattern-based model, makes medical data management more transparent for service seekers and providers. The giant step of blockchain is going to revolutionize the future e-healthcare the way it interacts with and stores data. But it’s not going to happen in the very next day. It can face certain smaller problems which can be mitigated at its nascent stage slowly. So this model hopes to solve the regulatory hurdles. Blockchain is emerging as the plateau of productivity for distributing information in health care. In this paper showcases a model that incorporates cryptographic evidence of work over multiple locations; this strong espionage among data blocks checks data against theft. It could influence a convergence point over an interoperable network and can decrease central administrative control. An electronic health record that uses blockchain is an integration of clinical data, medical certificate, etc.; it establishes in its entirety, medical data gathered across a range of smart devices, and hospital databases over the cloud and other sources could all be securely encrypted to individual demands for full chain access. Every service interaction is added with intrinsic security and by implementing healthcare blockchain.
Studying Retrieval Practice in an Intelligent Tutoring System

Retrieval practice (also known as testing effect or test-enhanced learning) is a well-studied and established technique for improving the retention of knowledge. Many previous works have confirmed the benefits of retrieval practice in laboratory experiments involving the memorization of words or facts. In this study, we build on these works and analyze retrieval practice in an intelligent tutoring system. Using a large data set composed of the actions of almost 4 million students studying math and chemistry, we look at the possible benefits of retrieval practice in the ALEKS adaptive learning and assessment system. We compare two different types of retrieval practice---one involving the assessment of learned material, and another involving the learning of closely related content that builds on the learned material---leveraging the scale of the available data to control for several confounding variables. Finally, we look at the timing of retrieval practice within the system and the possible effect it has on forgetting. The results indicate that a delay in retrieval practice is associated with better retention and that, while being assessed on learned material is beneficial, the learning of closely related content is associated with an even higher rate of retention.
Evolutionary computation and machine learning in cryptology

tutorial Open AccessEvolutionary computation and machine learning in cryptology Share on Authors: Stjepan Picek TU Delft, The Netherlands TU Delft, The NetherlandsView Profile , Domagoj Jakobovic University of Zagreb, Croatia University of Zagreb, CroatiaView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 1147–1173https://doi.org/10.1145/3377929.3389886Online:08 July 2020Publication History 1citation228DownloadsMetricsTotal Citations1Total Downloads228Last 12 Months86Last 6 weeks19 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF