Reviewerly/exHarmony huggingface dataset:
Trigger-based Blocking Mechanism for Access to Email-derived Phishing URLs with User Alert

Email is one of the important and indispensable Internet services, but in the meanwhile, the spread of emailderived phishing URLs has been one of the critical cyber threats for a long time. The security facilities in organization networks are suffering from monitoring and analyzing all traffic besides emails which consumes much computing resource. In this paper, we propose a trigger-based blocking mechanism for accessing the email-derived phishing URLs with user alert to protect the end users from phishing attacks. The proposed system practically uses the Domain Name System (DNS) and the Response Policy Zone (RPZ) feature to direct the triggered HTTP(S) access for the email-derived phishing URLs to a particular proxy. Then the HTTP(S) communication can be detected and blocked based on the users’ decisions by alerting them. A prototype for the proposed mechanism has been implemented and the preliminary feature evaluations in a local experimental network have been conducted. The evaluation results confirmed that all the HTTP(S) access for the email-derived phishing URLs was successfully directed to the pre-constructed particular HTTP(S) proxy, then an alert page was showed up to the end users, and the access was passed through or blocked based on the end users’ decisions.
When I use a word . . . . Data—usage and ownership

Etymology, grammar, and usage all support the view that “data” not only is a plural noun but also should be used as such in most instances. However, perhaps under the influence of the way in which it is used in news media, general usage is drifting towards singular usage. Nevertheless, regarding “data” as a plural noun also informs the more difficult question of who owns the data (the individual items) or the database (the collection). It suggests that each individual owns his or her own [plural] data, when the data exist as discrete pieces of information about the individual—using the word “information” in the general, non-technical sense, outside its use in computer science. However, no one individual or institution owns an anonymised collection of data in a database or databank, although those who collected the data or paid to have them collected may have a legitimate preferential claim. Nevertheless it is reasonable to expect that collections of data should be shared and that communally everybody owns them, while respecting the anonymity of the individuals whose data are represented.
An Efficient NPU-Aware Filter Pruning in Convolutional Neural Network

The neural processing unit (NPU)is a high-performance and low-power acceleration specialized in implementing artificial intelligence (AI) such as training and inference. The NPU needs a compressed network because it is used with low power and low latency to process the convolutional neural network (CNN). Therefore, in this paper, we propose an efficient NPU-aware filter pruning method for CNN to increase the efficiency of NPU. NPU-aware filter pruning is performed in multiples of the channel unit size, which is the operation unit of the NPU to reduce unnecessary computation and save memory storage space. In the experimental results with VGGNet-16 and ResNet-18 on the CIFAR10 dataset, the proposed method reduced hardware inefficient space and unnecessary computation by 1.86~6.78% compared to general pruning method without loss of accuracy.
Fast Virtual Keyboard Typing Using Vowel Hand Gesture Recognition

This paper proposes a fast virtual keyboard typing method that improves typing speed using hand gesture recognition. In the proposed method, five frequently used English vowels can be quickly input with five dedicated gestures. The proposed method reduces, not only the layer switching time of the multilayer keyboard layout, but also vowel typing time. To evaluate the performance of the proposed method, simulations are performed considering four scenarios according to the order of appearance of vowels and consonants. The experimental result shows that the proposed method improves typing speed by 23.07% on average compared to the previous method.
Automatic Conversation Turn-Taking Segmentation in Semantic Facet

Turn-taking is a significant aspect of a smooth conversation system. Detecting end-of-turn can be difficult for automatic conversation systems, and this can cause misleading conversation systems. To make a conversational system recognizing turn transition points, we propose a token-level turn-taking segmentation using linguistic features. This task imitates the automatic speech recognition environment by organizing several settings. Moreover, we utilize GPT-2, which is well known as a pretrained generative language model, to be able to predict in token-level live text stream. We evaluate our model compared to RNN series models in general conversation datasets and explore model prediction with test sample scenarios.
A Study of Optimization Algorithm based on Non-negative Matrix Factorization for Face Age Prediction

Face age prediction based on face image is an important research direction in the field of face recognition, and it is also a difficult point. Non-negative matrix factorization (NMF) dimensionality reduction method can fix the number of features after dimensionality reduction. In this paper, an optimization algorithm based on NMF is proposed for improving the accuracy of age prediction based on face images. It was confirmed that the maximum performance was shown for precision 0.19 in category 1 (21-24 ages), recall 0.20 in category 5 (40-44 ages), and f1-score 0.18 in categories 1-3 (21-34 ages) and 5. In addition, the mean absolute error (MAE) of the proposed algorithm is 1.92, which is about 31.85% lower than that of other algorithms.
Efficient Hardware Acceleration of Chinese Remainder Theorem for Fully Homomorphic Encryption

Fully homomorphic encryption (FHE) has recently received huge attention because of its ability to perform operations on encrypted data. FHE requires arithmetic operations on data with Large Arithmetic Word Size (LAWS) over 64-bit. The Chinese Remainder Theorem (CRT) is used to process such operations in 64-bit architecture. However, since the CRT itself involves the operations on LAWS data, long latency and many hardware resources are required to process the operations. In this paper, we propose a hardware architecture that performs LAWS operation of CRT and inverse CRT (iCRT) through recursive arithmetic operation of Small Arithmetic Word Size (SAWS) data, reducing resource usage and accelerating execution. The proposed hardware was implemented to operate at 100 MHz frequency on the FPGA, and showed latency of 91.2 us and 24.39 us, respectively, for executing CRT and iCRT with only 68 DSP and 65 LUTRAM, and a small number of LUTs and FFs.
Quality Assurance of Autonomous Vehicle’s Sensor Data based on Classifying Level of Difficulty

Self-driving technology using deep learning has achieved a lot of research and excellent performance, but the process of training a model requires a large amount of high-quality data and it is still difficult for humans to directly inspect the processed data and prove its quality. In addition, classifying the training difficulty of the data for contextual and functional separation of deep learning algorithm is also an important challenge. This paper proposes a framework for validating the quality of constructed new datasets, which also classifies the training difficulty of datasets, thereby ensuring the versatility of valid datasets and introducing strategies to classify contextual datasets. Experiments on the AI hub dataset proved its quality and were able to be reorganized into datasets classified by difficulty level.
Digital handwriting correction using deep learning

In this digital age, an increasing number of users are adopting tablets to compose writing. Unlike typing on a computer keyboard, it is difficult to correct handwriting automatically on a tablet. This study proposes a unified framework to correct handwriting on a tablet, based on a deep learning framework. First, we developed an optical character recognition and spell checker to correct typo errors in the input handwriting image. Next, we used a convolutional neural network (CNN) to classify the font of the input image. Finally, the corrected text is printed in a classified font. Experimental results provide a similar output to the user's handwriting.
Randomly generated lyrics using mixture models: the poem that Frank Sinatra never sang

In this article, we use a simple method to generate words from a pool of several songs. The approach is based on fitting a finite mixture of geometric probability mass functions to the distribution of the frequencies of the words as they occur in the pool. The classification of the lyrics with the estimated probabilities of belonging to each of the class yields a way of generating a given number words. This statistical procedure can be complemented with human composition of a poem using the generated lyrics. An application with 63 songs of Frank Sinatra illustrates how the methodology was used to compose the new poem Young.
Understanding the structure of knowledge graphs with ABSTAT profiles

While there has been a trend in the last decades for publishing large-scale and highly-interconnected Knowledge Graphs (KGs), their users often get overwhelmed by the task of understanding their content as a result of their size and complexity. Data profiling approaches have been proposed to summarize large KGs into concise and meaningful representations, so that they can be better explored, processed, and managed. Profiles based on schema patterns represent each triple in a KG with its schema-level counterpart, thus covering the entire KG with profiles of considerable size. In this paper, we provide empirical evidence that profiles based on schema patterns, if explored with suitable mechanisms, can be useful to help users understand the content of big and complex KGs. ABSTAT provides concise pattern-based profiles and comes with faceted interfaces for profile exploration. Using this tool we present a user study based on query completion tasks. We demonstrate that users who look at ABSTAT profiles formulate their queries better and faster than users browsing the ontology of the KGs. The latter is a pretty strong baseline considering that many KGs do not even come with a specific ontology to be explored by the users. To the best of our knowledge, this is the first attempt to investigate the impact of profiling techniques on tasks related to knowledge graph understanding with a user study.
Using AADL to Assess Architectural Concerns for Cyber Security

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;We describe how we apply the SAE AS 5506 Architecture and Analysis Design Language (AADL) [&lt;span class="xref"&gt;4&lt;/span&gt;] to reason about contextual and architectural concerns for cyber security. A system’s cyber security certification requires verification that the system’s cyber security mechanisms are correct, non-bypassable, and tamper-resistant. We can verify correctness by examining the mechanism itself, but verifying the other qualities requires us to examine the context in which that mechanism resides. Understanding that context and validating the system’s evolving design against that context is an objective for the Architecture Centric Virtual Integration Process (ACVIP), an AADL-based approach to model and detect system design defects before they become too costly to fix. We describe our work to apply AADL to assess non-bypassability and tamper-resistance. The results of our research - tool plugins for cyber security architectural validation - support system developers today in their ACVIP activities.&lt;/div&gt;&lt;/div&gt;
The LSTM-based automated phishing detection driven model for detecting multiple attacks on Tor hidden services

Phishing is a major problem on darknets. Phishing is the practice of attacking an unaware person by pretending to be someone else to steal their digital data. In anonymous platforms such as the dark web or deep web of Tor, detecting the attacker or phishing attacks is a much more complicated practice. Generic phishing attacks can be easy to spot. Today’s challenge is detecting the various attacks in the anonymous network is very hard. The intelligent factor of attacks can bypass traditional detection solutions. To solve the problem of complications in the Tor Network, this work focuses on the development of automated detection of vulnerable attacks in phishing-based Tor hidden services. The proposed model initially divides the attack parameters into three categories into Class A, Class B, and Class C based on technical perspectives and some defined threshold values. Next, the class A attacks (i.e. top level domain and protocol similarity) attacks are detected by a random forest (RF) classifier. Then, the class B attacks can be identified by the convolutional neural network (CNN). Finally, the LSTM model is applied for the accurate classification of multiple attacks in the Tor network. The experimental validation of the proposed model is tested using the CIRCL and AIL datasets. The experimental values highlighted the promising performance of the proposed model over other methods with a maximum overall detection accuracy of 95.60% and 95.77% on CIRCL and AIL datasets respectively. Therefore, the proposed model effectively detects multiple attacks in the Tor network under dynamic and real-time environments.
Truncated total variation in fractional B-spline wavelet transform for micro-CT image denoising

In medical applications, computed tomography (CT) is widely used to evaluate various sample characteristics. However, image quality of CT reconstruction can be degraded due to artifacts.To propose and test a truncated total variation (truncation TV) model to solve the problem of large penalties for the total variation (TV) model.In this study, a truncated TV image denoising model in the fractional B-spline wavelet domain is developed to obtain the best solution. The method is validated by the analysis of CT reconstructed images of actual biological Pigeons samples. For this purpose, several indices including the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and mean square error (MSE) are used to evaluate the quality of images.Comparing to the conventional truncated TV model that yields 22.55, 0.688 and 361.17 in PSNR, SSIM and MSE, respectively, using the proposed fractional B-spline-truncated TV model, the computed values of these evaluation indices change to 24.24, 0.898 and 244.98, respectively, indicating substantial reduction of image noise with higher PSNR and SSIM, and lower MSE.Study results demonstrate that compared with many classic image denoising methods, the new denoising algorithm proposed in this study can more effectively suppresses the reconstructed CT image artifacts while maintaining the detailed image structure.
ACAM-AD: Autocorrelation and attention mechanism-based anomaly detection in multivariate time series

Multivariate time series anomaly detection has made significant progress and has been studied in many fields. One of the difficulties in time-series data analysis is the complex nonlinear dependencies between multiple time steps and multiple variables. Therefore, detecting anomalies in these data is challenging. Although many studies used classical attention mechanisms to model the temporal patterns of data, few have combined multiple attention mechanisms and analyzed the data’s temporal characteristics and feature correlations. Therefore, we propose an autocorrelation and attention mechanism-based anomaly detection (ACAM-AD) framework that combines an autocorrelation model based on the Autoformer model, which is superior to the self-attention mechanism, a multi-head graph attention network, and a dot-product attention mechanism to model the complex dependencies of data considering temporal and feature dimensions. The autoregressive model is parallelized with the neural network, and a sparse autocorrelation mechanism and sparse graph attention network are used to reduce model complexity. Experiments on public datasets show that the model is effective and performs better than the baseline model.
Philosophy of Engineering and Design (Technological) Actions

We live in a world of technologies. Classical Philosophy of Science, Philosophy of Technology, Episte- mology, etc. philosophical disciplines appear insufficient for valid reflections on today's world. The Philo- sophy of Engineering and Design (Technological) Actions is seen promising to become a fruitful field of philosophical reflections and is offered from the perspective of the Philosophy of Action and Agency (Practical Philosophy). The foundations of the latter are presented in Part II. In the Part I, the Phi- losophy of Engineering and Design (Technological) Actions is outlined in a comparative with Philo- sophy of Technology, Humanities Philosophy of Technology, Philosophy of Engineering, Philosophy of Science, Epistemology, etc. plane. The paradoxes of E. Feenberg's technique, the interpretation of which is proposed in the part III, are involved for the illustrating of the maintained understanding. In the conclusive part (IV) the general danger of distortive philosophical understandings about the tech- nological common world of today are mentioned.
A polynomial-time hybrid solver for multi-agent motion navigation against deadlocks

In the cooperative multi-agent pathfinding and motion planning, given a unique start position and a unique goal position for each agent, all agents are able to pursue their own goals without colliding with each other. To aim at realizing the collision-free motion of the agents within the tractable time, this work proposes a polynomial-time solver, called the HBD-AOI, hybridizing centralized and decentralized schemes. Firstly, an algorithm of centralized pathfinding is utilized to plan the optimal paths of all agents. Afterwards, each of the agents updates the local motion pattern to tracks its own planned waypoints with the obstacle avoidance in a decentralized manner. Furthermore, to resolve unavoidable egoistic conflicts occurring in the decentralized scheme, a centralized intervener with the route replanning is invoked to coach the involved agents to abort the existing deadlocks. Bounded by an amount of time, the performances of the proposed and benchmarked algorithms are simulated on the same instance, from the evaluated testbeds that consists of various maps and scenarios. In the simulations, it is proved that this work outperforms other benchmarked algorithms for all presented instances in the term of the success rate. The experimental results are also demonstrated to verify the feasibility of the proposed methodology.
Stochastic Composition Optimization of Functions Without Lipschitz Continuous Gradient

In this paper, we study stochastic optimization of two-level composition of functions without Lipschitz continuous gradient. The smoothness property is generalized by the notion of relative smoothness which provokes the Bregman gradient method. We propose three stochastic composition Bregman gradient algorithms for the three possible relatively smooth compositional scenarios and provide their sample complexities to achieve an $$\epsilon $$ -approximate stationary point. For the smooth of relatively smooth composition, the first algorithm requires $$\mathcal {O}(\epsilon ^{-2})$$ calls to the stochastic oracles of the inner function value and gradient as well as the outer function gradient. When both functions are relatively smooth, the second algorithm requires $$\mathcal {O}(\epsilon ^{-3})$$ calls to the inner function value stochastic oracle and $$\mathcal {O}(\epsilon ^{-2})$$ calls to the inner and outer functions gradients stochastic oracles. We further improve the second algorithm by variance reduction for the setting where just the inner function is smooth. The resulting algorithm requires $$\mathcal {O}(\epsilon ^{-5/2})$$ calls to the inner function value stochastic oracle, $$\mathcal {O}(\epsilon ^{-3/2})$$ calls to the inner function gradient, and $$\mathcal {O}(\epsilon ^{-2})$$ calls to the outer function gradient stochastic oracles. Finally, we numerically evaluate the performance of these three algorithms over two different examples.
Toward an Operations-Dedicated Model for Space Systems

Current trends in the aerospace industry for the digitization of data, tools, and services call for novel solutions that can keep track of the latest technological advancements. Moreover, the increasing drive toward model-based approaches imposes the use of models for the development of complex systems, such as satellites. New types of methods and tools are needed in order to manage the still-growing complexity of embedded systems and their corresponding models. This paper addresses issues related to space systems (in-service) operations and proposes a multimodel approach to address them. The multimodel approach aims at facilitating the task of operational diagnosis by creating a monitoring and diagnosis-dedicated system model derived from existing system design (architecture, functional behavior, and safety) models. The purpose of the diagnosis-dedicated model is to enable the codesign of the system and its diagnosis tools in order to improve the performance of diagnosis activities and the system’s availability. This paper demonstrates the interests of the suggested approach with regard to the limitations of current practices.
Attention-based encoder–decoder network for depth estimation from color-coded light fields

Compressive light field cameras have attracted notable attention over the past few years because they can efficiently determine redundancy from light fields. However, much of the research has only concentrated on reconstructing the entire light field from compressed sampling, which ignores the possibility of directly extracting information such as depth from it. In this paper, we introduce a light field camera configuration with a random color-coded microlens array. Considering the color-coded light fields, we propose a novel attention-based encoder–decoder network. Specifically, the encoder part compresses the coded measurement into a low-dimensional representation that removes most redundancy, and the decoder part constructs the depth map directly from the latent representation. The attention mechanism enables the network to process spatial and angular features dynamically and effectively, thus significantly improving performance. Extensive experiments on synthetic and real-world datasets show that our method outperforms the state-of-the-art light field depth estimation method designed for non-coded light fields. To our knowledge, this is the first study that combines the color-coded light field with the attention-based deep learning approach, which provides a crucial insight into the design of enhanced light field photography systems.
Intrusion Attacks on Deep Learning Frameworks Employed in Self-Driving Vehicles

Deep convolutional networks have proven practical for autonomous vehicle applications as deep CNN technology has advanced. There has been a growing vogue for using end-to-end computational methods for the mechanization of vehicular activities. Preliminary studies, though, have demonstrated that deep learning network classifiers are sensitive to adversarial approaches. But, the impact of adversarial strategies on regression problems is not sufficiently known. We propose two white-box direct security breaches targeting progressive self-driving vehicles in this research. A prediction model is used in the navigation mechanism, which receives a picture as feed and returns a steering angle. By altering the input image, we may influence the actions of the automated driving unit. Two different attacks may be launched in practice on CPUs with no need for GPUs. The effectiveness of the threats is demonstrated by trials carried out in Udacity.