Reviewerly/exHarmony huggingface dataset:
Evasion Attack STeganography: Turning Vulnerability Of Machine Learning To Adversarial Attacks Into A Real-world Application

Evasion Attacks have been commonly seen as a weakness of Deep Neural Networks. In this paper, we flip the paradigm and envision this vulnerability as a useful application. We propose EAST, a new steganography and watermarking technique based on multi-label targeted evasion attacks. The key idea of EAST is to encode data as the labels of the image that the evasion attacks produce.Our results confirm that our embedding is elusive; it not only passes unnoticed by humans, steganalysis methods, and machine-learning detectors. In addition, our embedding is resilient to soft and aggressive image tampering (87% recovery rate under jpeg compression). EAST outperforms existing deep-learning-based steganography approaches with images that are 70% denser and 73% more robust and supports multiple datasets and architectures.We provide our algorithm and open-source code at https://github.com/yamizi/Adversarial-Embedding
Voting-Ensemble Classification for Email Spam Detection

On the Internet, e-mail spam continues to become an issue. Spam messages usually contain several duplications of the same text, advertising content and other meaningless documents. Different filtering techniques have been used in previous research to detect such emails. In this study, two phases are proposed. First, various effective traditional machine learning classifiers like Naive Bayes (NB), Support Vector machines (SVMs), k-Nearest Neighbor (k-NN), Decision Tress (DT), Random Forests (RF) and AdaBoost are implemented individually for the detection of spam with accuracy and other performance measures on the dataset. Second, an Ensemble Voting classifier is used, which sums the results of each individual classifier used and calculates the outcome class with the majority votes. Depending on the measures like recall, accuracy and F-score, an efficacy of the dataset is assessed. The research demonstrates that using voting-ensemble classification, the overall accuracy on the SMS Spam dataset improves.
Confidence intervals of proportion differences for stratified combined unilateral and bilateral data

In otolaryngologic studies, subjects may contribute either unilateral observations from only one ear or bilateral observations from a pair of ears. For bilateral subjects, each person contributes information from two ears, the values of which are generally highly correlated. To avoid the confounding effect in stratified otolaryngologic studies, stratified data analysis is an important research topic. Based on the dependence model in stratified designs, this article presents five simultaneous confidence intervals (CIs) and two bootstrap simultaneous CIs for proportion differences with combined unilateral and bilateral data. Six approximate CIs and two bootstrap CIs for the common proportion difference are also developed. The proposed CIs are evaluated by empirical coverage probability, empirical coverage width and the ratio of mesial non-coverage probability to the non-coverage probability. Simulation results show that the simultaneous CIs based on Wilson method, the inverse hyperbolic tangent transformation, score statistic and the bootstrap percentile simultaneous CIs perform well and hence be recommended for applications, score CI and the CI based on Cochran statistic for the common proportion difference behave well even under small sample sizes, other CIs produce good performance when sample size is not small. A real otolaryngology study data is used to illustrate the proposed methodologies.
Towards immersive software archaeology: regaining legacy systems’ design knowledge via interactive exploration in virtual reality

Many of today's software systems will become the legacy systems of tomorrow, comprised of outdated technology and inaccurate design documents. Preparing for their eventual re-engineering requires engineers to regain lost design knowledge and discover re-engineering opportunities. While tools and visualizations exist, comprehending an unfamiliar code base remains challenging. Hence, software archaeology suffers from a considerable entry barrier as it requires expert knowledge, significant diligence, tenacity, and stamina. In this paper, we propose a paradigm shift in how legacy systems' design knowledge can be regained by presenting our vision for an immersive explorable software visualization in virtual reality (VR). We propose innovative concepts leveraging benefits of VR for a) immersion in an exoteric visualization metaphor, b) effective navigation and orientation, c) guiding exploration, and d) maintaining a link to the implementation. By enabling immersive and playful legacy system exploration, we strive for lowering the entry barrier, fostering long-term engagement, strengthening mental-model building, and improving knowledge retention in an effort to ease coping with the increased number of tomorrow's legacy systems.
The mutual predictability of Bitcoin and web search dynamics

Abstract Economic theory predicts the price dynamics of an unbacked asset to be inherently unforecastable. The same applies to exchange rates of unbacked currencies. Albeit, empirically investors are found to be driven by online and offline news media. This study analyzes the Bitcoin cryptocurrency price series and web search queries with regard to their mutual predictability and cause‐effect delay structure. Chinese Baidu engine searches and compounded Baidu–Google search statistics predict Bitcoin price dynamics at relatively high frequencies ranging from 2 to 5 months. In the other direction, Granger‐causality runs from the cryptocurrency price to queries statistics across nearly all frequencies. In both directions, the reaction time computed from a phase delay measure for the relevant frequency bands with significant causality ranges from about 1 to 4 months. For either direction, out‐of‐sample forecasts are more accurate than forecasts of a benchmark stochastic process. Bivariate models including the Baidu Search Index slightly outperform competing models that include a Baidu–Google composite index. Predictive power seems less diluted if the September 2017 trade regulations by the Chinese government are controlled for.
Application of Artificial Intelligence in Computer Network Technology in big data era

In this era of big data network technology, computer and information technology are also making rapid progress. The rapid progress of big data network technology also makes the country and some large enterprises step up the research on artificial intelligence technology. This paper first defines the related concepts of big data and artificial intelligence, and analyzes the characteristics and technical advantages of artificial intelligence on this basis. At the same time, this paper discusses the application of artificial intelligence in computer network from the aspects of computer network security, system evaluation and network security management, so as to put forward a reference strategy for the better application of artificial intelligence in computer network technology. This study can not only greatly promote the application of artificial intelligence in the field of computer network technology, but also further promote the development of artificial intelligence and computer network.
Comparison of machine learning algorithms for mortality prediction in intensive care patients on multi-center critical care databases

Abstract Current scoring systems for mortality prediction in intensive care patients are usually applied once after 24 hours of admission, as all parameters needed for scoring are not yet available. In addition, several parameters are dynamic and may change according to patient conditions. It is hypothesized that mortality prediction should be made at the earliest when relevant information becomes available and continuously during patient stay. This study focuses on the development of algorithms for mortality prediction from vital signs and laboratory results based on the data from three recent critical care databases, i.e. the eICU collaborative research database, the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III) database, and the MIMIC-IV database. We employed logistic regression, κ-nearest neighbours, neural networks and tree-based classifiers for such problem. Our models had the area under the receiver operating characteristic curve (AUROC) ranging from 0.67 – 0.95. Reliable mortality prediction can be made as early as the first 4 hours after ICU admission. We provided comprehensive analysis on different time frames used for prediction, models trained with top attributes, models trained with data combination, and missing values. Our results provide guidelines and benchmarks for the development of such algorithm in local narratives.
A Novel Path Planning Algorithm Considering the Maximum Deflection Angle of Joint

The paper deals with a minimum potential energy algorithm considering the maximum deflection angle of joint (MPE-MDA algorithm) for three key factors including the maximum deflection angle of the joint, the total length of the path, and the minimum distance from obstacles.The algorithm is applied for space obstacle avoidance of hyper-redundant manipulators.On the path-following planning of the hyper-redundant manipulator, the MPE-MDA algorithm equates one link to multiple virtual small links and then derives the relationship between the length of the small links and the deflection angle of the small links.The planned path based on this relationship meets that the deflection angle of the link does not exceed the limited angle.The MPE-MDA algorithm resolves the problem that the traditional algorithm cannot limit the maximum deflection angle of the joint.Besides, the paper checks different paths by changing the step length, the direction of path planning, and other factors, and then selects the optimal path according to the evaluation function.Furthermore, by comparing the simulation results of path-following planning employing the MPE-MDA algorithm and the artificial potential field (APF) method, respectively.It is found that when the manipulator moves along the optimal path planned by the MPE-MDA algorithm, it can not only keep the specified distance from the obstacle but also can satisfy the condition that the deflection angle of the joint never exceeds the limited range, which verifies the reliability and effectiveness of the algorithm.
Adaptive Practical Nonlinear Model Predictive Control for Echo State Network Models

This article proposes an adaptive practical nonlinear model predictive (NMPC) control algorithm which uses an echo state network (ESN) estimated online as a process model. In the proposed control algorithm, the ESN readout parameters are estimated online using a recursive least-squares method that considers an adaptive directional forgetting factor. The ESN model is used to obtain online a nonlinear prediction of the system free response, and a linearized version of the neural model is obtained at each sampling time to get a local approximation of the system step response, which is used to build the dynamic matrix of the system. The proposed controller was evaluated in a benchmark conical tank level control problem, and the results were compared with three baseline controllers. The proposed approach achieved similar results as the ones obtained by its nonadaptive baseline version in a scenario with the process operating with the nominal parameters, and outperformed all baseline algorithms in a scenario with process parameter changes. Additionally, the computational time required by the proposed algorithm was one-tenth of that required by the baseline NMPC, which shows that the proposed algorithm is suitable to implement state-of-the-art adaptive NMPC in a computationally affordable manner.
A body contact-driven pupil response pet-robot for enhancing affinity

Dilated pupil response has effects on favorable impression to human. Focusing on the pupil response in human-robot interaction, we have developed a pupil response system using hemisphere displays and confirmed that the pupil response is effective for enhancing affective conveyance in human-robot interaction. In this study, for the basic research of realizing a friendly communication during embodied interaction between human and robot, we analyzed the pupil response during body contact that is closely related to affinity using a pupil measurement device. On the basis of the analysis results, we developed a pupil response pet-robot for enhancing affinity in human-robot interaction. This pet-robot generates the pupil response based on the body contact using small displays in which the 3D CG model of pupil and iris is represented, and can impress cuteness and familiarity. The effectiveness of the developed pet-robot with dilation of the pupil response at 2.0 times during body contact was demonstrated by sensory evaluations.
In-Class Multitasking with Smartphones and Laptops: Exploring Student Experiences and Perceptions

Although multitasking with laptops and mobile phones has become endemic on university campuses, we still don't have adequate understanding of this phenomenon. There is especially a lack of qualitative studies offering detailed and rich description of how and why students multitask. This exploratory study provides a nuanced understanding of in-class multitasking with smartphones and laptops. The study involves sixteen students from a comprehensive university in Hong Kong in in-depth interviews. The results indicate that the university students mainly multitask with mobile phones on activities unrelated to the ongoing class. Lectures and peer presentation are reported as the activities students felt least engaged with. The study has revealed a new type of multitasking behavior, that is learning-related yet off-task. The multitasking behavior also fluctuates on an array of factors associated with motivation, classroom context, and the presence of technology.
Balanced de Bruijn Sequences

The de Bruijn graph and its sequences have found many diverse applications in information theory as well as other areas of computer science and engineering such as interconnection networks, VLSI decomposition, and most recently in DNA storage. Binary balanced sequences have also been a subject to a large research during the last forty years with various applications and a lot of interest in information theory. There have been some works on classification of balanced sequences mainly based on their spectral-null order. This work generalizes the concept of de Bruijn sequences, based on the de Bruijn graph of order <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\ell$</tex> , where each edge is multiplied to a fixed number of multiple edges. This implies that in the sequences derived from the generalized graph each l-tuple has the same multiplicity. Using this generalization we form an interesting hierarchy between balanced sequences. Furthermore, another hierarchy is given by the derivatives of balanced sequences. Enumeration for each such family of sequences and efficient encoding and decoding algorithms are also provided.
Semantic Content-Based Image Retrieval System Using Deep Learning Model for Lung Cancer CT Images

When compared to other kinds of cancer, lung cancer is more prevalent worldwide. While computerised tomography (CT) images capture the required information for lung cancer diagnosis, clinicians must spend time analysing various sections of CT images. With the exponential rise of CT scans and the increasing prevalence of lung cancer, having an effective method for quickly analysing CT pictures can assist radiologists or doctors in planning for their treatment early. When compared to the usual method of categorising a patient’s kind of lung cancer, retrieving the nearest CT image already saved in the database gives additional information to the patient. This is accomplished through the use of a method for retrieving images depending on their content. In comparison to typical machine learning techniques, deep learning models extract more meaningful characteristics. As a consequence, this study analysis famous pre-trained deep learning models such as the VGG16 deep neural network and the ResNet residual neural network. Among these, the VGG16 model outperforms the others for the supplied hyper parameters. Thus, the characteristics restored through training the VGG16 model on the pre-processed picture are further processed by the KNN method to obtain comparable pictures to the input query picture. As a result of merging the VGG16 and KNN algorithms, this research produces a content-based picture retrieval system. The suggested CBIR system achieves an accuracy of 0.97 and a recall of 0.96.
Towards Labeling On-Demand IoT Traffic

A lack of transparency has accompanied the rapid proliferation of Internet of Things (IoT) devices. To this end, a growing body of work exists to classify IoT device traffic to identify unexpected or surreptitious device activity. However, this work requires fine-grained labeled datasets of device activity. This paper proposes a holistic approach for IoT device traffic collection and automated event labeling. Our work paves the way for future research by thoroughly examining different techniques for synthesizing and labeling on-demand traffic from IoT sensors and actuators. To demonstrate this approach, we instrumented a smart home environment consisting of 57 IoT devices spanning cameras, doorbells, locks, alarm systems, lights, plugs, environmental sensors, and hubs. We publish an open-source dataset consisting of 16,686 labeled events over 468,933 network flows. Our results indicate that vendor APIs, trigger-action frameworks, and companion notifications can be used to generate scientifically valuable labeled datasets of IoT traffic.
An Effective Scholarly Search by Combining Inverted Indices and Structured Search With Citation Networks Analysis

The rapid growth in the number of scholarly documents on the Web and in other digital platforms makes it challenging for researchers to find research publications most relevant to their information needs. This challenge has been mitigated to a greater extent by the major scholarly retrieval systems, such as Google Scholar, Semantic Scholar, PubMed, CiteSeerX, and others. The reason for the success of these retrieval solutions lies in the advances in ranking approaches. However, the existing studies advocate for the fact that we are still far from the method’s effectiveness ceiling, leaving ample room for further improvement to meet the scholarly needs of varying types of users. The existing methods adopt different approaches; some use classical Information Retrieval (IR), others use semantics-aware methods, including Knowledge Graph (KG) to support scholarly search. However, we hypothesize that combining the best of both worlds can further improve search relevance. In this context, this work incorporates inverted index from the classical IR with BM25 as the weighting scheme is combined with Citation Networks Analysis (CNA) for the baseline search results, which are then re-ranked, bypassing selected entities from the top-k initial search results as the search query to the KG. This way, not only the textual content but also the structural semantics of the research publications are well exploited in the retrieval processes. The goal is to exploit IR and KG-based retrieval techniques to gain insights into the behavior of both textual and structured information in the strategic ranking of scholarly articles. The proposed solution has been evaluated using the ACL Anthology Network (AAN) dataset. The results show that the proposed technique can comparatively improve the retrieval performance in terms of Normalized Discounted Cumulative Gain (nDCG) and precision rates.
Understanding the Implication of Non-Volatile Memory for Large-Scale Graph Neural Network Training

Graph neural networks (GNNs) can extract features by learning both the representation of each objects (i.e., graph nodes) as well as the relationship across different objects (i.e., the edges that connect nodes), achieving state-of-the-art performance on a wide range of graph-based tasks. Despite its strengths, utilizing these algorithms in a production environment faces several key challenges as the number of graph nodes and edges amount to several billions to hundreds of billions scale, requiring substantial storage space for training. Unfortunately, existing ML frameworks based on the in-memory processing model significantly hamper the productivity of algorithm developers as it mandates the overall working set to fit within DRAM capacity constraints. In this work, we first study state-of-the-art, large-scale GNN training algorithms. We then conduct a detailed characterization on utilizing capacity-optimized non-volatile memory solutions for storing memory-hungry GNN data, exploring the feasibility of SSDs for large-scale GNN training.
Almost fully anonymous attribute-based group signatures with verifier-local revocation and member registration from lattice assumptions

Attribute-Based Group Signature (ABGS) schemes permit any group member with required attributes to generate signatures for the sake of the group anonymously. Even though existing ABGS schemes with Verifier-local Revocation (VLR) method facilitate efficient user and attribute revocation, they cannot achieve stronger security for the users and user attributes. In this paper, we present a new approach to overcome this weakness delivering a new ABGS scheme with VLR that achieves stronger security, almost full anonymity, for both users and their attributes. We construct our scheme from lattices as lattice cryptography is quantum resist. Moreover, we present a simple member joining protocol and a new zero-knowledge argument system that supports the new scheme. Finally, we prove that the proposed scheme meets the security requirements of almost full anonymity, traceability, and non-frameability.
A threat intelligence framework for protecting smart satellite-based healthcare networks

Human-to-machine (H2M) communication is an important evolution in the industrial internet of health things (IIoHT), where many H2M interfaces are remotely interacting with industrial and medical assets. Lightweight protocols, such as constrained application protocol (CoAP), have been widely utilised in transferring sensing data of medical devices to end-users in smart satellite-based healthcare IIoT networks (SmartSat-IIoHT). However, such protocols are extensively deployed without appropriate security configurations, making attackers' mission easier for abusing these protocols to launch advanced cyber threats. This paper, therefore, presents a new threat intelligence framework to examine and model CoAP protocol's attacks in these systems. We present a ransom denial of service (RDoS) as a new threat that would exploit this protocol's vulnerabilities. We propose many RDoS attack's techniques to understand the attack indicators and analyse their behaviour on systems. Moreover, we present a real-time discovery of attacks' network behaviours using deep learning. The experiment results demonstrate that this proposed discovery model obtains a better performance in revealing RDoS than other conventional machine learning algorithms and accomplishing high fidelity of protecting SmartSat-IIoHT networks.