Reviewerly/exHarmony huggingface dataset:
Kill Chain Attack Modelling for Hidden Channel Attack Scenarios in Industrial Control Systems

The protection against Advanced Persistent Threats (APTs) is an important topic in nuclear and industrial information technology security since the last decade. Nowadays steganography, i.e. information hiding techniques are increasingly used by attackers in order to operate without being detected. The usage of hidden channel communication in APTs creates a novel form of attack scenarios for which the current defense mechanisms are usually ineffective. In order to defend industrial control systems against those attacks, it is necessary to understand and comprehend the attacks. Thus, this paper presents how attack modelling based on the Lockheed Martin Cyber Kill Chain can be used to analyze hidden channel APT attack scenarios and how it can be used to elaborate defense mechanisms and to reveal attack indicators along all phases of those attack scenarios.
Profile monitoring based on transfer learning of multiple profiles with incomplete samples

Profile monitoring is an important tool for quality control. Most existing profile monitoring approaches focus on monitoring a single profile. In practice, multiple profiles also widely exist and these profiles contain rich correlation information that can benefit the monitoring of interested/target profile. In this article, we propose a transfer learning framework to extract profile-to-profile inter-relationship to improve the monitoring performance. In this framework, profiles are modeled as a multi-output Gaussian process (MGP), and a specially designed covariance structure is proposed to reduce the computational load in optimizing the MGP parameters. More importantly, the proposed framework contains features for dealing with incomplete samples in each profile, which facilitates the information sharing among profiles with different data collection costs/availability. The proposed method is validated and compared with various benchmarks in extensive numerical studies and a case study of monitoring ice machine temperature profiles. The results show the proposed method can successfully transfer knowledge from related profiles to benefit the monitoring performance in the target profile. The R code of this paper would be available as on-line supplementary materials.
The method of path planning for AUV-group moving in desired formation in unknown environment with obstacles

The paper proposes a new method for trajectory formation of the AUVs group in "leader-follower" mode in the given formation in the unknown environment containing obstacles. In this mode, the AUV-leader defines the motion trajectory at the safe distance from the detected obstacles in accordance with the given mission. The AUVs-followers follow the leader and during obstacles avoidance they move along set in advance trajectories within the formation to ensure the safe distance between the AUVs-followers. In addition, the motion of the AUVs-followers along the predetermined trajectories allows to avoid additional data exchange between the AUVs with a view to match their positions.
Glider

Web automation scripts (tasklets) are used by personal AI assistants to carry out human tasks such as reserving a car or buying movie tickets. Generating tasklets today is a tedious job which requires much manual effort. We propose Glider, an automated and scalable approach to generate tasklets from a natural language task query and a website URL. A major advantage of Glider is that it does not require any pre-training. Glider models tasklet extraction as a state space search, where agents can explore a website's UI and get rewarded when making progress towards task completion. The reward is computed based on the agent's navigating pattern and the similarity between its trajectory and the task query. A hierarchical reinforcement learning policy is used to efficiently find the action sequences that maximize the reward. To evaluate Glider, we used it to extract tasklets for tasks in various categories (shopping, real-estate, flights, etc.); in 79% of cases a correct tasklet was generated.
The Dynamical Model of Flying-Qubit Control Systems

The control of flying qubits is crucial for the interconnection of quantum information processing units in the future applications. Physically, this class of problems can be modeled by the radiation of optical fields from a standing qubit (natural or artificial atoms). The photon statistics of the output field emitted from a quantum system coupled to multiple waveguides is complicated when the exciton number is not conserved, especially in presence of coherent driving that is crucial for control and optimization. In this paper, we use quantum stochastic differential equation (QSDE) to describe the photon generation process, and derive the dynamical jumps induced by photon emission. Numerical simulations show that this model can be applied to analyze the manipulation process of single qubits.
Efficient design of multicolumn RBF networks

To make radial basis function (RBF) networks efficient for large-scale learning tasks, the parallel technique provides a promising way for the construction of multicolumn RBF network (MCRN), where the task to be tackled is decomposed into several subtasks and RBF networks designed for these subtasks are integrated as a parallel structure to obtain the final model. This paper focuses on presenting an efficient design method for MCRN based on improved generalized hybrid constructive (GHC) learning algorithm such that desired performance and high efficiency are guaranteed. Specifically, a minimum difference scheme is firstly introduced to divide a large-scale dataset into several parts with similar distribution. For each subtask, a compact RBF network is designed by improved GHC learning algorithm. By fully considering the inherent properties of RBF networks, a K-nearest potential centers based criterion is established to calculate the output of testing sample. Experiments on some benchmark classification problems are conducted to verify the advantages of the proposed method. In terms of training and testing efficiencies and classification accuracy, it is shown that our model is superior over some existing ones.
Sentiment Analysis on Food Review using Machine Learning Approach

Interpersonal interaction correspondence has obtained a customary standard way to deal with web. Casual correspondence insinuates the use of web-based life destinations and applications. Twitter is one of the mainstream web-based media utilized in the present-day life. Individuals share their inclination with a post in many exercises of our everyday lives. Supposition analysis has become commonly notable. Regardless, stable Twitter thought portrayal execution stays dangerous due to different issues: generous class lopsidedness in a multi-class issue, illustrative extravagance issues for feeling signs, and the usage of different ordinary semantic models. These issues are perilous since various sorts of online life assessment rely upon exact shrouded Twitter thoughts. As necessities seem to be, a book examination structure is proposed for Twitter notion investigation. Estimation investigation by utilizing twitter information is well known in this recorded. Words and articulations bespeak the perspectives of people about the things, organizations, governments and events through electronic systems administration media. Eliminating positive, negative or nonpartisan polarities from electronic life content names task of suspicion assessment in the field of NLP. The outstanding improvement of solicitations for business affiliations and governments, affect experts to accomplish their examination in assumption examination. This exploration utilizes three front line ML classifiers SVM, Logistic Regression, Random Forest, Naive Bayes classifier for development of product review analysis. The tests are performed using Twitter yelp datasets. This data is available online on the web. The discussion conversation, review objections, destinations are a bit of the appraisal of rich resources where the study or posted articles is their inclination or all-around end towards the subject.
Integrating multiple blockchains to support distributed personal health records

Blockchain technologies have evolved in recent years, as have the use of personal health record (PHR) data. Initially, only the financial domain benefited from Blockchain technologies. Due to efficient distribution format and data integrity security, however, these technologies have demonstrated potential in other areas, such as PHR data in the healthcare domain. Applying Blockchain to PHR data faces different challenges than applying it to financial transactions via crypto-currency. To propose and discuss an architectural model of a Blockchain platform named “OmniPHR Multi-Blockchain” to address key challenges associated with geographical distribution of PHR data. We analyzed the current literature to identify critical barriers faced when applying Blockchain technologies to distribute PHR data. We propose an architecture model and describe a prototype developed to evaluate and address these challenges. The OmniPHR Multi-Blockchain architecture yielded promising results for scenarios involving distributed PHR data. The project demonstrated a viable and beneficial alternative for processing geographically distributed PHR data with performance comparable with conventional methods. Blockchain’s implementation tools have evolved, but the domain of healthcare still faces many challenges concerning distribution and interoperability. This study empirically demonstrates an alternative architecture that enables the distributed processing of PHR data via Blockchain technologies.
Contact Zones

Our food system is a socio-material, heterogeneous infrastructure whose complexity and interconnectedness often remains invisible to citizens. While moments of crisis expose the vulnerabilities and injustices underlying this system, this paper seeks to explore which processes and tools CSCW could purposely design to 'open up' food infrastructures and bring young and adult people in contact with different aspects of the food system to cultivate food citizenship from a more-than-human perspective. Through a collaboration with a local primary school and four different food organisations (a mushroom grower, a vegetable farm, a bread-baking community centre, and a food bank) in North East England, UK, we designed 'contact zones' that enabled a class of students aged 7 to 8 years to encounter socio-material food practices at each partnering organisation's site and in the classroom. Our insights show young people's rich engagement in the socio-materiality of place, food, and practices; how encountering food practices across very different sites helped surface the interconnectedness of the food system; and how the contact zones opened spaces to practice food citizenship. The paper offers design implications towards infrastructuring more-than-human food pedagogies. It discusses inherent power dynamics of more-than-human design collaborations, critically evaluates the role of technology in more-than-human relations, and presents three design opportunities towards a relational understanding of food.
No Need to Teach New Tricks to Old Malware: Winning an Evasion Challenge with XOR-based Adversarial Samples

Adversarial attacks to Machine Learning (ML) models became such a concern that tech companies (Microsoft and CUJO AI’s Vulnerability Research Lab) decided to launch contests to better understand their impact on practice. During the contest’s first edition (2019), participating teams were challenged to bypass three ML models in a white box manner. Our team bypassed all the three of them and reported interesting insights about models’ weaknesses. In the second edition (2020), the challenge evolved to an attack-and-defense model: the teams should either propose defensive models and attack other teams’ models in a black box manner. Despite the difficulty increase, our team was able to bypass all models again. In this paper, we describe our insights for this year’s contest regarding on attacking models, as well defending them from adversarial attacks. In particular, we show how frequency-based models (e.g., TF-IDF) are vulnerable to the addition of dead function imports, and how models based on raw bytes are vulnerable to payload-embedding obfuscation (e.g., XOR and base64 encoding).
Optimal Adaptive Strategies for Sequential Quantum Hypothesis Testing

We consider sequential hypothesis testing between two quantum states using adaptive and non-adaptive strategies. In this setting, samples of an unknown state are requested sequentially and a decision to either continue or to accept one of the two hypotheses is made after each test. Under the constraint that the number of samples is bounded, either in expectation or with high probability, we exhibit adaptive strategies that minimize both types of misidentification errors. Namely, we show that these errors decrease exponentially (in the stopping time) with decay rates given by the measured relative entropies between the two states. Moreover, if we allow joint measurements on multiple samples, the rates are increased to the respective quantum relative entropies. We also fully characterize the achievable error exponents for non-adaptive strategies and provide numerical evidence showing that adaptive measurements are necessary to achieve our bounds under some additional assumptions.
Defending co‐resident attack using reputation‐based virtual machine deployment policy in cloud computing

Abstract Cloud computing enables users to utilize IT resources conveniently with low‐level cost, but it also brings some new threats. The co‐resident attack is one of the typical examples, where malicious users steal information from legal users by starting a virtual machine (VM) and building a side‐channel between VMs on the same server. Most of current studies focus mainly on defending the side‐channel attack, which requires modifications to the existing underlying architecture of cloud platforms. Some studies focus on security‐aware VMs deployment policies that can defeat co‐resident attacks through reduction of co‐residences. However, most of these studies ignore workload‐balance and energy‐efficiency. In addition, they neglect that data held in different VMs should have different security levels. Motivated by these issues, in this article, we first formalize the multi‐attackers co‐resident attack problem. The co‐located VMs rate (CVR) metric is proposed to evaluate the security level in cloud and a security‐aware policy named least‐danger‐based‐on‐reputation (LDBR) is presented to minimize the metric. Also, we propose extensional LDBR (E‐LDBR) policy to balance security, workload‐balance and energy consumption, and weighted‐LDBR (W‐LDBR) policy that considers users' different security levels. Experimental results indicate that LDBR outperforms PSSF (previous‐selected‐server‐first) by 15.02%‐22.07% in security, E‐LDBR is more balanced in workload and consumes less energy consumption, and W‐LDBR can reduce security threats by 13.3% compared with PSSF.
Person Identification with Masked Face and Thumb Images under Pandemic of COVID-19

A novel coronavirus, named COVID-19 had resulted in an outbreak of viral pneumonia in China and became a pandemic in March 2020. According to WHO, 6.2 million people had affected by the disease till November 2020. Until recent updates, any proven cure has not been discovered yet. However, wearing a facemask and social distancing are predominant preventions. In the current scenario, person identification from masked images and without the use of biometric machines has become a challenge. In this paper, we have generated a dataset of almost 20000 pictures with the Raspberry Pi Camera Module. This dataset was divided into 20 classes, out of which, 10 classes are of masked face images and, 10 are of thumb images without using a biometric machine. In this article, we have proposed two less complex convolutional neural networks (CNN) based classifiers that have accurately classified the data and recognized the people in real-time using a Raspberry Pi 4 module. We achieved an accuracy of 97.67% in masked face recognition and 100% accuracy to recognize the thumb images.
Research Librarians' Experiences of Research Data Management Activities at an Academic Library in a Developing Country

University libraries have archaeologically augmented scientific research by collecting, organizing, maintaining, and availing research materials for access. Researchers reckon that with the expertise acquired from conventional cataloging, classification, and indexing coupled with that attained in the development, along with the maintenance of institutional repositories, it is only rational that libraries take a dominant and central role in research data management and further their capacity as curators. Accordingly, University libraries are expected to assemble capabilities, to manage and provide research data for sharing and reusing efficiently. This study examined research librarians' experiences of RDM activities at the UON Library to recommend measures to enhance managing, sharing and reusing research data. The study was informed by the DCC Curation lifecycle model and the Community Capability Model Framework (CCMF) that enabled the Investigator to purposively capture qualitative data from a sample of 5 research librarians at the UON Library. The data was analysed thematically to generate themes that enabled the Investigator to address the research problem. Though the UON Library had policies on research data, quality assurance and intellectual property, study findings evidenced no explicit policies to guide each stage of data curation and capabilities. There were also inadequacies in skills and training capability, technological infrastructure and collaborative partnerships. Overall, RDM faced challenges in all the examined capabilities. These challenges limited the managing, sharing, and reusing of research data. The study recommends developing an RDM unit within the UON Library to oversee the implementation of RDM activities by assembling all the needed capabilities (policy guidelines, skills and training, technological infrastructure and collaborative partnerships) to support data curation activities and enable efficient managing, sharing and reusing research data.
Gated SwitchGAN for Multi-Domain Facial Image Translation

Recent studies on multi-domain facial image translation have achieved impressive results. The existing methods generally provide a discriminator with an auxiliary classifier to impose domain translation. However, these methods neglect important information regarding domain distribution matching. To solve this problem, we propose a switch generative adversarial network (SwitchGAN) with a more adaptive discriminator structure and a matched generator to perform delicate image translation among multiple domains. A feature-switching operation is proposed to achieve feature selection and fusion in our conditional modules. We demonstrate the effectiveness of our model. Furthermore, we also introduce a new capability of our generator that represents attribute intensity control and extracts content information without tailored training. Experiments on the Morph, RaFD and CelebA databases visually and quantitatively show that our extended SwitchGAN (i.e., Gated SwitchGAN) can achieve better translation results than StarGAN, AttGAN and STGAN. The attribute classification accuracy achieved using the trained ResNet-18 model and the FID score obtained using the ImageNet pretrained Inception-v3 model also quantitatively demonstrate the superior performance of our models.
CT-Net: Cascade T-shape deep fusion networks for document binarization

Document binarization is a key step in most document analysis tasks. However, historical-document images usually suffer from various degradations, making this a very challenging processing stage. The performance of document image binarization has improved dramatically in recent years by the use of Convolutional Neural Networks (CNNs). In this paper, a dual-task, T-shaped neural network is proposed that has the main task of binarization and an auxiliary task of image enhancement. The neural network for enhancement learns the degradations in document images and the specific CNN-kernel features can be adapted towards the binarization task in the training process. In addition, the enhancement image can be considered as an improved version of the input image, which can be fed into the network for fine-tuning, making it possible to design a chained-cascade network (CT-Net). Experimental results on document binarization competition datasets (DIBCO datasets) and MCS dataset show that our proposed method outperforms competing state-of-the-art methods in most cases.
A quantum search decoder for natural language processing

Probabilistic language models, e.g. those based on an LSTM, often face the problem of finding a high probability prediction from a sequence of random variables over a set of tokens. This is commonly addressed using a form of greedy decoding such as beam search, where a limited number of highest-likelihood paths (the beam width) of the decoder are kept, and at the end the maximum-likelihood path is chosen. In this work, we construct a quantum algorithm to find the globally optimal parse (i.e. for infinite beam width) with high constant success probability. When the input to the decoder is distributed as a power-law with exponent $k>0$, our algorithm has runtime $R^{n f(R,k)}$, where $R$ is the alphabet size, $n$ the input length; here $f<1/2$, and $f\rightarrow 0$ exponentially fast with increasing $k$, hence making our algorithm always more than quadratically faster than its classical counterpart. We further modify our procedure to recover a finite beam width variant, which enables an even stronger empirical speedup while still retaining higher accuracy than possible classically. Finally, we apply this quantum beam search decoder to Mozilla's implementation of Baidu's DeepSpeech neural net, which we show to exhibit such a power law word rank frequency.
Verifiable and auditable authorizations for smart industries and industrial Internet-of-Things

Modern industrial systems are enriched by cyber–physical devices and interconnections with business processes that enable flexible production, remote monitoring, control and maintenance. These systems are typically subject to multiple authorities which must cooperate with each other, as in the case of segmented industrial environments and supply chains. In similar contexts, voluntary or unintentional damages may be caused by cyber attacks or by misbehaving authorized parties. We propose an original architecture that regulates accesses to industrial systems' resources through authorization delegation procedures. It guarantees several benefits that include the possibility of auditing authorizations released by delegated third parties, of detecting misconducts and possible attacks, and of assuring attribution of misconducts. The proposed solution is compatible with constraints characterizing industrial environments and with security and performance requirements of industrial architectures. The performance and latencies of the auditing mechanisms are evaluated through a prototype.