Reviewerly/exHarmony huggingface dataset:
A2G2V: Automatic Attack Graph Generation and Visualization and Its Applications to Computer and SCADA Networks

Securing cyber-physical systems (CPS) and Internet of Things (IoT) systems requires the identification of how interdependence among existing atomic vulnerabilities may be exploited by an adversary to stitch together an attack that can compromise the system. Therefore, accurate attack graphs play a significant role in systems security. A manual construction of the attack graphs is tedious and error-prone, this paper proposes a model-checking-based automated attack graph generator and visualizer (A2G2V). The proposed A2G2V algorithm uses existing model-checking tools, an architecture description tool, and our own code to generate an attack graph that enumerates the set of all possible sequences in which atomic-level vulnerabilities can be exploited to compromise system security. The architecture description tool captures a formal representation of the networked system, its atomic vulnerabilities, their pre-and post-conditions, and security property of interest. A model-checker is employed to automatically identify an attack sequence in the form of a counterexample. Our own code integrated with the model-checker parses the counterexamples, encodes those for specification relaxation, and iterates until all attack sequences are revealed. Finally, a visualization tool has also been incorporated with A2G2V to generate a graphical representation of the generated attack graph. The results are illustrated through application to computer as well as control (SCADA) networks.
Exact-K Recommendation via Maximal Clique Optimization

This paper targets to a novel but practical recommendation problem named exact-K recommendation. It is different from traditional top-K recommendation, as it focuses more on (constrained) combinatorial optimization which will optimize to recommend a whole set of K items called card, rather than ranking optimization which assumes that "better" items should be put into top positions. Thus we take the first step to give a formal problem definition, and innovatively reduce it to Maximum Clique Optimization based on graph. To tackle this specific combinatorial optimization problem which is NP-hard, we propose Graph Attention Networks (GAttN) with a Multi-head Self-attention encoder and a decoder with attention mechanism. It can end-to-end learn the joint distribution of the K items and generate an optimal card rather than rank individual items by prediction scores. Then we propose Reinforcement Learning from Demonstrations (RLfD) which combines the advantages in behavior cloning and reinforcement learning, making it sufficient-and-efficient to train the model. Extensive experiments on three datasets demonstrate the effectiveness of our proposed GAttN with RLfD method, it outperforms several strong baselines with a relative improvement of 7.7% and 4.7% on average in Precision and Hit Ratio respectively, and achieves state-of-the-art (SOTA) performance for the exact-K recommendation problem.
Grey Wolf Optimizer and Its Applications: A Survey

This paper describes Grey Wolf Optimizer (GWO) as a nature inspired metaheuristic algorithm. GWO is proposed in 2014. The leadership hierarchy and hunting behavior of the grey wolves is explained in GWO by developing a mathematical model. The mathematical model of GWO and the pseudocode are also discussed in this paper. Different applications of GWO have also been studied in this paper. This review will assist the future researcher to apply this algorithm in new fields of research.
Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation

Reinforcement learning aims at searching the best policy model for decision making, and has been shown powerful for sequential recommendations. The training of the policy by reinforcement learning, however, is placed in an environment. In many real-world applications, however, the policy training in the real environment can cause an unbearable cost, due to the exploration in the environment. Environment reconstruction from the past data is thus an appealing way to release the power of reinforcement learning in these applications. The reconstruction of the environment is, basically, to extract the casual effect model from the data. However, real-world applications are often too complex to offer fully observable environment information. Therefore, quite possibly there are unobserved confounding variables lying behind the data. The hidden confounder can obstruct an effective reconstruction of the environment. In this paper, by treating the hidden confounder as a hidden policy, we propose a deconfounded multi-agent environment reconstruction (DEMER) approach in order to learn the environment together with the hidden confounder. DEMER adopts a multi-agent generative adversarial imitation learning framework. It proposes to introduce the confounder embedded policy, and use the compatible discriminator for training the policies. We then apply DEMER in an application of driver program recommendation. We firstly use an artificial driver program recommendation environment, abstracted from the real application, to verify and analyze the effectiveness of DEMER. We then test DEMER in the real application of Didi Chuxing. Experiment results show that DEMER can effectively reconstruct the hidden confounder, and thus can build the environment better. DEMER also derives a recommendation policy with a significantly improved performance in the test phase of the real application.
Seamless Seams

Traditional crafting methods such as stitching, embroidering, dyeing and machine sewing can be enhanced to create novel techniques for embedding shape-changing and colour-changing actuation into soft fabrics. In this paper, we show how embedding Shape-Memory Alloy (SMA) wire, copper wire and thermochromic thread into needles and bobbins, we were able to successfully machine sew interactive morphological capabilities into textiles. We describe the results of extensive design experiments, which detail how differing actuations can be achieved through a matrix of parameters that directly influence a fabric's deformational behaviours. To demonstrate the usefulness of our 10 techniques, we then introduce and discuss an interactive artefact we produced, using a subset of these techniques. We contribute such new techniques for creating soft-interfaces, imbued with actuation through tactile and self-morphing capabilities without motors or LEDs. We draw insights from this on the potential of the proposed techniques for crafting interactive artefacts.
A Comparison of Word-Embeddings in Emotion Detection from Text using BiLSTM, CNN and Self-Attention

User profiling is becoming increasingly holistic by including aspects of the user that until a few years ago seemed irrelevant. The content that users produce on the Internet and social networks is an essential source of information about their habits, preferences, and behaviors in many situations. One factor that has proved to be very important for obtaining a complete user profile that includes her psychological traits are the emotions experienced. Therefore, it is of great interest to the research community to develop approaches for identifying emotions from the text that are accurate and robust in situations of everyday writing. In this work, we propose a classification approach based on deep neural networks, Bi-LSTM, CNN, and self-attention demonstrating its effectiveness on different datasets. Moreover, we compare three pre-trained word-embeddings for words encoding. The encouraging results obtained on state-of-the-art datasets allow us to confirm the validity of the model and to discuss what are the best word embeddings to adopt for the task of emotion detection. As a consequence of the great importance of deep learning in the research community, we promote our model as a starting point for further investigations in the domain.
A Graph Based Chatbot for Cancer Patients

World Health Organization (WHO) reports that, the second major cause of death is cancer. Life of people who have cancer is daunting. Their heart is open to all negative emotions like anger, fear, depression, guilt, helplessness, etc. People around them are also finding it difficult to cope with it. They require constant support to discuss their problems with and to provide them with factual information. This paper introduces a potential solution to provide them with what they are seeking for- a chatbot. The proposed chatbot is a cancer chatbot designed only for people dealing with cancer. People can ask about anything and everything about cancer- symptoms, treatments, survival and so on. The bot is trained by information collected from various cancer forums which have a wide range of information about cancer. Sentiment analysis is used to identify the mood of the users so that the bot can give a human-like behavior and comfort them.
Digital Transformation in Education

Digital technology in the modern world is not only a tool, but also a living environment that opens up new opportunities: learning at any convenient time, continuing education, etc. This article aims to describe the specificity of digital education, the current state of its implementation, the expected results and concerns in this respect. Having shown the core of the digital education and the state of its implementation in modern society, this type of education must be critically analyzed in terms of advantages and risks with reference to contemporary students and the effectiveness of the teaching – learning process, in which they participate. In the study pros and cons of digital learning are revealed. The paper concludes information about advantages of using electronic educational resources in teaching a foreign language based on the experimental work which was done in Naberezhnye Chelny Institute of Kazan Federal University. The success of the experiment presented in this paper is demonstrated by comparing the results of the test group who were taught using electronic educational resources with the reference group who were taught in a common traditional way. The statistical analysis shows that the test group students had better achievements compared to the reference group.
Objects with Intent

In HCI there is an increasing trend to approach computing artifacts as agents. In this article, we make a case for “Objects with Intent” (OwI's) as an emerging type of agents that take advantage of the meaning of everyday things as the site for their intelligence and agency. After reviewing relevant existing research in HCI and related fields, we demonstrate how OwI's provide a new perspective on human–agent interaction. We then elaborate on how the notion of OwI's is informed by Dennett's theory of intentionality and Leontiev's Activity Theory. Thereafter, we illustrate the application of OwI's through the design case of Fizzy, a robotic ball used to stimulate hospitalized children to engage in physical play. We end by discussing the nature and merit of OwI's and reflecting more broadly on the challenges involved in designing OwI's.
Subspace segmentation-based robust multiple kernel clustering

Multiple kernel clustering (MKC) is an important research topic during the last few decades. It optimally combines a group of pre-specified base kernels to improve clustering performance. Though demonstrating promising performance in various applications, this task is still challenging due to lack of reliable discriminative guidance for the base kernel combination. Moreover, noise from either corrupted data or inappropriately selected base kernel parameters would undermine the intrinsic manifold and makes the problem even harder. In this paper, we integrate subspace segmentation into MKC and propose a robust subspace segmentation-based multiple kernel clustering (SS-MKC) algorithm to address these issues. In our formulation, we unify the constrained kernel polarization and subspace segmentation into a single procedure, where the resultant affinity matrix embedded with robust subspace structural information is utilized to guide the linear combination of base kernels. In addition, we carefully design the noise representation matrix as well as two sparse constraints, i.e., the ℓ1-norm and the probability constraints, to eliminate the adverse effect of noise among base kernels. We then propose a novel Alternative Direction Method of Multiplier (ADMM)-based algorithm to solve the resulting optimization problem. Extensive experiments have been conducted on both synthetic and public benchmark datasets, and the results well demonstrate the superiority of the proposed algorithm when compared with the state-of-the-art MKC methods.
A simple PID-based strategy for particle swarm optimization algorithm

The particle swarm optimization (PSO) algorithm, which updates particles by considering their past momentum and current direction, has demonstrated its power in several optimization applications. However, the updating strategy followed by the standard PSO mainly aims to learn from the global optimum, which often leads to PSO suffering from premature convergence. Using the past momentum can result in the overshoot problem, which usually slows down convergence in complex optimization problems. Inspired by the massive success of the proportional-integral-derivative (PID) controller in automatic control, we first establish a connection between the PSO process and the PID controller-based control system. Thereafter, we propose a PID-based strategy for PSO (PBS-PSO) to accelerate convergence and adjust the search direction to get out of local optima. The proposed PBS-PSO utilizes the past, current, and change in global best together to update the search direction. We conduct experiments on the CEC2013 test suite benchmark. The experimental results demonstrate the effectiveness of our proposed PBS-PSO algorithm. Most importantly, we find that the proposed PID-controller-based strategy has good generalization ability because it can be combined with other PSO variants to improve convergence performance in most cases.
Convolution–deconvolution word embedding: An end-to-end multi-prototype fusion embedding method for natural language processing

Existing unsupervised word embedding methods have been proved to be effective to capture latent semantic information on various tasks of Natural Language Processing (NLP). However, existing word representation methods are incapable of tackling both the polysemous-unaware and task-unaware problems that are common phenomena in NLP tasks. In this work, we present a novel Convolution–Deconvolution Word Embedding (CDWE), an end-to-end multi-prototype fusion embedding that fuses context-specific information and task-specific information. To the best of our knowledge, we are the first to extend deconvolution (e.g. convolution transpose), which has been widely used in computer vision, to word embedding generation. We empirically demonstrate the efficiency and generalization ability of CDWE by applying it to two representative tasks in NLP: text classification and machine translation. The models of CDWE significantly outperform the baselines and achieve state-of-the-art results on both tasks. To validate the efficiency of CDWE further, we demonstrate how CDWE solves the polysemous-unaware and task-unaware problems via analyzing the Text Deconvolution Saliency, which is an existing strategy for evaluating the outputs of deconvolution.
Utilizing Blockchain and Smart Contracts to Enable Audit 4.0: From the Perspective of Accountability Audit of Air Pollution Control in China

ABSTRACT Industry 4.0 uses many technologies, such as smart sensors and IoT, to fundamentally improve manufacturing processes. These advanced tools can also be utilized by auditors for the purpose of achieving real-time auditing and monitoring, pushing the profession toward a new generation: “Audit 4.0.” Blockchains and smart contracts should be utilized to overcome new challenges in the transformation toward Audit 4.0. This paper explores the potential of blockchain and smart contracts to reengineer current audit procedures, thereby enabling Audit 4.0. First, this paper demonstrates a framework that summarizes where blockchain and smart contracts should be applied to help implement Audit 4.0. Then, it designs and implements a system to facilitate accountability audit for Chinese government officials regarding air pollution control. In this case, real air quality data are collected via crowdsourcing, verified and analyzed by blockchain and smart contracts to achieve a continuous audit of government officials' performance on air protection.
The Essential of Sentiment Analysis and Opinion Mining in Social Media : Introduction and Survey of the Recent Approaches and Techniques

With evolution of social network and Web 2.0, people not only consume content by downloading on web but also contribute and produce new contents. People became more eager to express and share their opinions on web regarding daily activities as well as local or global issues. Due to the proliferation of social media for instance Facebook, Twitter, Youtube and others, sentiment analysis and opinion mining grow rapidly. It branches out from the field of natural language processing and data mining particularly from web mining and text mining. Why sentiment analysis and also known as opinion mining is prevalent and relevant nowadays? When we try to decide to purchase a product, we are likely to get the opinions from friends or relatives and do some surveys before we purchase the product. Hence, opinions are undeniably the key influencer of our behavior as well as the central to nearly all of the activities. Within the opinions, we often find the neutral, positive and negative polarities in the sentences. Based on the sentiment analysis taxonomy, it has opinion mining to have the opinion polarity classification, subjectivity detection, opinion spam detection, opinion summarization and argument expression detection. On the other hand, emotion mining has the emotion polarity classification, emotion detection, emotion cause detection and emotion classification. If it is based on granularity level, it has sentence level, document level and aspect/entity level of sentiment analysis. As for the machine learning approaches, it has semi-supervised learning, unsupervised learning and supervised learning of sentiment analysis.
Robustness of Neural Networks: A Probabilistic and Practical Approach

Neural networks are becoming increasingly prevalent in software, and it is therefore important to be able to verify their behavior. Because verifying the correctness of neural networks is extremely challenging, it is common to focus on the verification of other properties of these systems. One important property, in particular, is robustness. Most existing definitions of robustness, however, focus on the worst-case scenario where the inputs are adversarial. Such notions of robustness are too strong, and unlikely to be satisfied by-and verifiable for-practical neural networks. Observing that real-world inputs to neural networks are drawn from non-adversarial probability distributions, we propose a novel notion of robustness: probabilistic robustness, which requires the neural network to be robust with at least (1 - ε) probability with respect to the input distribution. This probabilistic approach is practical and provides a principled way of estimating the robustness of a neural network. We also present an algorithm, based on abstract interpretation and importance sampling, for checking whether a neural network is probabilistically robust. Our algorithm uses abstract interpretation to approximate the behavior of a neural network and compute an overapproximation of the input regions that violate robustness. It then uses importance sampling to counter the effect of such overapproximation and compute an accurate estimate of the probability that the neural network violates the robustness property.
Improving Source Code Readability: Theory and Practice

There are several widely accepted metrics to measure code quality that are currently being used in both research and practice to detect code smells and to find opportunities for code improvement. Although these metrics have been proposed as a proxy of code quality, recent research suggests that more often than not, state-of-the-art code quality metrics do not successfully capture quality improvements in the source code as perceived by developers. More specifically, results show that there may be inconsistencies between, on the one hand, the results from metrics for cohesion, coupling, complexity, and readability, and, on the other hand, the interpretation of these metrics in practice. As code improvement tools rely on these metrics, there is a clear need to identify and resolve the aforementioned inconsistencies. This will allow for the creation of tools that are more aligned with developers' perception of quality, and can more effectively help source code improvement efforts. In this study, we investigate 548 instances of source code readability improvements, as explicitly stated by internal developers in practice, from 63 engineered software projects. We show that current readability models fail to capture readability improvements. We also show that tools to calculate additional metrics, to detect refactorings, and to detect style problems are able to capture characteristics that are specific to readability changes and thus should be considered by future readability models.
Negotiating Gender and Disability Identities in Participatory Design

Standpoint theory draws our attention toward how researchers' identities shape the production of knowledge. Their standpoint depends on previous experiences and their sense of identity, as well as on their social position relative to research participants and their communities. This is particularly the case in Participatory Design (PD), which entices researchers to develop personal relationships with participants through design. However, the way identities affect Participatory Design with children has so far been neglected in research, even though previous works focus on children's and researchers' roles in the design process or encourages auto-ethnography. In this paper, we build on case studies of how identities as they relate to gender and disability shape relationships between researchers and marginalised children through Participatory Design. We show how these identities are continuously negotiated throughout the design process, and how they shape outcomes. We close by proposing an approach to systematic reflexivity on identity in participatory design.
Versatile security analysis of measurement-device-independent quantum key distribution

Measurement-device-independent quantum key distribution (MDI-QKD) is the only known QKD scheme that can completely overcome the problem of detection side-channel attacks. Yet, despite its practical importance, there is no standard approach towards proving the security of MDI-QKD. Here, we present a simple numerical method that can efficiently compute almost-tight security bounds for any discretely modulated MDI-QKD protocol. To demonstrate the broad utility of our method, we use it to analyze the security of coherent-state MDI-QKD, decoy-state MDI-QKD with leaky sources, and a variant of twin-field QKD called phase-matching QKD. In all of the numerical simulations (using realistic detection models) we find that our method gives significantly higher secret key rates than those obtained with current security proof techniques. Interestingly, we also find that phase-matching QKD using only two coherent test states is enough to overcome the fundamental rate-distance limit of QKD. Taken together, these findings suggest that our security proof method enables a versatile, fast, and possibly optimal approach towards the security validation of practical MDI-QKD systems.