Reviewerly/exHarmony huggingface dataset:
Coffee With a Hint of Data: Towards Using Data-Driven Approaches in Personalised Long-Term Interactions

While earlier research in human-robot interaction pre-dominantly uses rule-based architectures for natural language interaction, these approaches are not flexible enough for long-term interactions in the real world due to the large variation in user utterances. In contrast, data-driven approaches map the user input to the agent output directly, hence, provide more flexibility with these variations without requiring any set of rules. However, data-driven approaches are generally applied to single dialogue exchanges with a user and do not build up a memory over long-term conversation with different users, whereas long-term interactions require remembering users and their preferences incrementally and continuously and recalling previous interactions with users to adapt and personalise the interactions, known as the lifelong learning problem. In addition, it is desirable to learn user preferences from a few samples of interactions (i.e., few-shot learning ). These are known to be challenging problems in machine learning, while they are trivial for rule-based approaches, creating a trade-off between flexibility and robustness. Correspondingly, in this work, we present the text-based Barista Datasets generated to evaluate the potential of data-driven approaches in generic and personalised long-term human-robot interactions with simulated real-world problems, such as recognition errors, incorrect recalls and changes to the user preferences. Based on these datasets, we explore the performance and the underlying inaccuracies of the state-of-the-art data-driven dialogue models that are strong baselines in other domains of personalisation in single interactions, namely Supervised Embeddings, Sequence-to-Sequence, End-to-End Memory Network, Key-Value Memory Network, and Generative Profile Memory Network. The experiments show that while data-driven approaches are suitable for generic task-oriented dialogue and real-time interactions, no model performs sufficiently well to be deployed in personalised long-term interactions in the real world, because of their inability to learn and use new identities, and their poor performance in recalling user-related data.
Multilabel Takagi-Sugeno-Kang Fuzzy System

Multilabel (ML) classification can effectively identify the relevant labels of an instance from a given set of labels. However, the modeling of the relationship between the features and the labels is critical to classification performance. To this end, in this article, we propose a new ML classification method, called ML Takagi-Sugeno-Kang fuzzy system (ML-TSK FS), to improve the classification performance. The structure of ML-TSK FS is designed using fuzzy rules to model the relationship between features and labels. The FS is trained by integrating fuzzy inference-based ML correlation learning with ML regression loss. The proposed ML-TSK FS is evaluated experimentally on 12 benchmark ML datasets. The results show that the performance of ML-TSK FS is competitive with existing methods in terms of various evaluation metrics, indicating that it is able to model the feature-label relationship effectively using fuzzy inference rules and enhances the classification performance.
A Method for Automatic Penetration Testing and Mitigation: A Red Hat Approach

Recently in the cybersecurity landscape, various figures have spread with different peculiarities. For instance there are the Black Hat hackers, aimed to perpetrate damage on the system or to silently exfiltrate sensitive information but there also the Ethical or White Hat hackers, aimed to investigate the vulnerabilities of a system under analysis only with the owner consent. In this context the Red Hat hackers, defined as vigilantes of the hacker world, are emerging. Their main aim is to independently found and solve vulnerabilities, by preventing cyberattacks. In this paper we propose a method aimed to automatise the vulnerability discover and mitigation process typically performed by Red Hat hackers. We exploit a tool-chain of several well-known tools and we evaluate the proposed method by exploiting the Metaesploitable Linux distro, showing that the proposed method is able to automatically mitigate vulnerabilities afflicting six widespread services..
V-DESIRR: Very Fast Deep Embedded Single Image Reflection Removal

Real world images often gets corrupted due to unwanted reflections and their removal is highly desirable. A major share of such images originate from smart phone cameras capable of very high resolution captures. Most of the existing methods either focus on restoration quality by compromising on processing speed and memory requirements or, focus on removing reflections at very low resolutions, there by limiting their practical deploy-ability. We propose a light weight deep learning model for reflection removal using a novel scale space architecture. Our method processes the corrupted image in two stages, a Low Scale Sub-network (LSSNet) to process the lowest scale and a Progressive Inference (PI) stage to process all the higher scales. In order to reduce the computational complexity, the sub-networks in PI stage are designed to be much shallower than LSSNet. Moreover, we employ weight sharing between various scales within the PI stage to limit the model size. This also allows our method to generalize to very high resolutions without explicit retraining. Our method is superior both qualitatively and quantitatively compared to the state of the art methods and at the same time 20× faster with 50× less number of parameters compared to the most recent state-of-the-art algorithm RAGNet. We implemented our method on an android smart phone, where a high resolution 12 MP image is restored in under 5 seconds.
Using Credal C4.5 for Calibrated Label Ranking in Multi-Label Classification

Multi-Label Classification (MLC) assumes that each instance belongs to a set of labels, unlike traditional classification, where each instance corresponds to a unique value of a class variable. Calibrated Label Ranking (CLR) is an MLC algorithm that determines a ranking of labels for a given instance by considering a binary classifier for each pair of labels. In this way, it exploits pairwise label correlations. Furthermore, CLR alleviates the class-imbalance problem that usually arises in MLC because, in this domain, very few instances often belong to a label. In order to build the binary classifiers in CLR, it is required to employ a standard classification algorithm. The Decision Tree method C4.5 has been widely used in this field. In this research, we show that a version of C4.5 based on imprecise probabilities recently proposed, known as Credal C4.5, is more appropriate than C4.5 to handle the binary classification tasks in CLR. Experimental results reveal that Credal C4.5 outperforms C4.5 when both methods are used in CLR and that the difference is more statistically significant as the label noise level is higher.
Stable Periodic Frequent Itemset Mining on Uncertain Datasets

In recent years, data mining has attracted great attention from the information industry. The main reason is that there is a large amount of data, which can be widely used, and there is an urgent need to convert these data into useful information and knowledge. Frequent itemset mining as an important research basis in data mining research has also been greatly developed. However, most of these frequent itemset mining algorithms only consider the frequency of itemsets and ignore the periodicity of itemsets, which makes the results incomplete. In order to solve the above problem, this paper presents a a stable periodic frequent itemset mining (SPFIM) algorithm on uncertain dataset, by considering both the frequency and periodicity of itemsets. The experimental evaluation on real datasets shows that the SPFIM algorithm is efficient and can find patterns that are not found by traditional algorithms.
Question Answering Chatbot for Troubleshooting Queries based on Transfer Learning

Open-Domain Question Answering (ODQA) is a technique for finding an answer to a given query from a large set of documents. In this paper, we present an experimentation study to compare ODQA candidate solutions in the context of troubleshooting documents. We mainly focus on a well known open-source framework which is called Haystack. This framework comprises two key components which are the Retriever and the Reader. The Haystack Framework comes with several Retriever-Reader combinations and the choice of the best one is still unanswered till now. In this paper, we conduct an experimentation study to compare different Retriever-Reader combinations. Our aim is to come up with the best combination of components in regard to the speed and the processing power within the context of troubleshooting queries.
UHP-SOT: An Unsupervised High-Performance Single Object Tracker

An unsupervised online object tracking method that exploits both foreground and background correlations is proposed and named UHP-SOT (Unsupervised High-Performance Single Object Tracker) in this work. UHP-SOT consists of three modules: 1) appearance model update, 2) background motion modeling, and 3) trajectory-based box prediction. A state-of-the-art discriminative correlation filters (DCF) based tracker is adopted by UHP-SOT as the first module. We point out shortcomings of using the first module alone such as failure in recovering from tracking loss and inflexibility in object box adaptation and then propose the second and third modules to overcome them. Both are novel in single object tracking (SOT). We test UHP-SOT on two popular object tracking benchmarks, TB-50 and TB-100, and show that it outperforms all previous unsupervised SOT methods, achieves a performance comparable with the best supervised deep-learning-based SOT methods, and operates at a fast speed (i.e. 22.7-32.0 FPS on a CPU).
A Unified Treatment of Partial Stragglers and Sparse Matrices in Coded Matrix Computation

The overall execution time of distributed matrix computations is often dominated by slow worker nodes (stragglers) within the computation clusters. Recently, coding-theoretic techniques have been utilized to mitigate the effect of stragglers where worker nodes are assigned the job of processing encoded submatrices of the original matrices. In many machine learning or optimization problems the relevant matrices are often sparse. Several prior coded computation methods operate with dense linear combinations of the original submatrices; this can significantly increase the worker node computation times and consequently the overall job execution time. Moreover, several existing techniques treat the stragglers as failures (erasures) and discard their computations. In this work, we present a coding approach which operates with limited encoding of the original submatrices and utilizes the partial computations done by the slower workers. While our scheme can continue to have the optimal threshold of prior work, it also allows us to trade off the straggler resilience with the worker computation speed for sparse input matrices. Extensive numerical experiments done over cloud platforms confirm that the proposed approach enhances the speed of the worker computations (and thus the whole process) significantly.
DeepFake Detection using a frame based approach involving CNN

This paper proposes a novel model to detect Deep-Fakes, which are hyper-realistic fake videos generated by advanced AI algorithms involving facial superimposition. With a growing number of DeepFakes involving prominent political figures that hold a lot of social capital, their misuse can lead to drastic repercussions. These videos can not only be used to circulate false information causing harm to reputations of individuals, companies and countries, but also has the potential to cause civil unrest through mass hysteria. Hence it is of utmost importance to detect these DeepFakes and promptly curb their spread. We therefore propose a CNN-based model that learns inherently distinct patterns that change between a DeepFake and a real video. These distinct features include pixel distortion, inconsistencies with facial superimposition, skin colour differences, blurring and other visual artifacts. The proposed model has trained a CNN (Convolutional Neural Network), to effectively distinguish DeepFake videos using a frame-based approach based on aforementioned distinct features. Herein, the proposed work demonstrates the viability of our model in effectively identifying Deepfake faces in a given video source, so as to aid security applications employed by social-media platforms in credibly tackling the ever growing threat of Deepfakes, by effectively gauging the authenticity of videos, so that they may be flagged or ousted before they can cause irreparable harm.
Segmentation of malignant tumours in mammogram images: A hybrid approach using convolutional neural networks and connected component analysis

Abstract The segmentation of breast lesions is an important step in the computer‐aided analysis of the mammogram. The presence of noise in mammograms makes lesion detection challenging particularly for complex malignant lesions. Pre‐processing techniques can deal with the noise issue but distorts the important shape features. This motivates us to propose a novel hybrid approach by combining a convolution neural network (CNN) with connected component analysis (CCA) to segment malignant breast lesions without any pre‐processing to avoid any distortion in image sharpness at the initial stages. Two well‐known segmentation techniques namely, K‐means (KM) and Fuzzy c‐means (FCM) are also used to compare the results. From a pool of 1045 mammographic cancer images acquired from the Digital Database for Screening Mammography (DDSM), 1016 are used for training and validation, and 29 are used for testing. All three results (Hybrid, KM and FCM) are compared against the results by the expert Radiologist. The results indicate that, among various segmentation techniques, the proposed hybrid approach achieves the highest accuracy (90%), Matthew's correlation coefficient (0.79), Jaccard index (0.73) and the Dice similarity coefficient (0.84). Other performance evaluation techniques such as; precision, sensitivity, specificity, false‐positive rate, false discovery rate, negative predictive value and false‐negative rate also show the superior performance of the proposed hybrid approach. Statistical analysis (Mann–Whitney U test, T ‐test, Chi‐square test, Kolmogorov–Smirnov test and Wilcoxon test), graphical analysis (Regression and Bland–Altman plots) and receiver operating characteristic curve further demonstrate the stability and consistency of the results.
High Spatial-Resolution Digital Phase-Stepping Shearography

Digital phase-stepping shearography is a speckle interferometric technique that uses laser speckles to generate the phase map of the displacement derivatives of a stressed object, and hence can map the stresses of a deformed object directly. Conventional digital phase-stepping shearography relies on the use of video cameras of relatively lower resolution, in the order of 5 megapixels or lower, operating at a video rate. In the present work, we propose a novel method of performing high spatial resolution phase stepping shearography. This method uses a 24 megapixel still digital imaging device (DSLR camera) and a Michelson-type shearing arrangement with an edge-clamped, center-loaded plate. Different phase-stepping algorithms were used, and all successfully generated shearograms. The system enabled extremely high-resolution phase maps to be generated from relatively large deformations applied to the test plate. Quantitative comparison of the maximum achieved spatial resolution is made with the video-rate cameras used in conventional shearography. By switching from conventional (video) imaging methods to still imaging methods, significantly higher spatial resolution (by about 5 times) can be achieved in actual phase-stepping shearography, which is of great usefulness in industrial non-destructive testing (NDT).
Feasibility of Digital Image Correlation for Fatigue Cracks Detection under Dynamic Loading

We address non-contact detection of defects in the railway rails under their dynamic loading and propose to combine digital image correlation (DIC) and finite element modeling (FEM). We show that accurate model of defect-free rail operating at the same loading conditions as the inspected one provides a reliable reference for experimental data. In this study, we tested the rail samples with artificial and fatigue defects under cyclic loading, calculated displacement and stress distributions at different locations of the cracks via DIC and validated the obtained results by FEM. The proposed DIC-FEM approach demonstrates high sensitivity to fatigue cracks and can be effectively used for remote control of rails as well as for non-destructive testing of various other objects operating under dynamic loads.
FedIPR: Ownership Verification for Federated Deep Neural Network Models

Federated learning models are collaboratively developed upon valuable training data owned by multiple parties. During the development and deployment of federated models, they are exposed to risks including illegal copying, re-distribution, misuse and/or free-riding. To address these risks, the ownership verification of federated learning models is a prerequisite that protects federated learning model intellectual property rights (IPR) i.e., FedIPR. We propose a novel federated deep neural network (FedDNN) ownership verification scheme that allows private watermarks to be embedded and verified to claim legitimate IPR of FedDNN models. In the proposed scheme, each client independently verifies the existence of the model watermarks and claims respective ownership of the federated model without disclosing neither private training data nor private watermark information. The effectiveness of embedded watermarks is theoretically justified by the rigorous analysis of conditions under which watermarks can be privately embedded and detected by multiple clients. Moreover, extensive experimental results on computer vision and natural language processing tasks demonstrate that varying bit-length watermarks can be embedded and reliably detected without compromising original model performances. Our watermarking scheme is also resilient to various federated training settings and robust against removal attacks.
Blockchain technology in food supply chain: A state of the art

The food industry is highly dependent upon food supply chains. It is highly critical for the quality of the food product that reaches the consumer, that all actors that interact with the corresponding supply chains act responsibly and according to the appropriate regulations. Because of that, monitoring of food supply chains is a necessity nowadays. The main problem of current food supply chain systems is the lack of transparency, traceability, and sustainability as those are required to operate in a complex ecosystem, involving multiple stakeholders across different countries. Blockchain technology has been proposed as the means to enhance existing systems by increasing transparency and traceability, and thus regain customer's trust. This paper aims at providing a state of the art with regards to the use of blockchain technology in the food industry and food supply chains in particular. The survey summarizes all relevant research efforts and concludes with the main findings, while it also identifies existing challenges and limitations.
Deep Learning‐Based Unsupervised Human Facial Retargeting

Abstract Traditional approaches to retarget existing facial blendshape animations to other characters rely heavily on manually paired data including corresponding anchors, expressions, or semantic parametrizations to preserve the characteristics of the original performance. In this paper, inspired by recent developments in face swapping and reenactment, we propose a novel unsupervised learning method that reformulates the retargeting of 3D facial blendshape‐based animations in the image domain. The expressions of a source model is transferred to a target model via the rendered images of the source animation. For this purpose, a reenactment network is trained with the rendered images of various expressions created by the source and target models in a shared latent space. The use of shared latent space enable an automatic cross‐mapping obviating the need for manual pairing. Next, a blendshape prediction network is used to extract the blendshape weights from the translated image to complete the retargeting of the animation onto a 3D target model. Our method allows for fully unsupervised retargeting of facial expressions between models of different configurations, and once trained, is suitable for automatic real‐time applications.
Efficient multi-agent epistemic planning: Teaching planners about nested belief

Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology for solving efficiently. Our approach represents an important step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.
Towards Applying Fuzzy Systems in Intelligent Agent-based CPS: A Case Study

Cyber-physical Systems (CPS) are complex systems that interact with the physical world where instant external changes exist. To tackle these systems, in the literature, software agents are considered as an option and their enhancement with fuzzy logic can also be employed. Agent-oriented approaches and particularly Belief-Desire-Intention (BDI) architecture is used in different application areas. In all these domains, autonomy and proactive behaviour as well as the human-like reasoning are important aspects to enhance the intelligence of a system. These features can also be applied in CPS where the systems are complex. In this study, it is aimed to enhance a CPS using fuzzy logic for intelligent agents so that intelligent decision making can be applied to eliminate inaccurate calculations in the CPS. To evaluate the proposed fuzzy-BDI approach, we used a CPU fan control system. First, the system is tested assuming that the temperature sensor is ideal without including any sampling noise. We applied traditional BDI and fuzzy-BDI separately. Then, we tested data sampling process considering ∓ 5 error rate. We concluded that fuzzy-BDI approach for both ideal and noisy data showed considerable improvement.