Reviewerly/exHarmony huggingface dataset:
Detecting Adversarial Examples for Time Series Classification and Its Performance Evaluation

Teraoka, Jun Tamura, KeiichiAs deep learning techniques have become increasingly used in real-world applications, their vulnerabilities have received significant attention from deep learning researchers and practitioners. In particular, adversarial example on deep neural networks and protection methods against them have been well-studied in recent years because such networks have serious vulnerabilities that threaten safety in the real world. This paper proposes a detection method against adversarial examples for time series classification. Time series classification is a task that predicts the class label that an unlabeled time series belongs to. To protect time series classification from attacks using adversarial examples, we propose three types of methods detecting adversarial examples for time series classification: 2n-class-based (2NCB) detection, 2-class (2CB) detection, and feature vector-based (FVB) detection methods. Moreover, we propose the ensemble method, which detects adversarial examples by using majority vote of the three aforementioned methods. Experimental results show that the proposed methods are superior to the conventional method.
On sampling error in evolutionary algorithms

The initial population in evolutionary algorithms (EAs) should form a representative sample of all possible solutions (the search space). While large populations accurately approximate the distribution of possible solutions, small populations tend to incorporate a sampling error. A low sampling error at initialization is necessary (but not sufficient) for a reliable search since a low sampling error reduces the overall random variations in a random sample. For this reason, we have recently presented a model to determine a minimum initial population size so that the sampling error is lower than a threshold, given a confidence level. Our model allows practitioners of, for example, genetic programming (GP) and other EA variants to estimate a reasonable initial population size.
An Adapted Approach of Image Steganography Using Pixel Mutation and Bit Augmentation

Saini, Ravi Joshi, Kamaldeep Nandal, RainuThis paper presents a new technique of data hiding in spatial domain using digital image as cover file. It uses two logical modules, binary converter and bit extractor in its processing. Binary converter module converts the identified pixel value into binary sequence of eight bits. Bit extractor module extracts the seven bits of pixel value except LSB. The proposed method hides the message in the seven bits of pixel value except LSB as LSB is the most sensitive bit and can be altered on the network by some error. It disburses the message evenly on the pixel intensity. The preliminary results show that it expels various anomalies associated with some state-of-the-art methods. It also provides better imperceptibility and hiding capacity.
Semi-supervised methodologies to tackle the annotated data scarcity problem in the field of HAR

In the field of Human Activity Recognition (HAR) the majority of approaches exploit fully supervised methodologies to process inertial sensor data collected from the users' wearable devices. Unfortunately, those solutions require users to collect a large number of annotated examples to train the recognition model, which is costly, unpractical, and time-consuming. In this paper, we propose diverse semi-supervised methodologies to tackle the data scarcity issue in the field of HAR. In particular, in Caviar and ProCaviar we introduce novel knowledge-based reasoning engines that exploiting the context data (e.g. semantic location, weather condition) allows a statistical classifier trained with a limited number of example to recognise a wide set of activities. Then, we propose FedHAR an hybrid semi-supervised and Federated-learning based system that enables distributing the training of an activity recognition model among a large number of subject, reducing the effort required from users to collect annotated data while preserving their privacy.
Blocks-World Cameras

For several vision and robotics applications, 3D geometry of man-made environments such as indoor scenes can be represented with a small number of dominant planes. However, conventional 3D vision techniques typically first acquire dense 3D point clouds before estimating the compact piece-wise planar representations (e.g., by plane-fitting). This approach is costly, both in terms of acquisition and computational requirements, and potentially unreliable due to noisy point clouds. We propose Blocks-World Cameras, a class of imaging systems which directly recover dominant planes of piece-wise planar scenes (Blocks-World), without requiring point clouds. The Blocks-World Cameras are based on a structured-light system projecting a single pattern with a sparse set of cross-shaped features. We develop a novel geometric algorithm for recovering scene planes without explicit correspondence matching, thereby avoiding computationally intensive search or optimization routines. The proposed approach has low device and computational complexity, and requires capturing only one or two images. We demonstrate highly efficient and precise planar-scene sensing with simulations and real experiments, across various imaging conditions, including defocus blur, large lighting variations, ambient illumination, and scene clutter.
Blockchain as messaging infrastructure for smart grids

This chapter explores the application of blockchain for supporting a messaging infrastructure within local electricity grids. Since communication is paramount in the optimization of the information flows, core concepts are introduced on established, as well as emerging blockchain-based protocols for communication in smart grids (SGs). A concrete initiative is presented, where a blockchain-enabled energy community trades locally produced renewable energy. The chapter presents also a discussion on benefits (e.g., transparency, immutability, data provenance) and challenges (e.g., economic feasibility/costs, privacy, scalability) in using the blockchain technology as a communication medium in SGs. Finally, overall conclusions and future research lines are presented.
Machine Learning Models Under the Copyright Microscope: Is EU Copyright Fit for Purpose?

Abstract This paper is based on a presentation given in December 2019 at the Lund’s University conference ‘Artificial Intelligence (AI), Data Protection and Intellectual Property in a European context’. The purpose of this article is to analyse the suitability of the copyright system over Machine Learning (ML) models, the so-called ‘core components’ of ML systems. Computer programs protection has always caused certain difficulties for intellectual property law. Internationally, agreement was reached in the 1970s to protect computer programs as literary works of copyright. ML models have been called ‘learning algorithms’, ‘AI computer programs’, and ‘software 2.0’. Yet there is no unanimity about what they are technically. This is relevant from a copyright perspective, because the regime of protection granted by copyright will be different depending on whether the ML model qualifies as a computer program, as a mathematical method, or as another type of work. Additionally, all proprietary and open source software licensing relies on copyright protection. In most open licenses, the license is not triggered if it is applied to something that is not protected by copyright (or related rights). Thus, it seems pertinent to question whether EU copyright law provides adequate protection for the core components of machine learning systems, the ML models.
Composition, Separation of Roles and Model-Driven Approaches as Enabler of a Robotics Software Ecosystem

Abstract Successful engineering principles for building software systems rely on the separation of concerns for mastering complexity. However, just working on different concerns of a system in a collaborative way is not good enough for economically feasible tailored solutions. A successful approach for this is the composition of complex systems out of commodity building blocks. These come as is and can be represented as blocks with ports via data sheets. Data sheets are models and allow a proper selection and configuration as well as the prediction of the behavior of a building block in a specific context. This chapter explains how model-driven approaches can be used to support separation of roles and composition for robotics software systems. The models, open-source tools, open-source robotics software components and fully deployable robotics software systems shape a robotics software ecosystem.
Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces

Reinforcement learning algorithms have proven to be capable of solving complicated robotics tasks in an end-to-end fashion without any need for hand-crafted features or policies. Especially in the context of robotics, in which the cost of real-world data is usually extremely high, Reinforcement Learning solutions achieving high sample efficiency are needed. In this paper, we propose a framework combining the learning of a low-dimensional state representation, from high-dimensional observations coming from the robot’s raw sensory readings, with the learning of the optimal policy, given the learned state representation. We evaluate our framework in the context of mobile robot navigation in the case of continuous state and action spaces. Moreover, we study the problem of transferring what learned in the simulated virtual environment to the real robot without further retraining using real-world data in the presence of visual and depth distractors, such as lighting changes and moving obstacles. A video of our experiments can be found at: https://youtu.be/rUdGPKr2Wuo.
DT2VIS: A Focus+Context Answer Generation System to Facilitate Visual Exploration of Tabular Data

The visual analysis dialog system utilizing natural language interface is emerging as a promising data analysis tool. However, previous work mostly focused on accurately understanding the query intention of a user but not on generating answers and inducing explorations. A focus+context answer generation approach, which allows users to obtain insight and contextual information simultaneously, is proposed in this work to address the incomplete user query (i.e., input query cannot reflect all possible intentions of the user). A query recommendation algorithm, which applies the historical query information of a user to recommend a follow-up query, is also designed and implemented to provide an in-depth exploration. These ideas are implemented in a system called DT2VIS. Specific cases of utilizing DT2VIS are also provided to analyze data. Finally, the results show that DT2VIS could help users easily and efficiently reach their analysis goals in a comparative study.
Developing a parallel distributed memory system of stories: A preliminary report

The author’s basic design concept of an artificial cognitive system is that story generation is an essential dimension of the mind. In this paper, a brief preliminary report on the development of a memory system is presented. The memory system is positioned as a partial system of a cognitive system. Its fundamental roles are to organize a world as a collection of stories and form the forces of generating stories. The memory system is designed as a parallel distributed system. In particular, each memory item—stories, events, concepts, and schemas—corresponds to an internal agent that operates automatically. These memory agents are dynamically organized in a network form through incorporating external simple narrative texts. This organizational process is performed via parallel distributed operations of connection formation and spreading activation among memory agents. The system is implemented in a proof-of-concept fashion. Several preliminary analyses of the system behavior are also presented.
Privacy in Times of COVID-19: A Pilot Study in the Republic of Ireland

Contact tracing apps used in tracing and mitigating the spread of COVID-19 have sparked discussions and controversies worldwide with major concerns around privacy. COVID Tracker app used in the Republic of Ireland was praised in general for the way it addressed privacy and was used as baseline for other contact tracing apps worldwide. The success of the app is dependent on the general public uptake, hence their voice and attitude is the one that really matters. This paper focuses on developing a survey and the methods aiming to examine the attitudes toward privacy during COVID-19 of the general public in the Republic of Ireland and their impact on the uptake of the COVID tracker app. Various privacy models are used and health belief model as well in this purpose. A pilot study with 286 participants show a change in attitude towards privacy during COVID-19 pandemic, with more people willing to share their data in the interest of saving lives. However, privacy attitudes are shown to have impacted the adoption of the app in Ireland.
Rethinking Security Operations Centre Onboarding

Cyber security operations centres (CSOC) are an essential business differentiator for digital services. They perform the challenging tasks of monitoring digital services such that when an attack is detected against the monitored digital services, they can respond swiftly ensuring cyber incidents are appropriately managed through to recovery. To monitor business services, the CSOC must first on-board those business services onto their security monitoring and incident management platforms. This process of onboarding business services to CSOC's security monitoring and incident management platform is described as Cybersecurity Onboarding. Cybersecurity Onboarding is a specialist technical process of setting up and configuring digital business services to generate appropriate interaction telemetry, such as events, logs, messages, metrics, and observables. These telemetric indicators when correlated and analysed reveal whether the business service may have been compromised or not. In this paper we rethink the approach to onboarding services to CSOC. A new approach of cybersecurity onboarding that is cyclic, simpler, quicker, efficient, and cost optimised by leveraging cloud-native and cloud-enabled technologies is discussed.
The contribution of non‐destructive geophysical surveys to archaeological research of early medieval hillforts in Bohemia: A case study of the Tismice hillfort area

Abstract Geophysical surveys of different areas of fortified prehistoric or early medieval hillforts are one of the long‐term topics of non‐destructive Czech archaeology. Different stages of geophysical measurements at the site of the early medieval hillfort near Tismice have been carried out under various circumstances of archaeological study in the area. The total full‐area magnetometer survey of all agricultural areas of the hillfort produced new spatial information for subsequent archaeological research of the supra‐regional Slavic centre from before the demise of the Slavník dynasty and the rise of the Přemyslids.
Experimental Evaluation of a Hierarchical Operating Framework for Ground Robots in Agriculture

For mobile robots to be effectively applied to real world unstructured environments—such as large scale farming—they require the ability to generate adaptive plans that account both for limited onboard resources, and the presence of dynamic changes, including nearby moving individuals. This work provides a real world empirical evaluation of our proposed hierarchical framework for long-term autonomy of field robots, conducted on University of Sydney’s Swagbot agricultural robot platform. We demonstrate the ability of the framework to navigate an unstructured and dynamic environment in an effective manner, validating its use for long-term deployment in large scale farming, for tasks such as autonomous weeding in the presence of moving individuals.
Dynamic information enhancement for video classification

How to extract and integrate spatiotemporal information for video classification is a major challenge. Advanced approaches adopt 2D, and 3D convolution kernels, or their variants as a basis of a spatiotemporal modeling process. However, 2D convolution kernels perform poorly along the temporal dimension, while 3D convolution kernels tend to create confusion between the spatial and temporal sources of information, with an increased risk of explosion of the number of model parameters. In this paper, we develop a more explicit way to improve the spatiotemporal modeling capacity of a 2D convolution network, which integrates two components: (1) Using Motion Intensification Block (MIB) to mandate a specific subset of channels to explicitly encode temporal clues to complement the spatial patterns extracted by other channels, achieving controlled diversity in the convolution calculations. (2) Using Spatial-temporal Squeeze-and-excitation (ST-SE) block to intensify the fused features reflecting the importance of different channels. In this manner, we improve the spatiotemporal dynamic information within the 2D backbone network, without performing complex temporal convolutions. To verify the effectiveness of the proposed approach, we conduct extensive experiments on challenging benchmarks. Our model achieves a competitive result on Something-Something V1, Something-Something V2, and a state-of-the-art performance on the Diving48 dataset, providing supporting evidence for the merits of the proposed methodology of spatiotemporal information encoding and fusion for video classification.
A novel combination of Mycielski–Markov, regime switching and jump diffusion models for solar energy

With renewable energy sources growing, solar power generation is becoming ever more popular around the world, so forecasting and scenario analysis of Solar Photovoltaic production is beneficial for grid operators and investors. In this paper, we introduce a novel combination of a Mycielski–Markov model, standard regime switching, and jump diffusion models to generate 1-minute Global Horizontal Irradiance time series over any time scale. It can simulate different scenarios of solar irradiance in the future after being trained on empirical data. We verify our model using statistical tests to compare our simulations with those from an observed time-series in Mauritius. The resulting model is able to generate simulations retaining the statistical properties of the data. Further, we find the proposed calibration process to be robust, and identified that splitting the day into 16 periods to be perfect balance to counter overfitting. The proposed model has the potential to better understand the effects of including large scale Solar Photovoltaic generation into an energy network, value future investments, or even allow for a cost–benefit analysis of subsidies.
Research on Face Recognition Algorithm Based on Block CR

Partial absence of face information challenges the robustness of face recognition algorithms. In order to reduce the effect of partial information loss on face recognition, a Face Recognition Method based on partitioning Collaborative Representation (FRAPCR) is proposed in this paper. Firstly, the face image is divided into several sub-blocks. Secondly, the Collaborative Representation (CR) is used to calculate the minimum sparse representation coefficient of each sub-block and the residual between the sub-block and the corresponding samples of each class, taking the class corresponding to the minimum residual as the class to which the sub-block belongs. Thirdly, a voting mechanism is introduced to count the categories of all sub-blocks of each face image, and the category with the largest number of votes is the category to which the whole face image belongs. Through the experiments on face databases ORL, Extend Yale B, and AR by the proposed method (FRAPCR), the best partitioning way of the face image is obtained. When there is partial information missing (pixel information missing, corrosion block and occlusion) in the face image, the images in each face database is divided in its corresponding optimal partitioning way. And comparative experiments between the FRAPCR and traditional CR face recognition methods are performed. The results show that FRAPCR has high recognition rate and stable recognition effect when there is partial information missing in face images.
ISSP-tree: An improved fast algorithm for constructing a complete prefix tree using single database scan

The researchers have explored the frequent pattern mining problem by considering the fact that the complete set of information to be processed can be accommodated in systems main memory, and databases are static. However, any transactional or online database may get modified in real-life scenarios due to new transactions or deleting previous obsolete records. Moreover, the support threshold may get updated over time to generate a new set of frequent patterns from the updated database. An inefficient but straightforward method to deal with this problem is recomputing the fresh set of patterns for the updated database or updated support threshold. Most of the existing algorithms perform pattern mining using multiple database scans, which requires a massive amount of main memory and computational time to retain tedious candidate itemsets and prune out the unnecessary itemsets. The research community has developed a few methods to handle the incremental scenario without re-computation from scratch, and those methods are efficient in terms of database scan point of view. Although the approaches have solved the re-computation problem by constructing a complete pattern-tree data structure using only one database scan, they have significant issues such as massive disk I/O and colossal search space high tree construction time. Therefore, to improve the tree construction time, we propose an efficient tree data structure called ISSP-tree (Improved Single Scan Pattern Tree), which creates a complete tree to retain all the database transactions irrespective of the item frequencies using only one database scan. Moreover, the method is also adaptive to incremental and interactive mining.
Unravel legal references in defeasible deontic logic

Legal documents often contain references to either other documents, or other parts (of the same document). The use of references is meant to reduce the complexity of the documents; however, they pose serious concerns for the formal (logical) representation of the norms stipulated in the document itself. We propose an approach to directly model the references in a logic language and to resolve them during the computation of the legal effects in force in a case. The approach is proved to be computationally feasible and to have an efficient algorithmic implementation.
Reintroducing Indonesian Folk Songs to Children Using Augmented Reality Books

This chapter explores a new approach to reintroduce Indonesian folk songs to children using Augmented Reality (AR) children's books. Indonesian folk song's existence as intangible cultural heritage is declining nowadays because children prefer popular music with exciting visuals and presentations. At the same time, augmented reality technology assimilation to conventional media has started to emerge. Previous studies show the great potential of AR-based books for children and indicate that this technology enhances their learning experience. This project adapts Bruce Archer's design methodology, including programming, data collection, analysis, synthesis, development, and communication. There were two phases for testing the AR book: the alpha testing phase used a digital book as a marker, then a printed book was used for the beta testing phase. The AR book is expected to provide a tangible experience for children to enjoy and preserve Indonesian folk songs.