Reviewerly/exHarmony huggingface dataset:
Optimizing Data Augmentation for Semantic Segmentation on Small-Scale Dataset

Augment the training data has a significant effect for deep learning on small-scale dataset. For practical semantic segmentation applications, it is a hard work to collect and annotate enough training data for training the deep neural network. In this paper, we focus on which data augmentation (DA) method is better, and what combination of different DA methods can improve the network performance more. Our target application is highland AI-Ranch which is hard to collect many training data. We firstly collect and produce a small-scale open source of sheep segmentation dataset including hundreds images, referred to as SSG dataset. Seven frequently used data augmentation methods are evaluated, including global augmentation (augment for the whole image) such as flipping, and local augmentation (augment only for the region of interest) such as cropping, etc. Especially, a novel image compression global DA method is proposed which can achieve the best augmentation performance in global methods. Furthermore, we explore the performance of the cross-combination data augmentation technique when applying to a small-scale semantic segmentation dataset. As different DA method will cover the different sample distribution, more augmentation fed more good training data and meanwhile more bad training data to the network. Therefore, too much augmentation may pull down the performance sometimes. Experiment results show that the combination of compression, cropping and local shift can achieve the best augmentation performance for our AI-Ranch application, the average coverage mean-IoU improve from 73.3% to 91.3%, even better than the combination of whole augmentation methods.
Informatization of technical vocational schools: Theoretical foundations and practical approaches

The aim of this study was an experimental research in vocational schools intended to the development of the system of educational process informatization taking into account the structure of the ICT competence of skilled workers and the trends of the information society. Representative sample of over 800 students of 10 vocational schools from different regions of Ukraine, 80 teachers, 17 specialists of VET centers allowed to study and analyze the state of informatization of VET schools, as well as to determine the degree of influence of education informatization on the quality of workers’ training. The system of educational process informatization at a technical vocational school, i.e. the creation of the ICT-saturated educational environment was implemented. This is a set of interrelated activities that comprise learning management systems; information and analytical databases; computer simulation of professional actions; programs of control of knowledge; application of ICT for writing diploma papers; computer methods of competence diagnostics; simulation training complexes; expert and decision support systems. The experiment showed that the use of the developed methods of skilled workers’ informational training, of applying pedagogical software tools at technical vocational school and of training vocational school teachers how to use ICT contributes to the improvement of graduates’ professional competence.
Cooperative Co-evolution with Soft Grouping for Large Scale Global Optimization

Cooperative Co-evolution (CC) is a promising framework to scale up conventional evolutionary algorithms for large scale global optimization (LSGO) problems. However, how to group decision variables is still a problem while there is no prior knowledge about the dependence relationship between variables. In this paper, a new kind of CC algorithm called Soft Grouping Cooperative Co-evolution (SGCC) is proposed to tackle the problem. Instead of explicitly dividing variables into multiple groups, the algorithm softly assigns variables into multiple groups by controlling the degree of membership of variables to the groups. In this work, the degree of membership is controlled by a probability distribution function. The experimental investigation shows that Soft Grouping CC is better than the explicit grouping CC on partially separable and non-separable problems.
Decentralized collaborative transport of fabrics using micro-UAVs

Small unmanned aerial vehicles (UAVs) have generally little capacity to carry payloads. Through collaboration, the UAVs can increase their joint payload capacity and carry more significant loads. For maximum flexibility to dynamic and unstructured environments and task demands, we propose a fully decentralized control infrastructure based on a swarm-specific scripting language, Buzz. In this paper, we describe the control infrastructure and use it to compare two algorithms for collaborative transport: field potentials and spring-damper. We test the performance of our approach with a fleet of micro-UAVs, demonstrating the potential of decentralized control for collaborative transport.
Siamese Neural Networks for User Identity Linkage Through Web Browsing

Linking online identities of users among countless heterogeneous network services on the Internet can provide an explicit digital representation of users, which can benefit both research and industry. In recent years, user identity linkage (UIL) through the Internet has become an emerging task with great potential and many challenges. Existing works mainly focus on online social networks that consider inconsistent profiles, content, and networks as features or use sparse location-based data sets to link the online behaviors of a real person. To extend the UIL problem to a general scenario, we try to link the web-browsing behaviors of users, which can help to distinguish specific users from others, such as children or malicious users. More specifically, we propose a Siamese neural network (NN) architecture-based UIL (SAUIL) model that learns and compares the highest-level feature representation of input web-browsing behaviors with deep NNs. Although the number of matching and nonmatching pairs for the UIL problem is highly imbalanced, previous studies have not considered imbalanced UIL data sets. Therefore, we further address the imbalanced learning issue by proposing cost-sensitive SAUIL (C-SAUIL) model, which assumes higher costs for misclassifying the minority class. In the experiments, the proposed model is robust and exhibits a good performance on very large, real-world data sets collected from different regions with distinct characteristics.
Compact Hash Code Learning With Binary Deep Neural Network

Learning compact binary codes for image retrieval problem using deep neural networks has recently attracted increasing attention. However, training deep hashing networks is challenging due to the binary constraints on the hash codes. In this paper, we propose deep network models and learning algorithms for learning binary hash codes given image representations under both unsupervised and supervised manners. The novelty of our network design is that we constrain one hidden layer to directly output the binary codes. This design has overcome a challenging problem in some previous works: optimizing non-smooth objective functions because of binarization. In addition, we propose to incorporate independence and balance properties in the direct and strict forms into the learning schemes. We also include a similarity preserving property in our objective functions. The resulting optimizations involving these binary, independence, and balance constraints are difficult to solve. To tackle this difficulty, we propose to learn the networks with alternating optimization and careful relaxation. Furthermore, by leveraging the powerful capacity of convolutional neural networks, we propose an end-to-end architecture that jointly learns to extract visual features and produce binary hash codes. Experimental results for the benchmark datasets show that the proposed methods compare favorably or outperform the state of the art.
Comparing Techniques for Visualizing Moving Out-of-View Objects in Head-mounted Virtual Reality

Current head-mounted displays (HMDs) have a limited field-of-view (FOV). A limited FOV further decreases the already restricted human visual range and amplifies the problem of objects receding from view (e.g., opponents in computer games). However, there is no previous work that investigates how to best perceive moving out-of-view objects on head-mounted displays. In this paper, we compare two visualization approaches: (1) Overview+detail, with 3D Radar, and (2) Focus+context, with EyeSee360, in a user study to evaluate their performances for visualizing moving out-of-view objects. We found that using 3D Radar resulted in a significantly lower movement estimation error and higher usability, measured by the system usability scale. 3D Radar was also preferred by 13 out of 15 participants for visualization of moving out-of-view objects.
Architecture and usability of OntoKeeper, an ontology evaluation tool

The existing community-wide bodies of biomedical ontologies are known to contain quality and content problems. Past research has revealed various errors related to their semantics and logical structure. Automated tools may help to ease the ontology construction, maintenance, assessment and quality assurance processes. However, there are relatively few tools that exist that can provide this support to knowledge engineers. We introduce OntoKeeper as a web-based tool that can automate quality scoring for ontology developers. We enlisted 5 experienced ontologists to test the tool and then administered the System Usability Scale to measure their assessment. In this paper, we present usability results from 5 ontologists revealing high system usability of OntoKeeper, and use-cases that demonstrate its capabilities in previous published biomedical ontology research. To the best of our knowledge, OntoKeeper is the first of a few ontology evaluation tools that can help provide ontology evaluation functionality for knowledge engineers with good usability.
Deep360Up: A Deep Learning-Based Approach for Automatic VR Image Upright Adjustment

Spherical VR cameras can capture high-quality immersive VR images with a 360° field of view. However, in practice, when the camera orientation is not straight, the acquired VR image appears tilted when displayed on a VR headset, which diminishes the quality of the VR experience. To overcome this problem, we present a deep learning-based approach that can automatically estimate the orientation of a VR image and return its upright version. In contrast to existing methods, our approach does not require the presence of lines or horizon in the image, and thus can be applied on a wide range of scenes. Extensive experiments and comparisons with state-of-the-art methods have successfully confirmed the validity of our approach.
Cascade embedding model for knowledge graph inference and retrieval

Knowledge graphs are widely used in retrieval systems, question answering systems (QA), hypothesis generation systems, etc. Representation learning provides a way to mine knowledge graphs to detect missing relations; and translation-based embedding models are a popular form of representation model. Shortcomings of translation-based models however, limits their practicability as knowledge completion algorithms. The proposed model helps to address some of these shortcomings. The similarity between graph structural features of two entities was found to be correlated to the relations of those entities. This correlation can help to solve the problem caused by unbalanced relations and reciprocal relations. We used Node2vec, a graph embedding algorithm, to represent information related to an entity's graph structure, and we introduce a cascade model to incorporate graph embedding with knowledge embedding into a unified framework. The cascade model first refines feature representation in the first two stages (Local Optimization Stage), and then uses backward propagation to optimize parameters of all the stages (Global Optimization Stage). This helps to enhance the knowledge representation of existing translation-based algorithms by taking into account both semantic features and graph features and fusing them to extract more useful information. Besides, different cascade structures are designed to find the optimal solution to the problem of knowledge inference and retrieval. The proposed model was verified using three mainstream knowledge graphs: WIN18, FB15K and BioChem. Experimental results were validated using the [email protected] rate entity prediction task. The proposed model performed better than TransE, giving an average improvement of 2.7% on WN18, 2.3% on FB15k and 28% on BioChem. Improvements were particularly marked where there were problems with unbalanced relations and reciprocal relations. Furthermore, the stepwise-cascade structure is proved to be more effective and significantly outperforms other baselines.
PC<sup>2</sup>A: Predicting Collective Contextual Anomalies via LSTM With Deep Generative Model

Proactive anomaly detection and diagnosis play an essential role in ensuring the security and stability of a large-scale information technology (IT) system with thousands or even millions of components that are interacting with each other. Collective contextual anomalies (CCAs) carry the characteristics of both collective and contextual anomalies. This type of anomalies is common in IT system monitoring, often manifested as security risks to be ameliorated, service outages to be eliminated, or stragglers to be mitigated. However, most existing studies emphasize primarily on the detection of point anomalies while the prediction or early detection of CCA has been an underexplored topic. In this paper, we propose a framework for discovering and studying CCAs in multiple time series based on a combination of semi-supervised deep learning, time series modeling, and graph analysis. A primary advantage of the proposed framework is that it can effectively predict CCAs with no human intervention. In addition, the performance of the proposed method can be further enhanced via learning from a small amount of anomalous training data, if it is available. Finally, the proposed framework is of low computational complexity and is thus particularly suitable for large-scale data streams. Simulation studies are carried out to reveal the superior performance of the proposed method and underscore the significant benefits of combining deep neural networks with time series analysis and graph models for the prediction and analysis of CCAs.
Side-channel Timing Attack of RSA on a GPU

To increase computation throughput, general purpose Graphics Processing Units (GPUs) have been leveraged to accelerate computationally intensive workloads. GPUs have been used as cryptographic engines, improving encryption/decryption throughput and leveraging the GPU’s Single Instruction Multiple Thread (SIMT) model. RSA is a widely used public-key cipher and has been ported onto GPUs for signing and decrypting large files. Although performance has been significantly improved, the security of RSA on GPUs is vulnerable to side-channel timing attacks and is an exposure overlooked in previous studies. GPUs tend to be naturally resilient to side-channel attacks, given that they execute a large number of concurrent threads, performing many RSA operations on different data in parallel. Given the degree of parallel execution on a GPU, there will be a significant amount of noise introduced into the timing channel given the thousands of concurrent threads executing concurrently. In this work, we build a timing model to capture the parallel characteristics of an RSA public-key cipher implemented on a GPU. We consider optimizations that include using Montgomery multiplication and sliding-window exponentiation to implement cryptographic operations. Our timing model considers the challenges of parallel execution, complications that do not occur in single-threaded computing platforms. Based on our timing model, we launch successful timing attacks on RSA running on a GPU, extracting the private key of RSA. We also present an effective error detection and correction mechanism. Our results demonstrate that GPU acceleration of RSA is vulnerable to side-channel timing attacks. We propose several countermeasures to defend against this class of attacks.
Simultaneous unwrapping and low pass filtering of continuous phase maps based on autoregressive phase model and wrapped Kalman filtering

We propose a simultaneous noise filtering and phase unwrapping algorithm. Spatial evolution of phase is modeled as an autoregressive Gaussian Markov random field. Accordingly, phase value at a pixel is related to phase values at surrounding pixels in a probabilistic manner. The problem of estimation of these probabilities is formulated as state space analysis using the wrapped Kalman filter. Simulation and experimental results demonstrate the practical applicability of the proposed phase unwrapping algorithm.
Information visualisation methods and techniques: State-of-the-art and future directions

Visualisation research explores techniques for conveying information in a meaningful way to allow users to make sense of data. This paper aims to review state-of-the-art information visualisation methods and techniques particularly for farmed data in defence simulations. It also discusses some of the key visualisation design challenges and outlines a conceptual framework for future research directions in information visualisation. It further presents a concept demonstrator that has been designed and developed to visualise joint force simulations for the purpose of showcasing some of the key concepts in the proposed information visualisation conceptual framework.
Adaptively Secure MPC with Sublinear Communication Complexity

A central challenge in the study of MPC is to balance between security guarantees, hardness assumptions, and resources required for the protocol. In this work, we study the cost of tolerating adaptive corruptions in MPC protocols under various corruption thresholds. In the strongest setting, we consider adaptive corruptions of an arbitrary number of parties (potentially all) and achieve the following results: On a technical level, our results are based on laconic function evaluation (LFE) (Quach, Wee, and Wichs, FOCS'18) and shed light on an interesting duality between LFE and FHE. Next, we analyze adaptive corruptions of all-but-one of the parties and show a two-round SFE protocol in the threshold PKI model (where keys of a threshold FHE scheme are pre-shared among the parties) with communication complexity sublinear in the circuit size, assuming LWE and NIZK. Finally, we consider the honest-majority setting, and show a two-round SFE protocol with guaranteed output delivery under the same constraints.
Fast Online Segmentation of Activities from Partial Trajectories

Augmenting a robot with the capacity to understand the activities of the people it collaborates with in order to then label and segment those activities allows the robot to generate an efficient and safe plan for performing its own actions. In this work, we introduce an online activity segmentation algorithm that can detect activity segments by processing a partial trajectory. We model the transitions through activities as a hidden Markov model, which runs online by implementing an efficient particle-filtering approach to infer the maximum a posteriori estimate of the activity sequence. This process is complemented by an online search process to refine activity segments using task model information about the partial order of activities. We evaluated our algorithm by comparing its performance to two state-of-the-art activity segmentation algorithms on three human activity datasets. The proposed algorithm improved activity segmentation accuracy across all three datasets compared with the other two approaches, with a range from 11.3% to 65.5%, and could accurately recognize an activity through observation alone for 31.6% of the initial trajectory of that activity, on average. We also implemented the algorithm onto an industrial mobile robot during an automotive assembly task in which the robot tracked a human worker's progress and provided the worker with the correct materials at the appropriate time.
IoT Ignorance is Digital Forensics Research Bliss

Interactions with IoT devices generates vast amounts of personal data that can be used as a source of evidence in digital investigations. Currently, there are many challenges in IoT forensics such as the difficulty in acquiring and analysing IoT data/devices and the lack IoT forensic tools. Besides technical challenges, there are many concepts in IoT forensics that have yet to be explored such as definitions, experience and capability in the analysis of IoT data/devices and current/future challenges. A deeper understanding of these various concepts will help progress the field. To achieve this goal, we conducted a survey which received 70 responses and provided the following results: (1) IoT forensics is a sub-domain of digital forensics, but it is undecided what domains are included; (2) practitioners are already having to examine IoT devices even though they felt undertrained; (3) requirements for technical training, software and education are non-existent; (4) high priority on research should be to develop IoT forensic tools, how to preserve volatile data and methods to identify and acquire data from the cloud; (5) improvements to forensic tools should be aimed at data acquisition (imaging) and device disassembly / forensic process; (6) practitioners' perspectives on research direction differ slightly to non-practitioners in that the focus should be on breaking encryption on IoT devices rather than focus on cloud data forensics; (7) future research should focus on developing initiatives and strategies to overcome data encryption and trail obfuscation in the cloud and ongoing development of IoT forensic tools. The responses to the survey question on the definition of IoT forensics helped us formulate a working definition. This has provided a clearer understanding of the subject, which will help further advance the research area.
A Correctable Public Blockchain

A key property of current blockchain solutions is the immutability of data stored in the blockchain. While this is a desired feature e.g. for crypto currencies, it can be a show stopper for other use cases that still require ex-post correction or removal of stored data. Existing proposals for redactable blockchains address this issue, but support the correction of specific types of stored data only. To overcome these limitations, we propose and introduce a correctable blockchain architecture that supports correction of arbitrary data. The proposed solution uses a consensus-enforced voting mechanism for decentralized decision making on requested data corrections. Information on applied corrections is stored in a second chain, which ensures that the corrected chain can still be validated successfully. We have verified the security of the proposed correctable blockchain architecture by means of a detailed security analysis. Furthermore, we have evaluated the proposed solution by means of a first reference implementation, which demonstrates the solution's feasibility. First experiments conducted with this reference implementation indicate that the added data-correction functionality has only negligible impacts on the overall performance.