Reviewerly/exHarmony huggingface dataset:
Productive Multitasking for Industrial Robots

The application of robotic solutions to small-batch production is challenging: economical constraints tend to dramatically limit the time for setting up new batches. Organizing robot tasks into modular software components, called skills, and allowing the assignment of multiple concurrent tasks to a single robot is potentially game-changing. However, due to cycle time constraints, it may be necessary for a skill to take over without waiting on another to terminate, and the available literature lacks a systematic approach in this case. In the present article, we fill the gap by (a) establishing the specifications of skills that can be sequenced with partial executions, (b) proposing an implementation based on the combination of finite-state machines and behavior trees, and (c) demonstrating the benefits of such skills through extensive trials in the environment of ARIAC (Agile Robotics for Industrial Automation Competition).
Forensic Tools for IoT Device Investigations in regards to Human Trafficking

As the Internet of Things (IoT) environment rapidly grows, so do the distribution and utilisation of IoT devices as well as Cyber-Enabled and Dependent Crime activities. Investigators face challenges in regards to available tools which are able to efficiently access and analyse the content of IoT devices. Due to the fast development of technology these tools are limited especially in terms of operating systems (OS) compatibility and cross-border investigations.In this paper, we review a variety of most popular used digital forensic tools, specifically in terms of their limitations in investigating IoT devices. As an alternative we introduce a new tool, the Hybrid Forensic IoT Server (HFIoTS), to improve IoT Forensics efficiently and we prove that it is adequate to investigate a variety of cases such as human trafficking. HFIoTS is aimed towards a dynamic and complex environment, such as the IoT, to minimise the current investigative challenges of its forensics. To elaborate on the importance of improving the investigation of the IoT forensically, we establish the context of IoT forensics investigations and its current challenges as well as the most popular tools. Furthermore, we demonstrate our HFIoTS model framework as well as its interface for investigation purposes. Additionally, we present HFIoTS within a hypothetical case study of human trafficking to demonstrate its efficiency and necessity within the IoT forensic environment and discuss its benefits in comparison to the reviewed forensic tools overall.
Explainable Deep Learning for Readmission Prediction with Tree-GloVe Embedding

Preventable hospital readmissions have been identified as one of the primary targets for improving the efficiency of the current healthcare system. Over the past decade, several data-driven solutions for predicting readmissions have been presented. While maintaining high predictive accuracy is the obvious main goal for such solutions, ensuring explainability of the model and its predictions, is equally important for adoption in the healthcare domain. Unfortunately, most solutions have struggled to strike an optimal balance between accuracy and explainability. Linear models only provide moderately accurate results while complex machine learning models are non-explainable black boxes, which precludes them from being used effectively within the decision support systems in the hospitals. We propose a solution that integrates domain knowledge, in the form of a hierarchical taxonomy defined for disease codes, into the learning framework to advance state-of-the-art in readmission prediction. We first propose a novel tree-structured embedding method to map disease codes into an explainable domain-guided representation. Next, we propose an attention-driven recurrent deep learning architecture. Results on two healthcare claims data sets show that the proposed model outperforms state-of-the-art methods proposed for this task, both in terms of accuracy and explainability.
Learning Human-like Hand Reaching for Human-Robot Handshaking

One of the first and foremost non-verbal interactions that humans perform is a handshake. It has an impact on first impressions as touch can convey complex emotions. This makes handshaking an important skill for the repertoire of a social robot. In this paper, we present a novel framework for learning reaching behaviours for humanrobot handshaking behaviours for humanoid robots solely using third-person human-human interaction data. This is especially useful for non-backdrivable robots that cannot be taught by demonstrations via kinesthetic teaching. Our approach can be easily executed on different humanoid robots. This removes the need for re-training, which is especially tedious when training with human-interaction partners. We show this by applying the learnt behaviours on two different humanoid robots with similar degrees of freedom but different shapes and control limits.
Enhancing Scalability of Image Retrieval Using Visual Fusion of Feature Descriptors

Content-Based Image Retrieval (CBIR) is an approach of retrieving similar images from a large image database. Recently CBIR poses new challenges in semantic categorization of the images. Different feature extraction technique have been proposed to overcome the semantic breach problems, however these methods suffer from several shortcomings. This paper contributes an image retrieval system to extract the local features based on the fusion of scale-invariant feature transform (SIFT) and KAZE. The strength of local feature descriptor SIFT complements global feature descriptor KAZE. SIFT concentrates on the complete region of an image using high fine points of features and KAZE ponders on details of a boundary. The fusion of local feature descriptor and global feature descriptor boost the retrieval of images having diverse semantic classification and also helps in achieving the better results in large scale retrieval. To enhance the scalability of image retrieval bag of visual words (BoVW) is mainly used. The fusion of local and global feature representations are selected for image retrieval for the reason that SIFT effectively captures shape and texture and robust towards the change in scale and rotation, while KAZE have strong response towards boundary and changes in illumination. Experiments conducted on two image collections, namely, Caltech-256 and Corel 10k demonstrate the proposed scheme appreciably enhanced the performance of the CBIR compared to state-of-the-art image retrieval techniques.
Integrated Modeling and Development of Component-Based Embedded Software in Scala

Developing embedded software requires good frameworks, models, and programming languages. The languages typically used for embedded programming (e.g., C and C++) tend to be decoupled from the models and tend to favor efficiency and low-level expressivity over safety, high-level expressivity, and ease of use. In this work, we explore the use of Scala for integrated modeling and development of embedded systems represented as sets of interconnected components. Although Scala today is not suitable for this domain, several current efforts aim to develop Scala-like embedded languages, so it is conceivable that in the future, such a language will exist. We present four internal Scala DSLs, each of which supports an aspect of embedded software development, inspired by an actual C++ framework for programming space missions. The DSLs support programming of software components, hierarchical state machines, temporal logic monitors, and rule-based test generators. The effort required to develop these DSLs has been small compared to the similar C++ effort .
Analytic Beta-Wavelet Transform-Based Digital Image Watermarking for Secure Transmission

The rapid development in the information technology field has introduced digital watermark technologies as a solution to prevent unauthorized copying and redistribution of data. This article introduces a self-embedded image verification and integrity scheme. The images are firstly split into dedicated segments of the same block sizes. Then, different Analytic Beta-Wavelet (ABW) orthogonal filters are utilized for embedding a self-segment watermark for image segment using a predefined method. ABW orthogonal filter coefficients are estimated to improve image reconstruction under different block sizes. We conduct a comparative study comparing the watermarked images using three kinds of ABW filters for block sizes 64 × 64, 128 × 128, and 256 × 256. We embed the watermark using the ABW-based image watermarking method in the 2-level middle frequency sub-bands of the ABW digital image coefficients. The imperceptibility and robustness of the ABW-based image watermarking method image is evaluated based on the Peak Signal to Noise Ratio (PSNR) and Correlation coefficient values. From the implementation results, we came to know that this ABW-based image watermarking method can withstand many image manipulations compared to other existing methods.
On the Security of ECDSA with Additive Key Derivation and Presignatures

Two common variations of ECDSA signatures are additive key derivation and presignatures. Additive key derivation is a simple mechanism for deriving many subkeys from a single master key, and is already widely used in cryptocurrency applications with the Hierarchical Deterministic Wallet mechanism standardized in Bitcoin Improvement Proposal 32 (BIP32). Because of its linear nature, additive key derivation is also amenable to efficient implementation in the threshold setting. With presignatures, the secret and public nonces used in the ECDSA signing algorithm are precomputed. In the threshold setting, using presignatures along with other precomputed data allows for an extremely efficient "online phase" of the protocol. Recent works have advocated for both of these variations, sometimes combined together. However, somewhat surprisingly, we are aware of no prior security proof for additive key derivation, let alone for additive key derivation in combination with presignatures. In this paper, we provide a thorough analysis of these variations, both in isolation and in combination. Our analysis is in the generic group model (GGM). Importantly, we do not modify ECDSA or weaken the standard notion of security in any way. Of independent interest, we also present a version of the GGM that is specific to elliptic curves. This EC-GGM better models some of the idiosyncrasies (such as the conversion function and malleability) of ECDSA. In addition to this analysis, we report security weaknesses in these variations that apparently have not been previously reported. For example, we show that when both variations are combined, there is a cube-root attack on ECDSA, which is much faster than the best known, square-root attack on plain ECDSA. We also present two mitigations against these weaknesses: re-randomized presignatures and homogeneous key derivation. Each of these mitigations is very lightweight, and when used in combination, the security is essentially the same as that of plain ECDSA (in the EC-GGM).
Multi-agent Receding Horizon Search with Terminal Cost

We present a multi-agent approach to receding horizon path planning that utilizes terminal costs. We show that the value of the receding horizon paths produced using the proposed methods have a guaranteed lower bound that can be determined using any readily-available, naive solution. We present a modified sequentially allocated optimal path planner with terminal costs that is guaranteed to satisfy the assumptions required to provide a guaranteed lower bound. We utilize a slightly modified version of the Decentralized Monte Carlo Tree Search algorithm to solve for near-optimal paths within a short planning horizon with an appended terminal cost to demonstrate the flexibility of the proposed method. We compare these receding horizon methods that incorporate a terminal cost to related receding horizon methods that do not incorporate a terminal cost. Our approach is developed specifically for multiple agents engaged in search, but can be easily adapted for other information gathering applications.
Conversion of Gaussian states under incoherent Gaussian operations

The coherence resource theory needs to study the operational value and efficiency which can be broadly formulated as the question: when can one coherent state be converted into another under incoherent operations? We answer this question completely for one-mode continuous-variable systems by characterizing conversion of coherent Gaussian states under incoherent Gaussian operations in terms of their first and second moments. The no-go theorem of purification of coherent Gaussian states is also built. The structure of incoherent Gaussian operations of two-mode continuous-variable systems is discussed further and is applied to coherent conversion for pure Gaussian states with standard second moments. The standard second moments are images of all second moments under local linear unitary Bogoliubov operations. As concrete applications, we obtain some peculiarities of a Gaussian system: (1) there does not exist a maximally coherent Gaussian state which can generate all coherent Gaussian states; (2) the conversion between pure Gaussian states is reversible; (3) the coherence of input pure state and the coherence of output pure state are equal.
Strategies for Practical Hybrid Attack Graph Generation and Analysis

As an analytical tool in cyber-security, an attack graph (AG) is capable of discovering multi-stage attack vectors on target computer networks. Cyber-physical systems (CPSs) comprise a special type of network that not only contains computing devices but also integrates components that operate in the continuous domain, such as sensors and actuators. Using AGs on CPSs requires that the system models and exploit patterns capture both token- and real-valued information. In this article, we describe a hybrid AG model for security analysis of CPSs and computer networks. Specifically, we focus on two issues related to applying the model in practice: efficient hybrid AG generation and techniques for information extraction from them. To address the first issue, we present an accelerated hybrid AG generator that employs parallel programming and high performance computing (HPC). We conduct performance tests on CPU and GPU platforms to characterize the efficiency of our parallel algorithms. To address the second issue, we introduce an analytical regimen based on centrality analysis and apply it to a hybrid AG generated for a target CPS system to discover effective vulnerability remediation solutions.
A novel algorithm to track closely spaced road vehicles using a low density flash lidar

This paper develops a novel algorithm for tracking closely-spaced road vehicles using a low-density flash lidar. Low-density flash lidars are recent to the automotive market and have attracted attention due to their low cost. However, these sensors have a poor angular resolution which makes tracking the lateral motion of targets challenging. One such challenge, namely unresolved measurements from multiple vehicles, is addressed in this paper. Detections from multiple targets can become unresolved if the targets are closely spaced. Traditional tracking algorithms, which do not account for unresolved measurements, lead to degraded tracking performance in such scenarios. This paper proposes a novel method based on predicting the lateral motion states of targets forward in time, and truncating their probability density functions at points based on the unresolved measurements. The method proposed works with a computationally simple data association algorithm, requires no sensor modeling, and can track more than two closely spaced targets. The proposed algorithm is evaluated using simulations and on-road experiments, and the results demonstrate its capability in maintaining target tracks even in the presence of unresolved detections. Further, a comparison with the well known Joint Probabilistic Data Association-Merged algorithm showed that the proposed algorithm is computationally lighter.
Advances in Memetic Automaton: Toward Human-Like Autonomous Agents in Complex Multi-Agent Learning Problems

The meme-centric memetic automaton (MA) was recently proposed as an adaptive entity or a software agent wherein memes are defined as the building blocks of knowledge. The conceptualization of MA has led to the development of a large number of potentially rich meme-inspired designs that form a cornerstone of memetic computation as tools for problem-solving. In this study, we investigate the use of memetic multi-agent systems to develop more intelligent and human-like autonomous agents by taking MA as the essential backbone of the agent. Taking inspiration from a psychological Broadbent-Treisman Attenuation Model, we propose an attention intensity control method in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">meme expression</i> for enhancing agents’ perception of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">value</i> of all kinds of information captured from the environment, hence leading to a greater capability of meme knowledge generalization. Our particular focus is placed on the design of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">meme selection</i> for more effective knowledge transmission across the population. To this end, we introduce a bidirectional imitation strategy based on agents’ estimation of the importance and/or uncertainty of decision making in a dynamic environment. Experiments on a minefield navigation simulation as well as a commercial video game demonstrate the superior performance of our proposed method compared to state-of-the-art methods.
Assuage: Assembly Synthesis Using A Guided Exploration

Assembly programming is challenging, even for experts. Program synthesis, as an alternative to manual implementation, has the potential to enable both expert and non-expert users to generate programs in an automated fashion. However, current tools and techniques are unable to synthesize assembly programs larger than a few instructions. We present Assuage : ASsembly Synthesis Using A Guided Exploration, which is a parallel interactive assembly synthesizer that engages the user as an active collaborator, enabling synthesis to scale beyond current limits. Using Assuage, users can provide two types of semantically meaningful hints that expedite synthesis and allow for exploration of multiple possibilities simultaneously. Assuage exposes information about the underlying synthesis process using multiple representations to help users guide synthesis. We conducted a within-subjects study with twenty-one participants working on assembly programming tasks. With Assuage, participants with a wide range of expertise were able to achieve significantly higher success rates, perceived less subjective workload, and preferred the usefulness and usability of Assuage over a state of the art synthesis tool.
Development of Predictive Models for “Very Poor” Beach Water Quality Gradings Using Class-Imbalance Learning

Statistical water quality forecast models are useful tools to assist with beach management. In particular, multiple linear regression (MLR) models have been successfully developed for prediction of fecal indicator bacteria concentrations for beaches in river, lake, and marine environments. Nevertheless, an unresolved challenging issue is the reliable prediction of infrequent events of high bacterial concentrations to inform beach closure decisions to protect public health. The number of field data available for the infrequent events is typically an order of magnitude less than that for days when the water quality criterion is met-MLR models often perform poorly in predicting bacterial concentrations on days when the beaches should be closed. For beach management in Hong Kong, MLR models have been developed to predict beach water quality indices in terms of four gradings (BWQI-1 to 4) based on Escherichia coli (E. coli) concentrations. In this study, we propose an artificial intelligence (AI)-based binary classification (EasyEnsemble) model using class-imbalance learning to predict "very poor" occasions (BWQI-4)-when E. coli concentration exceeds 610 counts/100 mL. Models are developed for three marine beaches with different hydrographic and pollution characteristics using a 30 year data set spanning three periods with different water quality status. The model-data comparison over a wide range of conditions shows that the proposed method results in a significant improvement in the prediction of "very poor" water quality. The proposed class-imbalance method for predicting rare events has an F-score of 0.84, and it significantly outperforms MLR and classification tree (CT) models with corresponding F-scores of 0.39 and 0.69. A robust beach water quality forecast system can hence be developed using hybrid MLR-binary classification modeling.
A Power-Efficient Parameter Quantization Technique for CNN Accelerators

Quantization techniques are widely used in CNN inference to reduce the cost of hardware at the expense of small accuracy losses. However, after the quantization, there is still a multiplication cost for the fixed-point quantized CNN weights. Therefore, a novel CNN quantization technique is introduced, which can be implemented without using any multiplier. We evaluated our quantization technique using VGG-16 and Alexnet networks, and the Tiny ImageNet dataset. The quantization technique causes 0.39% and 0.98% accuracy losses for the 8-bit CNN weights compared to floating-point implementations of VGG-16 and Alexnet, respectively. After, a fine-tuning method for our quantization is introduced, which further reduces the accuracy loss. The fine-tuning reduced the accuracy losses on 8-bit quantized VGG-16 and Alexnet to 0.24% and 0.39%, respectively. Two different processing element architectures, which do not include any multiplier hardware, are designed to perform multiply-accumulate (MAC) operations of CNN models quantized by our technique. Two different systolic array prototypes are designed employing the two PE architectures to compare with the traditional fixed-point MAC implementation. The systolic array architectures containing our processing element designs reduced the power consumption of the systolic array up to 14.2% and 21.6%.
Learning ecology of music teachers’ TPACK

The article focuses on the Technological Pedagogical Content Knowledge (TPCK or TPACK) framework among in-service music teachers in primary schools. Narrative research has allowed us to identify the environment and important others that lead to the release and maintenance of sparks of interest in using technology within various educational models and supporting influential solutions. Longitudinal studies and identification of informal environments within learning ecologies have allowed us to formulate the optimal paths to achieving TPACK competence. The study of a specific group of music teachers encourages comparative research between various teacher groups; this means comparing TPACK-competent arts teachers with teachers of other subjects.
RectorDApp: Decentralized Application for Managing University Rector Elections

Blockchain is a distributed and secure database that can be applied to all types of transactions. Blockchain technology is growing in popularity because it allows for the development of applications whose information is traceable, immutable, transparent and reliable. Given the advantages that blockchain provides over other traditional systems, in this paper we present a decentralized application, called RectorDApp, for the management of university rector voting in a private, but transparent and immutable way, being able to verify publicly and in real time the election results. RectorDApp, capable of interacting with the Ethereum public blockchain network, was developed using the Truffle framework and the MetaMask software. The results demonstrate that RectorDApp is a highly useful application to address the digital transformation of university rector elections.