Reviewerly/exHarmony huggingface dataset:
Governance of Smart Contracts in Blockchain Institutions

We consider some of the techno-economic characteristics of smart contracts relating to real-world transactions, and the effectiveness of the governance regimes in which they will operate. We find that the requirement to code the precise executable steps ex ante in smart contracts necessitates a degree of precision and foresight about potential future eventualities that is unprecedented in all but the simplest of real-world interactions. The rules-based approach necessitated by pre-programming all transactions anticipates a state where complete contracting is feasible, so is not well-suited to transactions in real-world environments where unexpected outcomes can arise. This stands in direct contrast to the well-developed principles-based governance frameworks offered under contract law and the courts, which have developed ways to specifically address issues of contractual incompleteness. Thus despite objectives for blockchain-governed institutional (or even constitutional) frameworks to operate as fully self-contained environments within which all commercial interactions can be mediated via smart contracts, the inherent incompleteness of contracts due to limits to information availability, human cognition and communication renders such objectives illusory. A role will continue to exist for the real-world contract governance institutions as a complement to blockchain governance arrangements. Smart contracts may be suitable for routine, low-value highly-standardised and easily-coded spot transactions with predetermined outcomes and executed within a short time horizon of agreement taking place. However, the more complex and unique is the transaction, the higher is the value at risk, the harder it is to anticipate and precisely specify contingencies and measure and observe outcomes, and the longer is the time frame between agreement and execution, the less likely it is that smart contracting will be more efficient than real-world contracting. Indeed, in many cases a smart contract may prove more costly to administer.
Fast Image-matching Technique Robust to Rotation in Spherical Images

There are many studies on 3D reconstruction using image-matching algorithms for 360-degree spherical images. However, it is difficult to apply conventional planar-based matching algorithms because a spherical image is represented in 3D space with a geodesic grid. In this study, a fast image-matching algorithm especially robust to rotation in spherical images is proposed. With interleaving and hashing techniques, memory waste is reduced and memory access efficiency is improved in mapping a 2D flatten icosahedron corresponding to a 3D icosphere. In addition, a modified Features from Accelerated Segment Test–based matching technique is proposed to improve robust matching performance. The proposed matching technique uses a descriptor with directional information order to endure rotation of images.
New taxonomy of easy-to-understand access services

The Convention on the Rights of People with Disabilities states that access to information through written and electronic communications is essential for personal development and full participation in society. In digital scenarios, media services, such as audio descriptions, subtitles, or sign language, facilitate access to content with a focus on sensory barriers. Still, there are shortcomings in addressing other needs, such as cognitive ones. This article aims to suggest a taxonomy of the emerging easy-to-understand access services that cater for the needs of audiences who struggle with understanding audiovisual content for varied reasons, such as low literacy, reading or learning difficulties, temporary impairments, or insufficient language skills. The taxonomy uses Gottlieb’s (2005) semiotically-based classification to define E2U access services within the landscape of Audiovisual translation and to classify them according to their semiotic identity as compared to the standard access services.
A Semantic Similarity-based Subgraph Matching Method for Improving Question Answering over RDF

RDF question/answering (Q/A) system can explore RDF data by translating natural language questions into SPARQL queries. In this poster, we design a generation-and-ranking approach to translate natural language questions into SPARQL queries based on semantic similarity between questions and SPARQL queries. In the generation stage, we employ a directed super semantic query graph to extract the structural query intention of the question, based on which Q/A on RDF is reduced to the graph matching problem. After building the query graph, we generate a set of candidate queries of the question. In the ranking stage, we rank the query in the candidate query set according to the semantic similarity between the query and question. Finally, we pick the query with the highest semantic similarity to the original question.
An Empirical Study on Neuroevolutional Algorithm Based on Machine Learning for Crop Yield Prediction

Machine learning has been come out with high performance computation power leads to create a great prospect in multi-disciplinary domain. Here, we present a novel machine learning method for predicting crop yield. The classification technique using machine learning algorithm demonstrated the performance improvement in prediction of crop yield. It depends on the factors of weather which have relationship with climate change data, soil of that area, and water irrigations. Here, we have illustrated an approach of implementing neuroevolution model based on ANN for predicting wheat crop yield. Crop yield prediction at different months is considered from June to September; the yields predictions are computed based on weather and fertilizer utilized data. A major improvement in the prediction ability is observed that yield diverge as for the season changes based on weather data. Therefore, the result of the proposed model assists in decision making in advance for planting wheat crop. The outcomes are more functional for decision making as well as in transplantation of wheat in advance with various farm activities throughout various stages of the wheat crop growing. Also, the same model can also be utilized for predicting various agricultural data such as disease prediction and weather prediction.
Research on Collision Avoidance Control of Multi-Arm Medical Robot Based on C-Space

A multi-arm robot for mandible reconstruction assisted surgery is developed. It has three manipulators and eighteen degrees of freedom. In order to prevent the collision of the manipulator caused by the overlap of the motion space of the manipulator during the space positioning of the whole mandible reconstruction operation, a collision avoidance path planning method for manipulator based on C-space is proposed. In order to obtain the space non-interference posture of one manipulator, and the interference posture of the other manipulators is taken as the obstacle mapping in C-space to obtain the free space after removing obstacles. At the same time, combining with the space position of the system manipulator, the C-space mapping mathematical model of feature points and line segments is studied, and the mapping mathematical model of any point and line segment in the plane is obtained. By introducing admissibility proof lemma to weigh the evaluation function, the improved heuristic A* search algorithm is used to search the best path for collision avoidance. The collision avoidance experiment is carried out through simulation and robot platform, and finally the animal experiment is carried out. These experimental results verify the feasibility and validity of the collision avoidance path planning method for the space manipulator.
A FPGA implementation of a self-adaptive genetic algorithm

A genetic algorithm (GA) is an iterative procedure which performs several processes with the population individuals (chromosomes) to produce a new population, like in the biological evolution. To avoid the premature convergence, the paper proposes a self-adaptive algorithm, which adjusts parameters at the chromosome level and also at the population level, to solve a gender-based GA. Because the FPGA implementation of a self-adaptive GA requires more complicated logic units as for a conventional GA implementation, we propose to optimize this implementation by using a soft or hard processor embedded in the FPGA chip. Thus a part of the tasks will be solved by hardware blocks and a part of the tasks will be solved by the processor.
Why You Need Soft and Non-Technical Skills for Successful Data Librarianship

There are many courses available to teach research data management to librarians and researchers. While these courses can help with technical skills, like programming or statistics, and practical knowledge of data life cycles or data sharing policies, there are “soft skills” and non-technical skills that are needed to successfully start and run data services. While there are many important characteristics of a good data librarian, reference skills, relationship building, collaboration, listening, and facilitation are some of the most important. Giving consideration to these skills will help any data librarian with their multifaceted job.
Special Issue on “Optics for AI and AI for Optics”

We live in an era of information explosion and digital revolution that has resulted in rapid technological developments in different aspects of life [...]
Sensor-driven Learning of Time-Dependent Parameters for Prescriptive Analytics

Big data analytics is rapidly emerging as a key Internet of Things (IoT) initiative aiming at providing meaningful insights and supporting optimal decision making under time constraints. In this direction, prescriptive analytics has just started to emerge. Prescriptive analytics moves beyond descriptive and predictive analytics aiming at providing adaptive, automated, constrained, time-dependent and optimal decisions. The use of time-dependent parameters in prescriptive analytics models provide a more reliable and realistic representation of the complex and dynamic environment and the associated decision making process; however, their estimation poses significant challenges due to the uncertainty derived from inaccurate user input, noisy data, and non-stationarity of real-world data streams. Since feedback and learning mechanisms for tracking the prescriptive analytics are crucial enablers for self-configuration and self-optimization, this paper proposes an approach for sensor-driven learning of time-dependent parameters for prescriptive analytics models deployed in streaming computational environments. The proposed approach was validated in an Industry 4.0 use case, while it was further evaluated through extensive simulation experiments. The proposed approach overcomes challenges related to uncertainty derived from user's input, non-stationary data and sensor noise and provides estimates of time-dependent parameters that lead to more reliable prescriptions.
A Method for Learning Netytar: An Accessible Digital Musical Instrument

Accessible Digital Musical Instruments (ADMI) are increasingly raising interest within the scientific community, especially in the contexts of Sound and Music Computing and Human-Computer Interaction.In the past, Netytar has been proposed among these.Netytar is a software ADMI operated through the eyes using an eye tracker and an additional switch or sensor (e.g., a breath sensor).The instrument is dedicated to quadriplegic users: it belongs to the niche of gaze operated musical instruments, and has been proven effective and functional through testing.Although there are several other gaze operated ADMIs available in market and literature, a formal method for studying music with them has not yet been proposed.The present work introduces a simple study method based on a set of exercises.This can be useful for approaching musical performance with Netytar, but it's also potentially generalizable for learning other similar instruments.The exercises are illustrated, discussed and explained in view of an improvement.A simple musical notation is introduced.At the end of a learning cycle, a user is expected to be able to perform simple melodies, and have a basis with which to learn other new ones.In the future, the method will be tested with the target users.
An efficient novel color image encryption algorithm based on 3D Lü chaotic dynamical system and SHA-512

In this paper, an efficient novel dual permutation–substitution structure-based color image encryption algorithm is proposed. Initially, the Secure Hash Algorithm-512 (SHA-512) is applied to the input image to generate the initial values for the Lü system dynamically. In the first stage of permutation, inter-color-component pixel shuffling is carried out with a circular pixel-swapping mechanism. In the second stage, intra-color-component pixel shuffling is executed, based on pseudorandom positions generated by the Lü chaotic system. Pixel values are changed in the substitution stage, based on float-valued chaotic sequences generated by the Lü system. The performance of the proposed algorithm is evaluated with metrics such as key space, key sensitivity, histograms, correlation coefficients (vertical, horizontal and diagonal), information entropy, number of pixel changes rate (NPCR), unified average changing intensity (UACI), peak signal-to-noise ratio (PSNR), mean absolute error (MAE), contrast analysis, encryption time and the National Institute of Standards and Technology Special Publication 800-22 (NIST SP 800-22) statistical test. The experimental results obtained and performance assessment show that the proposed scheme has produced good results.
Performance Modeling in Predictable Cloud Computing

This paper deals with the problem of performance stability of software running in shared virtualized infrastructures.The focus is on the ability to build an abstract performance model of containerized application components, where real-time scheduling at the CPU level, along with traffic shaping at the networking level, are used to limit the temporal interferences among colocated workloads, so as to obtain a predictable distributed computing platform.A model for a simple client-server application running in containers is used as a case-study, where an extensive experimental validation of the model is conducted over a testbed running a modified OpenStack on top of a custom real-time CPU scheduler in the Linux kernel.
Robust entanglement distribution via telecom fibre assisted by an asynchronous counter-propagating laser light

Abstract Distributing entangled photon pairs over noisy channels is an important task for various quantum information protocols. Encoding an entangled state in a decoherence-free subspace (DFS) formed by multiple photons is a promising way to circumvent the phase fluctuations and polarisation rotations in optical fibres. Recently, it has been shown that the use of a counter-propagating coherent light as an ancillary photon enables us to faithfully distribute entangled photon with success probability proportional to the transmittance of the optical fibres. Several proof-of-principle experiments have been demonstrated, in which entangled photon pairs from a sender side and the ancillary photon from a receiver side originate from the same laser source. In addition, bulk optics have been used to mimic the noises in optical fibres. Here, we demonstrate a DFS-based entanglement distribution over 1 km optical fibre using DFS formed by using fully independent light sources at the telecom band, and obtain a high-fidelity entangled state. This shows that the DFS-based scheme protects the entanglement against collective noise in 1 km optical fibre. In the experiment, we utilise an interference between asynchronous photons from continuous wave pumped spontaneous parametric down conversion (SPDC) and mode-locked coherent light pulse. After performing spectral and temporal filtering, the SPDC photons and light pulse are spectrally indistinguishable. This property allows us to observe high-visibility interference without performing active synchronisation between fully independent sources.
Experimental Verification of Three-Degree-of-Freedom Electromagnetic Actuator for Image Stabilization

Image deteriorations due to vibrations have become a problem in an autonomous system. Conventional image stabilization systems have some problems such as the increase in size. In order to solve the problems, we proposed a novel three-degree-of-freedom electromagnetic actuator for an image stabilization. This paper describes experimental results of the proposed actuator. The frequency characteristics are investigated, and the effectiveness of the actuator is discussed.
Automatic Update of Feature Model After Direct Modeling Operation

Intuitive interactive methods for novice users to create 3D content and 3D geometry have eluded the CAGD community for several decades, and they continue to be critical yet unresolved challenges.The need for such methods is even more acute considering the fact that 3D printing is becoming a commodity.In contrast, modern geometric modeling tools are very complex and continue to be used primarily by professionals such as architects and engineers.Part of the difficulty of novice users to create 3D content might be found in the fact that input/output devices are mostly 2D.While novice users, in real life, do interact daily in 3D environments, typically, they do not create 3D content.In this work, we combine a Leap Motion device [42] / mouse+keyboard with the intuitive notion of sand art, or the creation of 3D artifacts on the seashore, using droplets of wet sand.The result is a very simple, yet highly interactive 3D modeling interface that is also demonstrated on several applications.
Subtitles to Segmentation: Improving Low-Resource Speech-to-TextTranslation Pipelines

In this work, we focus on improving ASR output segmentation in the context of low-resource language speech-to-text translation. ASR output segmentation is crucial, as ASR systems segment the input audio using purely acoustic information and are not guaranteed to output sentence-like segments. Since most MT systems expect sentences as input, feeding in longer unsegmented passages can lead to sub-optimal performance. We explore the feasibility of using datasets of subtitles from TV shows and movies to train better ASR segmentation models. We further incorporate part-of-speech (POS) tag and dependency label information (derived from the unsegmented ASR outputs) into our segmentation model. We show that this noisy syntactic information can improve model accuracy. We evaluate our models intrinsically on segmentation quality and extrinsically on downstream MT performance, as well as downstream tasks including cross-lingual information retrieval (CLIR) tasks and human relevance assessments. Our model shows improved performance on downstream tasks for Lithuanian and Bulgarian.
UAV-CPSs as a test bed for new technologies and a primer to Industry 5.0

The extensive Cloud pool of resources and infrastructure can deliver significant improvements to Unmanned Aerial Vehicle (UAV) Cyber-Physical Systems (UAV-CPSs) relying on data or code from a network to operate, but not all sensors, actuators, computation modules and memory depots from a single fixed structure. This chapter is organised around the potential benefits of the Cloud: (1) Big Data (BD) access to visual libraries containing representations/descriptive data, images, video, maps and flight paths, (2) Cloud Computing (CC) functionalities for Grid Computing (GC) on demand for statistical analysis, Machine Learning (ML) algorithms, Computational Intelligence (CI) applications and flight planning, (3) Collective UAV Learning (CUL) where UAVs share their trajectories, control guidelines and mission outcomes and (4) human-machine collaboration through crowdsourcing for analysing high-dimensional high-resolution (HDHR) images/ video, classification of scenes/objects/entities, learning and error correction/ concealment. The Cloud can also expand UAV-CPSs by offering (a) data sets, models, all sorts of literature, HDHR benchmarks and software/hardware simulators, (b) open competitions for UAV-CPS designs with Open Source Hardware (OSH) and (c) Open-Source Software (OSS). This chapter talks about some open challenges and new trends in UAV-CPSs.
An Extensive Survey of Digital Image Steganography: State of the Art

The need to protect sensitive information privacy during information exchange over the internet/intranet has led to wider adoption of cryptography and steganography. The cryptography approaches convert the information into an unreadable format however draws the attention of cryptanalyst owing to the uncommon random nature flow of the bytes when viewing the flowing structured bytes on a computer. While steganography, in contrast, conceals the very existence of covert communication using digital media. Although any digital media (text, image, video, audio) can covey the sensitive information, the media with higher redundant bits are more favorable for embedding the sensitive information without distorting the media. Digital images are majorly used in conveying sensitive information compared to others owing to their higher rate of tolerating distortions, highly available, smaller sizes with high redundant bits. However, the need for maximizing the redundancy bits for the optimum embedding of secret information has been a paramount issue due to the imperceptibility prerequisite which deteriorates with an increase in payload thus, resulting in a tradeoff. This has limited steganography to only applications with lower payload requirements, thus limiting the adoption for wider deployment. This paper critically analyzes the current steganographic techniques, recent trends, and challenges.
Reviewing Computing Education Papers

Peer review is a mainstay of academic publication - indeed, it is the peer-review process that provides much of the publications' credibility. This working group is examining the ways peer review is used in various computing education venues and will use this examination to articulate community standards for peer review in this discipline.
A study on liver tumor detection from an ultrasound image using deep learning

The ultrasound examination is a difficult operation because a doctor not only operates an ultrasound scanner but also interprets images in rea time, which may increase the risk of overlooking tumors. To prevent that, we study a liver tumor detection method using convolutional neural networks toward realizing computer-assisted diagnosis systems. In this paper, we propose a liver tumor detection method within a false positive reduction framework. The proposed method uses YOLOv3 [1] in order to find tumor candidate regions in real-time, and also uses VGG16 [2] to reduce false positives. The proposed method using YOLOv3 [1] and VGG16 [2] achieved an F-measure of 0.837, which showed the effectiveness of the proposed method for liver tumor detection. Future work includes the collection of training data from more hospitals and their effective use for improving the detection accuracy.
Stochastic Differential Equations with variational Wishart diffusions

We present a Bayesian non-parametric way of inferring stochastic differential equations for both regression tasks and continuous-time dynamical modelling. The work has high emphasis on the stochastic part of the differential equation, also known as the diffusion, and modelling it by means of Wishart processes. Further, we present a semi-parametric approach that allows the framework to scale to high dimensions. This successfully lead us onto how to model both latent and auto-regressive temporal systems with conditional heteroskedastic noise. We provide experimental evidence that modelling diffusion often improves performance and that this randomness in the differential equation can be essential to avoid overfitting.