Reviewerly/exHarmony huggingface dataset:
PregGAN: A prognosis prediction model for breast cancer based on conditional generative adversarial networks

Background and Objective: Generative adversarial network (GAN) is able to learn from a set of training data and generate new data with the same characteristics as the training data. Based on the characteristics of GAN, this paper developed its capability as a tool of disease prognosis prediction, and proposed a prognostic model PregGAN based on conditional generative adversarial network (CGAN). Methods: The idea of PregGAN is to generate the prognosis prediction results based on the clinical data of patients. PregGAN added the clinical data as conditions to the training process. Conditions were used as the input to the generator along with noises. The generator synthesized new samples using the noises vectors and the conditions. In order to solve the mode collapse problem during PregGAN training, Wasserstein distance and gradient penalty strategy were used to make the training process more stable. Results: In the prognosis prediction experiments using the METABRIC breast cancer dataset, PregGAN achieved good results, with the average accurate (ACC) of 90.6% and the average AUC (area under curve) of 0.946. Conclusions: Experimental results show that PregGAN is a reliable prognosis predictive model for breast cancer. Due to the strong ability of probability distribution learning, PregGAN can also be used for the prognosis prediction of other diseases.
Multi-codec ultra high definition 8K MPEG-DASH dataset

Many applications and online services produce and deliver multimedia traffic over the Internet. Video streaming services with a rapidly growing desire for more resources to provide better quality, such as Ultra High Definition (UHD) 8K content, are on the list. The HTTP Adaptive Streaming (HAS) technique defines standard baselines for audio-visual content streaming to balance the delivered media quality and minimize defects in streaming sessions. On the other hand, video codecs development and standardization help the progress toward improving such services by introducing efficient algorithms and technologies. Versatile Video Coding (VVC) is one of the latest advancements in video encoding technology that is still not fully optimized and not supported on all available platforms. Mentioned optimization of the video codecs and supporting more platforms require years of research and development. This paper provides multiple test assets in the form of a dataset that facilitates the research and development of the stated technologies. Our open-source dataset comprises Dynamic Adaptive Streaming over HTTP (MPEG-DASH) packaged multimedia assets, encoded with Advanced Video Coding (AVC), High Efficiency Video Coding (HEVC), AOMedia Video 1 (AV1), and VVC. We provide our dataset with resolutions of up to 7680x4320 or 8K. Our dataset has a maximum media duration of 322 seconds, and we offer our MPEG-DASH packaged content with two segments lengths, 4 and 8 seconds.
Microeconomics: machine learning model with behavioural intelligence to reduce credit card fraud

Credit card fraud - is the adversity complementary to a modern facile payment system. Over the years, credit card frauds have managed to cause massive losses to banks and credit companies globally. Where the extensive availability of data was envisaged to improve customer service, the fraudsters have managed to exploit this boon to amplify the volume, intensity, and value of frauds. While a complex and continually developing problem is hard to convey successfully using rule-based algorithms, machine-learning models present themselves as adept means of tackling the crisis. By combining supervised and unsupervised models, incorporating behavioural analytics and domain knowledge, and using artificial neural networks, fraudulent transactions detection is achievable with desirable accuracy and poses to be a promising tool for diminishing considerable losses.
PaSSAGE: A Demonstration of Player Modelling in Interactive Storytelling

This demonstration presents PaSSAGE (Player-Specific Stories via Automatically Generated Events), an Artificial Intelligence system that uses player modelling to dynamically select the content of an interactive story. Through player modelling, PaSSAGE provides game designers with the opportunity to delay some of their design decisions to run-time, allowing further refinement based on an automatically learned model of its current player's preferences. The hands-on component of this demonstration allows players to interact with a PaSSAGE-created story while simultaneously observing its inner workings on a secondary screen.
Generating Pictorial Storylines Via Minimum-Weight Connected Dominating Set Approximation in Multi-View Graphs

This paper introduces a novel framework for generating pictorial storylines for given topics from text and image data on the Internet. Unlike traditional text summarization and timeline generation systems, the proposed framework combines text and image analysis and delivers a storyline containing textual, pictorial, and structural information to provide a sketch of the topic evolution. A key idea in the framework is the use of an approximate solution for the dominating set problem. Given a collection of topic-related objects consisting of images and their text descriptions, a weighted multi-view graph is first constructed to capture the contextual and temporal relationships among these objects. Then the objects are selected by solving the minimum-weighted connected dominating set problem defined on this graph. Comprehensive experiments on real-world data sets demonstrate the effectiveness of the proposed framework.
Grover search under localized dephasing

Decoherence in quantum searches, and in the Grover search in particular, has already been extensively studied, leading very quickly to the loss of the quadratic speedup over the classical case, when searching for some target (marked) element within a set of size $N$. The noise models used were, however, global. In this paper we study Grover search under the influence of localized partially dephasing noise of rate $p$. We find, that in the case when the size $k$ of the affected subspace is much smaller than $N$, and the target is unaffected by the noise, namely when $kp\ll\sqrt{N}$, the quadratic speedup is retained. Once these restrictions are not met, the quadratic speedup is lost. In particular, if the target is affected by the noise, the noise rate needs to scale as $1/\sqrt{N}$ in order to keep the speedup. We observe also an intermediate region, where if $k\sim N^\mu$ and the target is unaffected, the speedup seems to obey $N^\mu$, which for $\mu>0.5$ is worse than the quantum, but better than the classical case. We put obtained results for quantum searches also into perspective of quantum walks and searches on graphs.
Diversity-Driven Extensible Hierarchical Reinforcement Learning

Hierarchical reinforcement learning (HRL) has recently shown promising advances on speeding up learning, improving the exploration, and discovering intertask transferable skills. Most recent works focus on HRL with two levels, i.e., a master policy manipulates subpolicies, which in turn manipulate primitive actions. However, HRL with multiple levels is usually needed in many real-world scenarios, whose ultimate goals are highly abstract, while their actions are very primitive. Therefore, in this paper, we propose a diversitydriven extensible HRL (DEHRL), where an extensible and scalable framework is built and learned levelwise to realize HRL with multiple levels. DEHRL follows a popular assumption: diverse subpolicies are useful, i.e., subpolicies are believed to be more useful if they are more diverse. However, existing implementations of this diversity assumption usually have their own drawbacks, which makes them inapplicable to HRL with multiple levels. Consequently, we further propose a novel diversity-driven solution to achieve this assumption in DEHRL. Experimental studies evaluate DEHRL with nine baselines from four perspectives in two domains; the results show that DEHRL outperforms the state-of-the-art baselines in all four aspects.
Cultural Heritage in Marker-Less Augmented Reality: A Survey

Augmented reality (AR) is considered as one of the most significant technologies in the field of computer graphics and is utilised in many applications. In this chapter, we have presented a brief comprehensive survey of cultural heritage using augmented reality systems. This survey describes the main objectives and characteristics of marker-less augmented reality systems through presenting up-to-date research results in this area. We describe the marker-less technologies in the area of AR, indoor marker-less AR, outdoor marker-less AR, real-time solutions to the tracking problem, real-time registration, cultural heritage in AR, 3D remonstration techniques, as well as presenting the problems in each research.
Extending the Reach of Research: Introducing Open Accessible Summaries at <i>Language Learning</i>

Abstract Open science practices, which include efforts to enhance accessibility to data and materials and to improve the dissemination of findings to broader audiences, have been at the forefront of the open science movement worldwide. Language Learning is proud to extend its support of open science practices by participating in the OASIS (Open Accessible Summaries in Language Studies) initiative, which aims to make language‐related research openly available and easily accessible, both physically and conceptually. As part of the OASIS initiative, Language Learning now publishes accessible summaries of all accepted articles, including reviews and syntheses. Written in nontechnical language, accessible summaries provide information about each study's goals, its design and approach, and its results, highlighting findings that may be of interest to those outside academia, such as language educators. We outline key rationales leading to the development of accessible summaries, describe their core features, and discuss some of their strengths and weaknesses.
Path planning of mobile robot based on multi-sensor information fusion

Mobile robot is a very important branch of robotics. In practice, the performance of mobile robot is required to be higher and higher. It is required that mobile robot can adapt to different complex environments through its own intelligent system to achieve the established functional goals. Compared with traditional path planning with single sensor information, Kalman filter is used to fuse multi-sensor information, and path planning method based on improved dynamic artificial potential field method is studied. Using the improved dynamic artificial potential field method, the robot can also achieve optimal path planning and obstacle avoidance in complex dynamic environment. The simulation results show that the proposed algorithm is feasible.
The McGill Face Database: Validation and Insights Into the Recognition of Facial Expressions of Complex Mental States

Current databases of facial expressions represent only a small subset of expressions, usually the basic emotions (fear, disgust, surprise, happiness, sadness, and anger). To overcome these limitations, we introduce a database of pictures of facial expressions reflecting the richness of mental states. A total of 93 expressions of mental states were interpreted by two professional actors, and high-quality pictures were taken under controlled conditions in front and side view. The database was validated in two experiments. First, a four-alternative forced-choice paradigm was employed to test the ability to select a term associated with each expression. Second, the task was to locate each face within a 2-D space of valence and arousal. Results from both experiments demonstrate that subjects can reliably recognize a great diversity of emotional states from facial expressions. While subjects’ performance was better for front view images, the advantage over the side view was not dramatic. This is the first demonstration of the high degree of accuracy human viewers exhibit when identifying complex mental states from only partially visible facial features. The McGill Face Database provides a wide range of facial expressions that can be linked to mental state terms and can be accurately characterized in terms of arousal and valence.
Witness of Things

Purpose The purpose of this paper is to develop a model for autonomous cars to establish trusted parties by combining distributed ledgers and self-driving cars in the traffic to provide single version of the truth and thus build public trust. Design/methodology/approach The model, which the authors call Witness of Things, is based on keeping decision logs of autonomous vehicles in distributed ledgers through the use of vehicular networks and vehicle-to-vehicle/vehicle-to-infrastructure (or vice versa) communications. The model provides a single version of the truth and thus helps enable the autonomous vehicle industry, related organizations and governmental institutions to discover the true causes of road accidents and their consequences in investigations. Findings In this paper, the authors explored one of the potential effects of blockchain protocol on autonomous vehicles. The framework provides a solution for operating autonomous cars in an untrusted environment without needing a central authority. The model can also be generalized and applied to other intelligent unmanned systems. Originality/value This study proposes a blockchain protocol-based record-keeping model for autonomous cars to establish trusted parties in the traffic and protect single version of the truth.
Visual and Textual Analysis for Image Trustworthiness Assessment within Online News

The majority of news published online presents one or more images or videos, which make the news more easily consumed and therefore more attractive to huge audiences. As a consequence, news with catchy multimedia content can be spread and get viral extremely quickly. Unfortunately, the availability and sophistication of photo editing software are erasing the line between pristine and manipulated content. Given that images have the power of bias and influence the opinion and behavior of readers, the need of automatic techniques to assess the authenticity of images is straightforward. This paper aims at detecting images published within online news that have either been maliciously modified or that do not represent accurately the event the news is mentioning. The proposed approach composes image forensic algorithms for detecting image tampering, and textual analysis as a verifier of images that are misaligned to textual content. Furthermore, textual analysis can be considered as a complementary source of information supporting image forensics techniques when they falsely detect or falsely ignore image tampering due to heavy image postprocessing. The devised method is tested on three datasets. The performance on the first two shows interesting results, with F1-score generally higher than 75%. The third dataset has an exploratory intent; in fact, although showing that the methodology is not ready for completely unsupervised scenarios, it is possible to investigate possible problems and controversial cases that might arise in real-world scenarios.
Managing Software Platforms and Ecosystems

We are happy to introduce to you the "Managing Software Platforms and Ecosystems" special issue of IEEE Software. This special issue is characterized by contributions from diverse theoretical fields, such as computer science, information science, and economics, with research applied in practical settings in software-intensive business. It is an overview of the current state of research and illustrates some of the largest innovations in the field as well as the gaps for future research.
Towards Understanding the Link Between Age and Smartphone Authentication

While previous work on smartphone (un)locking has revealed real world usage patterns, several aspects still need to be explored. In this paper, we fill one of these knowledge gaps: the interplay between age and smartphone authentication behavior. To do this, we performed a two-month long field study (N = 134). Our results indicate that there are indeed significant differences across age. For instance, younger participants were more likely to use biometric unlocking mechanisms and older participants relied more on auto locks.
Searching for meaning: Co-constructing ontologies with stakeholders for smarter search engines in agriculture

A key challenge in agriculture, as in other disciplines, is taking a large body of research-based knowledge and making it meaningful to the user-audience. Computer aided search engines potentially can offer widespread access to large repositories with relevant reports and publications, however the usefulness of such systems for the practitioners who are dealing with multi-faceted and context-related issues is often limited. Building search engines with user-centered ontologies offer a means of resolving this as it provides a vocabulary common to different stakeholders and can optimise the interaction between practitioner users and the expert system.The paper critically reflects on the methodology used to construct a user-centered ontology in the development of a search engine designed to help agricultural practitioners (farmers and advisers) find useful research outputs. This involved the iterative participation of domain experts, adviser practitioners and stakeholder communities in ten diverse case studies across Europe. Specifically it analyses the design, validation and evaluation phases of the ontology development drawing on qualitative data (reports, observations, interviews) from four case studies and asks: How effective is the process of co-constructing an ontology with experts, practitioners and other stakeholders in enabling the search for useful and meaningful knowledge? In doing this, it contributes to a deeper theoretical understanding of shared concepts and meanings in the context of digital communications in the agricultural arena by adapting Carlile's (2004) framework of syntactic, semantic and pragmatic capacities.
Qubit-driven Fault Simulation

Quantum data structures, methods, algorithms and software applications for parallel solution of combinatorial problems, including design and test, on classical computers by using extra memory are proposed. Quantum models and methods for design and test of specific logic functionalities are developed based on the use of vector qubit data structures and memory-driven architectures for parallel computing algorithms, in order to significantly improve the performance of testing and deductive simulation of digital systems-on-chips. A new quantum method of test generation for single stuck-at faults of logic functions through taking Boolean derivatives with respect to their qubit coverages is proposed. The quantum method of deductive simulation of stuck-at faults of logical functions through the synthesis of matrices of Boolean derivatives with respect to their qubit coverage has been developed. The performance of methods for test synthesis and their analysis has no analogues in algorithmic performance.
Methodology for ontology design and construction

In this paper we present a methodology for ontology design and construction which incorporates the most outstanding design principles and a thorough evaluation process. An ontology provides logical formulation of complex problems of decision sciences like risk management, decision making under uncertainty, statistics and forecasting, negotiation and financial analysis. The main stages of this methodology are: requirements specification, formal design, construction, and evaluation. At each stage of the methodology a series of tasks are defined together with methods and techniques to build the ontology considering quality characteristics. Description logics is used as the formal language during design, a set of informal competency questions is used to support ontology conceptualization; at the evaluation stage, the set of questions are translated to a formal reasoning language and are used for evaluation purposes. Many methodologies and tools have been reported in literature, but little attention has been paid in the creation of consistent, modular, coherent, usable and reusable ontologies as an objective from the beginning of the design process. A comparative analysis with other methodologies is discussed and an Ontological Model for Medical Diagnosis is presented.
Toward Privacy-Preserving Shared Storage in Untrusted Blockchain P2P Networks

The shared storage is essential in the decentralized system. A straightforward storage model with guaranteed privacy protection on the peer-to-peer network is a challenge in the blockchain technology. The decentralized storage system should provide the privacy for the parties since it contains numerous data that are sensitive and dangerous if misused by maliciously. In this paper, we present a model for shared storage on a blockchain network which allows the authorized parties to access the data on storage without having to reveal their identity. Ring signatures combined with several protocols are implemented to disguise the signer identity thereby the observer is unlikely to determine the identity of the parties. We apply our proposed scheme in the healthcare domain, namely, decentralized personal health information (PHI). In addition, we present a dilemma to improve performance in a decentralized system.
Analysis of DICOM Image Compression Alternative Using Huffman Coding

Compression, in general, aims to reduce file size, with or without decreasing data quality of the original file. Digital Imaging and Communication in Medicine (DICOM) is a medical imaging file standard used to store multiple information such as patient data, imaging procedures, and the image itself. With the rising usage of medical imaging in clinical diagnosis, there is a need for a fast and secure method to share large number of medical images between healthcare practitioners, and compression has always been an option. This work analyses the Huffman coding compression method, one of the lossless compression techniques, as an alternative method to compress a DICOM file in open PACS settings. The idea of the Huffman coding compression method is to provide codeword with less number of bits for the symbol that has a higher value of byte frequency distribution. Experiments using different type of DICOM images are conducted, and the analysis on the performances in terms of compression ratio and compression/decompression time, as well as security, is provided. The experimental results showed that the Huffman coding technique has the capability to compress the DICOM file up to 1 : 3.7010 ratio and up to 72.98% space savings.
Do You Know That Florence Is Packed with Visitors? Evaluating State-of-the-art Models of Speaker Commitment

When a speaker, Mary, asks “Do you know that Florence is packed with visitors?”, we take her to believe that Florence is packed with visitors, but not if she asks “Do you think that Florence is packed with visitors?”. Inferring speaker commitment (aka event factuality) is crucial for information extraction and question answering. Here, we explore the hypothesis that linguistic deficits drive the error patterns of existing speaker commitment models by analyzing the linguistic correlates of model error on a challenging naturalistic dataset. We evaluate two state-of-the-art speaker commitment models on the CommitmentBank, an English dataset of naturally occurring discourses. The CommitmentBank is annotated with speaker commitment towards the content of the complement (“Florence is packed with visitors” in our example) of clause-embedding verbs (“know”, “think”) under four entailment-canceling environments (negation, modal, question, conditional). A breakdown of items by linguistic features reveals asymmetrical error patterns: while the models achieve good performance on some classes (e.g., negation), they fail to generalize to the diverse linguistic constructions (e.g., conditionals) in natural language, highlighting directions for improvement.