Reviewerly/exHarmony huggingface dataset:
Bitcoin Payment-Channels for Resource Limited IoT Devices

Resource-constrained devices are unable to maintain a full copy of the Bitcoin Blockchain in memory. This paper proposes a bidirectional payment channel framework for IoT devices. This framework utilizes Bitcoin Lightning-Network-like payment channels with low processing and storage requirements. This protocol enables IoT devices to open and maintain payment channels with traditional Bitcoin nodes without a view of the blockchain. Unlike existing solutions, it does not require a trusted third party to interact with the blockchain nor does it burden the peer-to-peer network in the way SPV clients do. The contribution of this paper includes a secure and crypto-economically fair protocol for bidirectional Bitcoin payment channels. In addition, we demonstrate the security and fairness of the protocol by formulating it as a game in which the equilibrium is reached when all players follow the protocol.
Multi-Label Question Classification for Factoid and List Type Questions in Biomedical Question Answering

Biomedical experts and bio-curators are unable to quickly find short and precise information using typical search engines as the amount of biomedical literature is increasing exponentially.The research community is focusing on biomedical question answering (QA) systems so that anyone can find precise information nuggets from the massive amount of biomedical literature.Generally, the user queries fall under different categories such as factoid, list, yes/no, or summary.The existing state-of-the-art question answering systems deal with most of these question types.However, the research to improve the performance of individual question types is also on the rise.To improve QA system performance, question classification plays a vital role for factoid and list type questions as it allows the answer processing stage to narrow down the candidate answer space and assigns a higher rank to the correct answers.A single biomedical answer or entity may be associated with more than one biomedical category or semantic type, e.g., Coenzyme Q( 10) is classified under two categories in Unified Medical Language System (UMLS): organic chemical and biologically active substance.This inherent characteristic of biomedical entities makes question classification in the biomedical domain a multi-label classification problem, where one question might expect answers belonging to more than one semantic type.To the best of our knowledge, several QA systems deal with question classification as a multi-class classification problem and only one state-of-the-art system -OAQA -deals with it as a multi-label classification problem.In this paper, we analyze the pipeline of the OAQA system for factoid and list type questions, emphasizing the multi-label question classification.We use an improved question classification dataset with the copy transformation technique to improve the performance of list type questions.Moreover, we introduce a binary transformation in the pipeline of factoid questions to increase its performance.Our modified methodology enhances the performance of both list and factoid type questions by a margin of 2% and 3% evaluated on standard F 1 and Mean Reciprocal Rank measures, respectively.
Executable component-based semantics

The potential benefits of formal semantics are well known. However, a substantial amount of work is required to produce a complete and accurate formal semantics for a major language; and when the language evolves, large-scale revision of the semantics may be needed to reflect the changes. The investment of effort needed to produce an initial definition, and subsequently to revise it, has discouraged language developers from using formal semantics. Consequently, many major programming languages (and most domain-specific languages) do not yet have formal semantic definitions. To improve the practicality of formal semantic definitions, the PLanCompS project has developed a component-based approach. In this approach, the semantics of a language is defined by translating its constructs (compositionally) to combinations of so-called fundamental constructs, or 'funcons'. Each funcon is defined using a modular variant of Structural Operational Semantics, and forms a language-independent component that can be reused in definitions of different languages. A substantial library of funcons has been developed and tested in several case studies. Crucially, the definition of each funcon is fixed, and does not need changing when new funcons are added to the library. For specifying component-based semantics, we have designed and implemented a meta-language called CBS. It includes specification of abstract syntax, of its translation to funcons, and of the funcons themselves. Development of CBS specifications is supported by an integrated development environment. The accuracy of a language definition can be tested by executing the specified translation on programs written in the defined language, and then executing the resulting funcon terms using an interpreter generated from the CBS definitions of the funcons. This paper gives an introduction to CBS, illustrates its use, and presents the various tools involved in our implementation of CBS.
Technologies and Tools to Support Teaching and Learning Computer Graphics

Teaching computer graphics using traditional methods such as textbooks, whiteboards, presentation slides, websites, and so forth, can be challenging. There are two reasons for this: computer graphics combines a variety of skills, such as programming, mathematics, art, and spatial reasoning; and computer graphics involves many 3D concepts such as geometry, transformations, illumination and shading, projections and mappings. Hence computer graphics is often best learned by experimenting with computer graphics concepts and interacting with the resulting renderings. Over the past three decades a variety of tools and technologies have been proposed to improve computer graphics learning. Technological changes over this period have affected what material is taught and how it is taught, as well as have opened new avenues to support computer graphics teaching. In this literature study, we will identify technologies and tools that have been used in the process of teaching and learning computer graphics, we classify them, and discuss to what extend they assist learning.
Colorization-based image coding using graph Fourier transform

This paper deals with the colorization-based image coding algorithm. In this algorithm, a color image is compressed by encoding its luminance image by a standard coding method such as JPEG coding and by storing several color pixels called as representative pixels (RPs). In decoding phase, a color image is restored from a luminance image and some color information of RPs using the image colorization technique. While previous studies have achieved a high coding performance, the compression method of RPs has not been considered because the positions of RPs are inhomogeneous. In order to improve the image coding performance, this paper proposes the RPs compression algorithm using the graph Fourier transform, where the chrominance image is transformed to graph spectrum and compressed. Using this RPs compression algorithm and a colorization technique, a new colorization-base image coding algorithm is proposed. Numerical results show that the proposed algorithm achieves better performance than some previous studies and JPEG2000 coding.
Towards High-Efficient Transaction Commitment in a Virtualized and Sustainable RDBMS

The relational database service in cloud usually achieves energy efficiency by using virtualization technology, in which it consolidates multiple independent database systems into a single physical machine while enforcing the hardware-level isolation among them. However, the disk I/O performance is inevitably hurt due to the resource contention on the shared device. We propose VMSQL, a novel disk I/O model for the virtualized relational database management system (RDBMS). VMSQL has two innovations over the original disk model of virtualized database systems. First, VMSQL enforces the synchronous operation in guest operating system to handle with the transaction commitment. Due to its simplicity, a portion of CPU cycles is decoupled from I/O buffer management and then used to serve the upcoming requests, thereby improving their response times. Second, in host system, VMSQL asynchronizes the storage path of transactions which are committed from the different co-located guest databases. An obvious advantage of this procedure is that systems can apply host-level improvements into the disk I/O performance of virtualized RDBMS, relieving the random I/O and enhancing the throughput of whole system. We implement a prototype of this Sync-Async model in QEMU-KVM hypervisor, in which the InnoDB engine is deployed in the guest operating system. Extensive experiments are conducted to verify its advantages and the results are positive without any loss of ACID-compliance. In the meanwhile, VMSQL incurs moderate overhead at the hypervisor layer.
Image Denoising via Nonlocal Low Rank Approximation With Local Structure Preserving

The nuclear norm minimization method emerged from a patch-based low-rank model leads to an excellent image denoising performance, where the non-local self-similarity over image patches is exploited.However, natural images are normally with complex and irregular image patches, which cannot be well represented using only a low-rank model, and thus most of them suffer from the over-penalty problem especially for images with lots of local irregular structures (e.g., fine details or sharp edges), and then results in over-smoothing problem after denoising.On the other hand, in order to represent the irregular components, edges defined over pixel level are often exploited.While the total variation (TV) is a well-known prior to remove noises and preserve edges, it might yield undesired staircase artifacts.The total generalized variation (TGV), a generalization of TV, can largely alleviate such staircase artifacts.Consequently, in order to deal with the over-smoothing problem aroused by a low-rank model, we propose a re-weighted TGV regularized nuclear norm minimization model for local structure preserving image denoising.Thanks to the split Bregman method, our proposed model can be effectively solved.A re-weighted strategy is developed to adaptively update the weight parameters of TGV regularization.The encouraging experimental results on noisy images demonstrate the effectiveness of our proposed method.
ROMOP: a light-weight R package for interfacing with OMOP-formatted electronic health record data

Electronic health record (EHR) data are increasingly used for biomedical discoveries. The nature of the data, however, requires expertise in both data science and EHR structure. The Observational Medical Out-comes Partnership (OMOP) common data model (CDM) standardizes the language and structure of EHR data to promote interoperability of EHR data for research. While the OMOP CDM is valuable and more attuned to research purposes, it still requires extensive domain knowledge to utilize effectively, potentially limiting more widespread adoption of EHR data for research and quality improvement.We have created ROMOP: an R package for direct interfacing with EHR data in the OMOP CDM format.ROMOP streamlines typical EHR-related data processes. Its functions include exploration of data types, extraction and summarization of patient clinical and demographic data, and patient searches using any CDM vocabulary concept.ROMOP is freely available under the Massachusetts Institute of Technology (MIT) license and can be obtained from GitHub (http://github.com/BenGlicksberg/ROMOP). We detail instructions for setup and use in the Supplementary Materials. Additionally, we provide a public sandbox server containing synthesized clinical data for users to explore OMOP data and ROMOP (http://romop.ucsf.edu).
Inducing non-orthogonal and non-linear decision boundaries in decision trees via interactive basis functions

Decision Trees (DTs) are a machine learning technique widely used for regression and classification purposes. Conventionally, the decision boundaries of Decision Trees are orthogonal to the features under consideration. A well-known limitation of this is that the algorithm may fail to find optimal partitions, or in some cases any partitions at all, depending on the underlying distribution of the data. To remedy this limitation, several modifications have been proposed that allow for oblique decision boundaries. The objective of this paper is to propose a new strategy for generating flexible decision boundaries by means of interactive basis functions (IBFs). We show how oblique decision boundaries can be obtained as a particular case of IBFs, and in addition how non-linear decision boundaries can be induced. One attractive aspect of the strategy proposed in this paper is that training Decision Trees with IBFs does not require custom software, since the functions can be precalculated for use in any existing implementation of the algorithm. Since the underlying mechanisms remain unchanged there is no substantial computational overhead compared to conventional trees. Furthermore, this also means that IBFs can be used in any extensions of the Decision Tree algorithm, such as evolutionary trees, boosting, and bagging. We conduct a benchmarking exercise to understand under which conditions the use of IBFs can improve model the performance. In addition, we present three empirical applications that illustrate the approach in classification and regression. As part of discussing the empirical applications, we introduce a device called decision charts to facilitate the interpretation of DTs with IBFs. Finally, we conclude the paper by outlining some directions for future research.
Multi-Label Metric Transfer Learning Jointly Considering Instance Space and Label Space Distribution Divergence

Multi-label learning deals with problems in which each instance is associated with a set of labels. Most multi-label learning algorithms ignore the potential distribution differences between the training domain and the test domain in the instance space and label space, as well as the intrinsic geometric information of the label space. These restrictive assumptions limit the ability of the existing multi-label learning algorithms to classify between domains. To solve this problem, in this paper, we propose a novel distribution-adaptation-based method, the multi-label metric transfer learning (MLMTL), to relax these two assumptions and handle more general multi-label learning tasks effectively. In particular, MLMTL extends the maximum mean discrepancy method into multi-label classification by learning and adjusting the weights for the multi-labeled training instances. In this way, MLMTL bridges the instance distribution and label distribution divergence between training and test datasets. In addition, based on the balanced multi-label training data, we explore the intrinsic geometric information of the label space by encoding it into a distance metric learning framework. Extensive experiments on five benchmark datasets show that the proposed approach significantly outperforms the state-of-the-art multi-label learning algorithms.
Posthumanism, open ontologies and bio-digital becoming: Response to Luciano Floridi’s <i>Onlife Manifesto</i>

In The Onlife Manifesto: Being Human in a Hyperconnected Era Luciano Floridi and his associates examine various aspects of the contemporary meaning of humanity. Yet, their insights give less thought to the political economy of techno-capitalism that in large measure creates ICTs and leads to their further innovation, development and commercialization. This article responses to Floridi’s work and examines political economy of the blurred distinction between human, machine and nature in the postdigital context. Taking lessons from early history of the Internet across the Eastern and the Western Bloc, it examines ideological underpinnings of development of information and communication technologies. The paper points towards the postdigital challenge of the rising importance of biological sciences, their mutual connection with information sciences, and the society at large. It introduces the concept of bio-informational capitalism, and closely examines the relationships between biology and information. Based on authors’ previous works the chapter introduces the concepts of homo economicus and homo collaborans, and claims that their mutual differences do not make a case for a dualist philosophy—instead, the two concepts should be understood as opposite poles of a continuum. However, it shows that the biological challenge is not a mere continuation of our existing (research) questions, and that it introduces new postdigital ways of understanding our reality. In conclusion, the chapter calls for developing a new, postdigital language of inquiry, which reinvents the digital challenge and invents its relationship to biology.
Constructions of Codebooks Asymptotically Achieving the Welch Bound With Additive Characters

Codebooks with small inner product correlation have wide applications in many fields, such as direct spread code-division multiple access communications, signal processing, and compressed sensing. It is extremely hard to produce optimal codebooks with respect to the Welch bound or the Levenstein bound. This letter focuses on constructing asymptotically optimal codebooks with additive characters of finite fields. Following the proposed constructions, two classes of asymptotically optimal codebooks with respect to the Welch bound are obtained. In addition, the resulting codebooks could have a small alphabet size.
A Multiscale Denoising Framework Using Detection Theory with Application to Images from CMOS/CCD Sensors

Output from imaging sensors based on CMOS and CCD devices is prone to noise due to inherent electronic fluctuations and low photon count. The resulting noise in the acquired image could be effectively modelled as signal-dependent Poisson noise or as a mixture of Poisson and Gaussian noise. To that end, we propose a generalized framework based on detection theory and hypothesis testing coupled with the variance stability transformation (VST) for Poisson or Poisson⁻Gaussian denoising. VST transforms signal-dependent Poisson noise to a signal independent Gaussian noise with stable variance. Subsequently, multiscale transforms are employed on the noisy image to segregate signal and noise into separate coefficients. That facilitates the application of local binary hypothesis testing on multiple scales using empirical distribution function (EDF) for the purpose of detection and removal of noise. We demonstrate the effectiveness of the proposed framework with different multiscale transforms and on a wide variety of input datasets.
A family of metainferential logics

We will present 12 different mixed metainferential consequence relations. Each one of them is specified using two different inferential Tarskian or non-Tarskian consequence relations: K3,LP,ST or TS+. We will show that it is possible to obtain a Tarskian logic with non-Tarskian inferential logics, but also a non-Tarskian logic with Tarskian inferential logics. Moreover, we will show how some of these metainferential logics work better than the corresponding inferential rivals. Finally, we will show how these logics prove that it is not enough to work with inferences as pairs of sets of formulas to obtain a contractive logic.
Copula Gaussian Graphical Models for Functional Data

We introduce a statistical graphical model for multivariate functional data, which are common in medical applications such as EEG and fMRI. Recently published functional graphical models rely on the multivariate Gaussian process assumption, but we relax it by introducing the functional copula Gaussian graphical model (FCGGM). This model removes the marginal Gaussian assumption but retains the simplicity of the Gaussian dependence structure, which is particularly attractive for large data. We develop four estimators for the FCGGM and establish the consistency and the convergence rates of one of them. We compare our FCGGM with the existing functional Gaussian graphical model by simulations, and apply our method to an EEG dataset to construct brain networks. Supplementary materials for this article are available online.
Evaluation of an intelligent team tutoring system for a collaborative two-person problem: Surveillance

This paper describes the development and evaluation of an Intelligent Team Tutoring System (ITTS) for pairs of learners working collaboratively to monitor an area. In the Surveillance Team Tutor (STT), learners performed a surveillance task in a virtual environment, communicating to track hostile moving soldiers. This collaborative problem solving task required significant communication to achieve the common goal of perfect surveillance. In a pilot evaluation, 16 two-person teams performed the task within one of three feedback conditions (Individual, Team, or None) across four trials each. The STT used a unique approach to filtering feedback so that teams in both individual and team conditions received a similar amount of feedback. In one performance measure, Team condition participants made fewer errors in one task than those in other conditions, though at a potential cost of mental workload. Feedback condition also significantly affected participants' subjective rating of both their own performance and their teammate's. This ITTS is one of the first automated team tutoring systems that provided real-time feedback during task execution. Recommendations are offered for the design of the optimal team task for future ITTSs that offer tutoring for small teams performing collaborative problem solving.
Challenging AI

Providing a challenging Artificial Intelligent opponent is an important aspect of making video games enjoyable and immersive. A game that is too easy, or conversely too hard may frustrate or bore players. Dynamic Difficulty Adjustment is a method that aims at improving the traditional methods of difficulty selection, by providing an opponent that tailors the challenge it presents to players such that it is at an optimal level for them. This research presents a player evaluation of three different Dynamic Difficulty Adjustment approaches using Monte Carlo Tree Search and measures their impact on player enjoyment, realism and perceived level of difficulty. In particular, it investigates the effect that different win/loss ratios, employed by Dynamic Difficulty Adjustment, have on player enjoyment.
Misogynistic Tweet Detection: Modelling CNN with Small Datasets

Online abuse directed towards women on the social media platform Twitter has attracted considerable attention in recent years. An automated method to effectively identify misogynistic abuse could improve our understanding of the patterns, driving factors, and effectiveness of responses associated with abusive tweets over a sustained time period. However, training a neural network (NN) model with a small set of labelled data to detect misogynistic tweets is difficult. This is partly due to the complex nature of tweets which contain misogynistic content, and the vast number of parameters needed to be learned in a NN model. We have conducted a series of experiments to investigate how to train a NN model to detect misogynistic tweets effectively. In particular, we have customised and regularised a Convolutional Neural Network (CNN) architecture and shown that the word vectors pre-trained on a task-specific domain can be used to train a CNN model effectively when a small set of labelled data is available. A CNN model trained in this way yields an improved accuracy over the state-of-the-art models.