Reviewerly/exHarmony huggingface dataset:
Methods to improve the dimensional measurement accuracy of a motion tracking system

Photogrammetry-based motion tracking systems are widely used in entertainment, medicine, sports, and other industries for low accuracy positioning applications. Camera-based systems track cooperative markers on objects to obtain their real-time positions and poses. There are few detailed studies in the literature on the metrological performance in three-dimensional (3D) space for such systems since they were primarily used in the entertainment industry and their off-the-shelf accuracy was not a key concern. However, applications for these systems have become more widespread due to their low cost and high repeatability (on the order of a few micrometers), and there is promise for applicability in metrology grade applications after suitable modifications. In this context, we investigate the error sources of a motion tracking system by error mapping in 3D space using a coordinate measuring machine (CMM) with two orders of magnitude lower positioning error. With the understanding gained from this study, we propose three different methods to improve the accuracy of motion tracking systems: interpolation based on an experimentally determined error map, error correction based on scale bar measurements, and weighted averaging using two motion tracking systems. Verification experiments are presented for each method. Our study is focused on the baseline errors of the motion tracking system, i.e., the system is used in the static mode where the coordinates are recorded when the probe (composed of markers) is stationary, not moving.
Robust graph regularised sparse matrix regression for two‐dimensional supervised feature selection

Bilinear matrix regression based on matrix data can directly select the features from matrix data by deploying several couples of left and right regression matrices. However, the existing matrix regression methods do not consider the local geometric structure of the samples, which results in poor classification performance. This study proposes a robust graph regularised sparse matrix regression method for two-dimensional supervised feature selection, where the intra-class compactness graph based on the manifold learning is used as the regularisation item, and the -norm as loss functions to establish the authors’ matrix regression model. An alternating optimisation algorithm is also devised to solve it and give its closed-form solutions in each iteration. The proposed method not only can learn the left and right regression matrices, but also can preserve the intrinsic geometry structure by using the label information. Extensive experiments on several data sets demonstrate the superiority of the proposed method.
Gaussian Guided Inter Prediction for Focal Stack Images Compression

Focal stack is an intermediate data representation obtained by projecting 4D light field (LF) in z-dimension. This kind of representation is fundamental for future interactive and immersive visual applications. However, focal stack images are a series of samples focused at varying depths of static scenes, which yields considerable redundancy among them. In this paper, we propose a Gaussian guided inter prediction model to eliminate the visual redundancy. In our work, the effect of the varying focus distance on plenoptic imaging system is characterized by the point spread function (PSF), and we propose a simplified Gaussian-like PSF to fit this characteristic according to the features of focal stack images. After that, Gaussian guided motion estimation and motion compensation are both implemented in this model. Experimental results show that our model can achieve smaller residual distribution. There are 10.33% bit rate saving and 0.397 dB PSNR gain on average in three configurations compared with HEVC anchor. Particularly, it brings about up to 16.60% bit rate saving with 0.649 dB PSNR increment in Low Delay P configuration.
SOTB: Semi-Supervised Oversampling Approach Based on Trigonal Barycenter Theory

The problem of classifying imbalanced data is one of the active research directions in machine learning and bioinformatics. The imbalance of data will greatly degrade the accuracy of classifiers. Good oversampling methods can improve the diversity and validity of new samples, which cannot only solve the imbalance problem of sample data, but also greatly improve the classification accuracy. In this study, we propose the trigonal barycenter theory and a semi-supervised oversampling method, called SOTB (Semi-supervised Oversampling method based on Trigonal Barycenter theory). SOBT works to: (1) construct the non-intersecting triangles based on Mahalanobis distance; (2) combine the semi-supervised sampling method with trigonal barycenter theory to oversample the positive samples, which can cope with the data imbalance problem without affecting the quality of data. Lastly, extensive experiments were conducted to verify the effectiveness of the proposed method. The results demonstrate that SOTB can improve the validity, diversity and rationality on the distribution of the newly generated samples as well as alleviate the phenomena of over-fitting which is popular in existing oversampling approaches. In particular, when compared with the state-of-the-art oversampling methods, the results show SOTB can achieve the best classification performance.
Managing Scientific Literature with Software from the PORTAL-DOORS Project

Scholarly research associated with finding and citing scientific literature in the 21st century requires new approaches to address the continuing problems that occur with the provenance of content in the literature as well as the peer and editorial review process for publishing this literature. The PORTAL-DOORS Project (PDP) has developed software for the Nexus-PORTAL-DOORS-Scribe (NPDS) cyberinfrastructure in support of identifying, describing, locating and linking things on the internet, web and grid with both lexical and semantic tools and applications. This presentation of our PDP software will highlight Discoverable Data with Reproducible Results for Equivalent Entities with Accessible Attributes and Manageable Metadata with the DREAM principles, and the Fair Acknowledgment of Information Records also called the Fair Attribution to Indexed Reports with the FAIR metrics. This software demonstration will explain use of the network of metadata repositories for scientific literature accessible from www.portaldoors.org, and use of the open source software that powers the NPDS cyberinfrastructure, PDP web sites and PDP web services. Our PDP software for the NPDS cyberinfrastructure will be released publicly at this presentation of the software where we will also discuss challenges in the peer review process that include plagiarism detection.
A Scalable System for Neural Architecture Search

Building reliable systems for neural architecture search requires careful design consideration due to the high computational demands coupled with the necessity of fault-tolerance. In this domain, it is not uncommon for applications to crash due to GPU memory exhaustion, which makes fault-tolerance and even more important attribute of a distributed neural architecture search system. We propose an RPC-based system that is robust to node failures and provides elastic compute abilities, allowing the system to add or remove computational resources as needed. The system is demonstrated on the task of neural architecture search for image classification using the CIFAR-10 dataset. Our system achieves near linear scaling and is robust to multiple GPU node failures, allowing the failed nodes to restart and rejoin.
An activity theory approach to information security non-compliance

Purpose The purpose of this paper is to introduce activity theory (AT) as a new theoretical lens to the field of information security non-compliance by explaining how research in that field can benefit from AT and to suggest eight propositions for future research. Design/methodology/approach Based on AT, the paper suggests that employees, IT systems, task characteristics, information security policies (ISPs), community and division of labor can be viewed to form an ensemble that is labeled activity. Their characteristics and/or the relationships that exist between them in organizational contexts are hypothesized to influence non-compliance behaviors. Findings The paper suggests that AT provides a broad lens that can be useful for explaining a large variety of non-compliant behaviors related to information security. Research limitations/implications The paper focuses only on non-compliant behaviors that employees undertake with non-malicious intentions and offers avenues for future research based on the propositions that are developed in the paper. Originality/value The paper provides a useful step toward a better understanding of non-compliant ISP behaviors. In addition, it proposes and explains new research areas in the non-compliance field.
Different Word Representation for Text Classification: A Comparative Study

Due to the large amounts of words usually present in documents, some of their appearances can complicate the classification process and make it less accurate. Accordingly, word representation methods have been employed to handle this issue through the use of a comparative study. In this study, we compare the effectiveness of both word embedding and TF-IDF weighting schema by applying four classifiers to assess the accuracy of the classification. To evaluate the effectiveness of our study, it was tested on the popular 20Newsgroup text document dataset. Following our experimentation, we found that using the TF-IDF method and ANN classifiers on the 20Newsgroup dataset greatly enhanced the text documents' classification compared against the use of word embedding and other classifiers.
SmartScore-Short Answer Scoring Made Easy Using Sem-LSH

In this paper, approaches to minimizing the efforts involved in creating annotated instances when training supervised automatic short answer scoring (ASAS) systems are explored since training supervised ASAS systems require a huge amount of annotated sets and also annotating training sets is a time-consuming, expensive and tedious task. To address the problem, we proposed a method that helps the annotators in selecting a small proportion of short answers to be annotated. The system utilizes a semantic intelligent k-means model which will be later used to train the scoring engine using semantic-based locality sensitive hashing (Sem-LSH). Sem-LSH uses annotated answers to score the remaining unscored answers. When a new answer arrives, Sem-LSH uses the same set of hash functions to map it into buckets and retrieves all answers from the buckets using the defined similarity function and similarity threshold value. The score of the closest answers among the retrieved ones will be assigned to each new answer using Sem-LSH. To evaluate the performance of the proposed approach, we experimented with the data sets provided by the Hewlett Foundation at Kaggle for ASAS competition. The performance of the Sem-LSH ASAS system was evaluated and compared with two other baselines. The best accuracy of 90.2% was achieved by using only 40% of short training responses selected with semantic intelligent k-means and manually annotated by the teacher.
Multi-Hop Quantum Communication Based on Greenberger-Horne-Zeilinger States and Teleportation

Quantum teleportation faithfully transfers the quantum state of a physical system, instead of the system itself, which underlies the proposal of global quantum communication network.In this paper, we propose a faithful multi-hop quantum communication scheme.In a quantum network, different types of Greenberger-Horne-Zeilinger (GHZ) states are previously shared on quantum links.To implement qubits transmission, the sender and intermediate nodes perform quantum measurements in parallel, and then send their measurement results and the types of previously shared GHZ states to the receiver independently.A general formula is deduced for the receiver, who chooses an appropriate unitary operation to perform on the local particle, the original qubit can be retrieved.Our scheme shows advantages in quantum communication delay.
Detection of seismograph signal using fiber bundle sensor

The improvement of an external fiber optic sensor both in design and in operational was conducted based on displacement measurement. The purpose of the investigation is to measure the frequency and the amplitude of seismograph signal using the fiber-optic displacement sensor. This sensor setup consists of an optical fiber transmitter, an optical fiber bundle probe, photodiode detector and a seismograph stimulator. The use of stimulator is as a volcano signal model for the testing process. The sensor has the capability to measure the seismograph signal from 0.5 to 1.5 Hz and the linearity is more than 99 %. The proposed sensor is simple, sensitive, provides high dynamic range, and offers lower cost of the fabrication and thus it is suitable to be applied in real field. Furthermore, this sensor generates more excellent result in accuracy and reliability.
Prosodic Structure Prediction using Deep Self-attention Neural Network

Prosodic structure prediction is a key part of the text analysis front-end of the text-to-speech (TTS) system. It predicts prosodic boundary tags given the input text context, which is essential to the naturalness of synthesized speech. Conventional methods such as conditional random fields (CRF) and recurrent neural network (RNN) have been successfully applied to this task. However, the lack of modeling temporal dependencies at different scopes (the short-term dependency as well as the long-span dependency across the entire sentence) limits their performance. In this paper, we propose a self-attention network with semantic features extracted by a pre-trained bidirectional encoder representations from Transformers (BERT) model to predict the prosodic structure. Experimental results show that the proposed approach outperforms the strong baseline CRF model with an absolute improvement of 3.4% in total accuracy.
Relating to materials in digital fabrication: Transform materials to transform yourself

The paper aims at investigating how children relate to and make sense of different materials in digital fabrication workshops. Our analysis focuses on how their ways of relating produced different: (1) becoming of the materials, (2) becoming of the to-be-designed project and (3) becoming of the children-as-makers. To this end, we present six vignettes of representative situations encountered in two digital fabrication workshops with primary school children. We used these cases as a foreground to open some initial paths for methodological reflections on digital fabrication in education and its potential as a terrain to work on personal transformative processes.
The UX of Things: Exploring UX Principles to Inform Security and Privacy Design in the Smart Home

Smart home devices have been successful in fulfilling functional requirements but have often failed at incorporating user-centric security and privacy. This research project addresses the problem of security and privacy in the smart home through the lens of User Experience (UX) Design. Using qualitative interviews with users and designers, we explore the relationship between UX design, security, and privacy in the smart home. This is followed by participatory design workshops with smart home stakeholders to gain an in-depth knowledge of UX design challenges of security and privacy. Our results are further broadened by the development of a conceptual framework for UX design of security and privacy in the smart home.
Towards a New Reward and Punishment Approach for Blockchain-based System

In this paper, we will introduce a new approach to calculate rewards and penalties for Blockchain-based systems. The purpose of our proposal is to retain the participants (nodes) and encourage them to actively cooperate in the stability and security of the Blockchain network to which they belong. We have studied and analyzed existing systems that, in generally favour the monopoly of rewards by assigning them either only to the leader node (elected by a consensus algorithm), or letting the leader profit from a large portion of the rewards and distribute the remainder of these rewards to a short list of unselected participants. The result of our study clearly indicate that our approach offers more opportunities and benefits to participants by ensuring them permanent, dynamic and proportionate rewards based on their scores and compliance rates. The latter is part of a gradual penalization system that verifies the conformity of each node with the rules of the Blockchain protocol.
The Method of Detection of Hidden Information in a Digital Image Using Steganographic Methods of Analysis

In this work it is proposed the method of detection of data in a digital image that is hidden using steganographic methods. The proposed method of analysis is based on detecting presence of hidden data embedded in a digital image using bit modification methods of color components of an image based on the RGB color model. This method analyzes the distribution of number of single bits in the conditional blocks of the bit planes of the color components of a digital image and parameters of their variation. In this work we can see the results of the research of the proposed method of detection of hidden data and checking its presence in a digital image using the formed matrix of conditional blocks of color components. Also, the mentioned method allows to determine the class of hidden data, namely, belonging to a linguistic group, if a message is text information. It was conducted the research of the proposed method for different classes of digital image and linguistic groups based on the linguistic ratio parameter.
Exploring the Feature Selection of the EEG Signal Time and Frequency Domain Features for k - NN and Weighted k-NN

Emotion recognition based on EEG signals has become a prospective research area because of its potential application. The features used for classifier input play an important role in the classification results, while there is no exact method for defining the best feature for a classifier. In this paper, we use nine types of time frequency domains as features. We also use some feature selection methods to select the best feature for classification. We compare the results of classifications from each method of feature selection. This method is implemented using a DEAP (Dataset for emotion analysis using psychological signal) dataset. The results of the experiment demonstrate that the time-frequency domain feature extraction shows the best performance when using PCA feature selection and the k-NN classifier with a 60.68% recognition rate. This experiment also shows that there is no significant result between k-NN and weighted k-NN classification for each feature selection. In this case, we can conclude that the DEAP data needs a more reliable feature selection method.
Reducing Code Complexity through Code Refactoring and Model-Based Rejuvenation

Over time, software tends to grow more complex, hampering understandability and further development. To reduce accidental complexity, model-based rejuvenation techniques have been proposed. These techniques combine reverse engineering (extracting models) with forward engineering (generating code). Unfortunately, model extraction can be error-prone, and validation can often only be performed at a late stage by testing the generated code. We intend to mitigate the aforementioned challenges, making model-based rejuvenation more controlled. We describe an exploratory case study that aims to rejuvenate an industrial embedded software component implementing a nested state machine. We combine two techniques. First, we develop and apply a series of small, automated, case-specific code refactorings that ensure the code (a) uses well-known programming idioms, and (b) easily maps onto the type of model we intend to extract. Second, we perform model-based rejuvenation focusing on the high-level structure of the code. The above combination of techniques gives ample opportunity for early validation, in the form of code reviews and testing, as each refactoring is performed directly on the existing code. Moreover, aligning the code with the type of model we intend to extract significantly simplifies the extraction, making the process less error-prone. Hence, we consider code refactoring to be a useful stepping stone towards model-based rejuvenation.
Nonparametric Bayesian Methods and the Dependent Pitman-Yor Process for Modeling Evolution in Multiple Object Tracking

In this paper, we propose a family of dependent Pitman-Yor (DPY) processes to model the state prior for multiple object tracking. This process is shown to be more flexible and a better match than the dependent Dirichlet process in tracking a time-varying number of objects. The DPY model directly incorporates learning multiple parameters from correlated information. Integrated with a Dirichlet process mixture model, the overall approach estimates time dependent object cardinality, provides object labeling, and identifies object associated measurements. Using Markov chain Monte Carlo sampling methods, the performance of the DPY based approach is demonstrated and compared to the labeled multi Bernoulli tracker.
Design Exposition Discussion Documents for Rich Design Discourse in Applied Visualization

We present and report on Design Exposition Discussion Documents (DExDs), a new means of fostering collaboration between visualization designers and domain experts in applied visualization research. DExDs are a collection of semi-interactive web-based documents used to promote design discourse: to communicate new visualization designs, and their underlying rationale, and to elicit feedback and new design ideas. Developed and applied during a four-year visual data analysis project in criminal intelligence, these documents enabled a series of visualization re-designs to be explored by crime analysts remotely - in a flexible and authentic way. The DExDs were found to engender a level of engagement that is qualitatively distinct from more traditional methods of feedback elicitation, supporting the kind of informed, iterative and design-led feedback that is core to applied visualization research. They also offered a solution to limited and intermittent contact between analyst and visualization researcher and began to address more intractable deficiencies, such as social desirability-bias, common to applied visualization projects. Crucially, DExDs conferred to domain experts greater agency over the design process - collaborators proposed design suggestions, justified with design knowledge, that directly influenced the re-redesigns. We provide context that allows the contributions to be transferred to a range of settings.