Reviewerly/exHarmony huggingface dataset:
Detecting Vehicle Anomaly in the Edge via Sensor Consistency and Frequency Characteristic

Autonomous vehicles are expected to significantly enhance the human mobility. However, recently researchers have discovered and demonstrated some attacks on vehicles, which have caused a panic among the public. Furthermore, these attacks have demonstrated that the security issue is still one of the major challenges of vehicles. In this paper, we propose a novel edge computing based anomaly detection, coined edge computing based vehicle anomaly detection (EVAD), which exploits edge based sensor data fusion to identify the anomaly events. The time domain property, i.e., the correlation between different intra-vehicle sensors, and the frequency domain property of sensor data are utilized to judge whether an anomaly has occurred within the vehicle. Especially, to reduce the computation overhead and improve the performance, multiple sensors will be organized as ring architecture, which is a tradeoff of detection accuracy and complexity. In addition, the major components (e.g., anomaly detection module) of EVAD are embedded in edge computing devices, which make the anomaly detection be more efficient and privacy preserving. Meanwhile, a more appropriate model is generated on the cloud server, of which computation overhead maybe heavy for edge computing devices. This paper evaluates the performance of EVAD under different scenarios, and the experimental results demonstrate its feasibility and efficiency. The average true positive rate achieves 99.5% with 1% false positive rate.
(Dual) server-aided revocable attribute-based encryption with decryption key exposure resistance

Attribute-based encryption (ABE) is a promising approach that enables scalable access control on encrypted data. However, one of the main efficiency drawbacks of ABE is the lack of practical user revocation mechanisms. In ESORICS 2016, Cui et al. proposed the first cloud server-aided revocable ABE scheme to achieve efficient user revocation. However, the cloud server cannot be fully compromised by an adversary. Otherwise, it will suffer from local decryption key exposure (DKE) attacks. In this paper, we first revisit Cui et al. security model, and enhance it by capturing the DKE attacks on user’s local decryption keys and meanwhile allowing the adversary to fully corrupt the cloud server. We then construct a server-aided revocable ABE based on Rouselakis–Waters ciphertext-policy ABE (CCS 2013). It was showed that our scheme is secure in the new security model and maintains the outstanding properties of efficient user revocation, short local ciphertext size and fast local decryption. Further, we propose a dual framework for server-aided revocable ABE, in which the update keys are distributed to local users rather than the cloud server. With the exception of interaction with the KGC, the local user still has the same efficiency as that of first scheme.
Sketching and Sublinear Data Structures in Genomics

Large-scale genomics demands computational methods that scale sublinearly with the growth of data. We review several data structures and sketching techniques that have been used in genomic analysis methods. Specifically, we focus on four key ideas that take different approaches to achieve sublinear space usage and processing time: compressed full-text indices, approximate membership query data structures, locality-sensitive hashing, and minimizers schemes. We describe these techniques at a high level and give several representative applications of each.
Improved inverse gravity moment term weighting for text classification

Text classification is one of the popular high dimensional classification problems where providing better feature vector representations explicitly improve classification performances. Thus, assigning appropriate weights to features or terms are crucial for obtaining effective feature vector representations. The methods used for weighting terms in text classification are called term weighting schemes. Although there exist some term weighting schemes for text classification, they are not fully effective and researchers still focus on proposing new term weighting schemes. In this study, two novel term weighting schemes namely SQRT_TF-IGMimp and TF-IGMimp derived from standard inverse gravity moment formula are proposed to improve weighting behaviors of existing TF-IGM scheme especially for some extreme cases. The performances of proposed schemes are compared with two standard IGM based schemes and five other state-of-the-art term weighting methods on both unbalanced (Reuters-21578) and balanced (20 Mini Newsgroups and 20 Newsgroups) datasets with KNN, SVM, and NN classifiers. Micro-F1 and macro-F1 are used as success measures. The experiments are conducted with various different feature sizes to examine the effects of the feature size on the success of weighting. The experimental results showed that the proposed SQRT_TF-IGMimp method generally outperformed all schemes including both standard TF-IGM and SQRT_TF-IGM schemes. However, the proposed TF-IGMimp scheme also showed mostly better performance than standard TF-IGM. To demonstrate validity of the proposed weighting scheme having maximum performance, t-test is also used and it can be stated that the performance gains obtained by the proposed SQRT_TF-IGMimp weighting scheme compared to standard SQRT_TF-IGM are statistically significant.
Blockchain in Healthcare: Challenges and Solutions

The main challenge in distributing electronic health records (EHRs) for patient-centered research, market analysis, medicine investigation, healthcare data mining etc., is data privacy. Handling the large-scale data and preserving the privacy of patients has been a challenge to researchers for a long period of time. On the contrary, blockchain technology has alleviated some of the problems by providing a protected and distributed platform. Sadly, existing electronic health record (EHR) management systems suffer from data manipulation, delayed communication, and trustless cooperation in data collection, storage, and distribution. This chapter discusses the current issues of healthcare data privacy and existing and upcoming regulations on this sector. This chapter also includes an overview of the architecture, existing issues, and future scope of blockchain technology for successfully handling privacy and management of current and future medical records. This chapter also presents few blockchain solutions that advocate the future research scopes in healthcare, big data, and blockchain.
Leveraging cyber threat intelligence for a dynamic risk framework

One of the most important goals in an organization is to have risks under an acceptance level along the time. All organizations are exposed to real-time security threats that could have an impact on their risk exposure levels harming the entire organization, their customers and their reputation. New emerging techniques, tactics and procedures (TTP) which remain undetected, the complexity and decentralization of organization assets, the great number of vulnerabilities proportional to the number of new type of devices (IoT) or still the high number of false positives, are only some examples of real risks for any organization. Risk management frameworks are not integrated and automated with near real-time (NRT) risk-related cybersecurity threat intelligence (CTI) information. The contribution of this paper is an integrated architecture based on the Web Ontology Language (OWL), a semantic reasoner and the use of Semantic Web Rule Language (SWRL) to approach a Dynamic Risk Assessment and Management (DRA/DRM) framework at all levels (operational, tactic and strategic). To enable such a dynamic, NRT and more realistic risk assessment and management processes, we created a new semantic version of STIX™v2.0 for cyber threat intelligence as it is becoming a de facto standard for structured threat information exchange. We selected an international leading organization in cybersecurity to demonstrate new dynamic ways to support decision making at all levels while being under attack. Semantic reasoners could be our ideal partners to fight against threats having risks under control along the time, for that, they need to understand the data. Our proposal uses an unprecedented mix of standards to cover all levels of a DRM and ensure easier adoption by users.
Self-attention Based Model for Punctuation Prediction Using Word and Speech Embeddings

This paper proposes to use self-attention based model to predict punctuation marks for word sequences. The model is trained using word and speech embedding features which are obtained from the pre-trained Word2Vec and Speech2Vec, respectively. Thus, the model can use any kind of textual data and speech data. Experiments are conducted on English IWSLT2011 datasets. The results show that the self-attention based model trained using word and speech embedding features outperforms the previous state-of-the-art single model by up to 7.8% absolute overall F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score. The results also show that it obtains performance improvement by up to 4.7% absolute overall F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score against the previous best ensemble model.
Probabilistic forecasting of high-resolution clear-sky index time-series using a Markov-chain mixture distribution model

This study presents a Markov-chain mixture (MCM) distribution model for forecasting the clear-sky index—normalized global horizontal irradiance. The model is presented in general, but applied to, and tested on, minute resolution clear-sky index data for the two different climatic regions of Norrköping, Sweden, and Hawaii, USA. Model robustness is evaluated based on a cross-validation procedure and on that basis a reference configuration of parameter settings for evaluating the model performance is obtained. Simulation results are compared with persistence ensemble (PeEn) and quantile regression (QR) model simulations for both data sets and for D=1,…,5 steps ahead forecasting scenarios. The results are evaluated by a set of probabilistic forecasting metrics: reliability mean absolute error (reliability MAE), prediction interval normalized average width (PINAW), continuous ranked probability score (CRPS) and continuous ranked probability skill score (skill). Both in terms of reliability MAE and CRPS, the MCM model outperforms PeEn for all simulated scenarios. In terms of reliability MAE, the QR model outperforms the MCM model for most simulated scenarios. However, in terms of mean CRPS, the MCM model outperforms the QR model in most simulated scenarios. A point forecasting estimate is also provided. The MCM model is concluded to be a computationally inexpensive, accurate and parameter insensitive probabilistic model. Based on this, it is suggested as a candidate benchmark model in probabilistic forecasting, in particular for solar irradiance forecasting. For applicability, a Python script of the MCM model is available as SheperoMah/MCM-distribution-forecasting at GitHub.
Wearable-sensor-based pre-impact fall detection system with a hierarchical classifier

Fall is a major threat to elder health, and fall detection has attracted considerable research attention recently. In our study, a novel method is proposed to detect falls prior to impact during walking. Angle and angular-velocity data from the waist and thigh are collected using two wearable sensors. By extracting and selecting distinctive features, we aim to identify falls at an early stage. To improve detection accuracy and reduce false alarms, a hierarchical classifier based on Fisher discriminant analysis is developed. With the hierarchical classifier, human activities are classified into three categories: non-fall, backward fall and forward fall. It can achieve average lead times of 376 ms for backward fall and 404 ms for forward fall. Meanwhile, it can achieve a sensitivity of 95.5% and specificity of 97.3%. The method can achieve a high accuracy for classifier, and a long lead time for pre-impact fall detection. Compared with single-sensor-based methods, the multi-sensor-based method achieves a better performance. The preliminary results indicate that our study has potential application in a fall-injury prevention system.
Disseminating digital innovation in school – leading second-order educational change

Using digital technology effectively in schools requires profound changes in traditional teaching and learning activities. Pedagogical innovations often start small-scale and developing good ideas into shared practice across schools is challenging in many ways, especially if the innovation requires second-order change, i.e. challenges to fundamental beliefs about teaching and learning. This study investigates how a validated pedagogical method requiring integrated Information and Communication Technology (ICT) use and second-order change can be disseminated and sustained over time. We surveyed 92 primary school teachers who at different times over a 5-year period participated in a training course designed to implement an innovative technology-supported teaching method, Write To Learn, across an entire city. We found that organized teacher development programs can drive second-order change, but this requires considerable, active, and sustained effort from leaders at both school and district level. Additional factors include immediate and extended social systems and handling diversity among teachers. The results are useful for both practitioners and researchers since they contribute to a deeper understanding of the opportunities and challenges involved in disseminating effective ICT-based methods that requires profound changes of thinking about teaching and learning to guide the transformation of teaching practice.
A Domain Ontology for Software Requirements Change Management in Global Software Development Environment

Ontology and its role in both software engineering and knowledge management fields have been widely reported in the literature. Numerous studies have proven the effectiveness of using ontologies to support the process of requirements engineering. One of the main problems facing requirements engineering is that the requirements are exposed to change. In fact, this problem swells even more in global software development (GSD) environment. Given that this problem is unavoidable, we need to improve the requirements change management (RCM) process. In this paper, a systematic domain ontology for RCM especially in GSD environment is proposed. A hybrid method combining Methontology and the 101 method is used for its development; Web Ontology Language (OWL) for its representation; and Protégé for its implementation. It was validated using ontology content evaluation and competency question evaluation methods. Also, it was verified using both ontology taxonomy evaluation and FOCA evaluation methods. Validation and verification results showed that the proposed ontology was successfully built. Building this ontology can be considered as a base contribution in supporting the RCM process in GSD.
Multiple-Choice Item Distractor Development Using Topic Modeling Approaches

Writing a high-quality, multiple-choice test item is a complex process. Creating plausible but incorrect options for each item poses significant challenges for the content specialist because this task is often undertaken without implementing a systematic method. In the current study, we describe and demonstrate a systematic method for creating plausible but incorrect options, also called distractors, based on students' misconceptions. These misconceptions are extracted from the labeled written responses. One thousand five hundred and fifteen written responses from an existing constructed-response item in Biology from Grade 10 students were used to demonstrate the method. Using a topic modeling procedure commonly used with machine learning and natural language processing called latent dirichlet allocation, 22 plausible misconceptions from students' written responses were identified and used to produce a list of plausible distractors based on students' responses. These distractors, in turn, were used as part of new multiple-choice items. Implications for item development are discussed.
Designing 'True Colors'

Vulnerability is a common experience in everyday life and is frequently perceived as a flaw to be excised in technology design. Yet, research indicates it is an essential aspect of wholehearted living among others. In this paper, we present the design and deployment of 'True Colors', a novel wearable device intended to support social interaction in a live action roleplay game (LARP) setting. We describe the Research-through-Design process that helped us to discover and articulate the possibility space of vulnerability in the design of social wearables, as support for producing a sense of social empowerment and connection among wearers within the LARP. We draw conclusions that may be of value to others designing wearables and related technologies aimed at supporting co-located social interaction in games/play.
Improved Faster R-CNN With Multiscale Feature Fusion and Homography Augmentation for Vehicle Detection in Remote Sensing Images

Vehicle detection in remote sensing images has attracted remarkable attention for its important role in a variety of applications in traffic, security, and military fields. Motivated by the stunning success of region convolutional neural network (R-CNN) techniques, which have achieved the state-of-the-art performance in object detection task on benchmark data sets, we propose to improve the Faster R-CNN method with better feature extraction, multiscale feature fusion, and homography data augmentation to realize vehicle detection in remote sensing images. Extensive experiments on representative remote sensing data sets related to vehicle detection demonstrate that our method achieves better performance than the state-of-the-art approaches. The source code will be made available (after the review process).
TrackCap

The latest generation of consumer market Head-mounted displays (HMD) now include self-contained inside-out tracking of head motions, which makes them suitable for mobile applications. However, 3D tracking of input devices is either not included at all or requires to keep the device in sight, so that it can be observed from a sensor mounted on the HMD. Both approaches make natural interactions cumbersome in mobile applications. TrackCap, a novel approach for 3D tracking of input devices, turns a conventional smartphone into a precise 6DOF input device for an HMD user. The device can be conveniently operated both inside and outside the HMD's field of view, while it provides additional 2D input and output capabilities.
A user study of neural interactive translation prediction

Machine translation (MT) on its own is generally not good enough to produce high-quality translations, so it is common to have humans intervening in the translation process to improve MT output. A typical intervention is post-editing (PE), where a human translator corrects errors in the MT output. Another is interactive translation prediction (ITP), which involves an MT system presenting a translator with translation suggestions they can accept or reject, actions the MT system then uses to present them with new, corrected suggestions. Both Macklovitch (2006) and Koehn (2009) found ITP to be an efficient alternative to unassisted translation in terms of processing time. So far, phrase-based statistical ITP has not yet proven to be faster than PE (Koehn 2009; Sanchis-Trilles et al. 2014; Underwood et al. 2014; Green et al. 2014; Alves et al. 2016; Alabau et al. 2016). In this paper we present the results of an empirical study on translation productivity in ITP with an underlying neural MT system (NITP). Our results show that over half of the professional translators in our study translated faster with NITP compared to PE, and most preferred it over PE. We also examine differences between PE and ITP in other translation productivity indicators and translators’ reactions to the technology.
Sensor Glove Based on Novel Inertial Sensor Fusion Control Algorithm for 3-D Real-Time Hand Gestures Measurements

In recent years, there has been an increasing interest in hand movements in various application fields. However, the human hand is too dexterous and only has limited space. Developing a proper method to monitor and capture hand motion is meaningful. Conventionally, an optical measurement method is widely used to capture hand gestures. Nevertheless, this method is costly and has the problem of line-of-sight shielding. In this paper, a sensor fusion algorithm is proposed to calculate a more accurate result by calculating acceleration, magnetic field strength, and angular speed from the inertial measurement unit (IMU). The core concept of this algorithm is based on the idea of feedback control. To validate the performance, the active rotary platform is set up to compare the measured results to the references directly. Most of the error results are less than 3°, and the standard deviations are less than 1°. The measurement results ensure the feasibility of the inertial measurement method with the sensor fusion algorithm proposed in this paper.
Queer(ing) HCI

The increasing corpus on queer research within HCI, which started by focusing on sites such as location-based dating apps, has begun to expand to other topics such as identity formation, mental health and physical well-being. This Special Interest Group (SIG) aims to create a space for discussion, connection and camaraderie for researchers working with queer populations, queer people in research, and those using queer theory to inform their work. We aim to facilitate a broad-ranging, inclusive discussion of where queer HCI research goes next.
Multi-label learning with kernel extreme learning machine autoencoder

In multi-label learning, in order to improve the accuracy of classification, many scholars have considered the relationship between features and features, features and labels or labels and labels, but how to combine the correlation among them is rarely studied. Based on this, this paper proposes a multi-label learning algorithm with kernel extreme learning machine autoencoder. Firstly, the label space is reconstructed by using the non-equilibrium labels completion method in the label space. Then, the non-equilibrium labels space information is added to the input node of the kernel extreme learning machine autoencoder network, and the input features are output as the target. Finally, the kernel extreme learning machine is used for classification. Our method implements the information fusion between features and features, between labels and features, and between labels and labels. Compared with the traditional autoencoder network, the extreme learning machine autoencoder has no iterative process, which reduces the network training time and improves the classification accuracy. The experimental results of the proposed algorithm in the opening benchmark multi-label data sets show that the KELM-AE algorithm has some advantages over other comparative multi-label learning algorithms and the statistical hypothesis testing and stability analysis further illustrate the effectiveness of the proposed algorithm.
Vulnerability of quantum classification to adversarial perturbations

It is expected that the quantum advantage of certain classification problems, like quantum-enhanced machine learning algorithms, will become more pronounced as the Hilbert-space dimension grows, but care must be taken with perturbations on the system, as they lead to security breaches that can be explored by untrusted parties. Here it is shown that the amount of perturbation needed for an adversary to induce a misclassification scales inversely with the dimensionality of the space, but security can be restored if we restrict the class of states to be classified.