Reviewerly/exHarmony huggingface dataset:
Context from the data reuser’s point of view

Purpose Taking the researchers’ perspective, the purpose of this paper is to examine the types of context information needed to preserve data’s meaning in ways that support data reuse. Design/methodology/approach This paper is based on a qualitative study of 105 researchers from three disciplinary communities: quantitative social science, archaeology and zoology. The study focused on researchers’ most recent data reuse experience, particularly what they needed when deciding whether to reuse data. Findings Findings show that researchers mentioned 12 types of context information across three broad categories: data production information (data collection, specimen and artifact, data producer, data analysis, missing data, and research objectives); repository information (provenance, reputation and history, curation and digitization); and data reuse information (prior reuse, advice on reuse and terms of use). Originality/value This paper extends digital curation conversations to include the preservation of context as well as content to facilitate data reuse. When compared to prior research, findings show that there is some generalizability with respect to the types of context needed across different disciplines and data sharing and reuse environments. It also introduces several new context types. Relying on the perspective of researchers offers a more nuanced view that shows the importance of the different context types for each discipline and the ways disciplinary members thought about them. Both data producers and curators can benefit from knowing what to capture and manage during data collection and deposit into a repository.
An Effective Style Token Weight Control Technique for End-to-End Emotional Speech Synthesis

In this letter, we propose a high-quality emotional speech synthesis system, using emotional vector space, i.e., the weighted sum of global style tokens (GSTs). Our previous research verified the feasibility of GST-based emotional speech synthesis in an end-to-end text-to-speech synthesis framework. However, selecting appropriate reference audio (RA) signals to extract emotion embedding vectors to the specific types of target emotions remains problematic. To ameliorate the selection problem, we propose an effective way of generating emotion embedding vectors by utilizing the trained GSTs. By assuming that the trained GSTs represent an emotional vector space, we first investigate the distribution of all the training samples depending on the type of each emotion. We then regard the centroid of the distribution as an emotion-specific weighting value, which effectively controls the expressiveness of synthesized speech, even without using the RA for guidance, as it did before. Finally, we confirm that the proposed controlled weight-based method is superior to the conventional emotion label-based methods in terms of perceptual quality and emotion classification accuracy.
Intriguing Properties of Randomly Weighted Networks: Generalizing While Learning Next to Nothing

Training deep neural networks results in strong learned representations that show good generalization capabilities. In most cases, training involves iterative modification of all weights in the network via back-propagation. In Extreme Learning Machines, it has been suggested to set the first layer of a network to fixed random values instead of learning it. In this paper, we propose to take this approach a step further and fix almost all layers of a deep convolutional neural network, allowing only a small portion of the weights to be learned. As our experiments show, fixing even the majority of the parameters of the network often results in performance which is on par with the performance of learning all of them. The implications of this intriguing property of deep neural networks are discussed and we suggest practical ways to harness it to create more robust and compact representations.
Location-Invariant Physical Layer Identification Approach for WiFi Devices

Recently, Radio Frequency Fingerprinting (RFF) becomes a promising technique which augments existing multifactor authentication schemes at the device level to counter forgery and related threats.As RFF leverages the discriminable hardware imperfections reflected in Radio Frequency (RF) signals for device identification, it has a good property of scalability, accuracy, energy-efficiency and tamper resistance.However, its identification accuracy might be compromised when the locations of training and testing are different, which is a more realistic assumption in practical scenarios.To address this issue, we study the location-invariant RFF feature extraction and identification method for WiFi Network Interface Cards (NICs).Firstly, we present an RFF feature extraction approach named Differential Phase of Pilots (DPoP).To further address the low-dimensional feature space problem, we propose another novel RFF extraction approach named Amplitude of Quotient (AoQ).AoQ exploits the fact that the RFFs of two Long Training Sequences (LTSs) in WiFi frames exhibit semi-steady characteristics and two LTSs in the same frame have similar channel frequency responses.Next, we use Euclidean distance and Deep Neural Network (DNN) for AoQ authentication and identification, respectively.Experimental results verify the effectiveness of our proposed AoQ method among 55 WiFi NICs of 5 models.The identification accuracy is higher than 95% and the Equal Error Rate (EER) is around 4% when SNR is higher than 40 dB.
Dataspace: A Reconfigurable Hybrid Reality Environment for Collaborative Information Analysis

Immersive environments have gradually become standard for visualizing and analyzing large or complex datasets that would otherwise be cumbersome, if not impossible, to explore through smaller scale computing devices. However, this type of workspace often proves to possess limitations in terms of interaction, flexibility, cost and scalability. In this paper we introduce a novel immersive environment called Dataspace, which features a new combination of heterogeneous technologies and methods of interaction towards creating a better team workspace. Dataspace provides 15 high-resolution displays that can be dynamically reconfigured in space through robotic arms, a central table where information can be projected, and a unique integration with augmented reality (AR) and virtual reality (VR) headsets and other mobile devices. In particular, we contribute novel interaction methodologies to couple the physical environment with AR and VR technologies, enabling visualization of complex types of data and mitigating the scalability issues of existing immersive environments. We demonstrate through four use cases how this environment can be effectively used across different domains and reconfigured based on user requirements. Finally, we compare Dataspace with existing technologies, summarizing the trade-offs that should be considered when attempting to build better collaborative workspaces for the future.
A learning-based approach for automatic construction of domain glossary from source code and documentation

A domain glossary that organizes domain-specific concepts and their aliases and relations is essential for knowledge acquisition and software development. Existing approaches use linguistic heuristics or term-frequency-based statistics to identify domain specific terms from software documentation, and thus the accuracy is often low. In this paper, we propose a learning-based approach for automatic construction of domain glossary from source code and software documentation. The approach uses a set of high-quality seed terms identified from code identifiers and natural language concept definitions to train a domain-specific prediction model to recognize glossary terms based on the lexical and semantic context of the sentences mentioning domain-specific concepts. It then merges the aliases of the same concepts to their canonical names, selects a set of explanation sentences for each concept, and identifies "is a", "has a", and "related to" relations between the concepts. We apply our approach to deep learning domain and Hadoop domain and harvest 5,382 and 2,069 concepts together with 16,962 and 6,815 relations respectively. Our evaluation validates the accuracy of the extracted domain glossary and its usefulness for the fusion and acquisition of knowledge from different documents of different projects.
Refining the r-index

Gagie, Navarro and Prezza's r-index (SODA, 2018) promises to speed up DNA alignment and variation calling by allowing us to index entire genomic databases, provided certain obstacles can be overcome. In this paper we first strengthen and simplify Policriti and Prezza's Toehold Lemma (DCC '16; Algorithmica, 2017), which inspired the r-index and plays an important role in its implementation. We then show how to update the r-index efficiently after adding a new genome to the database, which is likely to be vital in practice. As a by-product of this result, we obtain an online version of Policriti and Prezza's algorithm for constructing the LZ77 parse from a run-length compressed Burrows-Wheeler Transform. Our experiments demonstrate the practicality of all three of these results. Finally, we show how to augment the r-index such that, given a new genome and fast random access to the database, we can quickly compute the matching statistics and maximal exact matches of the new genome with respect to the database.
Black Box Attacks on Deep Anomaly Detectors

The process of identifying the true anomalies from a given set of data instances is known as anomaly detection. It has been applied to address a diverse set of problems in multiple application domains including cybersecurity. Deep learning has recently demonstrated state-of-the-art performance on key anomaly detection applications, such as intrusion detection, Denial of Service (DoS) attack detection, security log analysis, and malware detection. Despite the great successes achieved by neural network architectures, models with very low test error have been shown to be consistently vulnerable to small, adversarially chosen perturbations of the input. The existence of evasion attacks during the test phase of machine learning algorithms represents a significant challenge to both their deployment and understanding.
DepecheMood++: A Bilingual Emotion Lexicon Built Through Simple Yet Powerful Techniques

Several lexica for sentiment analysis have been developed; while most of these come with word polarity annotations (e.g., positive/negative), attempts at building lexica for finer-grained emotion analysis (e.g., happiness, sadness) have recently attracted significant attention. They are often exploited as a building block for developing emotion recognition learning models, and/or used as baselines to which the performance of the models can be compared. In this work, we contribute two new resources, that we call DepecheMood++ (DM++): a) an extension of an existing and widely used emotion lexicon for English; and b) a novel version of the lexicon, targeting Italian. Furthermore, we show how simple techniques can be used, both in supervised and unsupervised experimental settings, to boost performance on datasets and tasks of varying degree of domain-specificity. Also, we report an extensive comparative analysis against other available emotion lexica and state-of-the-art supervised approaches, showing that DepecheMood++ emerges as the best-performing non-domain-specific lexicon in unsupervised settings. We also observe that simple learning models on top of DM++ can provide more challenging baselines. We finally introduce embedding-based methodologies to perform a) vocabulary expansion to address data scarcity and b) vocabulary porting to new languages in case training data is not available.
Sentiment Analysis for a Resource Poor Language—Roman Urdu

Sentiment analysis is an important sub-task of Natural Language Processing that aims to determine the polarity of a review. Most of the work done on sentiment analysis is for the resource-rich languages of the world, but very limited work has been done on resource-poor languages. In this work, we focus on developing a Sentiment Analysis System for Roman Urdu, which is a resource-poor language. To this end, a dataset of 11,000 reviews has been gathered from six different domains. Comprehensive annotation guidelines were defined and the dataset was annotated using the multi-annotator methodology. Using the annotated dataset, state-of-the-art algorithms were used to build a sentiment analysis system. To improve the results of these algorithms, four different studies were carried out based on: word-level features, character level features, and feature union. The best results showed that we could reduce the error rate by 12% from the baseline (80.07%). Also, to see if the improvements are statistically significant, we applied t-test and Confidence Interval on the obtained results and found that the best results of each study are statistically significant from the baseline.
Ludo game-based metaheuristics for global and engineering optimization

This paper proposes a Ludo game-based strategy to enhance the ability of swarm algorithms to solve numerous global optimization problems. The proposed strategy simulates the rules of playing the game Ludo using two or four players to perform the update process for different swarm intelligent behaviors. The proposed approach is named the Ludo Game-based Swarm Intelligence (LGSI) Algorithm. The LGSI algorithm uses the concepts of two and four players to enhance the exploration and exploitation of the optimization methods. In the proposed LGSI, a player is represented by a swarm algorithm, for example, in the two-player concept; Moth Flame Optimization (MFO) and the Grasshopper Optimization Algorithm (GOA) are selected, while in the four-player version, two other algorithms, the Sine Cosine Algorithm (SCA) and Gray Wolf Optimization (GWO), are added. In the proposed LGSI algorithm, the functional behaviors of all the used algorithms are different; also, there is no similarity among algorithmic behaviors except for convergence towards the global optimum, which is a common interest for all. However, the other algorithms share the same platform with this strategy, so in this case, competitive behavior may not be underestimated. The proposed LGSI algorithm shares positions among all the algorithms used during the search for the optimal solution. The performance of the LGSI algorithm is tested on a set of CEC2005 benchmark problems and engineering problems and is compared with the original versions of the utilized algorithms and a variety of other state-of-the-art algorithms. The experimental results show that the LGSI algorithm can provide promising and competitive results.
A heterogeneous online learning ensemble for non-stationary environments

Learning in non-stationary environments is a challenging task which requires the updating of predictive models to deal with changes in the underlying probability distribution of the problem, i.e., dealing with concept drift. Most work in this area is concerned with updating the learning system so that it can quickly recover from concept drift, while little work has been dedicated to investigating what type of predictive model is most suitable at any given time. This paper aims to investigate the benefits of online model selection for predictive modelling in non-stationary environments. A novel heterogeneous ensemble approach is proposed to intelligently switch between different types of base models in an ensemble to increase the predictive performance of online learning in non-stationary environments. This approach is Heterogeneous Dynamic Weighted Majority (HDWM). It makes use of "seed" learners of different types to maintain ensemble diversity, overcoming problems of existing dynamic ensembles that may undergo loss of diversity due to the exclusion of base learners. The algorithm has been evaluated on artificial and real-world data streams against existing well-known approaches such as a heterogeneous Weighted Majority Algorithm (WMA) and a homogeneous Dynamic Weighted Majority (DWM). The results show that HDWM performed significantly better than WMA in non-stationary environments. Also, when recurring concept drifts were present, the predictive performance of HDWM showed an improvement over DWM.
Context-Aware Implicit Authentication of Smartphone Users Based on Multi-Sensor Behavior

Implicit authentication is a new research direction to enhance the privacy protection of smartphones. However, implicit authentication has low robustness due to its vulnerability to environment. Motivated by this, this paper proposes a context-aware implicit authentication, which is a scheme to improve the robustness of authentication by introducing context awareness module. In the scheme, multi-sensor data (including accelerometer, gyroscope, magnetometer, timestamp, pressure, touch size) is first captured in a fine-grained manner to characterize one's touch action. Then, based on various sensors data, gesture features and touch features are extracted by employing both statistical method and distance measurement method. Particularly, one's body posture when touch action happens can be used as context. In each context, we present a weighted sum fusion rule to fuse the results of different features obtained by One-Class SVM (OC-SVM). We have collected 10000+ sensor data from 87 participants for experimental evaluation. The results show that the authentication in an unrestricted environment can achieve a best equal error rate (EER) of 0.0071%, which is more than one percent lower than the non-context-aware authentication. The proposed method can effectively improve the reliability and practicability of implicit authentication.
Probabilistic Deep Autoencoder for Power System Measurement Outlier Detection and Reconstruction

A probabilistic deep autoencoder is proposed to reconstruct power system measurements in this paper, which can be utilized in outlier detection and reconstruction. A nonparametric distribution estimation method is employed to capture the uncertainty information of the measured data. The estimated confidence intervals of the measured data are extracted from the estimated distribution and used as input to the first layer of neural networks. Through multilayer encoding and decoding processes, the intervals of measurements are reconstructed, which are further applied to detect and replace outliers. Simulation results verify the effectiveness of the proposed method.
Modeling and Enforcing Blockchain-Based Choreographies

Distributed Ledger Technology (DLT) and blockchains in particular have been identified as promising foundations to realize inter-organizational business processes. Capabilities such as shared data and decision logic defined as smart contracts open up entirely new ways to implement process choreographies. However, current choreography modeling languages solely focus on direct interactions between organizations; they do not take into account the conceptually new features of blockchains, like shared data and smart contracts. To bridge the gap between choreography modeling and implementation, this paper critically analyzes the assumptions of choreography languages. We propose new language concepts specifically targeting blockchain capabilities, and we define their operational semantics. Our work is evaluated with a proof-of-concept implementation and an analysis of three real-world case studies from the private and the corporate sectors.
A Forensics-by-Design Management Framework for Medical Devices Based on Blockchain

The Internet of Medical Things (IoMT) provides ubiquitous healthcare services for patient monitoring and treatment. However, the interaction between doctors, patients, healthcare personnel and device manufacturers, with different and often conflicting security and privacy objectives, make such services vulnerable and subject to exploitation. In addition, since parties may require different access levels and the IoMT devices involve different functionalities, access control can be challenging. In this paper, we propose a blockchain-enabled authorization framework for managing both IoMT devices and medical files by creating a distributed chain of custody and health data privacy scheme. The core idea is to build trust domains for the various stakeholders and IoMT devices, in such a way that fine-grain access is enabled by taking into account critical attributes of the IoMT ecosystem such as a) the different roles and capabilities of the IoMT devices and b) their interaction with the users/stakeholders. A private blockchain is used in combination with on-chain smart contracts to allow for a forensics-by-design management architecture with audit trails for integrity and provenance guarantees as well as health data privacy. The private blockchain ecosystem is authenticated by a proof-of-medical-stake consensus mechanism that is tailored for medical applications.
Semantic Segmentation of Video Sequences with Convolutional LSTMs

Most of the semantic segmentation approaches have been developed for single image segmentation, and hence, video sequences are currently segmented by processing each frame of the video sequence separately. The disadvantage of this is that temporal image information is not considered, which improves the performance of the segmentation approach. One possibility to include temporal information is to use recurrent neural networks. However, there are only a few approaches using recurrent networks for video segmentation so far. These approaches extend the encoder-decoder network architecture of well-known segmentation approaches and place convolutional LSTM layers between encoder and decoder. However, in this paper it is shown that this position is not optimal, and that other positions in the network exhibit better performance. Nowadays, state-of-the-art segmentation approaches rarely use the classical encoder-decoder structure, but use multi-branch architectures. These architectures are more complex, and hence, it is more difficult to place the recurrent units at a proper position. In this work, the multi-branch architectures are extended by convolutional LSTM layers at different positions and evaluated on two different datasets in order to find the best one. It turned out that the proposed approach outperforms the pure CNN-based approach for up to 1.6 percent.