Reviewerly/exHarmony huggingface dataset:
Blockchain in IoT Systems: End-to-End Delay Evaluation

Providing security and privacy for the Internet of Things (IoT) applications while ensuring a minimum level of performance requirements is an open research challenge.Recently, Blockchain offers a promising solution to overcome the current peer-to-peer networks limitations.In the context of IoT, Byzantine Fault Tolerance-based (BFT) consensus protocols are used due to the energy efficiency advantage over other consensus protocols.The consensus process in BFT is done by electing a group of authenticated nodes.The elected nodes will be responsible for ensuring the data blocks' integrity through defining a total order on the blocks and preventing the concurrently appended blocks from containing conflicting data.However, the Blockchain Consensus Layer contributes the most performance overhead.Therefore, a performance study needs to be conducted especially for the IoT applications that are subject to maximum delay constraints.In this paper, we obtain a mathematical expression to calculate the end-to-end delay with different network configurations, i.e., number of network hops and replica machines.We validate the proposed analytical model with simulation.Our results show that the unique characteristics of IoT traffic have an undeniable impact on the end-to-end delay requirement.
Natural Scene Text Recognition Based on Encoder-Decoder Framework

Aiming at the situation that complex natural scene text is difficult to recognize a scene text recognition method based on an encoder-decoder framework is proposed. The method converts the natural text recognition into a sequence mark by combining the connection time classification (CTC) and attention mechanism under the encoder-decoder framework, in order to overcome the problem of character segmentation, using the correlation between image and text sequence. First of all, a convolutional neural network (CNN) is used to generate an ordered feature sequence from the entire word image. Then, the generated feature sequence is feature-coded using the bidirectional long short-term memory (Bi-LSTM) network. Finally, an integrated module of the CTC and attention mechanism is designed to decode and output the text sequence. The experiments show that compared with the comparison method, the recognition accuracy of the method is improved obviously.
Nighttime image enhancement using a new illumination boost algorithm

Nighttime images are often obtained with low brightness, deficient contrast, and latent colours. Thus, it is important to improve such aspects in order to obtain acceptable quality images. Hence, a new illumination boost algorithm is proposed in this study, in which it can improve the brightness, ameliorate contrast and process the colours of nighttime images properly. Accordingly, the proposed algorithm utilises only a small number of steps and uses several processing concepts to achieve the desired results. Intensive experiments and tests with various natural-degraded nighttime images are made to validate the performance of the proposed algorithm. In addition, it is compared with eight contemporary algorithms, and the obtained results from these comparisons are evaluated using two specialised image quality assessment metrics. Using the results of the achieved experiments and comparisons, it became evident that the proposed algorithm can provide satisfactory outcomes, in which it provided visually pleasing results and outperformed the comparison algorithms in terms of scored accuracy and visual quality.
No-Reference Video Quality Estimation Based on Machine Learning for Passive Gaming Video Streaming Applications

Recent years have seen increasing growth and popularity of gaming services, both interactive and passive. While interactive gaming video streaming applications have received much attention, passive gaming video streaming, in-spite of its huge success and growth in recent years, has seen much less interest from the research community. For the continued growth of such services in the future, it is imperative that the end user gaming quality of experience (QoE) is estimated so that it can be controlled and maximized to ensure user acceptance. Previous quality assessment studies have shown not so satisfactory performance of existing No-reference (NR) video quality assessment (VQA) metrics. Also, due to the inherent nature and different requirements of gaming video streaming applications, as well as the fact that gaming videos are perceived differently from non-gaming content (as they are usually computer generated and contain artificial/synthetic content), there is a need for application-specific light-weight, no-reference gaming video quality prediction models. In this paper, we present two NR machine learning-based quality estimation models for gaming video streaming, NR-GVSQI, and NR-GVSQE, using NR features, such as bitrate, resolution, and temporal information. We evaluate their performance on different gaming video datasets and show that the proposed models outperform the current state-of-the-art no-reference metrics, while also reaching a prediction accuracy comparable to the best known full reference metric.
Adaptive Hypergraph Embedded Semi-Supervised Multi-Label Image Annotation

Multilabel image annotation attracts a lot of research interest due to its practicability in multimedia and computer vision fields, while the need for a large amount of labeled training data to achieve promising performance makes it a challenging task. Fortunately, unlabeled and relevant data are widely available and these data can be used to serve the annotation task. To this end, we propose a novel adaptive hypergraph learning (AHL) method for multilabel image annotation in a semisupervised way, in which both the limited labeled data and abundant unlabeled data are utilized to facilitate the annotation performance. In detail, we seek a multilabel propagation scheme by learning a hypergraph which is used to preserve the local geometric structures of data in a high-order manner. Meanwhile, a feature projection is integrated into AHL to obtain a latent feature space where unlabeled instances can be effectively and robustly assigned with multiple labels. Experiments on six widely used image datasets are conducted to evaluate our model and the results demonstrate that the proposed AHL outperforms other state-of-the-art semisupervised methods.
Toward Robust Audio Spoofing Detection: A Detailed Comparison of Traditional and Learned Features

Automatic speaker verification, such as every other biometric system, is vulnerable to spoofing attacks. Using only a few minutes of recorded voice from a genuine client of a speaker verification system, attackers can develop a variety of spoofing attacks that might trick such systems. Detecting these attacks using the audio cues present in the recordings is an important challenge. Most existing spoofing detection systems depend on knowing the used spoofing technique. With this research, we aim at overcoming this limitation, by examining robust audio features, both traditional and those learned through an autoencoder, which is generalizable to different types of replay spoofing. Furthermore, we provide a detailed account of all the steps necessary in setting up the state-of-the-art audio feature detection, preprocessing, and postprocessing, such that the (non-audio expert) machine learning researcher can implement such systems. Finally, we evaluate the performance of our robust replay spoofing detection system with a wide variety and different combinations of both extracted and machine-learned audio features on the “out in the wild” ASVspoof 2017 dataset. This dataset contains a variety of new replay spoofing configurations. Since our focus is on examining which features will ensure robustness, we base our system on a traditional Gaussian mixture model-universal background model (GMM-UBM). We then systematically investigate the relative contribution of each feature set. The fused models based on both the known audio features and the machine learned features, respectively, have a comparable performance with an equal error rate (EER) of 12. The final best performing model, which obtains an EER of 10.8, is a hybrid system that contains both known and machine-learned features and is trained on an augmented dataset, thus revealing the importance of incorporating both types of features when developing a robust spoofing prediction model.
AsmDB

The large instruction working sets of private and public cloud workloads lead to frequent instruction cache misses and costs in the millions of dollars. While prior work has identified the growing importance of this problem, to date, there has been little analysis of where the misses come from, and what the opportunities are to improve them. To address this challenge, this paper makes three contributions. First, we present the design and deployment of a new, always-on, fleet-wide monitoring system, AsmDB, that tracks front-end bottlenecks. AsmDB uses hardware support to collect bursty execution traces, fleet-wide temporal and spatial sampling, and sophisticated offline post-processing to construct full-program dynamic control-flow graphs. Second, based on a longitudinal analysis of AsmDB data from real-world online services, we present two detailed insights on the sources of front-end stalls: (1) cold code that is brought in along with hot code leads to significant cache fragmentation and a corresponding large number of instruction cache misses; (2) distant branches and calls that are not amenable to traditional cache locality or next-line prefetching strategies account for a large fraction of cache misses. Third, we prototype two optimizations that target these insights. For misses caused by fragmentation, we focus on memcmp, one of the hottest functions contributing to cache misses, and show how fine-grained layout optimizations lead to significant benefits. For misses at the targets of distant jumps, we propose new hardware support for software code prefetching and prototype a new feedback-directed compiler optimization that combines static program flow analysis with dynamic miss profiles to demonstrate significant benefits for several large warehouse-scale workloads. Improving upon prior work, our proposal avoids invasive hardware modifications by prefetching via software in an efficient and scalable way. Simulation results show that such an approach can eliminate up to 96% of instruction cache misses with negligible overheads.
Revisiting kd-tree for Nearest Neighbor Search

\kdtree \citefriedman1976algorithm has long been deemed unsuitable for exact nearest-neighbor search in high dimensional data. The theoretical guarantees and the empirical performance of \kdtree do not show significant improvements over brute-force nearest-neighbor search in moderate to high dimensions. \kdtree has been used relatively more successfully for approximate search \citemuja2009flann but lack theoretical guarantees. In the article, we build upon randomized-partition trees \citedasgupta2013randomized to propose \kdtree based approximate search schemes with $O(d łog d + łog n)$ query time for data sets with n points in d dimensions and rigorous theoretical guarantees on the search accuracy. We empirically validate the search accuracy and the query time guarantees of our proposed schemes, demonstrating the significantly improved scaling for same level of accuracy.
Towards an Autonomous Bot for Automatic Source Code Refactoring

Continuous refactoring is necessary to maintain source code quality and to cope with technical debt. Since manual refactoring is inefficient and error-prone, various solutions for automated refactoring have been proposed in the past. However, empirical studies have shown that these solutions are not widely accepted by software developers and most refactorings are still performed manually. For example, developers reported that refactoring tools should support functionality for reviewing changes. They also criticized that introducing such tools would require substantial effort for configuration and integration into the current development environment. In this paper, we present our work towards the Refactoring-Bot, an autonomous bot that integrates into the team like a human developer via the existing version control platform. The bot automatically performs refactorings to resolve code smells and presents the changes to a developer for asynchronous review via pull requests. This way, developers are not interrupted in their workflow and can review the changes at any time with familiar tools. Proposed refactorings can then be integrated into the code base via the push of a button. We elaborate on our vision, discuss design decisions, describe the current state of development, and give an outlook on planned development and research activities.
Ontop-spatial: Ontop of geospatial databases

In this paper, we propose an OBDA approach for accessing geospatial data stored in relational databases using the R2RML mappings and OGC standard GeoSPARQL. On the theoretical side, we provide a formalization of GeoSPARQL in terms of SPARQL entailment regime. For a practical query answering algorithm, we introduce an extension to the existing SPARQL-to-SQL translation method to support GeoSPARQL features. Our approach has been implemented in the system Ontop-spatial, an extension of the OBDA system Ontop for creating virtual geospatial RDF graphs on top of geospatial relational databases. We present an experimental evaluation of our system using and extending a state-of-the-art benchmark. To measure the performance of our system, we compare it to two state-of-the-art geospatial RDF stores –a free and open-source one, and a commercial one– and confirm its efficiency.
The Missing Pieces of Artificial Intelligence in Medicine

Stakeholders across the entire healthcare chain are looking to incorporate artificial intelligence (AI) into their decision-making process. From early-stage drug discovery to clinical decision support systems, we have seen examples of how AI can improve efficiency and decrease costs. In this Opinion, we discuss some of the key factors that should be prioritized to enable the successful integration of AI across the healthcare value chain. In particular, we believe a focus on model interpretability is crucial to obtain a deeper understanding of the underlying biological mechanisms and guide further investigations. Additionally, we discuss the importance of integrating diverse types of data within any AI framework to limit bias, increase accuracy, and model the interdisciplinary nature of medicine. We believe that widespread adoption of these practices will help accelerate the continued integration of AI into our current healthcare framework.
Tighter Proofs of CCA Security in the Quantum Random Oracle Model

We revisit the construction of IND-CCA secure key encapsulation mechanisms (KEM) from public-key encryption schemes (PKE). We give new, tighter security reductions for several constructions. Our main result is an improved reduction for the security of the $$U^{\not \bot }$$ -transform of Hofheinz, Hövelmanns, and Kiltz (TCC’17) which turns OW-CPA secure deterministic PKEs into IND-CCA secure KEMs. This result is enabled by a new one-way to hiding (O2H) lemma which gives a tighter bound than previous O2H lemmas in certain settings and might be of independent interest. We extend this result also to the case of PKEs with non-zero decryption failure probability and non-deterministic PKEs. However, we assume that the derandomized PKE is injective with overwhelming probability. In addition, we analyze the impact of different variations of the $$U^{\not \bot }$$ -transform discussed in the literature on the security of the final scheme. We consider the difference between explicit ( $$U^{\bot }$$ ) and implicit ( $$U^{\not \bot }$$ ) rejection, proving that security of the former implies security of the latter. We show that the opposite direction holds if the scheme with explicit rejection also uses key confirmation. Finally, we prove that (at least from a theoretic point of view) security is independent of whether the session keys are derived from message and ciphertext ( $$U^{\not \bot }$$ ) or just from the message ( $$U^{\not \bot }_m$$ ).
Class Imbalance Evolution and Verification Latency in Just-in-Time Software Defect Prediction

Just-in-Time Software Defect Prediction (JIT-SDP) is an SDP approach that makes defect predictions at the software change level. Most existing JIT-SDP work assumes that the characteristics of the problem remain the same over time. However, JIT-SDP may suffer from class imbalance evolution. Specifically, the imbalance status of the problem (i.e., how much underrepresented the defect-inducing changes are) may be intensified or reduced over time. If occurring, this could render existing JIT-SDP approaches unsuitable, including those that re-build classifiers over time using only recent data. This work thus provides the first investigation of whether class imbalance evolution poses a threat to JIT-SDP. This investigation is performed in a realistic scenario by taking into account verification latency -- the often overlooked fact that labeled training examples arrive with a delay. Based on 10 GitHub projects, we show that JIT-SDP suffers from class imbalance evolution, significantly hindering the predictive performance of existing JIT-SDP approaches. Compared to state-of-the-art class imbalance evolution learning approaches, the predictive performance of JIT-SDP approaches was up to 97.2% lower in terms of g-mean. Hence, it is essential to tackle class imbalance evolution in JIT-SDP. We then propose a novel class imbalance evolution approach for the specific context of JIT-SDP. While maintaining top ranked g-means, this approach managed to produce up to 63.59% more balanced recalls on the defect-inducing and clean classes than state-of-the-art class imbalance evolution approaches. We thus recommend it to avoid overemphasizing one class over the other in JIT-SDP.
Applying augmented reality in physical education on motor skills learning

Physical education is a course that integrates knowledge of sports with skill drilling. Augmented Reality (AR)-assisted instruction has infrequently been applied in sport skill drilling. Video-assisted instruction has frequently applied to physical sports; however, it neither involves interactive practice nor embodies both textbook learning and the practice of sporting skills simultaneously. As a complement to video-assisted instruction, AR can apply virtual messages to learning objects so that 3-dimensional models can be superimposed into textbooks, which allows learners to read books while operating 3D character models. To verify the effects of learning outcomes, motor skills, and learning motivation with AR-assisted instruction and the effects of different difficulty levels on instruction materials, two experimental studies were implemented. Simple and basic running actions and more difficult Mark exercise actions were chosen as the teaching content. The experiments adopted a quasi-experimental design. The findings indicate that AR-assisted instruction is more effective than video-assisted instruction, and the effects are better for more difficult motor skills learning.
Exemplar Guided Face Image Super-Resolution Without Facial Landmarks

Nowadays, due to the ubiquitous visual media there are vast amounts of already available high-resolution (HR) face images. Therefore, for super-resolving a given very low-resolution (LR) face image of a person it is very likely to find another HR face image of the same person which can be used to guide the process. In this paper, we propose a convolutional neural network (CNN)-based solution, namely GWAInet, which applies super-resolution (SR) by a factor 8x on face images guided by another unconstrained HR face image of the same person with possible differences in age, expression, pose or size. GWAInet is trained in an adversarial generative manner to produce the desired high quality perceptual image results. The utilization of the HR guiding image is realized via the use of a warper subnetwork that aligns its contents to the input image and the use of a feature fusion chain for the extracted features from the warped guiding image and the input image. In training, the identity loss further helps in preserving the identity related features by minimizing the distance between the embedding vectors of SR and HR ground truth images. Contrary to the current state-of-the-art in face super-resolution, our method does not require facial landmark points for its training, which helps its robustness and allows it to produce fine details also for the surrounding face region in a uniform manner. Our method GWAInet produces photo-realistic images in upscaling factor 8x and outperforms state-of-the-art in quantitative terms and perceptual quality.
Sparse and low-redundant subspace learning-based dual-graph regularized robust feature selection

Feature selection can reduce the dimension of data and select the representative features. The available researches have shown that the underlying geometric structures of both the data and the feature manifolds are important for feature selection. However, few feature selection methods utilize the two geometric structures simultaneously in subspace learning. To solve this issue, this paper proposes a novel algorithm, called sparse and low-redundant subspace learning-based dual-graph regularized robust feature selection (SLSDR). Based on the framework of subspace learning-based graph regularized feature selection, SLSDR extends it by introducing the data graph. Specifically, both data graph and feature graph are introduced into subspace learning, so SLSDR preserves the geometric structures of the data and feature manifolds, simultaneously. Consequently, the features which best preserve the manifold structures are selected. Additionally, the inner product regularization term, which guarantees the sparsity of rows and considers the correlations between features, is imposed on the feature selection matrix to select the representative and low-redundant features. Meanwhile, the l2,1-norm is imposed on the residual matrix of subspace learning to ensure the robustness to outlier samples. Experimental results on twelve benchmark datasets show that the proposed SLSDR is superior to the six state-of-the-art algorithms from the literature.
Digital neocolonialism and massive open online courses (MOOCs): colonial pasts and neoliberal futures

Through evaluating dominant MOOC platforms created by Western universities, I argue that MOOCs on such platforms tend to embed Western-centric epistemologies and propagate this without questioning their global relevance. Consequently, such MOOCs can be detrimental when educating diverse and complex participants as they erode local and indigenous knowledge systems. Arguing that the digital divide is an exacerbation of historical inequalities, I draw parallels between colonial education, specifically across Sub-Saharan Africa, and 'digital neocolonialism' through Western MOOC platforms. I analyse similarities in ideology, assumptions, and methods of control. Highlighting evolving forms of coloniality, I include contemporary problems created by neoliberal techno-capitalist agendas, such as the commodification of education. Balance is needed between the opportunities offered through MOOCs and the harms they cause through overshadowing marginalised knowledges and framing disruptive technologies as the saviour. While recommending solutions for inclusion of marginalised voices, further problems such as adverse incorporation are raised.