Reviewerly/exHarmony huggingface dataset:
A Grid in Flux: Sound and Timing in Electronic Dance Music

Abstract Researchers have argued that temporal microdeviations from the metric grid, such as those produced by musicians in performance, are crucial to making a musical rhythm groovy and danceable. It is curious, then, that the music currently dominating the dance floor, “electronic dance music” or EDM, is typically characterized by grid-based rhythms. But is such a “mechanistic,” grid-based aesthetic necessarily devoid of microrhythmic nuance? In this article, we aim to show that the microrhythmic component of an engaging groove involves the manipulation of more than simply the onset locations of rhythmic events—the sonic features fundamentally contribute to shaping the groove as well. In particular, we seek to demonstrate that EDM producers, with their preference for a grid-based microtiming aesthetic, are very sensitive to and adept at manipulating such sonic features for expressive effect. Drawing on interviews with EDM producers, we show that producers are often concerned with both sonic and temporal features, as well as their interactions. We argue that sonic features are crucial to shaping groove and feel at the micro level of rhythm. Moreover, such features also tend to introduce an indirect microtiming aspect to the grid-based aesthetic of EDM through the ways in which they shape timing at the perceptual level.
Security Framework for Identifying threats in Smart Manufacturing Systems Using STRIDE Approach

Cyber security is still the main argument in any system development lifecycle that needs more concern. There are still numerous challenges associated with conducting a threat modeling approach for smart manufacturing systems. One of these challenges is the lack of a threat database based on the expertise of highly skilled researchers in this field. Hence this study attempts to address this gap by developing a components catalog and a rule-based threat database to address potential security threats in smart manufacturing systems saving time and effort. Specifically, it performs STRIDE-based threat modeling against a smart factory use case using Microsoft Threat Modelling Tool. The threat evaluation process is conducted with this research to determine the severity of threats and give a preliminary estimation of the overall system's risk.
Learning with Label Noise for Image Retrieval by Selecting Interactions

Learning with noisy labels is an active research area for image classification. However, the effect of noisy labels on image retrieval has been less studied. In this work, we propose a noise-resistant method for image retrieval named Teacher-based Selection of Interactions, T-SINT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , which identifies noisy interactions, i.e. elements in the distance matrix, and selects correct positive and negative interactions to be considered in the retrieval loss by using a teacher-based training setup which contributes to the stability. As a result, it consistently outperforms state-of-the-art methods on high noise rates across benchmark datasets with synthetic noise and more realistic noise.
“Standardizing information security – a structurational analysis”

Given that there are an increasing number of information security breaches, organizations are being driven to adopt best practice for coping with attacks. Information security standards are designed to embody best practice and the legitimacy of these standards is a core issue for standardizing organizations. This study uncovers how structures at play in de jure standard development affect the input and throughput legitimacy of standards. We participated as members responsible for standards on information security and our analysis revealed two structures: consensus and warfare. A major implication of the combination of these structures is that legitimacy claims based on appeals to best practice are futile because it is difficult to know which the best practice is.
A study on the use of vulnerabilities databases in software engineering domain

Over the last decade several software vulnerability databases have been introduced to guide researchers and developers in developing more secure and reliable software. While the Software Engineering research community is increasingly becoming aware of these vulnerabilities databases, no comprehensive literature survey exists that studies how they are used in software development. The objective of our survey is to provide insights on how the software vulnerability database (SVDBs) research landscape has evolved over the past 17 years and outline some open challenges associated with their use in non-security domain. More specifically, we introduce a semi-automated methodology based on topic modeling, to discover relevant topics from our dataset of 99 relevant SE research articles. We find 24 topics discussing the use of SVDBs in SE domain. The results shows that i) topics describing the use of SVDBs range from security empirical (case) studies to tools for generating security test cases; ii) the majority of the surveyed papers cover a limited number of software engineering contributions or activities (e.g., maintenance) and iii) that most of the surveyed articles rely on only one SVDB as their knowledge source. Dataset and results are available at https://github.com/isultane/svdbs_dataset
Requirements, Limitations and Recommendations for Enabling End-to-End Quality of Context-Awareness in IoT Middleware

Satisfying a context consumer's quality of context (QoC) requirements is important to context management platforms (CMPs) in order to have credibility. QoC indicates the contextual information's quality metrics (e.g., accuracy, timeliness, completeness). The outcomes of these metrics depend on the functional and quality characteristics associated with all actors (context consumers (or) context-aware applications, CMPs, and context providers (or) IoT-data providers) in context-aware IoT environments. This survey identifies and studies such characteristics and highlights the limitations in actors' current functionalities and QoC modelling approaches to obtain adequate QoC and improve context consumers' quality of experience (QoE). We propose a novel concept system based on our critical analysis; this system addresses the functional limitations in existing QoC modelling approaches. Moreover, we highlight those QoC metrics affected by quality of service (QoS) metrics in CMPs. These recommendations provide CMP developers with a reference system they could incorporate, functionalities and QoS metrics to maintain in order to deliver an adequate QoC.
Bipartite graph capsule network

Abstract Graphs have been widely adopted in various fields, where many graph models are developed. Most of previous research focuses on unipartite or homogeneous graph analysis. In this graphs, the relationships between the same type of entities are preserved in the graphs. Meanwhile, the bipartite graphs that model the complex relationships among different entities with vertices partitioned into two disjoint sets, are becoming increasing popular and ubiquitous in many real life applications. Though several graph classification methods on unipartite and homogenous graphs have been proposed by using kernel method, graph neural network, etc. However, these methods are unable to effectively capture the hidden information in bipartite graphs. In this paper, we propose the first bipartite graph-based capsule network, namely Bipartite Capsule Graph Neural Network (BCGNN), for the bipartite graph classification task. BCGNN exploits the capsule network and obtains information between the same type vertices in the bipartite graphs by constructing the one-mode projection. Extensive experiments are conducted on real-world datasets to demonstrate the effectiveness of our proposed method.
One-Class Learned Encoder-Decoder Network with Adversarial Context Masking for Novelty Detection

Novelty detection is the task of recognizing samples that do not belong to the distribution of the target class. During training, the novelty class is absent, preventing the use of traditional classification approaches. Deep autoencoders have been widely used as a base of many novelty detection methods. In particular, context autoencoders have been successful in the novelty detection task because of the more effective representations they learn by reconstructing original images from randomly masked images. However, a significant drawback of context autoencoders is that random masking fails to consistently cover important structures of the input image, leading to suboptimal representations - especially for the novelty detection task. In this paper, to optimize input masking, we introduce a Mask Module that learns to generate optimal masks and a Reconstructor that aims to reconstruct masked images. The networks are trained in an adversarial setting in which the Mask Module seeks to maximize the reconstruction error that the Reconstructor is minimizing. When applied to novelty detection, the proposed approach learns semantically richer representations compared to context autoencoders and enhances novelty detection at test time through more optimal masking. Novelty detection experiments on the MNIST and CIFAR-10 image datasets demonstrate the proposed approach’s superiority over cutting-edge methods. In a further experiment on the UCSD video dataset for novelty detection, the proposed approach achieves a frame-level Area Under the Curve (AUC) of 99.02% and an Equal Error Rate (EER) of 5.4%, exceeding recent state-of-the-art models. Code available at https://github.com/jewelltaylor/OLED.
Variational Stacked Local Attention Networks for Diverse Video Captioning

While describing spatiotemporal events in natural language, video captioning models mostly rely on the en-coder’s latent visual representation. Recent progress on the encoder-decoder model attends encoder features mainly in linear interaction with the decoder. However, growing model complexity for visual data encourages more explicit feature interaction for fine-grained information, which is currently absent in the video captioning domain. Moreover, feature aggregations methods have been used to un-veil richer visual representation, either by the concatenation or using a linear layer. Though feature sets for a video semantically overlap to some extent, these approaches result in objective mismatch and feature redundancy. In addition, diversity in captions is a fundamental component of expressing one event from several meaningful perspectives, currently missing in the temporal, i.e., video captioning domain. To this end, we propose Variational Stacked Local Attention Network (VSLAN), which exploits low-rank bilinear pooling for self-attentive feature interaction and stacking multiple video feature streams in a discount fashion. Each feature stack’s learned attributes contribute to our proposed diversity encoding module, followed by the decoding query stage to facilitate end-to-end diverse and natural captions without any explicit supervision on attributes. We evaluate VSLAN on MSVD and MSR-VTT datasets in terms of syntax and diversity. The CIDEr score of VSLAN outperforms current off-the-shelf methods by 7.8% on MSVD and 4.5% on MSR-VTT, respectively. On the same datasets, VSLAN achieves competitive results in caption diversity metrics.
Blockchain for the Internet of Drones: Applications, Challenges, and Future Directions

Drones, or unmanned aerial vehicles (UAVs), which started as a byproduct of military research, are passing through their most popular period and are being used on broader horizons, and more possibilities are being explored in commercial applications. The evolution of drones is quite interesting, and this article provides some of the key chronology of different time quanta related to the emergence of drones in the global market. During this journey, the size-shape-form of drones has also evolved; thus, different drone topologies are also discussed here. In addition, we have considered the wide range of drone applications (every nook and cranny) for a comprehensive discussion. This article discusses the emergence of the Internet of Drones and its industrial application perspective. Currently, its advancement brings key concerns, and security has always been a concern for these manless machines. Thus, we highlight the key security concerns and then propose the trending blockchain as a key solution for these concerns. However, blockchain-drone coalescence has always witnessed key concerns in the research community. We discuss these concerns, and furthermore present some derived blockchain architectures that tend to resolve these concerns. The latter part of this article presents a case study for drone-based community delivery in pandemic-like situations using a derived blockchain architecture. The article concludes with the key open issues and future challenges.
PERF-Net: Pose Empowered RGB-Flow Net

In recent years, many works in the video action recognition literature have shown that two stream models (combining spatial and temporal input streams) are necessary for achieving state-of-the-art performance. In this paper we show the benefits of including yet another stream based on human pose estimated from each frame — specifically by rendering pose on input RGB frames. At first blush, this additional stream may seem redundant given that human pose is fully determined by RGB pixel values — however we show (perhaps surprisingly) that this simple and flexible addition can provide complementary gains. Using this insight, we propose a new model, which we dub PERF-Net (short for Pose Empowered RGB-Flow Net), which combines this new pose stream with the standard RGB and flow based input streams via distillation techniques and show that our model outperforms the state-of-the-art by a large margin in a number of human action recognition datasets while not requiring flow or pose to be explicitly computed at inference time. The proposed pose stream is also part of the winner solution of the ActivityNet Kinetics Challenge 2020 [1].
Statistical methods for evaluating the fine needle aspiration cytology procedure in breast cancer diagnosis

Statistical issues present while evaluating a diagnostic procedure for breast cancer are non rare but often ignored, leading to biased results. We aimed to evaluate the diagnostic accuracy of the fine needle aspiration cytology(FNAC), a minimally invasive and rapid technique potentially used as a rule-in or rule-out test, handling its statistical issues: suspect test results and verification bias.We applied different statistical methods to handle suspect results by defining conditional estimates. When considering a partial verification bias, Begg and Greenes method and multivariate imputation by chained equations were applied, however, and a Bayesian approach with respect to each gold standard was used when considering a differential verification bias. At last, we extended the Begg and Greenes method to be applied conditionally on the suspect results.The specificity of the FNAC test above 94%, was always higher than its sensitivity regardless of the proposed method. All positive likelihood ratios were higher than 10, with variations among methods. The positive and negative yields were high, defining precise discriminating properties of the test.The FNAC test is more likely to be used as a rule-in test for diagnosing breast cancer. Our results contributed in advancing our knowledge regarding the performance of FNAC test and the methods to be applied for its evaluation.
ResNet50-Based Effective Model for Breast Cancer Classification Using Histopathology Images

Breast cancer is considered an immense threat and one of the leading causes of mortality in females. It is curable only when detected at an early stage. A standard cancer diagnosis approach involves detection of cancer-related anomalies in tumour histopathology images. Detection depends on the accurate identification of the landmarks in the visual artefacts present in the slide images. Researchers are continuously striving to develop automatic machine-learning algorithms for processing medical images to assist in tumour detection. Nowadays, computer-based automated systems play an important role in cancer image analysis and help healthcare experts make rapid and correct inferences about the type of cancer. This study proposes an effective convolutional neural network-based (CNN-based) model that exploits the transfer-learning technique for automatic image classification between malignant and benign tumour, using histopathology images. Resnet50 architecture has been trained on new dataset for feature extraction, and fully connected layers have been fine-tuned for achieving highest training, validation and test accuracies. The result illustrated state-of-the-art performance of the proposed model with highest training, validation and test accuracies as 99.70%, 99.24% and 99.24%, respectively. Classification accuracy is increased by 0.66% and 0.2% when compared with similar recent studies on training and test data results. Average precision and F1 score have also improved, and receiver operating characteristic (RoC) area has been achieved to 99.1%. Thus, a reliable, accurate and consistent CNN model based on pre-built Resnet50 architecture has been developed.
A fusion of dolphin swarm optimization and improved sine cosine algorithm for automatic detection and classification of objects from surveillance videos

Surveillance is the utmost extensively used technology in the current scenario. In real-time, it is immensely applicable in all domains to monitor, identifying the moving objects, and tracking through computer vision. The object detection and classification is an important process in surveillance video. During this task, the visual appearance will change according to the viewing angles, lightening, and distance from the camera. It is necessary to improve the efficiency in real-time detection and classification of objects from surveillance videos. To obtain this, we proposed a fusion of Dolphin Swarm Optimization (DSO) and improved Sine Cosine algorithm (ISCA) based Support Vector Machine (SVM) classifier which includes the following steps: Frame differencing for foreground segmentation, Histogram of Oriented Gradients (HOG) for feature extraction, and DSO-ISCA-SVM classifier for classification. Initially, the surveillance videos are collected and the acquisition of images from the surveillance video camera. Secondly, the moving objects are detected by frame differencing in which the difference between two frames are estimated and compared with the threshold value. Then the shadow and noise are removed. Thirdly, the HOG capture local shapes through gradients. Finally, the proposed DSO-ISCA-SVM classifier accurately classifies the objects from the surveillance video, the DSO-ISCA is used to find the SVM parameters. This proposed technique effectively detects and classify the objects from the surveillance videos. The proposed technique results are compared with other existing methods. The experimental results prove that the proposed method efficiency is better than the existing methods in terms of different evaluation metrics.
Attention - Based Non-Profiled Side-Channel Attack

Profiled attacks play a fundamental role in evaluating the security of cryptographic algorithm implementations in the worst case. In recent years, deep learning has been introduced as a new powerful alternative to achieve these attacks. Many works have demonstrated that deep learning could solve the desynchronization issue and break the masking countermeasures. From the early years when researchers explore the machine learning methods, there is a road map that the profiled attacks could be converted to non-profiled ones by on-the-fly strategy. In TCHES 2019, the Differential Deep Learning Analysis (DDLA) combining the deep learning and non-profiled attacks by on-the-fly strategy was proposed. The DDLA mainly uses the sensitivity analysis as the attack metric. In this paper, we propose a new metric, attention mechanism, to conduct deep learning-based non-profiled attacks. As a part of the feed-forward structure, when attention is used as the metric, it does not need additional gradient computations like the DDLA. Meanwhile, the attention probabilities also indicate where the intermediate leaks, not on the input traces but on the abstract feature vectors, which gives the attacker more insights into how the network makes the decision. Finally, we test our attention-based non-profiled attack on practical traces. The results show that it works well on desynchronized traces and masked implementations.
Disentangled Graph Recurrent Network for Document Ranking

Abstract BERT-based ranking models are emerging for its superior natural language understanding ability. All word relations and representations in the concatenation of query and document are modeled in the self-attention matrix as latent knowledge. However, some latent knowledge has none or negative effect on the relevance prediction between query and document. We model the observable and unobservable confounding factors in a causal graph and perform do-query to predict the relevance label given an intervention over this graph. For the observed factors, we block the back door path by an adaptive masking method through the transformer layer and refine word representations over this disentangled word graph through the refinement layer. For the unobserved factors, we resolve the do-operation query from the front door path by decomposing word representations into query related and unrelated parts through the decomposition layer. Pairwise ranking loss is mainly used for the ad hoc document ranking task, triangle distance loss is introduced to both the transformer and refinement layers for more discriminative representations, and mutual information constraints are put on the decomposition layer. Experimental results on public benchmark datasets TREC Robust04 and WebTrack2009-12 show that DGRe outperforms state-of-the-art baselines more than 2% especially for short queries.
Transformer Deep Learning Model for Bangla–English Machine Translation

Bangla is a widely spoken language, but unfortunately, very few researches in Machine Translation (MT) for Bangla have been reported in the literature. This research aims at developing an MT system for Bangla–English translation. MT is language-dependent as data preparation is different from language to language. Moreover, the vital part of MT is a model which requires training to adjust the particular language pair along with their grammar and phrase rules. Modern deep learning-based transformer model has been used for this language pair as it worked well for other language pairs. A transformer model comprising encoders and decoders is adapted by tuning the different parameter sets to identify the best performing model for Bangla–English translation. The proposed model is tested on a benchmark of Bangla–English corpus, which outperformed some prominent existing MT methods.
Robust dynamic phase-shifting common-path shearography using LCPG and pixelated micropolarizer array

A concise and dependable dynamic phase-shifting shearography system based on common-path interferometer is proposed. Two lateral sheared images with common optical path are generated using a liquid crystal polarization grating (LCPG). The sheared images of the object under test interfere with each other, and a pixelated micropolarizer array is used to introduce pixelated carrier, enabling the measurement of dynamic deformation using dynamic phase-shifting. Compared with the ordinary imaging system, the shearography system only needs to insert LCPG between an imaging lens and a camera with pixelated micropolarizer array. The system is resilient to environmental disturbances benefitting from the compact common-path and dynamic phase-shifting method. Theoretical analysis and experiments have been completed, showing that the system can fully exploit the advantages of shearography and improve the reliability of shearography applications.