Reviewerly/exHarmony huggingface dataset:
Localization-aware channel pruning for object detection

Channel pruning is one of the important methods for deep model compression. Most of existing pruning methods mainly focus on classification. Few of them conduct systematic research on object detection. However, object detection is different from classification, which requires not only semantic information but also localization information. In this paper, based on discrimination-aware channel pruning (DCP) which is state-of-the-art pruning method for classification, we propose a localization-aware auxiliary network to find out the channels with key information for classification and regression so that we can conduct channel pruning directly for object detection, which saves lots of time and computing resources. In order to capture the localization information, we first design the auxiliary network with a contextual RoIAlign layer which can obtain precise localization information of the default boxes by pixel alignment and enlarges the receptive fields of the default boxes when pruning shallow layers. Then, we construct a loss function for object detection task which tends to keep the channels that contain the key information for classification and regression. Extensive experiments demonstrate the effectiveness of our method. On MS COCO, we prune 70% parameters of the SSD based on ResNet-50 with modest accuracy drop, which outperforms the-state-of-art method.
CAVIAR: Context-driven Active and Incremental Activity Recognition

Activity recognition on mobile device sensor data has been an active research area in mobile and pervasive computing for several years. While the majority of the proposed techniques are based on supervised learning, semi-supervised approaches are being considered to reduce the size of the training set required to initialize the model. These approaches usually apply self-training or active learning to incrementally refine the model, but their effectiveness seems to be limited to a restricted set of physical activities. We claim that the context which surrounds the user (e.g., time, location, proximity to transportation routes) combined with common knowledge about the relationship between context and human activities could be effective in significantly increasing the set of recognized activities including those that are difficult to discriminate only considering inertial sensors, and the highly context-dependent ones. In this paper, we propose CAVIAR, a novel hybrid semi-supervised and knowledge-based system for real-time activity recognition. Our method applies semantic reasoning on context-data to refine the predictions of an incremental classifier. The recognition model is continuously updated using active learning. Results on a real dataset obtained from 26 subjects show the effectiveness of our approach in increasing the recognition rate, extending the number of recognizable activities and, most importantly, reducing the number of queries triggered by active learning. In order to evaluate the impact of context reasoning, we also compare CAVIAR with a purely statistical version, considering features computed on context-data as part of the machine learning process.
Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

Breast Cancer is a common dangerous disease for women. Around the world, many women have died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues, there are several techniques and methods. The image processing, machine learning, and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to save a women's life. To detect the breast masses, microcalcifications, and malignant cells,different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for breast cancer survival, it is essential to improve the methods or techniques to diagnose it at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are also challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.
Real-Time Acceleration-Continuous Path-Constrained Trajectory Planning With Built-In Tradeoff Between Cruise and Time-Optimal Motions

In this article, a novel real-time acceleration-continuous path-constrained trajectory planning algorithm is proposed with an appealing built-in tradeoff mechanism between the cruise motion and time-optimal motion. Different from existing approaches, the proposed approach smoothens time-optimal trajectories with bang-bang input structures to generate acceleration-continuous trajectories while preserving the completeness property. More importantly, a novel built-in tradeoff mechanism is proposed and embedded into the trajectory planning framework so that the proportion of the cruise motion and time-optimal motion can be flexibly adjusted by changing a user-specified functional parameter. Thus, the user can easily apply the trajectory planning algorithm for various tasks with different requirements on motion efficiency and cruise proportion. Moreover, it is shown that feasible trajectories are computed more quickly than optimal trajectories. Rigorous mathematical analysis and proofs are presented for those aforementioned theoretical results. Comparative simulations and experimental results on an omnidirectional wheeled mobile robot demonstrate that flexible tunings between the cruise and time-optimal motions can be achieved in a higher computational efficiency manner by the proposed algorithm. Note to Practitioners-This article is motivated by the time-optimal and smooth motion planning problem for mobile robots along given paths. Existing approaches generally use the piecewise polynomial interpolations to smoothen and adjust feasible trajectories. This article proposes a novel path-constrained trajectory planning approach, which preserves properties of completeness and a high-efficient tradeoff mechanism between the optimal and cruise motions when achieving a globally optimal and acceleration-continuous trajectory. Comparative experimental results with other methods show the effectiveness of the proposed approach. In future research, we will attempt to integrate the proposed approach with typical path planning methods to achieve a complete and high-efficient motion planning framework.
Source Domain Data Selection for Improved Transfer Learning Targeting Dysarthric Speech Recognition

This paper presents an improved transfer learning framework applied to robust personalised speech recognition models for speakers with dysarthria. As the baseline of transfer learning, a state-of-the-art CNN-TDNN-F ASR acoustic model trained solely on source domain data is adapted onto the target domain via neural network weight adaptation with the limited available data from target dysarthric speakers. Results show that linear weights in neural layers play the most important role for an improved modelling of dysarthric speech evaluated using UASpeech corpus, achieving averaged 11.6% and 7.6% relative recognition improvement in comparison to the conventional speaker-dependent training and data combination, respectively. To further improve the transferability towards target domain, we propose an utterance-based data selection of the source domain data based on the entropy of posterior probability, which is analysed to statistically obey a Gaussian distribution. Compared to a speaker-based data selection via dysarthria similarity measure, this allows for a more accurate selection of the potentially beneficial source domain data for either increasing the target domain training pool or constructing an intermediate domain for incremental transfer learning, resulting in a further absolute recognition performance improvement of nearly 2% added to transfer learning baseline for speakers with moderate to severe dysarthria.
Exploiting Semantics for Face Image Deblurring

In this paper, we propose an effective and efficient face deblurring algorithm by exploiting semantic cues via deep convolutional neural networks. As the human faces are highly structured and share unified facial components (e.g., eyes and mouths), such semantic information provides a strong prior for restoration. We incorporate face semantic labels as input priors and propose an adaptive structural loss to regularize facial local structures within an end-to-end deep convolutional neural network. Specifically, we first use a coarse deblurring network to reduce the motion blur on the input face image. We then adopt a parsing network to extract the semantic features from the coarse deblurred image. Finally, the fine deblurring network utilizes the semantic information to restore a clear face image. We train the network with perceptual and adversarial losses to generate photo-realistic results. The proposed method restores sharp images with more accurate facial features and details. Quantitative and qualitative evaluations demonstrate that the proposed face deblurring algorithm performs favorably against the state-of-the-art methods in terms of restoration quality, face recognition and execution speed.
CD-VulD: Cross-Domain Vulnerability Discovery Based on Deep Domain Adaptation

A major cause of security incidents such as cyber attacks is rooted in software vulnerabilities. These vulnerabilities should ideally be found and fixed before the code gets deployed. Machine learning-based approaches achieve state-of-the-art performance in capturing vulnerabilities. These methods are predominantly supervised. Their prediction models are trained on a set of ground truth data where the training data and test data are assumed to be drawn from the same probability distribution. However, in practice, the test data often differs from the training data in terms of distribution because they are from different projects or they differ in the types of vulnerability. In this article, we present a new system for <u>C</u>ross <u>D</u>omain Software <u>Vul</u>nerability <u>D</u>iscovery (<i>CD-VulD</i>) using deep learning (DL) and domain adaptation (DA). We employ DL because it has the capacity of automatically constructing high-level abstract feature representations of programs, which are likely of more cross-domain useful than the handcrafted features driven by domain knowledge. The divergence between distributions is reduced by learning cross-domain representations. First, given software program representations, CD-VulD converts them into token sequences and learns the token embeddings for generalization across tokens. Next, CD-VulD employs a deep feature model to build abstract high-level presentations based on those sequences. Then, the metric transfer learning framework (MTLF) technique is employed to learn cross-domain representations by minimizing the distribution divergence between the source domain and the target domain. Finally, the cross-domain representations are used to build a classifier for vulnerability detection. Experimental results show that CD-VulD outperforms the state-of-the-art vulnerability detection approaches by a wide margin. We make the new datasets publicly available so that our work is replicable and can be further improved.
Automatically Learning Patterns for Self-Admitted Technical Debt Removal

Technical Debt (TD) expresses the need for improvements in a software system, e.g., to its source code or architecture.In certain circumstances, developers "self-admit" technical debt (SATD) in their source code comments.Previous studies investigate when SATD is admitted, and what changes developers perform to remove it.Building on these studies, we present a first step towards the automated recommendation of SATD removal strategies.By leveraging a curated dataset of SATD removal patterns, we build a multi-level classifier capable of recommending six SATD removal strategies, e.g., changing API calls, conditionals, method signatures, exception handling, return statements, or telling that a more complex change is needed.SARDELE (SAtd Removal using DEep LEarning) combines a convolutional neural network trained on embeddings extracted from the SATD comments with a recurrent neural network trained on embeddings extracted from the SATD-affected source code.Our evaluation reveals that SARDELE is able to predict the type of change to be applied with an average precision of 55%, recall of 57%, and AUC of 0.73, reaching up to 73% precision, 63% recall, and 0.74 AUC for certain categories such as changes to method calls.Overall, results suggest that SATD removal follows recurrent patterns and indicate the feasibility of supporting developers in this task with automated recommenders.
Is FIDO2 the Kingslayer of User Authentication? A Comparative Usability Study of FIDO2 Passwordless Authentication

The newest contender for succeeding passwords as the incumbent web authentication scheme is the FIDO2 standard.Jointly developed and backed by the FIDO Alliance and the W3C, FIDO2 has found support in virtually every browser, finds increasing support by service providers, and has adoptions beyond browser-software on its way.While it supports MFA and 2FA, its single-factor, passwordless authentication with security tokens has received the bulk of attention and was hailed by its supporters and the media as the solution that will replace text-passwords on the web.Despite its obvious security and deployability benefits-a setting that no prior solution had in this strong combination-the paradigm shift from a familiar knowledge factor to purely a possession factor raises questions about the acceptance of passwordless authentication by end-users.This paper presents the first large-scale lab study of FIDO2 single-factor authentication to collect insights about end-users' perception, acceptance, and concerns about passwordless authentication.Through hands-on tasks our participants gather first-hand experience with passwordless authentication using a security key, which they afterwards reflect on in a survey.Our results show that users are willing to accept a direct replacement of text-based passwords with a security key for single-factor authentication.That is an encouraging result in the quest to replace passwords.But, our results also identify new concerns that can potentially hinder the widespread adoption of FIDO2 passwordless authentication.In order to mitigate these factors, we derive concrete recommendations to try to help in the ongoing proliferation of passwordless authentication on the web.
Comparison of Machine Learning Techniques for Software Quality Prediction

Software quality prediction is one the most challenging tasks in the development and maintenance of software. Machine learning (ML) is widely being incorporated for the prediction of the quality of a final product in the early development stages of the software development life cycle (SDLC). An ML prediction model uses software metrics and faulty data from previous projects to detect high-risk modules for future projects, so that the testing efforts can be targeted to those specific ‘risky' modules. Hence, ML-based predictors contribute to the detection of development anomalies early and inexpensively and ensure the timely delivery of a successful, failure-free and supreme quality software product within budget. This article has a comparison of 30 software quality prediction models (5 technique * 6 dataset) built on five ML techniques: artificial neural network (ANN); support vector machine (SVMs); Decision Tree (DTs); k-Nearest Neighbor (KNN); and Naïve Bayes Classifiers (NBC), using six datasets: CM1, KC1, KC2, PC1, JM1, and a combined one. These models exploit the predictive power of static code metrics, McCabe complexity metrics, for quality prediction. All thirty predictors are compared using a receiver operator curve (ROC), area under the curve (AUC), and accuracy as performance evaluation criteria. The results show that the ANN technique for software quality prediction is promising for accurate quality prediction irrespective of the dataset used.
Large-Scale Unsupervised Pre-Training for End-to-End Spoken Language Understanding

End-to-end Spoken Language Understanding (SLU) is proposed to infer the semantic meaning directly from audio features without intermediate text representation. In this paper, we explore unsupervised pre-training for End-to-end SLU models by learning representations from large-scale raw audios. The pre-trained model preserves semantic features which benefit the downstream SLU tasks as the learned model weights are further fine-tuned on the task specific training data. Our approach out-perform the state-of-the-art end-to-end SLU system with over 18.33% error reduction.
A Dense U-Net with Cross-Layer Intersection for Detection and Localization of Image Forgery

In this paper, we apply cross-layer intersection mechanism to dense u-net for image forgery detection and localization. We first train DenseNet for binary classification. Spatial rich model (SRM) filters are adopted for capturing residual signals in the detected images. Then we propose a new approach to preserve complete feature maps of fully connected layer and consider them as the spatial decision information for image segmentation. In addition, these features in downsampling path are transferred more effectively and densely to upsampling path through multiscale upsampling and concatenation. A multi-stage training scheme is then applied to improve the convergence of the network. The experimental results show that the proposed method works well on several standard datasets.
How not to prove your election outcome

The Scytl/SwissPost e-voting solution was intended to provide complete verifiability for Swiss government elections.We show failures in both individual verifiability and universal verifiability (as defined in Swiss Federal Ordinance 161.116), based on mistaken implementations of cryptographic components.These failures allow for the construction of "proofs" of an accurate election outcome that pass verification though the votes have been manipulated.Using sophisticated cryptographic protocols without a proper consideration of what properties they offer, and under which conditions, can introduce opportunities for undetectable fraud even though the system appears to allow verification of the outcome.Our findings are immediately relevant to systems in use in Switzerland and Australia, and probably also elsewhere.
Re-Translation Strategies for Long Form, Simultaneous, Spoken Language Translation

We investigate the problem of simultaneous machine translation of long-form speech content. We target a continuous speech-to-text scenario, generating translated captions for a live audio feed, such as a lecture or play-by-play commentary. As this scenario allows for revisions to our incremental translations, we adopt a re-translation approach to simultaneous translation, where the source is repeatedly translated from scratch as it grows. This approach naturally exhibits very low latency and high final quality, but at the cost of incremental instability as the output is continuously refined. We experiment with a pipeline of industry-grade speech recognition and translation tools, augmented with simple inference heuristics to improve stability. We use TED Talks as a source of multilingual test data, developing our techniques on English-to-German spoken language translation. Our minimalist approach to simultaneous translation allows us to scale our final evaluation to several other target languages, dramatically improving incremental stability for all of them.
Towards Scalable Threshold Cryptosystems

The resurging interest in Byzantine fault tolerant systems will demand more scalable threshold cryptosystems.Unfortunately, current systems scale poorly, requiring time quadratic in the number of participants.In this paper, we present techniques that help scale threshold signature schemes (TSS), verifiable secret sharing (VSS) and distributed key generation (DKG) protocols to hundreds of thousands of participants and beyond.First, we use efficient algorithms for evaluating polynomials at multiple points to speed up computing Lagrange coefficients when aggregating threshold signatures.As a result, we can aggregate a 130,000 out of 260,000 BLS threshold signature in just 6 seconds (down from 30 minutes).Second, we show how "authenticating" such multipoint evaluations can speed up proving polynomial evaluations, a key step in communicationefficient VSS and DKG protocols.As a result, we reduce the asymptotic (and concrete) computational complexity of VSS and DKG protocols from quadratic time to quasilinear time, at a small increase in communication complexity.For example, using our DKG protocol, we can securely generate a key for the BLS scheme above in 2.3 hours (down from 8 days).Our techniques improve performance for thresholds as small as 255 and generalize to any Lagrange-based threshold scheme, not just threshold signatures.Our work has certain limitations: we require a trusted setup, we focus on synchronous VSS and DKG protocols and we do not address the worst-case complaint overhead in DKGs.Nonetheless, we hope it will spark new interest in designing large-scale distributed systems.
Destination prediction using deep echo state network

Final destination prediction based on initial partial trajectories plays an important role in location-based services (LBSs) and urban computing. Several studies on destination prediction make use of extra information which is often unavailable and reveal privacy of data owners. Many existing approaches are based on various Markov chain models, while they have significant drawbacks due to the Markov property. In general, the solution based on neural networks performs much better than the above approaches, in which Recurrent Neural Networks (RNNs) provide an effective temporal processing capacity for destination prediction. The Echo State Network (ESN) is the state-of-the-art approach for the design of efficiently trained RNNs, which provides an architecture and supervised learning principle. In this work, we adapt the deep Echo State Network (deepESN) model to taxi destination prediction, which uses only historic trajectories and achieves good results. Based on the original deepESN, we propose a novel model called deepESN with Dual Input (deepESN-DI) to optimize the time complexity of deepESN in training with large amount of data, and combine the Reservoir Computing (RC) with Multi-Layer Perceptron (MLP) to improve the prediction accuracy, which achieves better prediction results. Experimental results using the dataset provided by the Kaggle taxi trajectory prediction challenge show that our deepESN-DI and deepESN-DI-MLP approaches outperform other destination prediction models based on neural networks, namely MLP, RNN and the shallow ESN architectures.
Comparison of Heuristic techniques:A case of TSP

In the current scenario, heuristic approaches are broadly used in research in interdisciplinary categories. Heuristic Algorithms are used to get better and optimized results. the limitations are occur when we have to do selective and situational use of the algorithms amongst the all. In this paper we compared the results of Genetic Algorithm, Harmony Search and Firefly Algorithm on one of the popular problem Travelling Salesman problem(TSP). On TSP we proposed our own algorithm for the optimized cost of length of the tour.