Reviewerly/exHarmony huggingface dataset:
Type-level computations for Ruby libraries

Many researchers have explored ways to bring static typing to dynamic languages. However, to date, such systems are not precise enough when types depend on values, which often arises when using certain Ruby libraries. For example, the type safety of a database query in Ruby on Rails depends on the table and column names used in the query. To address this issue, we introduce CompRDL, a type system for Ruby that allows library method type signatures to include type-level computations (or comp types for short). Combined with singleton types for table and column names, comp types let us give database query methods type signatures that compute a table’s schema to yield very precise type information. Comp types for hash, array, and string libraries can also increase precision and thereby reduce the need for type casts. We formalize CompRDL and prove its type system sound. Rather than type check the bodies of library methods with comp types—those methods may include native code or be complex—CompRDL inserts run-time checks to ensure library methods abide by their computed types. We evaluated CompRDL by writing annotations with type-level computations for several Ruby core libraries and database query APIs. We then used those annotations to type check two popular Ruby libraries and four Ruby on Rails web apps. We found the annotations were relatively compact and could successfully type check 132 methods across our subject programs. Moreover, the use of type-level computations allowed us to check more expressive properties, with fewer manually inserted casts, than was possible without type-level computations. In the process, we found two type errors and a documentation error that were confirmed by the developers. Thus, we believe CompRDL is an important step forward in bringing precise static type checking to dynamic languages.
Fifteen years of phishing: can technology save us?

With the first incidents dating back to 2003, phishing has now been a problem for over a decade and a half. Unfortunately, just like malware, it's proven to be the cyber security equivalent of an unwanted genie that we can't put back in the bottle. Despite many efforts to educate users and provide safeguards, people are still falling victim. Although it is a long-established threat, phishing continues to grow and represents a significant issue for individuals and organisations. Steven Furnell, Kieran Millet and Maria Papadaki of the Centre for Security, Communications and Network Research, University of Plymouth, UK, examine the scale of the problem and look at the effectiveness of some of our technology-based defences. And they present the results of a practical assessment of the capabilities of email services to identify and highlight phishing based upon message content.
A Survey of State-of-the-Art Short Text Matching Algorithms

The short text matching task uses an NLP model to predict the semantic relevance of two texts. It has been used in many fields such as information retrieval, question answering and dialogue systems. This paper will review several state-of-the-art neural network based text matching algorithms in recent years. We aim to provide a quick start guide to beginners on short text matching. The representation based model DSSM is first introduced, which uses a neural network model to represent texts as feature vectors, and the cosine similarity between vectors is regarded as the matching score of texts. Word interaction based models such as DRMM, MatchPyramid and BERT are then introduced, which extract semantic matching features from the similarities of word pairs in two texts to capture more detailed interaction information between texts. We analyze the applicable scenes of each algorithm based on the effectiveness and time complexity, which will help beginners to choose appropriate models for their short text matching applications.
Examining the challenges in development data pipeline

The developing world has increasingly relied on data driven policies. Numerous development agencies have pushed for on-ground data collection to support the development work they pursue. Many governments have launched their own efforts for frequent information gathering. Overall, the amount of data collected is tremendous, yet there are significant issues in doing useful analysis. Most of these barriers manifest in data cleaning and merging, and require a data engineer to support some parts of the analysis. In this paper, we investigate the challenges of cleaning development data through an interview based study. We conducted face to face interviews of 13 stakeholders, eight from international development organizations and five government workers from Pakistan, including both managers and data analysts. From analysis of the interviews we identified common challenges faced in processing development data including correcting open text fields, merging hierarchical data, and extracting data from textual formats such as PDF. We construct a basic taxonomy of data cleaning challenges, and identify areas where support tools can improve the process. Ultimately, the objective is to empower regular data users to easily do the necessary data cleaning and scrubbing for analysis.
Localization-Aware Meta Tracker Guided With Adversarial Features

Deep learning has recently shown great potentials in learning powerful features for visual tracking.However, most deep learning-based trackers neglect localization accuracy in the evaluation process of candidates.What's more, they usually over-rely on the discriminative features in a single frame in the training process.Consequently, they may fail when the discriminative features are occluded or changed in the tracking phase.In this paper, we propose a novel localization-aware meta tracker (LMT) guided with adversarial features to address the above issues.First of all, we design a novel intersection over union guided method to effectively balance the problem of classification and localization accuracy.To further improve the robustness of our classifier, we creatively use adversarial features during offline training phase.Those adversarial features can effectively guide the classifier in learning how to better deal with the situation where the discriminative features are occluded or changed.Finally, benefiting from meta learning, our algorithm only needs to perform one iterative update on the first frame and it can perform well on the tracking sceneries.The extensive experiments demonstrate the outstanding performance of our LMT compared with the stateof-the-art trackers on three benchmarks: OTB-2015, VOT-2016, and VOT-2018.
Good Clusterings Have Large Volume

In “Good Clusterings Have Large Volume,” S. Borgwardt and F. Happach study the strong contrast between the favorable performance of clustering algorithms in practice and their bad theoretical worst cases. They explain this contrast using polyhedral theory. Classical clustering algorithms can be interpreted as the search for vertices in the so-called bounded-shape partition polytopes, following a "random" direction. The vertices correspond to clusterings with extraordinary separation properties, and the quality of this separation depends on the volume of the normal cone of the vertex—the larger the better. This gives rise to a new quality criterion for clusterings and explains why good clusterings are also the most likely to be found. The authors further characterize the edges of the polytopes, which allows for an explicit description of the normal cone, and in turn the computation of a best separation between the clusters of a given clustering.
Cryptography for #MeToo

Abstract Reporting sexual assault and harassment is an important and difficult problem. Since late 2017, it has received increased attention as the viral #MeToo movement has brought about accusations against high-profile individuals and a wider discussion around the prevalence of sexual violence. Addressing occurrences of sexual assault requires a system to record and process accusations. It is natural to ask what security guarantees are necessary and achievable in such a system. In particular, we focus on detecting repeat offenders: only when a set number of accusations are lodged against the same party will the accusations be revealed to a legal counselor. Previous solutions to this privacy-preserving reporting problem, such as the Callisto Protocol of Rajan et al., have focused on the confidentiality of accusers. This paper proposes a stronger security model that ensures the confidentiality of the accuser and the accused as well as the traceability of false accusations. We propose the WhoToo protocol to achieve this notion of security using suitable cryptographic techniques. The protocol design emphasizes practicality, preferring fast operations that are implemented in existing software libraries. We estimate that an implementation would be suitably performant for real-world deployment.
ELM-SOM+: A continuous mapping for visualization

This paper presents a novel dimensionality reduction technique based on ELM and SOM: ELM-SOM+. This technique preserves the intrinsic quality of Self-Organizing Map (SOM): it is nonlinear and suitable for big data. It also brings continuity to the projection using two Extreme Learning Machine (ELM) models, the first one to perform the dimensionality reduction and the second one to perform the reconstruction. ELM-SOM+ is tested successfully on nine diverse datasets. Regarding reconstruction error, the new methodology shows considerable improvement over SOM and brings continuity.
Using cognitive dimensions to evaluate the usability of security APIs: An empirical investigation

Usability issues of security Application Programming Interfaces (APIs) are a main factor for mistakes programmers make that could result in introducing security vulnerabilities into applications they develop. This has become a common problem as there is no methodology to evaluate the usability of security APIs. A usability evaluation methodology for security APIs would allow API developers to identify usability issues of security APIs and fix them. A Cognitive Dimensions Framework (CDF) based usability evaluation methodology has been proposed in previous research to empirically evaluate the usability of security APIs. In this research, we evaluated the proposed CDF based methodology through four security APIs (Google Authentication API, Bouncy Castle light weight Crypto API, Java Secure Socket Extension API, OWASP Enterprise Security API). We conducted four experiments where in each experiment we recruited programmers and they completed a programming task using one of the four security APIs. Participants’ feedback on each cognitive dimension of the particular API was collected using the cognitive dimensions questionnaire. Usability issues of each API was identified based on this feedback. Results of the four experiments revealed that over 83% of the usability issues in a security API could be identified by this methodology with a considerably good validity and reliability. The proposed CDF based usability evaluation methodology provides a good platform to conduct usability evaluation for security APIs.
US-Net for Robust and Efficient Nuclei Instance Segmentation

We present a novel neural network architecture, US-Net, for robust nuclei instance segmentation in histopathology images. The proposed framework integrates the nuclei detection and segmentation networks by sharing their outputs through the same foundation network, and thus enhancing the performance of both. The detection network takes into account the high-level semantic cues with contextual information, while the segmentation network focuses more on the low-level details like the edges. Extensive experiments reveal that our proposed framework can strengthen the performance of both branch networks in an integrated architecture and outperforms most of the state-of-the-art nuclei detection and segmentation networks.
Classification of Vehicle Make Based on Geometric Features and Appearance-Based Attributes Under Complex Background

Vehicle detection and recognition is an important task in the area of advanced infrastructure and movement administration. Many researchers are working on this area with different approaches to solve the problem since it has a many challenge. Every vehicle has its on own unique features for recognition. This paper focus on identifying the vehicle brand based on its geometrical features and diverse appearance-based attributes like colour, occlusion, shadow and illumination. These attributes will make the problem very challenging. In the proposed work, system will be trained with different samples of vehicles belongs to the different make. Classify those samples into different classes of models belongs to same make using Neural Network Classifier. Exploratory outcomes display promising possibilities efficiently.
KNN-Based Overlapping Samples Filter Approach for Classification of Imbalanced Data

Imbalanced data classification is one of the most interesting problems in various real-world data sets. The class distribution of imbalanced data set strongly affects the classification rate of learning classifiers. If the class distribution problems can't be solved before implementing the learning algorithms, the predictions of learning classifiers tend to support a large number of samples (majority class) and ignore the other samples (minority class). In addition, the class overlapping problem can increase the difficulty to classify the minority class samples correctly. In this paper, we propose an effective under-sampling method for the classification of imbalanced and overlapping data by using KNN-based overlapping samples filter approach. Besides, this paper summarizes the performance analysis of three ensemble-based learning classifiers for the proposed method. Experimental results on fifteen imbalanced data sets indicate that the proposed under-sampling method can effectively improve the five representative algorithms in terms of three popular metrics; area under the curve (AUC), G-mean and F-measure.
Asymmetric broadcasting of quantum correlations

In this work, we exhaustively investigate $1 \rightarrow 2$ local and nonlocal broadcasting of entanglement as well as correlations beyond entanglement (geometric discord) using asymmetric Pauli cloners with most general two qubit state as the resource. We exemplify asymmetric broadcasting of entanglement using Maximally Entangled Mixed States. We demonstrate the variation of broadcasting range with the amount of entanglement present in the resource state as well as with the asymmetry in the cloner. We show that it is impossible to optimally broadcast geometric discord with the help of these asymmetric Pauli cloning machines. We also study the problem of $1 \rightarrow 3$ broadcasting of entanglement using non-maximally entangled state (NME) as the resource. For this task, we introduce a method we call successive broadcasting which involves application of $1 \rightarrow 2$ optimal cloning machines multiple times. We compare and contrast the performance of this method with the application of direct $1 \rightarrow 3$ optimal cloning machines. We show that $1 \rightarrow 3$ optimal cloner does a better job at broadcasting than the successive application of $1 \rightarrow 2$ cloners and the successive method can be beneficial in the absence of $1 \rightarrow 3$ cloners. We also bring out the fundamental difference between the tasks of cloning and broadcasting in the final part of the manuscript. We create examples to show that there exist local unitaries which can be employed to give a better range for broadcasting. Such unitary operations are not only economical, but also surpass the best possible range obtained using existing cloning machines enabling broadcasting of lesser entangled states. This result opens up a new direction in exploration of methods to facilitate broadcasting which may outperform the standard strategies implemented through cloning transformations.
Partial face detection in the mobile domain

Face detection algorithms do not perform well in the mobile domain due to significant presence of occluded and partially visible faces. One promising technique for handling the challenge of partial faces is to design face detectors based on facial segments. In this paper two different approaches of facial segment-based face detection are discussed, namely, proposal-based detection and detection by end-to-end regression. Methods that follow the first approach rely on generating face proposals that contain facial segment information. The three detectors following this approach, namely Facial Segment-based Face Detection (FSFD), SegFace and DeepSegFace, discussed in this paper, perform binary classification on each proposal based on features learned from facial segments. The process of proposal generation, however, needs to be handled separately, which can be very time consuming, and may not be necessary given the nature of the active authentication problem. Hence a novel algorithm, Deep Regression-based User Image Detector (DRUID) is proposed, which shifts from the classification to the regression paradigm, thus avoiding the proposal generation step. DRUID has an unique network architecture with customized loss functions, is trained using a relatively small amount of data by utilizing a novel data augmentation scheme and is fast since it outputs the bounding boxes of a face and its segments in a single pass. Being robust to occlusion by design, the facial segment-based face detection methods, especially DRUID show superior performance over other state-of-the-art face detectors in terms of precision-recall and ROC curve on two mobile face datasets.
Estimating the Fundamental Limits is Easier Than Achieving the Fundamental Limits

We show through case studies that it is easier to estimate the fundamental limits of data processing than to construct the explicit algorithms to achieve those limits. Focusing on binary classification, data compression, and prediction under logarithmic loss, we show that in the finite space setting, when it is possible to construct an estimator of the limits with vanishing error with n samples, it may require at least n ln n samples to construct an explicit algorithm to achieve the limits.
Part-Level Convolutional Neural Networks for Pedestrian Detection Using Saliency and Boundary Box Alignment

Pedestrians in videos have a wide range of appearance factors, such as body poses, occlusions, and complex backgrounds, which make their detection difficult.Moreover, a proposal shift problem causes the loss of body parts, such as head and legs in pedestrian detection, which further degrades the detection accuracy.In this paper, we propose part-level convolutional neural networks (CNNs) for pedestrian detection using saliency and boundary box (BB) alignment.The proposed network consists of two subnetworks: detection and alignment.In the detection subnetwork, we use saliency to remove false positives, such as lamp posts and trees, by combining a fully convolutional network and a class activation map to extract deep features.Subsequently, we adopt the BB alignment on detection proposals in the alignment subnetwork to overcome the proposal shift problem by applying the part-level CNN to recall the lost body parts.The experimental results on various datasets demonstrate that the proposed method remarkably improves accuracy in pedestrian detection and outperforms the existing state-of-the-art techniques.
Temporally Coherent Video Harmonization Using Adversarial Networks

Compositing is one of the most important editing operations for images and videos. The process of improving the realism of composite results is often called harmonization. Previous approaches for harmonization mainly focus on images. In this paper, we take one step further to attack the problem of video harmonization. Specifically, we train a convolutional neural network in an adversarial way, exploiting a pixel-wise disharmony discriminator to achieve more realistic harmonized results and introducing a temporal loss to increase temporal consistency between consecutive harmonized frames. Thanks to the pixel-wise disharmony discriminator, we are also able to relieve the need of input foreground masks. Since existing video datasets which have ground-truth foreground masks and optical flows are not sufficiently large, we propose a simple yet efficient method to build up a synthetic dataset supporting supervised training of the proposed adversarial network. The experiments show that training on our synthetic dataset generalizes well to the real-world composite dataset. In addition, our method successfully incorporates temporal consistency during training and achieves more harmonious visual results than previous methods.
Design of a hybrid measure for image similarity: a statistical, algebraic, and information-theoretic approach

Image similarity or distortion assessment is fundamental to a wide range of applications throughout the field of image processing and computer vision. Many image similarity measures have been proposed to treat specific types of image distortions. Most of these measures are based on statistical approaches, such as the classic SSIM. In this paper, we present a different approach by interpolating the information theory with the statistic, because the information theory has a high capability to predict the relationship among image intensity values. Our unique hybrid approach incorporates information theory (Shannon entropy) with a statistic (SSIM), as well as a distinctive structural feature provided by edge detection (Canny). Correlative and algebraic structures have also been utilized. This approach combines the best features of Shannon entropy and a joint histogram of the two images under test, and SSIM with edge detection as a structural feature. The proposed method (ISSM) has been tested versus SSIM and FSIM under Gaussian noise, where good results have been obtained even under a wide range of PSNR. Simulation results using the IVC and TID2008 image databases show that the proposed approach outperforms the SSIM and FSIM approaches in similarity and recognition of the image.
Demistifying Ethereum Technology

A cryptocurrency is a digital or virtual currency designed to work as a medium of exchange. It uses cryptography to secure and verify transactions as well as to control the creation of new units of a particular cryptocurrency. Essentially, cryptocurrencies are limited entries in a database that no one can change unless specific conditions are fulfilled. Here, in this chapter, the authors demystify Ethereum cryptocurrency. Initially, they introduce the said topic. While explaining, they follow a layered approach and explain each layer that is contributing in transactions. Eventually, they show the challenges for Ethereum cryptocurrency.
Nudging the particle filter

Abstract We investigate a new sampling scheme aimed at improving the performance of particle filters whenever (a) there is a significant mismatch between the assumed model dynamics and the actual system, or (b) the posterior probability tends to concentrate in relatively small regions of the state space. The proposed scheme pushes some particles toward specific regions where the likelihood is expected to be high, an operation known as nudging in the geophysics literature. We reinterpret nudging in a form applicable to any particle filtering scheme, as it does not involve any changes in the rest of the algorithm. Since the particles are modified, but the importance weights do not account for this modification, the use of nudging leads to additional bias in the resulting estimators. However, we prove analytically that nudged particle filters can still attain asymptotic convergence with the same error rates as conventional particle methods. Simple analysis also yields an alternative interpretation of the nudging operation that explains its robustness to model errors. Finally, we show numerical results that illustrate the improvements that can be attained using the proposed scheme. In particular, we present nonlinear tracking examples with synthetic data and a model inference example using real-world financial data.