Reviewerly/exHarmony huggingface dataset:
Cross-language question retrieval with multi-layer representation and layer-wise adversary

In cross-language question retrieval (CLQR), users employ a new question in one language to search the community question answering (CQA) archives for similar questions in another language. In addition to the ranking problem in monolingual question retrieval, one needs to bridge the language gap in CLQR. The existing adversarial models for cross-language learning normally rely on a single adversarial component. Since natural languages consist of units of different abstract levels, we argue that crossing the language gap adaptatively on different levels with multiple adversarial components should lead to smoother text representation and better CLQR performance. To this end, we first encode questions into multi-layer representations of different abstract levels with a CNN based model which enhances conventional models with diverse kernel shapes and the corresponding pooling strategy so as to capture different aspects of a text segment. We then impose a set of adversarial components on different layers of question representation so as to decide the appropriate abstract levels and their role in performing cross-language mapping. Experimental results on two real-world datasets demonstrate that our model outperforms state-of-the-art models for CLQR, which is on par with the strong machine translation baselines and most monolingual baselines.
Are Registration Uncertainty and Error Monotonically Associated?

In image-guided neurosurgery, current commercial systems usually provide only rigid registration, partly because it is harder to predict, validate and understand non-rigid registration error. For instance, when surgeons see a discrepancy in aligned image features, they may not be able to distinguish between registration error and actual tissue deformation caused by tumor resection. In this case, the spatial distribution of registration error could help them make more informed decisions, e.g., ignoring the registration where the estimated error is high. However, error estimates are difficult to acquire. Probabilistic image registration (PIR) methods provide measures of registration uncertainty, which could be a surrogate for assessing the registration error. It is intuitive and believed by many clinicians that high uncertainty indicates a large error. However, the monotonic association between uncertainty and error has not been examined in image registration literature. In this pilot study, we attempt to address this fundamental problem by looking at one PIR method, the Gaussian process (GP) registration. We systematically investigate the relation between GP uncertainty and error based on clinical data and show empirically that there is a weak-to-moderate positive monotonic correlation between point-wise GP registration uncertainty and non-rigid registration error.
The Modal Logics of the Poison Game

The poison game is a two-player zero-sum game played on directed graphs, first introduced by Duchet and Meyniel (1993) in the context of graph theory, where one of the players travels along the edges of a graph, while the other modifies the underlying structure by marking vertices. In this paper, we investigate the poison game from the perspective of modal logic, as a natural case study of the use of modal languages equipped with model-changing operators to describe evolving relational structures. In particular, to model the poison game, we consider three memory logics of decreasing expressive power but increasing fit with the game. We begin with $$\mathcal {ML}_\emptyset $$, the basic memory logic restricted to the initial class of models with an empty memory (see Areces et al. 2011). We then identify two fragments of $$\mathcal {ML}_\emptyset $$, which we respectively, denote as $$\mathcal {PML}$$ and $$\mathcal {PSL}$$, and whose modal operators capture operations on models that mimic more closely the moves of both players. We show that these logics form a chain in expressive power with $$\mathcal {PSL}<\mathcal {PML}<\mathcal {ML}_\emptyset $$, and we introduce suitable notions of bisimulation for the two new logics presented in this paper. We then show that model checking for both $$\mathcal {PML}$$ and $$\mathcal {PSL}$$ is PSPACE-complete. The construction also establishes that determining the existence of a winning strategy in the poison game is PSPACE-hard. We conclude by proving that $$\mathcal {PML}$$, while strictly less expressive than $$\mathcal {ML}_\emptyset $$, nonetheless has an undecidable satisfiability problem.
Global-and-Local Aware Data Generation for the Class Imbalance Problem

In many real-world classification applications such as fake news detection, the training data can be extremely imbalanced, which brings challenges to existing classifiers as the majority classes dominate the loss functions of classifiers. Oversampling techniques such as SMOTE are effective approaches to tackle the class imbalance problem by producing more synthetic minority samples. Despite their success, the majority of existing oversampling methods only consider local data distributions when generating minority samples, which can result in noisy minority samples that do not fit global data distributions or interleave with majority classes. Hence, in this paper, we study the class imbalance problem by simultaneously exploring local and global data information since: (i) the local data distribution could give detailed information for generating minority samples; and (ii) the global data distribution could provide guidance to avoid generating outliers or samples that interleave with majority classes. Specifically, we propose a novel framework GL-GAN, which leverages the SMOTE method to explore local distribution in a learned latent space and employs GAN to capture the global information, so that synthetic minority samples can be generated under even extremely imbalanced scenarios. Experimental results on diverse real data sets demonstrate the effectiveness of our GL-GAN framework in producing realistic and discriminative minority samples for improving the classification performance of various classifiers on imbalanced training data. Our code is available at https://github.com/wentao-repo/GL-GAN.MSC codesimbalanced dataadversarial learning
Network Reconstruction from Graph-stationary Signals with Hidden Variables

Network topology inference from nodal observations has attracted a lot of attention in different fields with a wide variety of applications. While most of the existing works assume that signal observations at all the nodes are available, this is not always the case. We investigate the problem of inferring the topology of an undirected network in the presence of hidden variables, meaning that only a subset of the nodes of the graph is being observed. To address this problem it is assumed that: (i) the nodal signals are stationary in the unknown underlying graph; and (ii) the number of observed nodes is considerably larger than the number of hidden variables. Graph stationarity implies that the covariance of the nodal signals can be expressed as a polynomial of the matrix representation of the whole graph. Rooted in this prior knowledge, the network topology inference is approached as a (low-rank and sparse) optimization problem where we aim to recover the matrix representation of the observed nodes without ignoring the influence of the hidden variables. Different convex relaxations are proposed and robust designs are presented. Finally, numerical experiments using simulations showcase the performance of the developed methods and compare them with existing alternatives.
Measuring Unfairness Through Game-Theoretic Interpretability

One often finds in the literature connections between measures of fairness and measures of feature importance employed to interpret trained classifiers. However, there seems to be no study that compares fairness measures and feature importance measures. In this paper we propose ways to evaluate and compare such measures. We focus in particular on SHAP, a game-theoretic measure of feature importance; we present results for a number of unfairness-prone datasets.
Topics in Graph Signal Processing: Convolution and Modulation

Graph signals are signals indexed by nodes of the graph. Graph Signal Processing (GSP) extends Discrete Signal Processing (DSP) to graph signals.In this paper, we explore shifting, convolution, and modulation for graph signals. We begin by reviewing some basic concepts in GSP. Then, we define M, the spectral domain shift analogous to the vertex domain shift, A. Finally, we use A and M and polynomial filters, P (A) and P (M), to illustrate convolution and modulation for graph signals in both the vertex and spectral domain.
Security Protocol for Internet of Things (IoT): Blockchain-based Implementation and Analysis

The combination of blockchain technologies and Internet of Things devices on a network reduces overall network usage by centralizing communication that is broadcasted to all devices via a server. A blockchain-based key management protocol lessens the need for a centralized key authority while retaining similar security compared to its centralized counterpart.
Using an Inverted Index Synopsis for Query Latency and Performance Prediction

Predicting the query latency by a search engine has important benefits, for instance, in allowing the search engine to adjust its configuration to address long-running queries without unnecessarily sacrificing its effectiveness. However, for the dynamic pruning techniques that underlie many commercial search engines, achieving accurate predictions of query latencies is difficult. We propose the use of index synopses—which are stochastic samples of the full index—for attaining accurate timing predictions. Indeed, we experiment using the TREC ClueWeb09 collection, and a large set of real user queries, and find that using small index synopses it is possible to very accurately estimate properties of the larger index, including sizes of posting list unions and intersections. Thereafter, we demonstrate that index synopses facilitate two key use cases: first, for query efficiency prediction, we show that predicting the query latencies on the full index and classifying long-running queries can be accurately achieved using index synopses; second, for query performance prediction, we show that the effectiveness of queries can be estimated more accurately using a synopsis index post-retrieval predictor than a pre-retrieval predictor. Overall, our experiments demonstrate the value of such a stochastic sample of a larger index at predicting the properties of the larger index.
Are CAPTCHAs preventing robotic intrusion or accessibility for impaired users?

Is the World Wide Web for everyone? Long story short: no and unfortunately it is not only a matter of infrastructures, level of instruction or economic conditions. People affected by visual impairments have often difficulties in navigating web pages for a wide range of reasons. One of the biggest obstacles nowadays is the use of CAPTCHAs, powerful tools against bot attacks but also potential virtual barriers for the aforementioned category of users. In this paper we tested various categories of CAPTCHAs with people affected by visual impairment and not, to understand how discriminatory can be these cybersecurity measures.
Convergence of Password Guessing to Optimal Success Rates

Password guessing is one of the most common methods an attacker will use for compromising end users. We often hear that passwords belonging to website users have been leaked and revealed to the public. These leaks compromise the users involved but also feed the wealth of knowledge attackers have about users' passwords. The more informed attackers are about password creation, the better their password guessing becomes. In this paper, we demonstrate using proofs of convergence and real-world password data that the vulnerability of users increases as a result of password leaks. We show that a leak that reveals the passwords of just 1% of the users provides an attacker with enough information to potentially have a success rate of over 84% when trying to compromise other users of the same website. For researchers, it is often difficult to quantify the effectiveness of guessing strategies, particularly when guessing different datasets. We construct a model of password guessing that can be used to offer visual comparisons and formulate theorems corresponding to guessing success.
Discovering Cricket Stroke Classes in Trimmed Telecast Videos

Activity recognition in sports telecast videos is challenging, especially, in outdoor field events, where there is a lot of camera motion. Generally, camera motions like zoom, pan, and tilt introduce noise in the low-level motion features, thereby, effecting the recognition accuracy, but in some cases, such camera motion can have a pattern which can be useful for action recognition in trimmed videos. In this work, we experimentally discover the types of strokes in a Cricket strokes dataset using direction information of dense optical flow. We use trimmed videos of Cricket strokes taken from Cricket telecast videos of match highlights. The predominant direction of motion is found by summing up the histograms of optical flow directions, taken for significant pixels, over the complete Cricket stroke clip. We show that such a quantized representation of the optical flow direction for the complete video clip can be used for Cricket stroke recognition. Our method uses an unsupervised K-Means clustering of the extracted clip feature vectors and we evaluate our results for 3-cluster K-Means by manually annotating the clusters as Left strokes, Right strokes and Ambiguous strokes. The accuracy for different set of feature vectors was obtained by varying the bin granularity of the histogram and the optical flow magnitude threshold. The best result we obtained, for 3-cluster K-Means and 562 stroke instances, was $$87.72\%$$.
CNN hardware acceleration on a low-power and low-cost APSoC

Deep learning and Convolutional Neural Networks (CNNs) in particular, are currently one of the most promising and widely used classes of algorithms in the field of artificial intelligence, being employed in a wide range of tasks. However, their high computational complexity and storage demands limit their efficient deployment on resource-limited embedded systems and IoT devices. To address this problem, in recent years a wide landscape of customized FPGA-based hardware acceleration solutions has been presented in literature, focused on combining high performance and power efficiency. Most of them are implemented on mid- to high-range devices including different computing cores, and target intensive models such as AlexNet and VGG16. In this work, we implement a CNN inference accelerator on a compact and cost-optimized device, the Minized development board from Avnet, integrating a single-core Zynq 7Z007S. We measure the execution time and energy consumption of the developed accelerator, and we compare it with a CPU-based software implementation. The results show that the accelerator achieves a frame rate of 13 fps on the end-to-end execution of ALL-CNN-C model, and 4 fps on DarkNet. Compared with the software implementation, it was 5 times faster providing up to 10.62 giga operations per second (GOPS) at 80 MHz while consuming 1.08 W of on-chip power.
A Framework for Requirements Prioritization Process in Agile Software Development

Abstract Requirements engineering is a crucial phase of software engineering, and requirements prioritization is an essential stage of requirements engineering particularly in agile software development. Requirements prioritization goals at eliciting which requirements of software need to be covered in a particular release. The key point is which requirement will be selected in the next iteration and which one will be delayed to other iterations for minimizing risk during development and meeting stakeholders’ needs. There are many existing techniques for requirement prioritization, but most of these techniques do not cover continuous growth and change of requirements or cover requirements dependencies. So, most of these prioritization techniques need to be more continuous, scalable, implemented merely and integrated with software development life cycle and not work separately. This paper introduces a framework to prioritize requirements in agile software development. This framework tries to find solutions for the challenges facing this prioritization process such as how to make this prioritization continuous and scalable and how to deal with rapidly requirement changes and its dependencies.
Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space

As machine learning becomes an important part of many real world applications affecting human lives, new requirements, besides high predictive accuracy, become important. One important requirement is transparency, which has been associated with model interpretability. Many machine learning algorithms induce models difficult to interpret, named black box. Black box models are difficult to validate. Moreover, people have difficulty to trust models that cannot be explained. Explainable artificial intelligence is an active research area. In particular for machine learning, many groups are investigating new methods able to explain black box models. These methods usually look inside the black models to explain their inner work. By doing so, they allow the interpretation of the decision making process used by black box models. Among the recently proposed model interpretation methods, there is a group, named local estimators, which are designed to explain how the label of particular instance is predicted. For such, they induce interpretable models on the neighborhood of the instance to be explained. Local estimators have been successfully used to explain specific predictions. Although they provide some degree of model interpretability, it is still not clear what is the best way to implement and apply them. Open questions include: how to best define the neighborhood of an instance? How to control the trade-off between the accuracy of the interpretation method and its interpretability? How to make the obtained solution robust to small variations on the instance to be explained? To answer these questions, we propose and investigate two strategies: (i) using data instance properties to provide improved explanations, and (ii) making sure that the neighborhood of an instance is properly defined by taking the geometry of the domain of the feature space into account. We evaluate these strategies in a regression task and present experimental results that show that they can improve local explanations.
Minimal Solvers for Rectifying from Radially-Distorted Scales and Change of Scales

This paper introduces the first minimal solvers that jointly estimate lens distortion and affine rectification from the image of rigidly-transformed coplanar features. The solvers work on scenes without straight lines and, in general, relax strong assumptions about scene content made by the state of the art. The proposed solvers use the affine invariant that coplanar repeats have the same scale in rectified space. The solvers are separated into two groups that differ by how the equal scale invariant of rectified space is used to place constraints on the lens undistortion and rectification parameters. We demonstrate a principled approach for generating stable minimal solvers by the Gröbner basis method, which is accomplished by sampling feasible monomial bases to maximize numerical stability. Synthetic and real-image experiments confirm that the proposed solvers demonstrate superior robustness to noise compared to the state of the art. Accurate rectifications on imagery taken with narrow to fisheye field-of-view lenses demonstrate the wide applicability of the proposed method. The method is fully automatic.
Kinship Verification Through Facial Images Using CNN-Based Features

The use of facial images in the kinship verification is a challenging research problem in soft biometrics and computer vision.In our work, we present a kinship verification system that starts with pair of facial images of the child and parent, then as a final result is determine whether two persons have a kin relation or not.our approach contains five steps as follows: (i) the face preprocessing step to get aligned and cropped facial images of the pair (ii), extracting deep features based on the deep learning model called Visual Geometry Group (VGG) Face, (iii) applying our proposed pair feature representation function alongside with a features normalization, (iv) the use of Fisher Score (FS) to select the best discriminative features, (v) decide whether there is a kinship or not based on the Support Vector Machine (SVM) classifier.We conducted several experiments to demonstrate the effectiveness of our approach that we tested on five benchmark databases (Cornell KinFace, UB KinFace, Familly101, KinFace W-I, and KinFace W-II).Our results indicate that our system is robust compared to other existing approaches.
ExpressPass++: Credit-Effecient Congestion Control for Data Centers

For data center networks (DCNs), the network fabric evolves a lot to serve global users, but network congestion inevitability occurs when traffic bursts as well. To efficient manage congestion in current high-speed DCNs, researchers devote much attention to the proactive congestion control which has the merits of fast convergence, low latency, and low buffer occupancy. ExpressPass, as a state-of-the-art proactive scheme, leverages credit reservation mechanism to achieve fast transmission, where each credit schedules one maximum transmission unit (MTU) data packet. However, for workloads with a high volume of very short flows that shorter than an MTU, the credit efficiency is degraded and the link bandwidth is wasted. This paper proposes ExpressPass++ to improve the credit scheduling mechanism of ExpressPass. ExpressPass++ develops a token-scheduled protocol, each credit corresponds to an MTU size tokens which can schedule an MTU amount of data. Since a credit may schedule multiple small packets, data packets will carry reduplicative credit sequence number that used for feedback control and the credit rate will be inaccurately regulated. To address this problem, ExpressPass++ improves the sequence-based feedback control of ExpressPass. Compared to ExpressPass, the results show that ExpressPass++ greatly improves in terms of average FCT and network utilization, and ExpressPass++ maintains the ExpressPass's advantages of the bounded queue and fast convergence.
An ID-Based Privacy-Preserving Integrity Verification of Shared Data Over Untrusted Cloud

Data sharing is considered as a favorite service provided by cloud storage servers in which the stored data is shared among a group of users. However, auditing such shared data is a big security issue for cloud users. In this paper, we propose an ID-Based public shared data integrity auditing scheme, in which all the group users are able to update, delete or insert new blocks into the shared data. Besides, the cloud server can revoke a misbehaving user with a minimum overhead. The scheme is secure against an untrusted cloud server and also preserves data privacy against the public verifier. Furthermore, overhead analysis shows the efficiency of proposed scheme in comparison to the existing well-known schemes.