Reviewerly/exHarmony huggingface dataset:
More powerful logrank permutation tests for two-sample survival data

Weighted logrank tests are a popular tool for analysing right-censored survival data from two independent samples. Each of these tests is optimal against a certain hazard alternative, for example, the classical logrank test for proportional hazards. But which weight function should be used in practical applications? We address this question by a flexible combination idea leading to a testing procedure with broader power. Besides the test's asymptotic exactness and consistency, its power behaviour under local alternatives is derived. All theoretical properties can be transferred to a permutation version of the test, which is even finitely exact under exchangeability and showed a better finite sample performance in our simulation study. The procedure is illustrated in a real data example.
PEN: Pose-Embedding Network for Pedestrian Detection

In the past years, pedestrian detection has achieved significant progress via improving the visual description. However, the visual description is not robust to discover the occluded pedestrian, which is the bottleneck of the existing pedestrian methods. Targeting to overcome the shortcoming of visual description, we employ the human pose information, which is complementary to the visual description, to address the occlusion and false positive failure problems in pedestrian detection. The advantage of using human pose information is that the pose estimation model can localize the local part of the pedestrian once the pedestrian is occluded. By embedding the human pose information with the visual description, we propose a novel Pose-Embedding Network for pedestrian detection, which consists of two components: a Region Proposal Network, and a Pedestrian Recognization Network. The Region Proposal Network targets to generate lots of candidate proposals and corresponding confidence scores. Once obtaining the candidate proposals, the Pedestrian Recognization Network is proposed to distinguish pedestrian proposals by taking the visual information and pose information into consideration to refine the confidence scores and eliminate the false positives. Given the proposal image, the visual information is extracted with the Visual Feature Module. The Human Pose Module, which is proposed based on the pose estimation model, is used to predict the pose information. Further, the Classification Module is employed to fuse the visual and pose information and generates a pose-embedding pedestrian description. Extensive experiments on three challenging datasets, i.e., Caltech, CityPersons, and COCOPersons, show that the proposed approach achieves a significant improvement upon the state-of-the-art methods.
Chronological pattern indexing: An efficient feature extraction method for hand gesture recognition with Leap Motion

Recently, Hand-Gesture-Recognition (HGR) systems has appreciably change the way of interaction between humans and computers thanks to advanced sensor technologies like the Leap-Motion-Controller (LMC). Despite the success achieved by many state-of-the-art methods, they have not worked on the rich temporal information existing in the sequential hand gesture data and characterizing the discriminative representation of different hand gesture classes. In this paper, we suggest a novel Chronological-Pattern-Indexing (CPI) approach which encodes the temporal orders of patterns for hand gesture time series data acquired by the LMC sensor. We extract a set of temporal patterns from different optimized projections. Then, we compare their temporal order and we encode the whole sequence with the index of the first coming pattern. We repeat these steps until we generate an efficient feature vector modeling the chronological dynamics of the hand gesture. The experiments demonstrate the potential of the proposed CPI approach for HGR systems.
Generating correctness proofs with neural networks

Foundational verification allows programmers to build software which has been empirically shown to have high levels of assurance in a variety of important domains. However, the cost of producing foundationally verified software remains prohibitively high for most projects, as it requires significant manual effort by highly trained experts. In this paper we present Proverbot9001, a proof search system using machine learning techniques to produce proofs of software correctness in interactive theorem provers. We demonstrate Proverbot9001 on the proof obligations from a large practical proof project, the CompCert verified C compiler, and show that it can effectively automate what were previously manual proofs, automatically producing proofs for 28% of theorem statements in our test dataset, when combined with solver-based tooling. Without any additional solvers, we exhibit a proof completion rate that is a 4X improvement over prior state-of-the-art machine learning models for generating proofs in Coq.
PhishPreventer: A Secure Authentication Protocol for Prevention of Phishing Attacks in Mobile Environment with Formal Verification

In the mobile payment systems flow of confidential data is one of the essential and vital services. The customer's sensitive data is always kept safe from the various kind of attacks, such as phishing and man-in-the-middle attacks. The current mobile authentication protocols put an extra burden on mobile device users to detect and avoid phishing attacks. In this paper, we propose a novel authentication protocol that deals with an Authentication Server (AS), which sends a nonce message to the mobile customer device to be signed, so that he/she can avoid phishing attacks. The phishing attacks are fraudulent e-mail messages appearing to come from legitimate enterprises to access the private information and to commit identity theft. On the other hand, over the Internet, so many associated attacks are also possible, and it can quickly spread across the Internet and cause severe damage to our society. In this paper, we mainly focus on a phishing attack in the mobile environment with the help of an authentication server. Our We simulate our proposed approach with the verification model checking tool Scyther, which rigorously analyses our proposed scheme and shows that our proposed method is secure and safe from phishing attacks.
Question selection for interactive program synthesis

Interactive program synthesis aims to solve the ambiguity in specifications, and selecting the proper question to minimize the rounds of interactions is critical to the performance of interactive program synthesis. In this paper we address this question selection problem and propose two algorithms. SampleSy approximates a state-of-the-art strategy proposed for optimal decision tree and has a short response time to enable interaction. EpsSy further reduces the rounds of interactions by approximating SampleSy with a bounded error rate. To implement the two algorithms, we further propose VSampler, an approach to sampling programs from a probabilistic context-free grammar based on version space algebra. The evaluation shows the effectiveness of both algorithms.
Hyperspectral image restoration via CNN denoiser prior regularized low-rank tensor recovery

Hyperspectral images (HSIs) are widely used in various tasks such as mineral detection and food safety. However, during the imaging process, they are often contaminated by various noises. In this paper for HSIs restoration tasks, we firstly investigate the advantages of traditional physical restoration models and the denoising convolutional neural networks (CNN). For the physical prior of HSIs, a Tucker decomposition based low-rank tensor approximation can fully explore the global correlations in both the spatial and spectral domains. And for the implicit prior, a CNN based method can represent the prior which cannot be designed by mathematical theory tools. Then, we combine the advantages of the two methods to introduce the HSI restoration CNN with the low-rank tensor approximation based regularization in the flexible and extensible plug-and-play framework. The proposed model can be quickly solved using the alternating direction method of multipliers method. Experiments with simulated data and real data show that, compared with competitive methods, the proposed method achieves better HSI restoration results in various quantitative evaluation indicators.
A modified salp swarm algorithm for task assignment problem

The task assignment problem (TAP) is one of the standard combinatorial optimization problems (COPs) in the field of discrete optimization. TAP is known to be an NP-complete problem due to the difficulty to obtain the exact solution of it in polynomial time. Thus, it is essential to develop and/or propose an optimization algorithm to solve this problem. In TAPs, a set of tasks is assigned to a set of machines to both effectively minimize the communication and execution cost. This paper presents a modified Salp Swarm Algorithm (SSA), to solve not only task assignment problems but also, fundamental combinatorial optimization problems in engineering and real-world scientific domains. The modified salp algorithm takes place in the integration with the Local Refinement Heuristic (LRH) approach to enhance a given assignment along with operators. The modified algorithm is named Modified Salp Local Refinement Heuristic (MSLRH). To the best of our knowledge, this paper is the first of its kind to attempt using the SSA in task assignment problems. The MSLRH algorithm is tested on different benchmark datasets (tree structure and general graph), including various tasks and machines for each dataset. In addition, it compared with the most known meta-heuristic algorithms such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, and Jaya algorithm (JAYA) to investigate the effectiveness of the MSLRH algorithm in terms of average assignment allocation cost. From tree structure dataset (e.g., 200 tasks assigned to 8 machines), the proposed MSLRH algorithm has achieved a minimum average assignment cost better than GA by 62% and better than PSO and JAYA by 42%. From the general graph dataset (e.g., 209 tasks assigned to 16 machines), the proposed MSLRH algorithm has better results than other algorithms up to 60%. From various and extensive experimental results, the proposed algorithm has proven the effectiveness in solving the task assignment problem.
Fully-automated liberalism? Blockchain technology and international cooperation in an anarchic world

Abstract A recent wave of scholarship attests that the liberal world order is under threat. Although there is disagreement about the underlying reasons for this diagnosis, there are few attempts to further our understanding of how the liberal order can be reinvigorated. This paper probes the potential of blockchain technology to promote international cooperation. Blockchain technology is a data structure that enables global governance stakeholders to establish decentralized governance systems which provide high-powered incentives for enhanced cooperation. By outlining the contours of a blockchain-based global governance system for climate policy, the paper illustrates that blockchain technology holds theoretical promise to foster cooperation in three ways: leveraging new sources of information through blockchain-based prediction markets; allaying coordinating problems through reducing the cost of transactions for side payments; and allowing states and other global governance actors to make more credible commitments given guaranteed execution of blockchain-enabled smart contracts. By empowering local knowledge holders and non-state actors that traditionally lacked the means to coordinate efforts to influence global politics, blockchain technology also promises to advance an international order based on liberal values. In actuality, however, emerging blockchain-based global governance systems will fall short of the libertarian ideal of ‘fully-automated liberalism’ as their design and operation will remain under the shadow of power.
Understanding Technology as Situated Practice: Everyday use of Voice User Interfaces Among Diverse Groups of Users in Urban India

Abstract As smartphones have become ubiquitous across urban India, voice user interfaces (VUIs) are increasingly becoming part of diverse groups of users’ daily experiences. These technologies are now generally accessible as a result of improvements in mobile Internet access, [-8.5pc]Biography is Required. Please provide. introduction of low-cost smartphones and the ongoing process of their localisation into Indian languages. However, when people engage with technologies in their everyday lives, they not only enact the material attributes of the artifact but also draw on their skills, social positions, prior experience and societal norms and expectations to make use of the artifact. Drawing on Orlikowski’s analytical framework of “technologies-in-practice” we engage in an interview-based exploratory study among diverse groups of users in urban India to understand use of VUIs as situated practice. We identify three technologies-in-practice emerging through enactment of VUIs on users’ smartphones: looking up, learning and leisure. We argue that – instead of asking why and how users appropriate VUIs – identifying different kinds of enactments of VUIs present researchers and practitioners with a more nuanced understanding of existing and potential use of VUIs across varied contexts.
Armada: low-effort verification of high-performance concurrent programs

Safely writing high-performance concurrent programs is notoriously difficult. To aid developers, we introduce Armada, a language and tool designed to formally verify such programs with relatively little effort. Via a C-like language and a small-step, state-machine-based semantics, Armada gives developers the flexibility to choose arbitrary memory layout and synchronization primitives so they are never constrained in their pursuit of performance. To reduce developer effort, Armada leverages SMT-powered automation and a library of powerful reasoning techniques, including rely-guarantee, TSO elimination, reduction, and alias analysis. All these techniques are proven sound, and Armada can be soundly extended with additional strategies over time. Using Armada, we verify four concurrent case studies and show that we can achieve performance equivalent to that of unverified code.
Multimodal Future Localization and Emergence Prediction for Objects in Egocentric View With a Reachability Prior

In this paper, we investigate the problem of anticipating future dynamics, particularly the future location of other vehicles and pedestrians, in the view of a moving vehicle. We approach two fundamental challenges: (1) the partial visibility due to the egocentric view with a single RGB camera and considerable field-of-view change due to the egomotion of the vehicle; (2) the multimodality of the distribution of future states. In contrast to many previous works, we do not assume structural knowledge from maps. We rather estimate a reachability prior for certain classes of objects from the semantic map of the present image and propagate it into the future using the planned egomotion. Experiments show that the reachability prior combined with multi-hypotheses learning improves multimodal prediction of the future location of tracked objects and, for the first time, the emergence of new objects. We also demonstrate promising zero-shot transfer to unseen datasets.
Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation

With the increasing availability of videos, how to edit them and present the most interesting parts to users, i.e., video highlight, has become an urgent need with many broad applications. As users' visual preferences are subjective and vary from person to person, previous generalized video highlight extraction models fail to tailor to users' unique preferences. In this paper, we study the problem of personalized video highlight recommendation with rich visual content. By dividing each video into non-overlapping segments, we formulate the problem as a personalized segment recommendation task with many new segments in the test stage. The key challenges of this problem lie in: the cold-start users with limited video highlight records in the training data and new segments without any user ratings at the test stage. To tackle these challenges, an intuitive idea is to formulate a user-item interaction graph and perform inductive graph neural network based models for better user and item embedding learning. However, the graph embedding models fail to generalize to unseen items as these models rely on the item content feature and item link information for item embedding calculation. To this end, we propose an inductive Graph based Transfer learning framework for personalized video highlight Recommendation (TransGRec). TransGRec is composed of two parts: a graph neural network followed by an item embedding transfer network. Specifically, the graph neural network part exploits the higher-order proximity between users and segments to alleviate the user cold-start problem. The transfer network is designed to approximate the learned item embeddings from graph neural networks by taking each item's visual content as input, in order to tackle the new segment problem in the test phase. We design two detailed implementations of the transfer learning optimization function, and we show how the two parts of TransGRec can be efficiently optimized with different transfer learning optimization functions. Please note that, our proposed framework is generally applicable to any inductive graph based recommendation model to address the new node problem without any link structure. Finally, extensive experimental results on a real-world dataset clearly show the effectiveness of our proposed model.
Deep Generative Model for Robust Imbalance Classification

Discovering hidden pattern from imbalanced data is a critical issue in various real-world applications including computer vision. The existing classification methods usually suffer from the limitation of data especially the minority classes, and result in unstable prediction and low performance. In this paper, a deep generative classifier is proposed to mitigate this issue via both data perturbation and model perturbation. Specially, the proposed generative classifier is modeled by a deep latent variable model where the latent variable aims to capture the direct cause of target label. Meanwhile, the latent variable is represented by a probability distribution over possible values rather than a single fixed value, which is able to enforce uncertainty of model and lead to stable prediction. Furthermore, this latent variable, as a confounder, affects the process of data (feature/label) generation, so that we can arrive at well-justified sampling variability considerations in statistics, and implement data perturbation. Extensive experiments have been conducted on widely-used real imbalanced image datasets. By comparing with the state-of-the-art methods, experimental results demonstrate the superiority of our proposed model on imbalance classification task.
Impact of False Data Injection Attacks on Deep Learning Enabled Predictive Analytics

Industry 4.0 is the latest industrial revolution primarily merging automation with advanced manufacturing to reduce direct human effort and resources. Predictive maintenance (PdM) is an industry 4.0 solution, which facilitates predicting faults in a component or a system powered by state-of-the- art machine learning (ML) algorithms (especially deep learning algorithms) and the Internet-of-Things (IoT) sensors. However, IoT sensors and deep learning (DL) algorithms, both are known for their vulnerabilities to cyber-attacks. In the context of PdM systems, such attacks can have catastrophic consequences as they are hard to detect due to the nature of the attack. To date, the majority of the published literature focuses on the accuracy of DL enabled PdM systems and often ignores the effect of such attacks. In this paper, we demonstrate the effect of IoT sensor attacks (in the form of false data injection attack) on a PdM system. At first, we use three state-of-the-art DL algorithms, specifically, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN) for predicting the Remaining Useful Life (RUL) of a turbofan engine using NASA's C-MAPSS dataset. The obtained results show that the GRU-based PdM model outperforms some of the recent literature on RUL prediction using the C-MAPSS dataset. Afterward, we model and apply two different types of false data injection attacks (FDIA), specifically, continuous and interim FDIAs on turbofan engine sensor data and evaluate their impact on CNN, LSTM, and GRU-based PdM systems. The obtained results demonstrate that FDI attacks on even a few IoT sensors can strongly defect the RUL prediction in all cases. However, the GRU-based PdM model performs better in terms of accuracy and resiliency to FDIA. Lastly, we perform a study on the GRU-based PdM model using four different GRU networks with different sequence lengths. Our experiments reveal an interesting relationship between the accuracy, resiliency and sequence length for the GRU-based PdM models.
IoT or NoT: Identifying IoT Devices in a Short Time Scale

In recent years the number of IoT devices in home networks has increased dramatically. Whenever a new device connects to the network, it must be quickly managed and secured using the relevant security mechanism or QoS policy. Thus a key challenge is to distinguish between IoT and NoT devices in a matter of minutes. Unfortunately, there is no clear indication of whether a device in a network is an IoT. In this paper, we propose different classifiers that identify a device as IoT or non-IoT, in a short time scale, and with high accuracy.Our classifiers were constructed using machine learning techniques on a seen (training) dataset and were tested on an unseen (test) dataset. They successfully classified devices that were not in the seen dataset with accuracy above 95%. The first classifier is a logistic regression classifier based on traffic features. The second classifier is based on features we retrieve from DHCP packets. Finally, we present a unified classifier that leverages the advantages of the other two classifiers.