Reviewerly/exHarmony huggingface dataset:
The Lottery Ticket Hypothesis at Scale

Pruning is a well-established technique for removing unnecessary structure from neural networks after training to improve the performance of inference. Several recent results have explored the possibility of pruning at initialization time to provide similar benefits during training. In particular, the ticket conjectures that typical neural networks contain small subnetworks that can train to similar accuracy in a commensurate number of steps. The evidence for this claim is that a procedure based on iterative magnitude pruning (IMP) reliably finds such subnetworks retroactively on small vision tasks. However, IMP fails on deeper networks, and proposed methods to prune before training or train pruned networks encounter similar scaling limitations. In this paper, we argue that these efforts have struggled on deeper networks because they have focused on pruning precisely at initialization. We modify IMP to search for subnetworks that could have been obtained by pruning early in training (0.1% to 7% through) rather than at iteration 0. With this change, it finds small subnetworks of deeper networks (e.g., 80% sparsity on Resnet-50) that can complete the training process to match the accuracy of the original network on more challenging tasks (e.g., ImageNet). In situations where IMP fails at iteration 0, the accuracy benefits of delaying pruning accrue rapidly over the earliest iterations of training. To explain these behaviors, we study subnetwork stability, finding that - as accuracy improves in this fashion - IMP subnetworks train to parameters closer to those of the full network and do so with improved consistency in the face of gradient noise. These results offer new insights into the opportunity to prune large-scale networks early in training and the behaviors underlying the lottery ticket hypothesis
Trajectory Optimization for Target Localization With Bearing-Only Measurement

This paper considers the problem of two-dimensional constrained trajectory optimization of a point-mass aerial robot for constant-maneuvering target localization using bearing-only measurement. A performance metric that can be utilized in trajectory optimization to maximize target observability is proposed first based on geometric conditions. One-step optimal maneuver that maximizes the observability criterion is then derived analytically for moving targets. The heading angle constraint is also incorporated in the proposed optimal maneuver derivation to support practical application. Numerical simulations with some comparisons are presented to validate the analytical findings.
Information security climate and the assessment of information security risk among healthcare employees

Since 2009, over 176 million patients in the United States have been adversely impacted by data breaches affecting Health Insurance Portability and Accountability Act–covered institutions. While the popular press often attributes data breaches to external hackers, most breaches are the result of employee carelessness and/or failure to comply with information security policies and procedures. To change employee behavior, we borrow from the organizational climate literature and introduce the Information Security Climate Index, developed and validated using two pilot samples. In this study, four categories of healthcare professionals (certified nursing assistants, dentists, pharmacists, and physician assistants) were surveyed. Likert-type items were used to assess the Information Security Climate Index, information security motivation, and information security behaviors. Study results indicated that the Information Security Climate Index was related to better employee information security motivation and information security behaviors. In addition, there were observed differences between occupational groups with pharmacists reporting a more favorable climate and behaviors than physician assistants.
Density Peaks Clustering Based on Weighted Local Density Sequence and Nearest Neighbor Assignment

Density peaks clustering (DPC) is a density-based clustering algorithm with excellent clustering performance including accuracy, automatically detecting the number of clusters, and identifying center points. However, the local density of DPC strongly depends on the cutoff distance which must be prespecified; in addition, the strategy assigns each remaining point to the same cluster as its nearest neighbor of higher density in descending order of local density, which is likely to cause cluster label error propagation. To overcome these limitations, we propose an improved DPC by introducing weighted local density sequence and two-stage assignment strategies, called DPCSA. Many previous improved DPC algorithms neglect additional complexity, whereas DPCSA incorporates the nearest neighbor dynamic table to enhance clustering efficiency. The experimental results for 12 artificial and 11 real-world datasets, including Olivetti face, verify that the DPCSA clustering performance is significantly superior to DPC and DPC via heat diffusion (HDDPC), and slightly superior to fuzzy weighted k-nearest neighbors density peak clustering (FKNNDPC). In addition, the DPCSA is more computationally efficient than FKNNDPC and HDDPC, but less than DPC. The source code of DPCSA is available at https://www.github.com/Yu123456/DPCSA.
A Parallel Proof of Work to Improve Transaction Speed and Scalability in Blockchain Systems

A blockchain is a distributed ledger forming a distributed consensus on a history of transactions, and is the underlying technology for the Bitcoin cryptocurrency. However, its applications are far beyond the financial sector. The transaction verification process for cryptocurrencies is much slower than traditional digital transaction systems. One approach to increase transaction speed and scalability is to identify a solution that offers faster Proof of Work. In this paper, we propose a method for accelerating the process of Proof of Work based on parallel mining rather than solo mining. The goal is to ensure that no more than two or more miners put the same effort into solving a specific block. The proposed method includes a process for selection of a manager, distribution of work and a reward system. This method has been implemented in a test environment that contains all the characteristics needed to perform Proof of Work for Bitcoin and has been tested, using a variety of case scenarios, by varying the difficulty level and number of validators. Preliminary results show improvement in the scalability of Proof of Work up to 34% compared to the current system.
Data Privacy Protection for Edge Computing of Smart City in a DIKW Architecture

Current trend of shifting computing from centralized Cloud to Edge has not only empowered huge amount of IoT devices with the capability of the accumulation of individualized computing and storage as a flexible whole, but also brought along new privacy challenges originating in emerging new usage requests on the accumulated content or resources from multiple sources of various integrated devices at the Edge. In this work, we focus on modeling the privacy content of multiple sources through mapping them as resources of types of Data, Information and Knowledge in the well-known DIKW architecture. We propose to categorize content objects and relationships uniformly as typed resources of data, information, and knowledge, according to our formalized DIKW architecture composing a meta model of DIKW and extended Data Graph, Information Graph and Knowledge Graph. We further propose to categorize target privacy resources of data and information according to their presence in the modeled searching space in our DIKW architecture as explicit and implicit divisions. Thereafter we propose protection solutions according to explicit and implicit divisions for privacy target concerning typed data. The efficiency and performance potential of our processing solution originates in a multiple dimensional modeling strategy of typed data, which is modeled solely with various frequencies of various meta level dimensions.
A Study of the Effects of Stemming Strategies on Arabic Document Classification

Stemming is one of the most effective techniques, which has been adopted in many applications, such as machine learning, machine translation, document classification (DC), information retrieval, and natural language processing.The stemming technique is meant to be applied during the classification of documents to reduce the high dimensionality of the feature space, which, in turn, raises the functioning of the classification system, particularly with extreme modulated language, for instance, Arabic language.This paper aims to study the impact of stemming techniques, namely Information Science Research Institute (ISRI), Tashaphyne, and ARLStem on Arabic DC.The classification algorithms, namely Naïve Bayesian (NB), support vector machine (SVM), and K -nearest neighbors (KNN), are used in this paper.In addition, the chi-square feature selection is used to select the most relevant features.Experiments are conducted on CNN Arabic corpus, which is collected from Arabic websites to assess the performance of the classification system.In order to evaluate the classifiers, the K -fold cross-validation method and Micro-F1 are used.Findings of this paper indicate that the ARLStem outperforms the ISRI and Tashaphyne stemmers.The outcomes clearly showed the effectiveness of the SVM over the KNN and NB classifiers, which achieved 94.64% Micro-F1 value when using the ARLStem stemmer.
Deep Self-Organizing Maps for Unsupervised Image Classification

The deep self-organizing map (DSOM) was introduced to embed hierarchical feature abstraction capability to self-organizing maps (SOMs). This paper presents an extended version of the original DSOM algorithm (E-DSOM). E-DSOM enhances the DSOM in two ways-learning algorithm is modified to be completely unsupervised, and architecture is modified to learn features of different resolution in hidden layers. E-DSOM has three main advantages over the original DSOM: 1) improved classification accuracy; 2) improved generalization capability; and 3) need of fewer sequential layers (reduced training time). E-DSOM was tested on benchmark and real-world datasets and was compared against DSOM, SOM, sStacked autoencoder (AE), and stacked convolutional autoencoder (CAE). Experimental results showed that the E-DSOM outperformed DSOM with improvements of classification accuracy up to 15% while saving training time up to 19% on all datasets. Moreover, E-DSOM evidenced better generalization capability compared to the DSOM by showing superior performance on all datasets with induced noise. Further, E-DSOM showed comparable performance to the AE and the CAE while outperforming them on two datasets.
Fair compute loads enabled by blockchain: sharing models by alternating client and server roles

Abstract Objective Decentralized privacy-preserving predictive modeling enables multiple institutions to learn a more generalizable model on healthcare or genomic data by sharing the partially trained models instead of patient-level data, while avoiding risks such as single point of control. State-of-the-art blockchain-based methods remove the “server” role but can be less accurate than models that rely on a server. Therefore, we aim at developing a general model sharing framework to preserve predictive correctness, mitigate the risks of a centralized architecture, and compute the models in a fair way Materials and Methods We propose a framework that includes both server and “client” roles to preserve correctness. We adopt a blockchain network to obtain the benefits of decentralization, by alternating the roles for each site to ensure computational fairness. Also, we developed GloreChain (Grid Binary LOgistic REgression on Permissioned BlockChain) as a concrete example, and compared it to a centralized algorithm on 3 healthcare or genomic datasets to evaluate predictive correctness, number of learning iterations and execution time Results GloreChain performs exactly the same as the centralized method in terms of correctness and number of iterations. It inherits the advantages of blockchain, at the cost of increased time to reach a consensus model Discussion Our framework is general or flexible and can also address intrinsic challenges of blockchain networks. Further investigations will focus on higher-dimensional datasets, additional use cases, privacy-preserving quality concerns, and ethical, legal, and social implications Conclusions Our framework provides a promising potential for institutions to learn a predictive model based on healthcare or genomic data in a privacy-preserving and decentralized way.
A clique-based approach for co-location pattern mining

Co-location pattern mining refers to the task of discovering the group of features (geographic object types) whose instances (geographic objects) are frequently located close together in a geometric space. Current approaches on this topic adopt a prevalence threshold (a measure of a user's interest in a pattern) to generate prevalent co-location patterns. However, in practice, it is not easy to specify a suitable prevalence threshold. Thus, users have to repeatedly execute the program to find a suitable prevalence threshold. Besides, the efficiency of these approaches is limited because of the expensive cost of identifying row-instances of co-location patterns. In this paper, we propose a novel clique-based approach for discovering complete and correct prevalent co-location patterns. The proposed approach avoids identifying row-instances of co-location patterns thus making it much easier to find a proper prevalence threshold. First, two efficient schemas are designed to generate complete and correct cliques. Next, these cliques are transformed into a hash structure which is independent of the prevalence threshold. Finally, the prevalence of each co-location pattern is efficiently calculated using the hash structure. The experiments on both real and synthetic datasets show the efficiency and effectiveness of our proposed approaches.
Kinodynamic Motion Planning With Continuous-Time Q-Learning: An Online, Model-Free, and Safe Navigation Framework

This paper presents an online kinodynamic motion planning algorithmic framework using asymptotically optimal rapidly-exploring random tree (RRT*) and continuous-time Q-learning, which we term as RRT-Q⋆. We formulate a model-free Q-based advantage function and we utilize integral reinforcement learning to develop tuning laws for the online approximation of the optimal cost and the optimal policy of continuous-time linear systems. Moreover, we provide rigorous Lyapunov-based proofs for the stability of the equilibrium point, which results in asymptotic convergence properties. A terminal state evaluation procedure is introduced to facilitate the online implementation. We propose a static obstacle augmentation and a local replanning framework, which are based on topological connectedness, to locally recompute the robot's path and ensure collision-free navigation. We perform simulations and a qualitative comparison to evaluate the efficacy of the proposed methodology.
Fuzzing File Systems via Two-Dimensional Input Space Exploration

File systems, a basic building block of an OS, are too big and too complex to be bug free. Nevertheless, file systems rely on regular stress-testing tools and formal checkers to find bugs, which are limited due to the ever-increasing complexity of both file systems and OSes. Thus, fuzzing, proven to be an effective and a practical approach, becomes a preferable choice, as it does not need much knowledge about a target. However, three main challenges exist in fuzzing file systems: mutating a large image blob that degrades overall performance, generating image-dependent file operations, and reproducing found bugs, which is difficult for existing OS fuzzers. Hence, we present JANUS, the first feedback-driven fuzzer that explores the two-dimensional input space of a file system, i.e., mutating metadata on a large image, while emitting image-directed file operations. In addition, JANUS relies on a library OS rather than on traditional VMs for fuzzing, which enables JANUS to load a fresh copy of the OS, thereby leading to better reproducibility of bugs. We evaluate JANUS on eight file systems and found 90 bugs in the upstream Linux kernel, 62 of which have been acknowledged. Forty-three bugs have been fixed with 32 CVEs assigned. In addition, JANUS achieves higher code coverage on all the file systems after fuzzing 12 hours, when compared with the state-of-the-art fuzzer Syzkaller for fuzzing file systems. JANUS visits 4.19x and 2.01x more code paths in Btrfs and ext4, respectively. Moreover, JANUS is able to reproduce 88-100% of the crashes, while Syzkaller fails on all of them.
Multiscale Locality and Rank Preservation for Robust Feature Matching of Remote Sensing Images

As a fundamental and important task in many applications of remote sensing and photogrammetry, feature matching tries to seek correspondences between the two feature sets extracted from an image pair of the same object or scene. This paper focuses on eliminating mismatches from a set of putative feature correspondences constructed according to the similarity of existing well-designed feature descriptors. Considering the stable local topological relationship of the potential true correspondences, we propose a simple yet efficient method named multiscale Top K Rank Preservation (mTopKRP) for robust feature matching. To this end, we first search the K-nearest neighbors of each feature point and generate a ranking list accordingly. Then we design a metric based on the weighted Spearman's footrule distance to describe the similarity of two ranking lists specifically for the matching problem. We build a mathematical optimization model and derive its closed-form solution, enabling our method to establish reliable correspondences in linearithmic time complexity, which requires only tens of milliseconds to handle over 1000 putative matches. We also introduce a multiscale strategy for neighborhood construction, which increases the robustness of our method and can deal with different types of degradation, even when the image pair suffers from a large scale change, rotation, nonrigid deformation, or a large number of mismatches. Extensive experiments on several representative remote sensing image data sets demonstrate the superiority of our method over state of the art.
Separation logic

Separation logic is a key development in formal reasoning about programs, opening up new lines of attack on longstanding problems.
A Sample of One

First-person research (i.e., research that involves data collection and experiences from the researcher themselves) continues to become a viable addition and, possibly even, alternative to more traditional HCI methods. While we have seen the benefits of using methods such as autoethnography, autobiographical design, and autoethnographical research through design, we also see the need to further explore, define, and investigate the practices, techniques, tactics, and implications of first-person research in HCI. To address this, this one-day workshop aims to bring together a community of researchers, designers, and practitioners who are interested in exploring and reimagining research in HCI and interaction design, with an emphasis on first-person methods.
On the Limitations of Targeted Adversarial Evasion Attacks Against Deep Learning Enabled Modulation Recognition

Wireless communications has greatly benefited in recent years from advances in machine learning. A new subfield, commonly termed Radio Frequency Machine Learning (RFML), has emerged that has demonstrated the application of Deep Neural Networks to multiple spectrum sensing tasks such as modulation recognition and specific emitter identification. Yet, recent research in the RF domain has shown that these models are vulnerable to over-the-air adversarial evasion attacks, which seek to cause minimum harm to the underlying transmission to a cooperative receiver, while greatly lowering the performance of spectrum sensing tasks by an eavesdropper. While prior work has focused on untargeted evasion, which simply degrades classification accuracy, this paper focuses on targeted evasion attacks, which aim to masquerade as a specific signal of interest. The current work examines how a Convolutional Neural Network (CNN) based Automatic Modulation Classification (AMC) model breaks down in the presence of an adversary with direct access to its inputs. Specifically, the current work uses the adversarial perturbation power needed to change the classification from a specific source modulation to a specific target modulation as a proxy for the model's estimation of their similarity and compares this with the known hierarchy of these human engineered modulations. The findings conclude that the reference model breaks down in an intuitive way, which can have implications on progress towards hardening RFML models.
Design and development of a spatial mixed reality touring guide to the Egyptian museum

Many public services and entertainment industries utilise Mixed Reality (MR) devices to develop highly immersive and interactive applications. However, recent advancements in MR processing has prompted the tourist and events industry to invest and develop commercial applications. The museum environment provides an accessible platform for MR guidance systems by taking advantage of the ergonomic freedom of spatial holographical Head-mounted Displays (HMD). The application of MR systems in museums can enhance the typical visitor experience by amalgamating historical interactive visualisations simultaneously with related physical artefacts and displays. Current approaches in MR guidance research primarily focus on visitor engagement with specific content. This paper describes the design and development of a novel museum guidance system based on the immersion and presence theory. This approach examines the influence of interactivity, spatial mobility, and perceptual awareness of individuals within MR environments. The developmental framework of a prototype MR tour guide program named MuseumEye incorporates the sociological needs, behavioural patterns, and accessibility of the user. This study aims to create an alternative tour guidance system to enhance customer experience and reduce the number of human tour guides in museums. The data gathering procedure examines the functionality of the MuseumEye application in conjunction with pre-existing pharaonic exhibits in a museum environment. This methodology includes a qualitative questionnaire sampling 102 random visitors to the Egyptian Museum in Cairo. Results of this research study indicate a high rate of positive responses to the MR tour guide system, and the functionality of AR HMD in a museum environment. This outcome reinforces the suitability of the touring system to increase visitor experience in museums, galleries and cultural heritage sites.