Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Information Centrality Metric: A Simulation Approach

Identifying the central people in information flow networks is essential to understanding how people communicate and coordinate as well as who controls the information flows in the network. However, the appropriate usage of centrality metrics depends on an understanding of the type of network flow. Networks can vary in the way node-to-node transmission takes place, or in the way a course through the network is taken, thereby leading to different types of information flow processes. When metrics are used for an inappropriate flow process, the result of the metric can be misleading and often incorrect. In this paper we create a simulation of the flow of information in a network, and then we investigate the relation of information centrality as well as other network centralities, like betweenness, closeness and eigenvector along with the outcome of simulations with information flowing through walks rather than paths, trails or geodesics. We find that Information Centrality is more similar to Eigenvector and Degree centrality than to Closeness centrality as postulated by previous literature. We also find an interesting pattern emerge from the inter metric correlations.
Time is of the Essence: A Joint Hierarchical RNN and Point Process Model for Time and Item Predictions

In recent years session-based recommendation has emerged as an increasingly applicable type of recommendation. As sessions consist of sequences of events, this type of recommendation is a natural fit for Recurrent Neural Networks (RNNs). Several additions have been proposed for extending such models in order to handle specific problems or data. Two such extensions are 1. ) modeling of inter-session relations for catching long term dependencies over user sessions, and 2. ) modeling temporal aspects of user-item interactions. The former allows the session-based recommendation to utilize extended session history and inter-session information when providing new recommendations. The latter has been used to both provide state-of-the-art predictions for when the user will return to the service and also for improving recommendations. In this work, we combine these two extensions in a joint model for the tasks of recommendation and return-time prediction. The model consists of a Hierarchical RNN for the inter-session and intra-session items recommendation extended with a Point Process model for the time-gaps between the sessions. The experimental results indicate that the proposed model improves recommendations significantly on two datasets over a strong baseline, while simultaneously improving return-time predictions over a baseline return-time prediction model.
MemCloak: Practical Access Obfuscation for Untrusted Memory

Access patterns over untrusted memory have long been exploited to infer sensitive information like program types or even secret keys. Most existing obfuscation solutions hide real memory accesses among a sufficiently large number of dummy memory accesses. Such solutions lead to a heavy communication overhead and more often apply to the client/server scenario instead of the CPU/memory architecture. Sporadic obfuscation solutions strive for an affordable memory bandwidth cost at the expense of security degradation. For example, they may have to obfuscate accesses over a limited range of memory space to control the overhead. In this paper, we present MemCloak to obfuscate accesses throughout the entire memory space with an 0(1) communication overhead. We advocate leveraging data redundancy to achieve extremely efficient obfuscation. Loading multiple duplicates of a data block in memory, MemCloak enables the CPU to fetch the same data by accessing different memory locations. This breaks the condition for snooping the access pattern. Moreover, we leverage data aggregation to improve memory utilization. It enables the CPU to fetch the same aggregated data block times from the same memory location but each time for a different data block therein. This further prohibits an attacker from correlating memory accesses. We propose a series of optimization techniques to compress the position that tracks memory layout. The optimized position map is hundreds of times smaller than the traditional position map. It takes only several megabytes for protecting a 4 GB memory and can fit in an on-chip cache or buffer. We implement MemCloak using the gem5 simulator and validate its performance using highly memory-intensive MiBench benchmarks.
Metabolic Changes Induced by High-Fat Meal Evoke Different Microvascular Responses in Accordance with Adiposity Status

Background Frequently, ingestion of lipids exceeds our daily requirements and constantly exposes humans to circulating lipid overload which may lead to endothelial dysfunction (ED), the earliest marker of atherosclerosis. Nailfold videocapillaroscopy (NVC) technique can detect ED on microcirculation. Using NVC, we aimed to demonstrate if metabolic alterations evoked by high-fat meals can act differently on microvascular endothelial reactivity in lean and women with obesity. Methods Women, aged between 19 and 40 years, were allocated to control group (CG) and with obesity group (OBG) and were subjected to blood analysis for determination of glucose, total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-c) and high-density lipoprotein cholesterol (HDL-c) and NVC evaluation at fasting and 30, 60, 120, and 180-min after high-fat meal ingestion. NVC technique evaluated microvascular reactivity through the following variables: red blood cell velocity (RBCV) at rest and after 1-min ischemia (maximal red blood cell velocity, RBCVmax) and time taken to reach it (TRBCVmax). A P value ≤0.05 was considered significant. Results High-fat meal promoted a two-phase response in both groups: one until 60-min, associated with glucose and insulin levels, and the other after 120-min, associated with TG levels. Significant differences between groups were observed concerning insulin and HDL-c concentrations only at fasting and TC, TG, and LDL-c levels in all-time points. Regarding microvascular reactivity, RBCV, RBCVmax, and TRBCVmax were significantly different in OBG at 30-min compared to baseline. RBCVmax and TRBCVmax were significantly different in CG at 30-min and 60-min comparing to fasting. In all-time points, OBG presented RBCV, RBCVmax , and TRBCVmax significantly different in comparison to CG. Conclusion High-fat meal worsened ED on microcirculation in women with obesity and induced impairment of endothelial function in lean ones, reinforcing the association between high-fat meal and atherosclerosis.
Hiding in the Shadows: Empowering ARM for Stealthy Virtual Machine Introspection

ARM has become the leading processor architecture for mobile and IoT devices, while it has recently started claiming a bigger slice of the server market pie as well. As such, it will not be long before malware more regularly target the ARM architecture. Therefore, the stealthy operation of Virtual Machine Introspection (VMI) is an obligation to successfully analyze and proactively mitigate this growing threat. Stealthy VMI has proven itself perfectly suitable for malware analysis on Intel's architecture, yet, it often lacks the foundation required to be equally effective on ARM.
Parallel Metric Tree Embedding Based on an Algebraic View on Moore-Bellman-Ford

A metric tree embedding of expected stretch α ≥ 1 maps a weighted n-node graph G = (V, E, ω) to a weighted tree T = (VT, ET , ωT) with V ⊑ VT such that, for all v,w ∈ V, dist(v, w, G) ≤ dist(v, w, T), and E[dist(v, w, T)] ≤ α dist(v, w, G). Such embeddings are highly useful for designing fast approximation algorithms as many hard problems are easy to solve on tree instances. However, to date, the best parallel polylog n)-depth algorithm that achieves an asymptotically optimal expected stretch of α ∈ O(log n) requires Ω (n2) work and a metric as input. In this article, we show how to achieve the same guarantees using polylog n depth and Õ(m1+ϵ) work, where m = |E| and ϵ > 0 is an arbitrarily small constant. Moreover, one may further reduce the work to Õ(m + n1+ε) at the expense of increasing the expected stretch to O(ε−1 log n). Our main tool in deriving these parallel algorithms is an algebraic characterization of a generalization of the classic Moore-Bellman-Ford algorithm. We consider this framework, which subsumes a variety of previous “Moore-Bellman-Ford-like” algorithms, to be of independent interest and discuss it in depth. In our tree embedding algorithm, we leverage it to provide efficient query access to an approximate metric that allows sampling the tree using polylog n depth and Õ(m) work. We illustrate the generality and versatility of our techniques by various examples and a number of additional results. Specifically, we (1) improve the state of the art for determining metric tree embeddings in the Congest model, (2) determine a (1 + εˆ)-approximate metric regarding the distances in a graph G in polylogarithmic depth and Õ(n(m+n1 + ε )) work, and (3) improve upon the state of the art regarding the k-median and the buy-at-bulk network design problems.
Set Cross Entropy: Likelihood-based Permutation Invariant Loss Function for Probability Distributions

We propose a permutation-invariant loss function designed for the neural networks reconstructing a set of elements without considering the order within its vector representation. Unlike popular approaches for encoding and decoding a set, our work does not rely on a carefully engineered network topology nor by any additional sequential algorithm. The proposed method, Set Cross Entropy, has a natural information-theoretic interpretation and is related to the metrics defined for sets. We evaluate the proposed approach in two object reconstruction tasks and a rule learning task.
Network Layer Weight Calculation Method for Social Rumor Communication Network

Rumors are evolving by developing spreading mechanisms based on the use of multiple vectors of propagation. Finding important nodes in the network and taking action can effectively control the range and speed of the rumor. Most of the current research on the influential spreaders is on the single-layer network level. However, compared with single-layer network, the influential spreaders of multiplex networks will be more realistic. In particular, based on the susceptible-infected-susceptible (SIS) propagation model of multiplex complex network, we design a network layer weight calculation method for multiplex complex network. We show that this method is effective for calculating the importance of nodes in multiplex complex networks.
Transfer learning‐based online multiperson tracking with Gaussian process regression

Most existing tracking‐by‐detection approaches are affected by abrupt pedestrian pose changes, lighting conditions, scale changes, and real‐time processing, which leads to issues such as detection errors and drifts. To deal with these issues, we present a novel multi‐person tracking framework by introducing a new Gaussian Process Regression based observation model, which learns in a semi‐supervised manner. The background information is taken into consideration to build the discriminative tracker, training samples are re‐weighted appropriately to ease the impact of the potential sample misalignment and noisy during model updating. Unlabeled samples from the current frame provide rich information, which is used for enhancing the tracking inference. Experimental results show that the proposed approach outperforms a number of state‐of‐the‐art methods on some benchmark datasets.
Implementation of a non‐linear solver on heterogeneous architectures

Heterogeneous architectures seem to be not only the present but also the future of the HPC world (eg, see the Exascale Project of U.S. Department of Energy or the European Horizon 2020 FET Proactive ‐ High Performance Computing Call). A lot of work has been done in developing software libraries useful to solve problem described by linear equations on such computing systems. Instead, not the same effort is spent in such context for the implementation of software modules to be used to solve non‐linear problem. In this work, we present some experiences related with the implementation of a Quasi‐Newton method able to exploit, using a combination of “Task Scheduling,” “matrix‐free,” and “look‐ahead” approaches, both the CPUs and the GP‐GPUs components of a heterogeneous system.
Cardiovascular Manifestations of Erdheim-Chester's Disease: A Case Series

Erdheim-Chester Disease is a rare entity, classified as an inflammatory myeloid neoplasm, with an unknown incidence, occurring preferentially in men after 50 years of age. Classically, it has a multisystemic presentation, with the skeletal system being the most frequently affected (90% of the patients), followed by genitourinary involvement in 60% of cases and central nervous system in the pituitary and diabetes insipidus in 25% of the cases. Cardiovascular manifestations are present in more than half of the patients, with aortic infiltration and atrial pseudotumor being the most common forms.
Expert Systems With Applications

Malware creators have been getting their way for too long now. String-based similarity measures can leverage ground truth in a scalable way and can operate at a level of abstraction that is diﬃcult to combat from the code level. At the string level, information theory and, speciﬁcally, entropy play an important role related to detecting patterns altered by concealment strategies, such as polymorphism or encryption. Controlling the entropy levels in different parts of a disk resident executable allows an analyst to detect malware or a black hat to evade the detection. This paper shows these two perspectives into two scalable entropy-based tools: EnTS and EEE . EnTS, the detection tool, shows the effectiveness of detecting entropy patterns, achieving 100% precision with 82% accuracy. It outperforms VirusTotal for accuracy on combined Kaggle and VirusShare malware. EEE , the evasion tool, shows the effectiveness of entropy as a concealment strategy, attacking binary-based state of the art detectors. It learns their detection patterns in up to 8 generations of its search process, and increments their false negative rate from range 0–9%, up to the range 90–98.7%. © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. ( http://creativecommons.org/licenses/by/4.0/ )
Rare Event Detection Using Disentangled Representation Learning

This paper presents a novel method for rare event detection from an image pair with class-imbalanced datasets. A straightforward approach for event detection tasks is to train a detection network from a large-scale dataset in an end-to-end manner. However, in many applications such as building change detection on satellite images, few positive samples are available for the training. Moreover, an image pair of scenes contains many trivial events, such as in illumination changes or background motions. These many trivial events and the class imbalance problem lead to false alarms for rare event detection. In order to overcome these difficulties, we propose a novel method to learn disentangled representations from only low-cost negative samples. The proposed method disentangles the different aspects in a pair of observations: variant and invariant factors that represent trivial events and image contents, respectively. The effectiveness of the proposed approach is verified by the quantitative evaluations on four change detection datasets, and the qualitative analysis shows that the proposed method can acquire the representations that disentangle rare events from trivial ones.
Typing-Proof: Usable, Secure and Low-Cost Two-Factor Authentication Based on Keystroke Timings

Two-factor authentication (2FA) systems provide another layer of protection to users' accounts beyond password. Traditional hardware token based 2FA and software token based 2FA are not burdenless to users since they require users to read, remember, and type a onetime code in the process, and incur high costs in deployments or operations. Recent 2FA mechanisms such as Sound-Proof, reduce or eliminate users' interactions for the proof of the second factor; however, they are not designed to be used in certain settings (e.g., quiet environments or PCs without built-in microphones), and they are not secure in the presence of certain attacks (e.g., sound-danger attack and co-located attack). To address these problems, we propose Typing-Proof, a usable, secure and low-cost two-factor authentication mechanism. Typing-Proof is similar to software token based 2FA in a sense that it uses password as the first factor and uses a registered phone to prove the second factor. During the second-factor authentication procedure, it requires a user to type any random code on a login computer and authenticates the user by comparing the keystroke timing sequence of the random code recorded by the login computer with the sounds of typing random code recorded by the user's registered phone. Typing-Proof can be reliably used in any settings and requires zero user-phone interaction in the most cases. It is practically secure and immune to the existing attacks to recent 2FA mechanisms. In addition, Typing-Proof enables significant cost savings for both service providers and users.
Experimenting with Clojure on Extracting Medication Information from Clinical Narratives

The path towards electronic healthcare records is nonetheless not free of challenges including the large amount of clinical information buried in narrative content. Medication information is one of the most important types of clinical data in electronic healthcare records. It is critical for healthcare safety and quality, as well as for clinical research to have such information identified correctly. Natural language processing (NLP) is essential to phenotyping the medication data. However, recognizing medication patterns based on general NLP techniques fail short to identify such patterns with great accuracy even if they were trained with relevant clinical treebanks or corpuses. This article describes how Clojure and OpenNLP API can be used to identify medication patterns and train the clinical narrative chuncker to accurately identify given medication patterns.
Transferring Knowledge across Learning Processes

In complex transfer learning scenarios new tasks might not be tightly linked to previous tasks. Approaches that transfer information contained only in the final parameters of a source model will therefore struggle. Instead, transfer learning at a higher level of abstraction is needed. We propose Leap, a framework that achieves this by transferring knowledge across learning processes. We associate each task with a manifold on which the training process travels from initialization to final parameters and construct a meta-learning objective that minimizes the expected length of this path. Our framework leverages only information obtained during training and can be computed on the fly at negligible cost. We demonstrate that our framework outperforms competing methods, both in meta-learning and transfer learning, on a set of computer vision tasks. Finally, we demonstrate that Leap can transfer knowledge across learning processes in demanding reinforcement learning environments (Atari) that involve millions of gradient steps.
TorchProteinLibrary: A computationally efficient, differentiable representation of protein structure

Predicting the structure of a protein from its sequence is a cornerstone task of molecular biology. Established methods in the field, such as homology modeling and fragment assembly, appeared to have reached their limit. However, this year saw the emergence of promising new approaches: end-to-end protein structure and dynamics models, as well as reinforcement learning applied to protein folding. For these approaches to be investigated on a larger scale, an efficient implementation of their key computational primitives is required. In this paper we present a library of differentiable mappings from two standard dihedral-angle representations of protein structure (full-atom representation "$\phi,\psi,\omega,\chi$" and backbone-only representation "$\phi,\psi,\omega$") to atomic Cartesian coordinates. The source code and documentation can be found at this https URL.
Midfoot Charcot Neuroarthropathy

Citation: Alqubaisi M, Ross C, Ferguson Z, Pillai A (2018) Midfoot Charcot Neuroarthropathy. Int J Foot Ankle 2:021. Accepted: December 27, 2018; Published: December 29, 2018 Copyright: © 2018 Alqubaisi M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
LSCP: Locally Selective Combination in Parallel Outlier Ensembles

In unsupervised outlier ensembles, the absence of ground truth makes the combination of base outlier detectors a challenging task. Specifically, existing parallel outlier ensembles lack a reliable way of selecting competent base detectors, affecting accuracy and stability, during model combination. In this paper, we propose a framework---called Locally Selective Combination in Parallel Outlier Ensembles (LSCP)---which addresses the issue by defining a local region around a test instance using the consensus of its nearest neighbors in randomly selected feature subspaces. The top-performing base detectors in this local region are selected and combined as the model's final output. Four variants of the LSCP framework are compared with seven widely used parallel frameworks. Experimental results demonstrate that one of these variants, LSCP_AOM, consistently outperforms baselines on the majority of twenty real-world datasets.
Respiratory Complications of Adenotonsillectomy for Obstructive Sleep Apnea in the Pediatric Population

Objective To determine the prevalence of respiratory complications in the early postoperative period of children with sleep apnea who required adenotonsillectomy at a tertiary pediatric hospital and to establish recommendations for postoperative monitoring. Methods Retrospective cohort study of children with obstructive sleep apnea (OSA) diagnosed by polysomnogram (PSG), who underwent adenotonsillectomy for treatment of OSA. The prevalence of respiratory complications in the first 24 postoperative hours was measured. Patients with craniofacial malformations, obesity, and severe cardiovascular comorbidities were excluded. The prevalence of postoperative respiratory complications was compared with the severity of OSA according to the Apnea Hypopnea Index (AHI) and NADIR. All data were taken in patients residing in Bogotá city, Colombia, at 2.640 meters above sea level (m.a.s.l). Results Between May 2014 and February 2017, 167 patients (108 males) required adenotonsillectomy for OSA, with an age range of 1 and 15 years (mean 5.3 years +/- 2.7). The prevalence of postoperative respiratory complications was 3.59% (6/167). There was a statistically significant relationship between the presence of respiratory complication and AHI greater than 44/h (p <0.04). There was an inverse correlation between the AHI and NADIR values. Risk groups of patients younger than 3 years and NADIR less than 70% had a higher prevalence of respiratory complications; however, this correlation was not statistically significant (p <0.08 and 0.89, respectively). Conclusions The prevalence of respiratory complications in OSA patients undergoing adenotonsillectomy in high altitudes is similar to that reported in other heights. Preoperative AHI greater than 44/h could be considered a risk factor for early respiratory complication. We suggest ambulatory management after 6 hours in Postanesthetic Care Unit (PACU) observation in patients older than 3 years, with AHI less than 44/h and NADIR greater than 70% in altitudes higher than 2.500 m.a.s.l. Further research must be done to confirm this hypothesis.