Miracle-dz/UniFAR-FTUs huggingface dataset:
Pushing the Boundaries with bdrmapIT: Mapping Router Ownership at Internet Scale

Two complementary approaches to mapping network boundaries from traceroute paths recently emerged [27,31]. Both approaches apply heuristics to inform inferences extracted from traceroute measurement campaigns. bdrmap [27] used targeted traceroutes from a specific network, alias resolution probing techniques, and AS relationship inferences, to infer the boundaries of that specific network and the other networks attached at each boundary. MAPIT [31] tackled the ambitious challenge of inferring all AS-level network boundaries in a massive archived collection of traceroutes launched from many different networks. Both were substantial contributions to the state-of-the-art, and inspired a collaboration to explore the potential to combine the approaches. We present and evaluate bdrmapIT, the result of that exploration, which yielded a more complete, accurate, and general solution to this persistent and central challenge of Internet topology research. bdrmapIT achieves 91.8%-98.8% accuracy when mapping AS boundaries in two Internet-wide traceroute datasets, vastly improving on MAP-IT's coverage without sacrificing bdrmap's ability to map a single network. The bdrmapIT source code is available at https://git.io/fAsI0.
Dynamic Programming Based Content Placement Strategy for 5G and Beyond Cellular Networks

Optimal caching strategies of popular contents in heterogeneous cellular networks are studied. The increasing demand for data traffic by users of the wireless network can be handled by rapaciously caching most frequently accessed contents by users. Hence, we propose an efficient popular content placement strategy, the first step in the content caching process, typically for popular video files. To do so, we introduce a novel approach for caching popular contents. This caching strategy follows a dynamic programming approach to tackle the optimization complexity of selecting most popular files among a wide range of files, under certain constraints. The proposed strategy gives the combination of popular files to be cached that maximizes the optimal cache hit probability with a pseudo-polynomial time complexity. To that end, we used the well-known resourcing algorithm, called the 0/1-Knapsack problem, assuming that files are cached without partitioning.
EAWA: Energy-Aware Workload Assignment in Data Centers

One of the challenges that today's cloud computing infrastructures, and more specifically data centers, are struggling with is related to their energy consumption. Information technology (IT) equipment and cooling infrastructure are key parts of the total energy expenditure in a data center. A considerable amount of power is wasted due to workload management inefficiencies and the lack of coordination between cooling units and IT equipment. In this paper, server differences in terms of their cooling requirements and power consumption are taken into account for workload distribution. An optimal workload assignment problem that takes both server power consumption and thermal models into account is formulated. A simple low complexity algorithm is proposed. The algorithm not only assigns workload but it also adjusts the cooling unit set-point accordingly. Results show that the proposed algorithm can significantly reduce the total power consumed in a data center, in particular when compared to the uniform workload distribution algorithm.
Numerical studies of the influence of various geometrical features of a multispiked connecting scaffold prototype on mechanical stresses in peri-implant bone

Abstract The multispiked connecting scaffold (MSC-scaffold) prototype is an essential innovation in the fixation of components of resurfacing arthroplasty (RA) endoprostheses, providing their entirely non-cemented and bone-tissue-preserving fixation in peri-articular bone. An FE study is proposed to evaluate the influence of geometrical features of the MSC-scaffold on the transfer of mechanical load in peri-implant bone. For this study, an FE model of Ti-Alloy MSC-scaffold prototype embedded in a bilinear elastic, transversely isotropic bone material was built. For the compressive load on the MSC-scaffold, maps of Huber-Mises-Hencky (HMH) stress in peri-implant bone were determined. The influence of the distance between the bases of neighbouring spikes, the apex angle of spikes, and the height of the spherical cup of spikes of the MSC-scaffold were analysed. It was found that the changes in the distance between the bases of neighbouring spikes from 0.2 to 0.5 mm cause the HMH stress to increase in bone material by 32%. The changes of the apex angle of spikes from 2° to 4° decrease the HMH stress in bone material by 39%. The changes of height of the spherical cup of spikes from 0 to 0.12 mm increase the HMH stress in bone material by 24%. In conclusion, the spikes’ apex angle and the distance between the bases of spikes of the MSC-scaffold are the key geometrical features determining the appropriate MSC-scaffold prototype design. The built FE model was found to be useful in bioengineering design of the novel fixation system for RA endoprostheses by means of the MSC-scaffold.
NL-FIIT at IEST-2018: Emotion Recognition utilizing Neural Networks and Multi-level Preprocessing

In this paper, we present neural models submitted to Shared Task on Implicit Emotion Recognition, organized as part of WASSA 2018. We propose a Bi-LSTM architecture with regularization through dropout and Gaussian noise. Our models use three different embedding layers: GloVe word embeddings trained on Twitter dataset, ELMo embeddings and also sentence embeddings. We see preprocessing as one of the most important parts of the task. We focused on handling emojis, emoticons, hashtags, and also various shortened word forms. In some cases, we proposed to remove some parts of the text, as they do not affect emotion of the original sentence. We also experimented with other modifications like category weights for learning and stacking multiple layers. Our model achieved a macro average F1 score of 65.55%, significantly outperforming the baseline model produced by a simple logistic regression.
An mHealth intervention programme to promote healthy behaviours and prevent adolescent obesity (TeenPower): A study protocol

AIM This paper describes the design and rationale of multicenter practice-based research that aims to develop and evaluate an innovative mobile health (mHealth) intervention programme directed to promote healthy behaviours and prevent adolescent obesity. DESIGN This study is designed as a non-randomized controlled trial with a three-arm structure. METHODS Twelve to 16 years old participants will be recruited from schools, with access to the Internet and smartphone/tablet devices. The intervention group will be invited to engage in the TeenPower mHealth programme and divided into two subgroups: Group A (additionally engaged in a structured school-based intervention programme) and Group B (only engaged in the mHealth programme). The mHealth app includes educational resources, self-monitoring, social support, interactive training modules and motivational tools. The control group will only follow the structured school-based intervention programme. The intervention length will be 3 months, including the direct support of an interdisciplinary team (nursing, nutrition, sports, psychology, among others). This research was approved and funded in August 2017. DISCUSSION The positive evaluation of the intervention programme will stimulate the inclusion of technologies in the promotion of salutogenic behaviours and obesity prevention. IMPACT Adolescent obesity reached epidemic proportions. It is urgent to find effective prevention strategies to induce change at the individual, family and community level. If effective, this protocol can be used by health and exercise professionals in improving community interventions tailored to teenagers.
A Novel Framework for the Seamless Integration of FPGA Accelerators with Big Data Analytics Frameworks in Heterogeneous Data Centers

To face the increased network traffic in the cloud, data center operators have started adopting an heterogeneous approach in their infrastructures. Heterogeneous infrastructures, e.g. based on FPGAs, can provide higher performance and better energy-efficiency compared to the contemporary processors. However, FPGAs lack of an easy-to-use framework for the efficient deployment from high-level programming frameworks. In this paper, we present a novel framework that allows the seamless integration of FPGAs from high-level programming languages, like Java and Scala. The proposed approach provides all the required APIs for the utilization of FPGAs from these languages. The proposed scheme has been mapped on Amazon AWS f1 infrastructure and a performance evaluation is presented for two widely used machine learning algorithms.
Cognitive Neural Network Delay Predictor for High Speed Mobility in 5G C-RAN Cellular Networks

The future fifth Generation (5G) cellular network is expected to support one million connections per square kilometre with 1 ms end-to-end as a desired latency. The potential of this ultra-dense network motivated the researchers to develop a new architecture. Cloud Radio Access Network (C-RAN) technology was proposed to meet the demand of future networks, however, moving the baseband processing from multiple physical base stations on the ground within the cell site into the cloud brings many challenges. One of these challenges is how to acquire accurate Channel State Information (CSI) for a dense number of access points and User Equipment (UE), which are the future theme of 5G deployment. CSI reflects the instantaneous communication link status between the mobile user and the base station. Hence, the imperfect or delayed CSI can influence the performance of the whole network. In order to reduce the impact of this outdated CSI and to improve its accuracy in C-RAN architecture, a Cognitive Neural Network Delay Predictor (CNNDP) is proposed for compensating the transmission and acquisition delay of the CSI working simultaneously along with the conventional prediction technique for predicting the time variations of the communication channel. The results demonstrate a significant enhancement in the data throughput of the network with the proposed approach.
The Marchex 2018 English Conversational Telephone Speech Recognition System

In this paper, we describe recent improvements to the production Marchex speech recognition system for our spontaneous customer-to-business telephone conversations. We outline our semi-supervised lattice-free maximum mutual information (LF-MMI) training process which can supervise over full lattices from unlabeled audio. We also elaborate on production-scale text selection techniques for constructing very large conversational language models (LMs). On Marchex English (ME), a modern evaluation set of conversational North American English, for acoustic modeling we report a 3.3% ({agent, caller}:{3.2%, 3.6%}) reduction in absolute word error rate (WER). For language modeling, we observe a separate {1.3%, 1.2%} point reduction on {agent, caller} utterances respectively over the performance of the 2017 production system.
Crowdsourcing-Based Web Accessibility Evaluation with Golden Maximum Likelihood Inference

Web accessibility evaluation examines how well websites comply with accessibility guidelines which help people with disabilities to perceive, navigate and contribute to the Web. This demanding task usually requires manual assessment by experts with many years of training and experience. However, not enough experts are available to carry out the increasing number of evaluation projects while non-experts often have different opinions about the presence of accessibility barriers. Addressing these issues, we introduce a crowdsourcing system with a novel truth inference algorithm to derive reliable and accurate assessments from conflicting opinions of evaluators. Extensive evaluation on 23,901 complex tasks assessed by 50 people with and without disabilities shows that our approach outperforms state of the art approaches. In addition, we conducted surveys to identify frequent barriers that people with disabilities are facing in their daily lives and the difficulty to access Web pages when they encounter these barriers. The frequencies and severities of barriers correlate with their derived importance in our evaluation project.
Universal capture: image-based facial animation for "The Matrix Reloaded"

The VFX R&D stage for The Matrix Reloaded was kicked off in January 2000 with the challenge to create realistic human faces. We believed that traditional facial animation approaches like muscle deformers or blend shapes would simply never work, both because of the richness of facial movement and because of the human viewer's extreme sensitivity to facial nuances. Our task was further complicated as we had to recreate familiar actors such as Keanu Reeves and Lawrence Fishburne. Our team had been very successful at applying image-based techniques for photorealistic film set/location rendering, so we decided to approach the problem from the image-based side again. We wanted to produce a 3-d recording of the real actor's performance and be able to play it back from different angles and under different lighting conditions. Just as we can extract geometry, texture, or light from images, we are now able to extract movement. Universal Capture combines two powerful computer vision techniques: optical flow and photogrammetry.
Realistic human face rendering for "The Matrix Reloaded"

The ultimate challenge in photorealistic computer graphics is rendering believable human faces. We are trained to study the human face since birth, so our brains are intimately familiar with every nuance and detail of what human skin is supposed look like. The challenge of rendering human skin is further complicated by some technical issues such as the fact that skin is a highly detailed surface with noticeable features in the order of ~100 microns and the fact that skin is translucent. On The Matrix Reloaded we had to create completely photorealistic renderings for most of the principal actors including Keanu Reeves, Lawrence Fishborne, and Hugo Weaving.
Preliminary study of the reliability and validity of the Johns Hopkins Adapted Cognitive Exam (Chinese version) in neurological intensive care unit patients

ABSTRACT Objective: To evaluate the reliability and validity of the Chinese version of the Johns Hopkins Adapted Cognitive Exam (ACE) in neurological intensive care unit patients. Design: The English version of the ACE was translated and adapted into Chinese. The cognitive function of 40 critically ill NICU patients was assessed using the Chinese version of the ACE and the Mini-mental state examination (MMSE) battery. Scores on the ACE and MMSE were analyzed, and the scale’s content validity, construct validity, concurrent validity, internal consistency, inter-rater reliability, and test-retest reliability metrics were determined. Result: The coefficients for the Pearson correlations between individual item scores and total score ranged from 0.617 to 0.938, and content validity was good. ACE scores were significantly correlated with MMSE scores(r = 0.822, p<0.05). Five factors were extracted during the principal component analysis, the cumulative contribution of which was 85.90%. Overall, the factor loading of each item was 0.5, and the scale had good construct validity. The Chinese version of the ACE demonstrated good internal consistency (Cronbach’α = 0.756), inter-rater reliability (ICC>0.95), and test-retest reliability (ICC = 0.652–0.979). Conclusions: The results of this study suggest that the Chinese version of the ACE was a reliable and valid screening tool for cognitive impairment in NICU patients. List of abbreviations: ACE: (Johns Hopkins Adapted Cognitive Exam); NICU : (neurological intensive care unit); MMSE: (Mini-Mental State Examination); SPSS: (the Statistical package for the Social Sciences); ICC: (Intra-class Correlation Coefficients); SCCM: (Society of Critical Care Medicine); PICS: (post–intensive care syndrome); ARDS:(acute respiratory distress syndrome); MoCA; (Montreal Cognitive Assessment); EFA: (exploratory factor analysis).
Neural sentence generation from formal semantics

Sequence-to-sequence models have shown strong performance in a wide range of NLP tasks, yet their applications to sentence generation from logical representations are underdeveloped. In this paper, we present a sequence-to-sequence model for generating sentences from logical meaning representations based on event semantics. We use a semantic parsing system based on Combinatory Categorial Grammar (CCG) to obtain data annotated with logical formulas. We augment our sequence-to-sequence model with masking for predicates to constrain output sentences. We also propose a novel evaluation method for generation using Recognizing Textual Entailment (RTE). Combining parsing and generation, we test whether or not the output sentence entails the original text and vice versa. Experiments showed that our model outperformed a baseline with respect to both BLEU scores and accuracies in RTE.
Inactivity Benchmarking

Computational workloads that run out of work need some way to signal the intent to be inactive. With a proper inactivity signaling, the battery life can be extended, cloud costs can be reduced and the performance can be improved. In this paper, we study some properties of the inactivity signaling capabilities on virtualized platforms. The main contribution of the paper is a presentation of the inner workings of Spurg-Bench - a benchmarking tool developed by the author. The Spurg-Bench generates a load consisting of computation and inactivity. A secondary contribution is a small study on the inactivity behaviour of two different operating system (OS) kernels. Spurg-Bench generates a specified load on a system according to the utilization of the operations per second (OPS). By using the utilization notion of the load, Spurg-Bench generates a load occupying a fraction of the available processor time without any throughput guarantees. With the OPS notion, the generated load has a given throughput but has no guarantees on how much processor time it uses. Spurg-Bench generates load in cycles by executing an operation repeatedly and becoming inactive for a while. The parameters of a cycle in Spurg-Bench are the number of the operations and the duration of the inactivity. To achieve the desired load, the parameters of Spurg-Bench are dynamically adjusted using a control algorithm which minimizes the difference between the current load and the specified load. Spurg-Bench differs from similar tools by running the workload in the same loop as the control algorithm. The in-loop approach is crucial to the inactivity benchmarking because it enables a full control over the duration and the starting time of the inactivity periods.
Learning representations for sentiment classification using Multi-task framework

Most of the existing state of the art sentiment classification techniques involve the use of pre-trained embeddings. This paper postulates a generalized representation that collates training on multiple datasets using a Multi-task learning framework. We incorporate publicly available, pre-trained embeddings with Bidirectional LSTM’s to develop the multi-task model. We validate the representations on an independent test Irony dataset that can contain several sentiments within each sample, with an arbitrary distribution. Our experiments show a significant improvement in results as compared to the available baselines for individual datasets on which independent models are trained. Results also suggest superior performance of the representations generated over Irony dataset.
Mixing signals and modes in synchronous data-flow systems

Synchronous data-flow languages such as SCADE LUSTRE manage infinite sequences, or streams, as primitive values making them naturally adapted to the description of dominated systems. Their conservative extension with means to define control-structures or modes has been a long-term research topic through which several solutions have emerged.In this paper, we pursue this effort and generalize existing solutions by providing two constructs: a general form of state machines called parameterized state machines, and valued signals, as can be found in ESTEREL. Parameterized state machines greatly reduce the reliance on error-prone mechanisms such as shared memory in automaton-based programming. Signals provide a new way of programming with multi-rate data in synchronous dataflow languages. Together, they allow for a much more direct and natural programming of systems that combine dataflow and state-machines.The proposed extension is fully implemented in the new LUCID SYNCHRONE compiler.
Cortical astroglia undergo transcriptomic dysregulation in the G93A SOD1 ALS mouse model

Abstract Astroglia are the most abundant glia cell in the central nervous system, playing essential roles in maintaining homeostasis. Key functions of astroglia include, but are not limited to, neurotransmitter recycling, ion buffering, immune modulation, neurotrophin secretion, neuronal synaptogenesis and elimination, and blood–brain barrier maintenance. In neurological diseases, it is well appreciated that astroglia play crucial roles in the disease pathogenesis. In amyotrophic lateral sclerosis (ALS), a motor neuron degenerative disease, astroglia in the spinal cord and cortex downregulate essential transporters, among other proteins, that exacerbate disease progression. Spinal cord astroglia undergo dramatic transcriptome dysregulation. However, in the cortex, it has not been well studied what effects glia, especially astroglia, have on upper motor neurons in the pathology of ALS. To begin to shed light on the involvement and dysregulation that astroglia undergo in ALS, we isolated pure grey-matter cortical astroglia and subjected them to microarray analysis. We uncovered a vast number of genes that show dysregulation at end-stage in the ALS mouse model, G93A SOD1. Many of these genes play essential roles in ion homeostasis and the Wnt-signaling pathway. Several of these dysregulated genes are common in ALS spinal cord astroglia, while many of them are unique. This database serves as an approach for understanding the significance of dysfunctional genes and pathways in cortical astroglia in the context of motor neuron disease, as well as determining regional astroglia heterogeneity, and providing insight into ALS pathogenesis.
Interestingness measures for data mining: A survey

Interestingness measures play an important role in data mining, regardless of the kind of patterns being mined. These measures are intended for selecting and ranking patterns according to their potential interest to the user. Good measures also allow the time and space costs of the mining process to be reduced. This survey reviews the interestingness measures for rules and summaries, classifies them from several perspectives, compares their properties, identifies their roles in the data mining process, gives strategies for selecting appropriate measures for applications, and identifies opportunities for future research in this area.