Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Crowdworkers as a Proxy for Online Learners in Video-Based Learning Contexts

Crowdsourcing has emerged as an effective method of scaling-up tasks previously reserved for a small set of experts. Accordingly, researchers in the large-scale online learning space have begun to employ crowdworkers to conduct research about large-scale, open online learning. We here report results from a crowdsourcing study (N=135) to evaluate the extent to which crowdworkers and MOOC learners behave comparably on lecture viewing and quiz tasks---the most utilized learning activities in MOOCs. This serves to (i) validate the assumption of previous research that crowdworkers are indeed reliable proxies of online learners and (ii) address the potential of employing crowdworkers as a means of online learning environment testing. Overall, we observe mixed results---in certain contexts (quiz performance and video watching behavior) crowdworkers appear to behave comparably to MOOC learners, and in other situations (interactions with in-video quizzes), their behaviors appear to be disparate. We conclude that future research should be cautious if employing crowdworkers to carry out learning tasks, as the two populations do not behave comparably on all learning-related activities.
FlashBack: Immersive Virtual Reality on Mobile Devices via Rendering Memoization

Virtual reality head-mounted displays (VR HMDs) are attracting users with the promise of full sensory immersion in virtual environments. Creating the illusion of immersion for a near-eye display results in very heavy rendering workloads: low latency, high framerate, and high visual quality are all needed. Tethered VR setups in which the HMD is bound to a powerful gaming desktop limit mobility and exploration, and are difficult to deploy widely. Products such as Google Cardboard and Samsung Gear VR purport to offer any user a mobile VR experience, but their GPUs are too power-constrained to produce an acceptable framerate and latency, even for scenes of modest visual quality. We present FLASHBACK, an unorthodox design point for HMD VR that eschews all real-time scene rendering. Instead, FLASHBACK aggressively precomputes and caches all possible images that a VR user might encounter. FLASHBACK memoizes costly rendering effort in an offline step to build a cache full of panoramic images. During runtime, FLASHBACK constructs and maintains a hierarchical storage cache index to quickly lookup images that the user should be seeing. On a cache miss, FLASHBACK uses fast approximations of the correct image while concurrently fetching more closely-matching entries from its cache for future requests. Moreover, FLASHBACK not only works for static scenes, but also for dynamic scenes with moving and animated objects. We evaluate a prototype implementation of FLASHBACK and report up to a 8x improvement in framerate, 97x reduction in energy consumption per frame, and 15x latency reduction compared to a locally-rendered mobile VR setup. In some cases, FLASHBACK even delivers better framerates and responsiveness than a tethered HMD configuration on graphically complex scenes.
Influence of pennation angle on measurement of shear wave elastography: in vivo observation of shear wave propagation in human pennate muscle

Objective: To examine the influence of pennation angle on measurement of shear wave elastography, this study utilised the propagation view mode that can reveal shear wave propagation in human pennate muscle in vivo. Approach: The validity of the propagation view mode was qualitatively confirmed by observation of shear wave propagation in tissue-mimicking phantoms. Shear wave velocity of the medial gastrocnemius (MG) was evaluated in 15 healthy males. By changing the placement of the ultrasound probe on the skin surface, shear wave propagation in the MG was changed: shear waves propagated along the MG fascicles (horizontal condition) and across the fascicles (oblique condition). Main results: It was observed that shear wave propagation became faster when shear waves passed through stiffer parts of the phantoms and perimysium of the MG, which is concordant with the basic principle of shear wave elastography. Shear wave velocity in the horizontal condition (2.69 ± 0.71 m s−1) was significantly higher than that in the oblique condition (2.61 ± 0.73 m s−1) (p < 0.001), but the effect size was small (Cohen’s d = 0.11). Significance: Ultrasound shear wave elastography can evaluate tissue elasticity of pennate muscles along the fascicle direction, even if shear waves propagate across the obliquely arranged fascicles.
Double Adaptive Stochastic Gradient Optimization

Adaptive moment methods have been remarkably successful in deep learning optimization, particularly in the presence of noisy and/or sparse gradients. We further the advantages of adaptive moment techniques by proposing a family of double adaptive stochastic gradient methods~\textsc{DASGrad}. They leverage the complementary ideas of the adaptive moment algorithms widely used by deep learning community, and recent advances in adaptive probabilistic algorithms.We analyze the theoretical convergence improvements of our approach in a stochastic convex optimization setting, and provide empirical validation of our findings with convex and non convex objectives. We observe that the benefits of~\textsc{DASGrad} increase with the model complexity and variability of the gradients, and we explore the resulting utility in extensions of distribution-matching multitask learning.
Perceived Discrimination and Substance Use among Adolescents: Examining the Moderating Effect of Distress Tolerance and Negative Urgency

Abstract Background: Perceived discrimination has been found to increase substance use vulnerability among adolescent populations. However, less is known about individual level factors which may moderate this risk. Objectives: The aim of the current study is to examine whether two emotion-based personality traits (i.e., distress tolerance and negative urgency) moderates the effect of perceived discrimination on substance use. We hypothesized that high distress tolerance would decrease risk, while high negative urgency would increase risk for substance use as a consequence of discrimination. Methods: 108 youth ages 12–18 (68.6% male; 56.2% African-American) provided data on perceived discrimination, distress tolerance, negative urgency, and substance use (i.e., alcohol and marijuana use). Results: Contrary to our hypothesis, no moderating effect was observed for negative urgency. Distress tolerance was found to moderate the relationship, but in the opposite direction than expected. Conclusions: It is speculated that this counterintuitive finding may be due to the racial/ethnic composition of the sample, suggesting that distress tolerance may operate differently among minority youth. Further research examining these relationships among minority youth is warranted.
The Interoperability of Things: Interoperable solutions as an enabler for IoT and Web 3.0

This paper presents an overview of the interoperability concepts along with the challenges for the IoT domain and the upcoming Web 3.0. We identify four levels of interoperability and the relevant solutions for accomplishing vertical and horizontal compatibility between the various layers of a modern IoT ecosystem, referred to as: technological, syntactic, semantic, and organizational interoperability. The goal is to achieve cross-domain interaction and facilitate the proper usage and management of the provided IoT services and applications. An interoperability framework is also proposed where the involved system components can cooperate and offer the seamless operation from the device to the backend framework. This by-design end-to-end interoperation enables the interplay of several complex service composition settings and the management of the system via patterns. The overall proposal is adopted by the EU funded project SEMIoTICS as an enabler towards the IoT and Web 3.0, even when products from different vendors are utilized.
Assisting investors with collective intelligence

There is a growing interest in exploring and developing a new generation of financial services where automated advices generated by computers are offered to individuals, thereby helping them manage their investment portfolios in a way that is best suited for them without costly human expert involvement. In this paper, we describe the development of such a system. Our approach is based on a form of exploratory computing and generates some collective intelligence that investors may use to make informed investment and wealth management decisions.
5GENESIS: The Genesis of a flexible 5G Facility

This paper describes the pathway towards the realisation of a 5G Facility that will allow the validation of the major 5G Key Performance Indicators (KPIs). It reflects the approach that the 5GENESIS consortium will adopt in this direction. More precisely, it describes the key design principles of such Facility as well as the targeted use cases for the KPIs validation. The adopted approach for the Facility realisation includes the design of a common implementation blueprint that will be instantiated in five Platforms distributed across Europe. To maximise the diversity and the efficiency of the Facility, complementary performance objectives have been selected for the Platforms, while specific characteristics from different vertical industries have been allocated to each of them.
Analysis of a Classical Matrix Preconditioning Algorithm

We study a classical iterative algorithm for the problem of balancing matrices in the L∞ norm via a scaling transformation. This algorithm, which goes back to Osborne and Parlett & Reinsch in the 1960s, is implemented as a standard preconditioner in many numerical linear algebra packages. Surprisingly, despite its widespread use over several decades, no bounds were known on its rate of convergence. In this paper we prove that, for a large class of irreducible n x n (real or complex) input matrices~$A$, a natural variant of the algorithm converges in O(n3 log(nρ/ε)) elementary balancing operations, where ρ measures the initial imbalance of A and ε is the target imbalance of the output matrix. (The imbalance of A is maxi |log(aiout/aiin)|, where aiout,aiin are the maximum entries in magnitude in the ith row and column respectively. ) This bound is tight up to the log n factor. A balancing operation scales the ith row and column so that their maximum entries are equal, and requires O(m/n) arithmetic operations on average, where m is the number of non-zero elements in A. Thus the running time of the iterative algorithm is ~O(n2m). This is the first time bound of any kind on any variant of the Osborne-Parlett-Reinsch algorithm. The class of matrices for which the above analysis holds are those which satisfy a condition we call Unique Balance, meaning that the limit of the iterative balancing process does not depend on the order in which balancing operations are performed. We also prove a combinatorial characterization of the Unique Balance property, which had earlier been conjectured by Chen.
An Interface for Annotating Science Questions

Recent work introduces the AI2 Reasoning Challenge (ARC) and the associated ARC dataset that partitions open domain, complex science questions into an Easy Set and a Challenge Set. That work includes an analysis of 100 questions with respect to the types of knowledge and reasoning required to answer them. However, it does not include clear definitions of these types, nor does it offer information about the quality of the labels or the annotation process used. In this paper, we introduce a novel interface for human annotation of science question-answer pairs with their respective knowledge and reasoning types, in order that the classification of new questions may be improved. We build on the classification schema proposed by prior work on the ARC dataset, and evaluate the effectiveness of our interface with a preliminary study involving 10 participants.
Exploring and Learning Suicidal Ideation Connotations on Social Media with Deep Learning

The increasing suicide rates amongst youth and its high correlation with suicidal ideation expression on social media warrants a deeper investigation into models for the detection of suicidal intent in text such as tweets to enable prevention. However, the complexity of the natural language constructs makes this task very challenging. Deep Learning architectures such as LSTMs, CNNs, and RNNs show promise in sentence level classification problems. This work investigates the ability of deep learning architectures to build an accurate and robust model for suicidal ideation detection and compares their performance with standard baselines in text classification problems. The experimental results reveal the merit in C-LSTM based models as compared to other deep learning and machine learning based classification models for suicidal ideation detection.
A Transparent Server-Managed Object Storage System for HPC

On the road to exascale, the high-performance computing (HPC) community is seeing the emergence of multi-tier storage systems. However, existing data management solutions for HPC applications are no longer suitable for handling the increased level of storage complexity and currently delegate that task back to the user. We describe a novel object-based data abstraction that takes advantage of deep memory hierarchies by providing a simplified programming interface that enables autonomous, asynchronous, and transparent data movement with a server-driven architecture. Users can define a mapping between the application memory and abstract storage objects, creating a linkage between either all or part of an object's content without data copy or transfer, avoiding explicit management of complex data movement across multiple storage hierarchies. We evaluate our system by storing plasma physics simulation data with different storage layouts.
Uma Arquitetura de Virtualização de Funções de Rede para Proteção Automática e Eficiente contra Ataques

Intrusion Prevention Systems (IPSs) are expensive and inefficient when blocking all traffic from a source address. Therefore, the ability to apply countermeasures only to malicious traffic becomes an important security challenge. This article proposes a Network Function Virtualization (NFV) architecture to provide automatic, and efficient protection against attacks. Malicious flows are dynamically diverted through a Virtual Network Functions (VNFs) chain containing an application firewall and an IDS. A prototype was built using the Open Platform for NFV (OPNFV). The architecture is capable of blocking 99.12% of the attacks without affecting 93.6% of the benign traffic.
Automated learning of templates for data-to-text generation: comparing rule-based, statistical and neural methods

The current study investigated novel techniques and methods for trainable approaches to data-to-text generation. Neural Machine Translation was explored for the conversion from data to text as well as the addition of extra templatization steps of the data input and text output in the conversion process. Evaluation using BLEU did not find the Neural Machine Translation technique to perform any better compared to rule-based or Statistical Machine Translation, and the templatization method seemed to perform similarly or sometimes worse compared to direct data-to-text conversion. However, the human evaluation metrics indicated that Neural Machine Translation yielded the highest quality output and that the templatization method was able to increase text quality in multiple situations.
Awareness of what is learned as a characteristic of hippocampus-dependent memory

Significance Knowledge (or awareness) of what has been learned has been described as a key feature of hippocampus-dependent memory or as unhelpful for understanding hippocampal function. We recorded eye movements while memory-impaired patients with hippocampal lesions and controls viewed familiar scenes or familiar scenes that had been altered. Controls preferentially looked at the altered regions of the scenes, but only when they knew what had changed and where the change occurred. Patients had difficulty distinguishing altered and unaltered scenes and, overall, did not direct their viewing toward the altered regions. Nonetheless, they did exhibit this effect on the few occasions when they were aware of the change. The results indicate the importance of conscious awareness for understanding hippocampal function. We explored the relationship between memory performance and conscious knowledge (or awareness) of what has been learned in memory-impaired patients with hippocampal lesions or larger medial temporal lesions. Participants viewed familiar scenes or familiar scenes where a change had been introduced. Patients identified many fewer of the changes than controls. Across all of the scenes, controls preferentially directed their gaze toward the regions that had been changed whenever they had what we term robust knowledge about the change: They could identify that a change occurred, report what had changed, and indicate where the change occurred. Preferential looking did not occur when they were unaware of the change or had only partial knowledge about it. The patients, overall, did not direct their gaze toward the regions that had been changed, but on the few occasions when they had robust knowledge about the change they (like controls) did exhibit this effect. Patients did not exhibit this effect when they were unaware of the change or had partial knowledge. The findings support the idea that awareness of what has been learned is a key feature of hippocampus-dependent memory.
Deliberation and Resolution on Wikipedia

Resolving disputes in a timely manner is crucial for any online production group. We present an analysis of Requests for Comments (RfCs), one of the main vehicles on Wikipedia for formally resolving a policy or content dispute. We collected an exhaustive dataset of 7,316 RfCs on English Wikipedia over the course of 7 years and conducted a qualitative and quantitative analysis into what issues affect the RfC process. Our analysis was informed by 10 interviews with frequent RfC closers. We found that a major issue affecting the RfC process is the prevalence of RfCs that could have benefited from formal closure but that linger indefinitely without one, with factors including participants' interest and expertise impacting the likelihood of resolution. From these findings, we developed a model that predicts whether an RfC will go stale with 75.3% accuracy, a level that is approached as early as one week after dispute initiation.
Results of the WMT18 Metrics Shared Task: Both characters and embeddings achieve good performance

This paper presents the results of the WMT18 Metrics Shared Task. We asked participants of this task to score the outputs of the MT systems involved in the WMT18 News Translation Task with automatic metrics. We collected scores of 10 metrics and 8 research groups. In addition to that, we computed scores of 8 standard metrics (BLEU, SentBLEU, chrF, NIST, WER, PER, TER and CDER) as baselines. The collected scores were evaluated in terms of system-level correlation (how well each metric’s scores correlate with WMT18 official manual ranking of systems) and in terms of segment-level correlation (how often a metric agrees with humans in judging the quality of a particular sentence relative to alternate outputs). This year, we employ a single kind of manual evaluation: direct assessment (DA).
SDR – Half-baked or Well Done?

In speech enhancement and source separation, signal-to-noise ratio is a ubiquitous objective measure of denoising/separation quality. A decade ago, the BSS_eval toolkit was developed to give researchers worldwide a way to evaluate the quality of their algorithms in a simple, fair, and hopefully insightful way: it attempted to account for channel variations, and to not only evaluate the total distortion in the estimated signal but also split it in terms of various factors such as remaining interference, newly added artifacts, and channel errors. In recent years, hundreds of papers have been relying on this toolkit to evaluate their proposed methods and compare them to previous works, often arguing that differences on the order of 0.1 dB proved the effectiveness of a method over others. We argue here that the signal-to-distortion ratio (SDR) implemented in the BSS_eval toolkit has generally been improperly used and abused, especially in the case of single-channel separation, resulting in misleading results. We propose to use a slightly modified definition, resulting in a simpler, more robust measure, called scale-invariant SDR (SI-SDR). We present various examples of critical failure of the original SDR that SI-SDR overcomes.
Research on network rumor propagation model based on combustion theory

The rumor propagation in social networks has great harmfulness. Classic researches are mostly based on infectious disease model, without considering the characteristics of the users in the network itself. Based on the theory of combustion, this paper proposes a new network rumor propagation model CRP Model (Combustion-inspired Rumor Propagation Model) to explore the propagation rule of rumor among users. The model is based on three combustion elements to simulate the process of network rumor propagation including combustible material, combustion improver and ignition temperature. It can be seen from the experiment that the fire users' influence and the firefighters in the network play a very important role in the spreading of rumors.
Automatic Phrase Indexing for Document Retrieval: An Examination of Syntactic and Non-Syntactic Methods

An automatic phrase indexing method based on the term discrimination model is described, and the results of retrieval experiments on five document collections are presented. Problems related to this non-syntactic phrase construction method are discussed, and some possible solutions are proposed that make use of information about the syntactic structure of document and query texts.