Miracle-dz/UniFAR-FTUs huggingface dataset:
Advancing the Art of Internet Edge Outage Detection

Measuring reliability of edge networks in the Internet is difficult due to the size and heterogeneity of networks, the rarity of outages, and the difficulty of finding vantage points that can accurately capture such events at scale. In this paper, we use logs from a major CDN, detailing hourly request counts from address blocks. We discovered that in many edge address blocks, devices, collectively, contact the CDN every hour over weeks and months. We establish that a sudden temporary absence of these requests indicates a loss of Internet connectivity of those address blocks, events we call disruptions. We develop a disruption detection technique and present broad and detailed statistics on 1.5M disruption events over the course of a year. Our approach reveals that disruptions do not necessarily reflect actual service outages, but can be the result of prefix migrations. Major natural disasters are clearly represented in our data as expected; however, a large share of detected disruptions correlate well with planned human intervention during scheduled maintenance intervals, and are thus unlikely to be caused by external factors. Cross-evaluating our results we find that current state-of-the-art active outage detection over-estimates the occurrence of disruptions in some address blocks. Our observations of disruptions, service outages, and different causes for such events yield implications for the design of outage detection systems, as well as for policymakers seeking to establish reporting requirements for Internet services.
Lower Choline Rate in the Left Prefrontal Cortex Is Associated With Higher Amount of Alcohol Use in Alcohol Use Disorder

Excessive and long-term alcohol consumption produce metabolic changes, such as of choline, in many brain regions in alcohol use disorder (AUD) and in non-AUD subjects as well. This study examined the association of choline proportion in the prefrontal cortex with pattern of alcohol use in AUD patients. The choline metabolite was acquired through a single voxel Proton Magnetic Resonance Spectroscopy (1H MRS). Between-groups comparison corrected by age showed that the ratio of Choline/Creatine (Cho/Cr) was significantly smaller (p = 0.005) in the Left Prefrontal (LPF) of AUD patients when compared to paired non-AUD subjects. A multiple regression analysis corrected by age showed that decreasing ratios of Cho/Cr in the LPF was associated with increasing amount of alcohol consumption in drinks per day (p < 0.01) in AUD patients. Rates of Cho/Cr in the LPF was inversely related to amounts of alcohol consumption possibly indicating the severity of the AUD. Thus, low proportion of Cho/Cr in the LPF could indicate more severe AUD (higher alcohol intake).
Mediating Color Filter Exploration with Color Theme Semantics Derived from Social Curation Data

Despite the popularity of photo editors used to improve image attractiveness and expressiveness on social media, many users have trouble making sense of color filter effects and locating a preferred filter among a set of designer-crafted candidates. The problem gets worse when more computer-generated filters are introduced. To enhance filter findability, we semantically name and organize color effects leveraging data curated by creative communities online. We first model semantic mappings between color themes and keywords in everyday language. Next, we index and organize each filter by the derived semantic information. We conduct three separate studies to investigate the benefit of the semantic features on filter exploration. Our results indicate that color theme semantics constructed through social curation enhances filter findability, providing important implications into how to use the wisdom of the crowd to improve user experience with image editors.
Software Profiling for Hot Path Prediction: Less is More

Recently, there has been a growing interest in exploiting profile information in adaptive systems such as just-in-time compilers, dynamic optimizers and, binary translators. In this paper, we show that sophisticated software profiling schemes that provide highly accurate information in an offline setting are ill-suited for these dynamic code generation systems. We experimentally demonstrate that hot path predictions must be made early in order to control the rising cost of missed opportunity that result from the prediction delay. We also show that existing sophisticated path profiling schemes, if used in an online setting, offer no prediction advantages over simpler schemes that exhibit much lower runtime overheads.Based on these observation we developed a new low-overhead software profiling scheme for hot path prediction. Using an abstract metric we compare our scheme to path profile based prediction and show that our scheme achieves comparable prediction quality. In our second set of experiments we include runtime overhead and evaluate the performance of our scheme in a realistic application: Dynamo, a dynamic optimization system. The results show that our prediction scheme clearly outperforms path profile based prediction and thus confirm that less profiling as exhibited in our scheme will actually lead to more effective hot path prediction.
Treat the system like a human student: Automatic naturalness evaluation of generated text without reference texts

The current most popular method for automatic Natural Language Generation (NLG) evaluation is comparing generated text with human-written reference sentences using a metrics system, which has drawbacks around reliability and scalability. We draw inspiration from second language (L2) assessment and extract a set of linguistic features to predict human judgments of sentence naturalness. Our experiment using a small dataset showed that the feature-based approach yields promising results, with the added potential of providing interpretability into the source of the problems.
Bootstrapping Single-channel Source Separation via Unsupervised Spatial Clustering on Stereo Mixtures

Separating an audio scene into isolated sources is a fundamental problem in computer audition, analogous to image segmentation in visual scene analysis. Source separation systems based on deep learning are currently the most successful approaches for solving the underdetermined separation problem, where there are more sources than channels. Such systems are normally trained on sound mixtures where the ground truth decomposition is already known. In this work, we use an unsupervised spatial source separation on stereo mixtures which generates initial decompositions of mixtures to train a deep learning source separation model. These estimated decompositions vary greatly in quality across the training mixtures. To overcome this, we weight the data during training using a confidence measure that assesses which mixtures or parts of mixtures are well-separated by the unsupervised algorithm. Once trained, the model can be applied to separate single-channel mixtures, where no source direction information is available. The idea is to use simple, low-level processing to separate sources in an unsupervised fashion, identify easy conditions, and then use that knowledge to bootstrap a (self-)supervised source separation model for difficult conditions. We also explore using the two approaches in an ensemble.
Making a Living My Way

Entrepreneurship has long been used to create self-employment opportunities to guard against career uncertainty. Yet, little is known about how social technologies impact the day-to-day work of entrepreneurs in resource-constrained contexts. We performed a qualitative study involving interviews with 26 micro-entrepreneurs in Detroit and observations of entrepreneurship events. We found that micro-entrepreneurs in Detroit are often pushed into entrepreneurship in response to unexpected life disruptions, barriers to employment, and desire to benefit the community. Their resource-constrained contexts shape how they use social technologies, such as sharing economy tools and social media groups, particularly with respect to privacy, safety, and professional agency. We expand the discussion in CSCW around what it means to be an entrepreneur and provide implications for how social technologies can be designed to better meet the employment needs of people in resource-constrained communities.
Overexpression of Orange Carotenoid Protein Protects the Repair of PSII under Strong Light in Synechocystis sp. PCC 6803

Orange carotenoid protein (OCP) plays a vital role in the thermal dissipation of excitation energy in the photosynthetic machinery of the cyanobacterium Synechocystis sp. PCC 6803. To clarify the role of OCP in the protection of PSII from strong light, we generated an OCP-overexpressing strain of Synechocystis and examined the effects of overexpression on the photoinhibition of PSII. In OCP-overexpressing cells, thermal dissipation of energy was enhanced and the extent of photoinhibition of PSII was reduced. However, photodamage to PSII, as monitored in the presence of lincomycin, was unaffected, suggesting that overexpressed OCP protects the repair of PSII. Furthermore, the synthesis de novo of proteins in thylakoid membranes, such as the D1 protein which is required for the repair of PSII, was enhanced in OCP-overexpressing cells under strong light, while the production of singlet oxygen was suppressed. Thus, the enhanced thermal dissipation of energy via overexpressed OCP might support the repair of PSII by protecting protein synthesis from oxidative damage by singlet oxygen under strong light, with the resultant mitigation of photoinhibition of PSII.
Entombed: An archaeological examination of an Atari 2600 game

The act and experience of programming is, at its heart, a fundamentally human activity that results in the production of artifacts. When considering programming, therefore, it would be a glaring omission to not involve people who specialize in studying artifacts and the human activity that yields them: archaeologists. Here we consider this with respect to computer games, the focus of archaeology's nascent subarea of archaeogaming. One type of archaeogaming research is digital excavation, a technical examination of the code and techniques used in old games' implementation. We apply that in a case study of Entombed, an Atari 2600 game released in 1982 by US Games. The player in this game is, appropriately, an archaeologist who must make their way through a zombie-infested maze. Maze generation is a fruitful area for comparative retrogame archaeology, because a number of early games on different platforms featured mazes, and their variety of approaches can be compared. The maze in Entombed is particularly interesting: it is shaped in part by the extensive real-time constraints of the Atari 2600 platform, and also had to be generated efficiently and use next to no memory. We reverse engineered key areas of the game's code to uncover its unusual maze-generation algorithm, which we have also built a reconstruction of, and analyzed the mysterious table that drives it. In addition, we discovered what appears to be a 35-year-old bug in the code, as well as direct evidence of code-reuse practices amongst game developers. What further makes this game's development interesting is that, in an era where video games were typically solo projects, a total of five people were involved in various ways with Entombed. We piece together some of the backstory of the game's development and intoxicant-fueled design using interviews to complement our technical work. Finally, we contextualize this example in archaeology and lay the groundwork for a broader interdisciplinary discussion about programming, one that includes both computer scientists and archaeologists.
Stabiliteit van grondkribben en onderwatergolfbrekers opgebouwd uit zandworsten

In het Laboratorium voor Vloeistofmechanica van de faculteit Civiele Techniek van de TU Delft is modelonderzoek gedaan naar de werking en de stabiliteit van zandworsten. De volgende onderdelen zijn onderzocht: - stabiliteit van grondkribben onder stroming. - Golftransmissie over onderwatergolfbrekers - stabiliteit van onderwatergolfbrekers onder golfaanval Al deze grondkribben en golfbrekers zijn opgebouwd uit zandworsten. Verder is er theoretisch onderzoek gedaan naar de drie bovengenoemde onderdelen.
Deep 3D Flow Features for Human Action Recognition

The present work investigates the use of 3D flow information for performing Deep Learning (DL)-based human action recognition. Generally, 3D flow fields include rich and fine-grained information, regarding the motion dynamics of the observed human actions. However, despite the great potentials present, 3D flow has not been widely used, mainly due to challenges related to the efficient modeling of the flow information and the addressing of the respective computational complexity issues. In this paper, different techniques are investigated for incorporating 3D flow information in DL action recognition schemes. In particular, a novel sequence modeling approach is introduced, which combines the advantageous characteristics for spatial correlation estimation of Convolutional Neural Networks (CNNs) with the increased temporal modeling capabilities of Long Short Term Memory (LSTM) models. Additionally, an extended CNN - based deep flow model is proposed that extracts features from both the spatial and temporal domains, by applying 3D convolutions; hence, modeling the action dynamics within consecutive frames. Moreover, for compact and efficient 3D motion feature extraction, the combined use of CNNs with a ‘flow colorization’ approach is adopted. The proposed methods significantly outperform similar DL and hand-crafted 3D flow approaches, and compare favorably with most skeleton-based techniques in the currently most challenging public dataset, namely the NTU RGB-D.
Sound Quality Effects of an Adaptive Nonlinear Frequency Compression Processor with Normal-Hearing and Hearing-Impaired Listeners

Abstract Background: Frequency lowering (FL) technology offers a means of improving audibility of high-frequency sounds. For some listeners, the benefit of such technology can be accompanied by a perceived degradation in sound quality, depending on the strength of the FL setting. Purpose: The studies presented in this article investigate the effect of a new type of FL signal processing for hearing aids, adaptive nonlinear frequency compression (ANFC), on subjective speech quality. Research Design: Listener ratings of sound quality were collected for speech stimuli processed with systematically varied fitting parameters. Study Sample: Study 1 included 40 normal-hearing (NH) adult and child listeners. Study 2 included 11 hearing-impaired (HI) adult and child listeners. HI listeners were fitted with laboratory-worn hearing aids for use during listening tasks. Intervention: Speech quality ratings were assessed across test conditions consisting of various strengths of static nonlinear frequency compression (NFC) and ANFC speech. Test conditions included those that were fine-tuned on an individual basis per hearing aid fitting and conditions that were modified to intentionally alter the sound quality of the signal. Data Collection and Analysis: Listeners rated speech quality using the MUlti Stimulus test with Hidden Reference and Anchor (MUSHRA) test paradigm. Ratings were analyzed for reliability and to compare results across conditions. Results: Results show that interrater reliability is high for both studies, indicating that NH and HI listeners from both adult and child age groups can reliably complete the MUSHRA task. Results comparing sound quality ratings across experimental conditions suggest that both the NH and HI listener groups rate the stimuli intended to have poor sound quality (e.g., anchors and the strongest available parameter settings) as having below-average sound quality ratings. A different trend in the results is reported when considering the other experimental conditions across the listener groups in the studies. Speech quality ratings measured with NH listeners improve as the strength of ANFC decreases, with a range of bad to good ratings reported, on average. Speech quality ratings measured with HI listeners are similar and above-average for many of the experimental stimuli, including those with fine-tuned NFC and ANFC parameters. Conclusions: Overall, HI listeners provide similar sound quality ratings when comparing static and adaptive forms of frequency compression, especially when considering the individualized parameter settings. These findings suggest that a range in settings may result in above-average sound quality for adults and children with hearing impairment. Furthermore, the fitter should fine-tune FL parameters for each individual listener, regardless of type of FL technology.
LEAP: lightweight deterministic multi-processor replay of concurrent java programs

The technique of deterministic record and replay aims at faithfully reenacting an earlier program execution. For concurrent programs, it is one of the most important techniques for program understanding and debugging. The state of the art deterministic replay techniques face challenging efficiency problems in supporting multi-processor executions due to the unoptimized treatment of shared memory accesses. We propose LEAP: a deterministic record and replay technique that uses a new type of local order w.r. t. the shared memory locations and concurrent threads. Compared to the related work, our technique records much less information without losing the replay determinism. The correctness of our technique is underpinned by formal models and a replay theorem that we have developed in this work. Through our evaluation using both benchmarks and real world applications, we show that LEAP is more than 10x faster than conventional global-order based approaches and, in most cases, 2x to 10x faster than other local-order based approaches. Our recording overhead on the two large open source multi-threaded applications Tomcat and Derby is less than 10%. Moreover, as the evidence of the deterministic replay, LEAP is able to deterministically reproduce 7 out of 8 real bugs in Tomcat and Derby, 13 out of 16 benchmark bugs in IBM ConTest benchmark suite, and 100% of the randomly injected concurrency bugs.
Mobility and Epidemic Process in Temporal Networks

Complex networks are distinguished to have a particular structure due to its large dimension and its many interactions, which play an important role in its characteristic. In fact, these networks are the locus of many dynamical phenomena such as birth of community, opinion formation, information diffusion, and rumors or epidemic spreading and so on. User mobility is of critical important when you want to know how disease spreads in network. The synthetic models represents a good alternative to describe how humans move. In this paper, we study the dynamics of epidemic spreading in temporal network generated by synthetic mobility model such as Random Waypoint (RWP), Gauss Markov (GM) and Truncated Levy Walk (TLW). Then, we show how proximity in terms in distance could impact a spreading process like droplet or airborne modes of pathogen spreading. Finally, we will statistically evaluate the number of infected nodes by scaling the spreading rate depending on the proximity between agents.
CogCompTime: A Tool for Understanding Time in Natural Language

Automatic extraction of temporal information is important for natural language understanding. It involves two basic tasks: (1) Understanding time expressions that are mentioned explicitly in text (e.g., February 27, 1998 or tomorrow), and (2) Understanding temporal information that is conveyed implicitly via relations. This paper introduces CogCompTime, a system that has these two important functionalities. It incorporates the most recent progress, achieves state-of-the-art performance, and is publicly available at http://cogcomp.org/page/publication_view/844.
Online Process Monitoring Using Recursive Mutual Information-Based Variable Selection and Dissimilarity Analysis With No Prior Information

The traditional dissimilarity (DISSIM) method explores the underlying fault characteristic based on the data distribution and is sensitive to the structure change of the process. However, it fails to explore which variables are significant to the concerned faults and its monitoring performance, including fault detection and diagnosis performance, is seriously decreased by the noise brought by non-informative variables, especially in plant-wide process. Since mutual information (MI) can explore both the linear and nonlinear dependencies of variables, a recursive MI-based variable selection algorithm is proposed in this paper. It can efficiently extract the most informative variables to the faults online and reduce the computational complexity. Then based on the variables selected online, the dissimilarity index is calculated to detect the distribution changes from normal to a fault condition and an MI-based diagnosis method is developed to further investigate the responsible variables to the fault. With the variable selection, not only the local characteristic of the process can be highlighted by the informative variables but also the influence of the non-informative variables can be eliminated, thus the control limit can be adaptively updated and the sensitivity and accuracy of the monitoring performance can be significantly improved. Moreover, the MI-based diagnosis method explores the contribution of selected variables with a high-order statistic and overcomes shortage brought by variable variance. Case study on Tennessee Eastman (TE) benchmark process demonstrates the feasibility and efficiency of our method.
Second-order occlusion-aware volumetric radiance caching

Accurate simulation of light transport in participating media is expensive, due to the many scattering events. However, the band-limiting effect of scattering in media makes this kind of light transport very suitable for adaptive sampling and reconstruction techniques. In this work we present a novel algorithm that adaptively samples radiance from sparse points in the medium using up-to second-order occlusion-aware derivatives to determine when interpolation is appropriate. We derive our metric from each point's incoming light field. We use a proxy triangulation-based representation of the radiance reflected by the surrounding medium and geometry to efficiently compute the first- and second-order derivatives of the radiance at the cache points while accounting for occlusion changes. We validate the quality of our approach on a self-contained two-dimensional model for light transport in media. Then we show how our results generalize to practical three-dimensional scenarios, where we show much better results while reducing computation time up to a 30% compared to previous work.
MechanicalHeart

Listening to heart sounds is an important first step in evaluating the cardiovascular system and is important in the early detection of cardiovascular disease. We present and evaluate a framework for combining machine learning algorithms, crowd workers, and experts in the classification of heart sound recordings. The development of a hybrid human-machine framework is motivated by the past success in utilizing human computation to solve problems in medicine and the use of human-machine frameworks in other domains. We describe the methods that decide when and how to escalate the analysis of heart sounds to different resources and incorporate their decision into a final classification. Our framework was tested with a combination of machine classifiers and crowd workers from Amazon's Mechanical Turk. The results indicate a hybrid approach achieves greater performance than a baseline classifier alone, utilizing less expert resources while achieving similar performance, compared to a framework without the crowd.