Miracle-dz/UniFAR-FTUs huggingface dataset:
Approval Voting and Incentives in Crowdsourcing

The growing need for labeled training data has made crowdsourcing an important part of machine learning. The quality of crowdsourced labels is, however, adversely affected by three factors: (1) the workers are not experts; (2) the incentives of the workers are not aligned with those of the requesters; and (3) the interface does not allow workers to convey their knowledge accurately, by forcing them to make a single choice among a set of options. In this paper, we address these issues by introducing approval voting to %judiciously utilize the expertise of workers who have partial knowledge of the true answer, and coupling it with a ("strictly proper") incentive-compatible compensation mechanism. We show rigorous theoretical guarantees of optimality of our mechanism together with a simple axiomatic characterization. We also conduct preliminary empirical studies on Amazon Mechanical Turk which validate our approach.
This paper is included in the Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation.

Many large cloud providers operate two wide-area networks (WANs) — a software-defined WAN to carry inter-datacenter traffic and a standards-based WAN for Internet traffic. Our experience with operating two heterogeneous planet-scale WANs has revealed the operational complexity and cost in-efficiency of the split-WAN architecture. In this work, we present the unification of Microsoft’s split-WAN architecture consisting of SWAN and CORE networks into O NE WAN. O NE WAN serves both Internet and inter-datacenter traffic using software-defined control. O NE WAN grappled with the order of magnitude increase in network and routing table sizes. We developed a new routing and forwarding paradigm called traffic steering to manage the increased network scale using existing network equipment. Increased network and traffic matrix size posed scaling challenges to SDN traffic engineering in O NE WAN. We developed techniques to find paths in the network and chain multiple TE optimization solvers to compute traffic allocations within a few seconds. O NE WAN is the first to apply software-defined techniques in an Internet backbone and scales to a network that is 10 × larger than SWAN .
Clustering on the forward surfaces of migrating neutrophils of a novel GPI‐anchored protein that may regulate neutrophil adherence and migration

We previously reported a novel glycosylphosphatidylinositol (GPI)‐anchored glycoprotein (tentatively designated GPI‐80) on human leukocytes that may be involved in the regulation of neutrophil adherence and migration. In this study, we examined by immuno‐ and scanning electron microscopy, the distribution of GPI‐80 on neutrophil surfaces. GPI‐80 was diffusely distributed on the surface of resting neutrophils and on the peripheral areas of adherent cells after stimulation with N‐formyl‐methionyl‐leucyl‐phenylalanine. After longer stimulation (60 min), GPI‐80 decreased in number and was again diffusely distributed on the surfaces of round neutrophils. Few GPI‐80 were detected on the ventral surfaces of adherent neutrophils. Clusters of GPI‐80 were detected on the forward surfaces of neutrophils transmigrating through pores of nitrocellulose membranes. These results may give a morphological background of possible role of GPI‐80 for neutrophil extravasation.
Semantic hashtag relation classification using co-occurrence word information

Social Networking Service users express their thoughts and feelings using hashtags. Hashtags can be related to other hashtags and these hashtags and images are used together in a post that the user wrote. Understanding the meaning of a hashtag is one of the ways to learn latent semantic expressions of words. Existing methods for learning semantic analysis use large corpus. This research focuses on the classification of semantic words using a user's hashtag data and co-occurrence hashtag information.
MapReduce: Simplified Data Processing on Large Cluster

MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper. Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling the program's execution across a set of machines, handling machine failures, and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system. Our implementation of MapReduce runs on a large cluster of commodity machines and is highly scalable: a typical MapReduce computation processes many terabytes of data on thousands of machines. Programmers find the system easy to use: hundreds of MapReduce programs have been implemented and upwards of one thousand MapReduce jobs are executed on Google's clusters every day.
PANDA: Prioritization of autism‐genes using network‐based deep‐learning approach

Understanding the genetic background of complex diseases and disorders plays an essential role in the promising precision medicine. The evaluation of candidate genes, however, requires time‐consuming and expensive experiments given a large number of possibilities. Thus, computational methods have seen increasing applications in predicting gene‐disease associations. We proposed a bioinformatics framework, Prioritization of Autism‐genes using Network‐based Deep‐learning Approach (PANDA). Our approach aims to identify autism‐genes across the human genome based on patterns of gene–gene interactions and topological similarity of genes in the interaction network. PANDA trains a graph deep learning classifier using the input of the human molecular interaction network and predicts and ranks the probability of autism association of every node (gene) in the network. PANDA was able to achieve a high classification accuracy of 89%, outperforming three other commonly used machine learning algorithms. Moreover, the gene prioritization ranking list produced by PANDA was evaluated and validated using an independent large‐scale exome‐sequencing study. The top 10% of PANDA‐ranked genes were found significantly enriched for autism association.
AutoCoEv – a high-throughput in silico pipeline for predicting novel protein-protein interactions

Protein-protein interactions govern the cellular decision-making and a multitude of various functions. Thus, identifying new interactions between proteins can significantly facilitate our understanding on the mechanistic principles of protein functions. Coevolution between proteins is a sign of functional communication and, as such, provides a powerful approach to search for novel molecular partners. However, evolutionary analyses of large arrays of proteins, in silico, is a highly time-consuming effort, which has limited the usage of this method to protein pairs or small protein families. Here, we developed AutoCoEv, a user-friendly computational pipeline for the search of coevolution between a large number of proteins. Driving over 10 individual programs, with CAPS2 as a key software for coevolution detection, AutoCoEv achieves seamless automation and parallelization of the workflow. Importantly, we provide a patch to CAPS2 source code to strengthen its statistical output, allowing for multiple comparisons correction. We apply the method to inspect coevolution among 324 individual proteins identified to be in, or close to, the lipid rafts of B lymphocytes. We successfully detected multiple strong coevolutionary relations between the proteins, predicting many novel partners and previously unidentified clusters of interacting molecules. We conclude that AutoCoEv can be used to predict protein interaction from large datasets of hundreds and, with the aid of super-computing resources even thousands of proteins in a time and cost-efficient manner.
Whole-Genome Sequencing and Comparative Genome Analysis of Bacillus subtilis Strains Isolated from Non-Salted Fermented Soybean Foods

Bacillus subtilis is the main component in the fermentation of soybeans. To investigate the genetics of the soybean-fermenting B. subtilis strains and its relationship with the productivity of extracellular poly-γ-glutamic acid (γPGA), we sequenced the whole genome of eight B. subtilis stains isolated from non-salted fermented soybean foods in Southeast Asia. Assembled nucleotide sequences were compared with those of a natto (fermented soybean food) starter strain B. subtilis BEST195 and the laboratory standard strain B. subtilis 168 that is incapable of γPGA production. Detected variants were investigated in terms of insertion sequences, biotin synthesis, production of subtilisin NAT, and regulatory genes for γPGA synthesis, which were related to fermentation process. Comparing genome sequences, we found that the strains that produce γPGA have a deletion in a protein that constitutes the flagellar basal body, and this deletion was not found in the non-producing strains. We further identified diversity in variants of the bio operon, which is responsible for the biotin auxotrophism of the natto starter strains. Phylogenetic analysis using multilocus sequencing typing revealed that the B. subtilis strains isolated from the non-salted fermented soybeans were not clustered together, while the natto-fermenting strains were tightly clustered; this analysis also suggested that the strain isolated from “Tua Nao” of Thailand traces a different evolutionary process from other strains.
SPEAKER-INDEPENDENT MALAY SYLLABLE RECOGNITION USING SINGULAR AND MODULAR NEURAL NETWORKS

The paper investigates the use of Singular and Modular Neural Networks in classifying the Malay syllable sounds in a speaker-independent manner. The syllable sounds are initialized with plosives and followed by vowels. The speech tokens are sampled at 16 kHz with 16-bit resolution. Linear Predictive Coding (LPC) is used to extract the speech features. The Neural Networks utilize standard three-layer Multi-Layer Perceptron (MLP) as the speech sound classifier. The MLPs are trained with stochastic Back-Propagation (BP). The weights of the networks are updated after presentation of each training token and the sequence of the epoch is randomized after every epoch. The speech training and test tokens are obtained from 25 (17 females and 8 males) and 4 (all females) Malay adult speakers respectively. The total training and test token number are 1600 and 320 respectively. The result shows that modular neural networks outperform singular neural network with a recognition rate of about 92%.
Structure and Content Scoring for XML

XML repositories are usually queried both on structure and content. Due to structural heterogeneity of XML, queries are often interpreted approximately and their answers are returned ranked by scores. Computing answer scores in XML is an active area of research that oscillates between pure content scoring such as the well-known tf*idf and taking structure into account. However, none of the existing proposals fully accounts for structure and combines it with content to score query answers. We propose novel XML scoring methods that are inspired by tf*idf and that account for both structure and content while considering query relaxations. Twig scoring, accounts for the most structure and content and is thus used as our reference method. Path scoring is an approximation that loosens correlations between query nodes hence reducing the amount of time required to manipulate scores during top-k query processing. We propose efficient data structures in order to speed up ranked query processing. We run extensive experiments that validate our scoring methods and that show that path scoring provides very high precision while improving score computation time.
Supertagging: An Approach to Almost Parsing

In this paper, we have proposed novel methods for robust parsing that integrate the flexibility of linguistically motivated lexical descriptions with the robustness of statistical techniques. Our thesis is that the computation of linguistic structure can be localized if lexical items are associated with rich descriptions (supertags) that impose complex constraints in a local context. The supertags are designed such that only those elements on which the lexical item imposes constraints appear within a given supertag. Further, each lexical item is associated with as many supertags as the number of different syntactic contexts in which the lexical item can appear. This makes the number of different descriptions for each lexical item much larger than when the descriptions are less complex, thus increasing the local ambiguity for a parser. But this local ambiguity can be resolved by using statistical distributions of supertag co-occurrences collected from a corpus of parses. We have explored these ideas in the context of the Lexicalized Tree-Adjoining Grammar (LTAG) framework. The supertags in LTAG combine both phrase structure information and dependency information in a single representation. Supertag disambiguation results in a representation that is effectively a parse (an almost parse), and the parser need "only" combine the individual supertags. This method of parsing can also be used to parse sentence fragments such as in spoken utterances where the disambiguated supertag sequence may not combine into a single structure.
Heating of large format filters in sub-mm and fir space optics

Most FIR and sub-mm space borne observatories use polymer-based quasi-optical elements like filters and lenses, due to their high transparency and low absorption in such wavelength ranges. Nevertheless, data from those missions have proven that thermal imbalances in the instrument (not caused by filters) can complicate the data analysis. Consequently, for future, higher precision instrumentation, further investigation is required on any thermal imbalances embedded in such polymer-based filters. Particularly, in this paper the heating of polymers when operating at cryogenic temperature in space will be studied. Such phenomenon is an important aspect of their functioning since the transient emission of unwanted thermal radiation may affect the scientific measurements. To assess this effect, a computer model was developed for polypropylene based filters and PTFE-based coatings. Specifically, a theoretical model of their thermal properties was created and used into a multi-physics simulation that accounts for conductive and radiative heating effects of large optical elements, the geometry of which was suggested by the large format array instruments designed for future space missions. It was found that in the simulated conditions, the filters temperature was characterized by a time-dependent behaviour, modulated by a small scale fluctuation. Moreover, it was noticed that thermalization was reached only when a low power input was present.
of Huddersfield Repository

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Load Testing of Web Sites

Developers typically measure a Web application's quality of service in terms of response time, throughput, and availability. Poor QoS translates into frustrated customers, which can lead to lost business opportunities. At the same time, company expenditures on a Web site's IT infrastructure are a function of the site's expected traffic. Ideally, you want to spend enough, and no more, allocating resources where they will generate the most benefit. For example, you should not upgrade your Web servers if customers experience most delays in the database server or load balancer. Thus, to maximize your ROI, you must determine when and how to upgrade IT infrastructure. One way to assess IT infrastructure performance is through load testing, which lets you assess how your Web site supports its expected workload by running a specified set of scripts that emulate customer behavior at different load levels. I describe the QoS factors load testing addresses, how to conduct load testing, and how it addresses business needs at several requirement levels.
Multi-Model Medical Image Segmentation Using Multi-Stage Generative Adversarial Networks

Image segmentation is a challenging problem in medical applications. Medical imaging has become an integral part of machine learning research, as it enables inspecting interior human body with no surgical intervention. Much research has been conducted to study brain segmentation. However, prior studies usually employ one-stage models to segment brain tissues, which could lead to a significant information loss. In this paper, we propose a multi-stage Generative Adversarial Network (<inline-formula> <tex-math notation="LaTeX">$GAN$ </tex-math></inline-formula>) model to resolve existing issues of one-stage models. To do this, we apply a <italic>coarse-to-fine</italic> method to improve brain segmentation using a multi-stage <inline-formula> <tex-math notation="LaTeX">$GAN$ </tex-math></inline-formula>. In the first stage, our model generates a <italic>coarse</italic> outline for both the background and brain tissues. Then, in the second stage, the model generates a <italic>refine</italic> outline for the white matter (<inline-formula> <tex-math notation="LaTeX">$WM$ </tex-math></inline-formula>), gray matter (<inline-formula> <tex-math notation="LaTeX">$GM$ </tex-math></inline-formula>), and cerebrospinal fluid (<inline-formula> <tex-math notation="LaTeX">$CSF$ </tex-math></inline-formula>). We perform a fusion of the <italic>coarse</italic> and <italic>refine</italic> outlines to achieve high results. Despite using very limited data, we obtain an improved Dice Coefficient (DC) accuracy of up to 5% compared to one-stage models. We conclude that our model is more efficient and accurate in practice for brain segmentation of both infants and adults. In addition, we observe that our multi-stage model is 2.69–13.93 minutes faster than prior models. Moreover, our multi-stage model achieves higher performance with only a few-shot learning, in which only limited labeled data is available. Therefore, for medical images, our solution is applicable to a wide range of image segmentation applications for which convolution neural networks and one-stage methods have failed. This helps to advance the process of analyzing brain images, thus providing many advantages to the healthcare system, especially in critical health situations where urgent intervention is needed.
Quantitative study of undersampled recoverability for sparse images in computed tomography

Image reconstruction methods based on exploiting image sparsity, motivated by compressed sensing (CS), allow reconstruction from a signiﬁcantly reduced number of projections in X-ray computed tomography (CT). However, CS provides neither theoretical guarantees of accu- rate CT reconstruction, nor any relation between sparsity and a suﬃcient number of measurements for recovery. In this paper, we demonstrate empirically through computer simulations that mini- mization of the image 1-norm allows for recovery of sparse images from fewer measurements than unknown pixels, without relying on artiﬁcial random sampling patterns. We establish quantitatively an average-case relation between image sparsity and suﬃcient number of measurements for recovery, and we show that the transition from non-recovery to recovery is sharp within well-deﬁned classes of simple and semi-realistic test images. The speciﬁc behavior depends on the type of image, but the same quantitative relation holds independently of image size.
Mining What Developers Are Talking About Deep Learning LYU1801

Deep learning are gaining more and more attentions these days, which attracts lots of engineers and researchers jumping into this area. Understanding the questions raised by developers in public discussion forum is helpful for industry practitioners and research scientists to capture the trends of deep learning and comprehend the common concerns about designing deep learning techniques. Also, detecting major bugs of different platforms and concerns of users helps increase the reliability of deep learning tools. In this paper, we crawl over 10,000 questions about deep learning in StackOverflow, and simulate the Attention-based Aspect Extraction [1] model and IDentify Emerging App issues (IDEA) model [2] to extract common aspects embedded in these questions. Since questions have its own distinguishing features, a specific topic interpretation method is proposed. To figure out the outcomes of each approach, we propose to combine the Automatic Topic Coherence Evaluation model [3] . Finally, we evaluate the model performance quantitively and visualize the results to facilitate readers’ observation.
Targeting Inflation with a Prominent Role for Money

This paper demonstrates how a target for money growth can be beneficial for an inflation targeting central bank acting under discretion. Because the growth rate of money is closely related to the change in the interest rate and the growth of real output, delegating a money growth target to the central bank makes discretionary policy more inertial, leading to better social outcomes. This delegation scheme is also compared with other schemes suggested in the literature. Although other delegation schemes are sometimes more efficient, the results indicate that giving a prominent role to a money growth indicator can be a sensible strategy for monetary policy.
Computer money - a systematic overview of electronic payment systems

When there are many people who don't need to expect something more than the benefits to take, we will suggest you to have willing to reach all benefits. Be sure and surely do to take this computer money a systematic overview of electronic payment systems that gives the best reasons to read. When you really need to get the reason why, this computer money a systematic overview of electronic payment systems book will probably make you feel curious.
This paper is included in the Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation.

Video analytics applications use edge compute servers for processing videos. Compressed models that are deployed on the edge servers for inference suffer from data drift where the live video data diverges from the training data. Continuous learning handles data drift by periodically retraining the models on new data. Our work addresses the challenge of jointly supporting inference and retraining tasks on edge servers, which requires navigating the fundamental tradeoff between the retrained model’s accuracy and the inference accuracy. Our solution Ekya balances this tradeoff across multiple models and uses a micro-profiler to identify the models most in need of retraining. Ekya ’s accuracy gain compared to a base-line scheduler is 29% higher, and the baseline requires 4 × more GPU resources to achieve the same accuracy as Ekya .