Miracle-dz/UniFAR-FTUs huggingface dataset:
LARGE SCALE SENSOR DATA PROCESSING BASED ON DEEP STACKED AUTOENCODER NETWORK

Recently, Internet of Things (IoT) extremely populated by massive amounts of connected embedded devices, which are gathering large volumes of real-time heterogeneous data. Hence, IoT becomes an archetypal instance of Big Data. The collected IoT Big Data may not be profitable unless we evaluate and accurately exploit them. Providing mining for large scales of raw sensor data is an open challenge. To cope with this challenge, we proposed a system that operates in two modes, which are preparation and processing. The preparation mode converges on reducing the factors that hinder making efficient processing by focusing on three stages. First, handling missing data by applying interval-valued fuzzy-rough feature selection methodology. It highlights the most important features that contain missing data and gets rid of the others. Then, Maximum Likelihood (ML) approach is used for estimating the missing values. Second, anomalies are detected by initially utilizing K-nearest neighbors (KNN) algorithm then removing the detected ones from the data. Third, the dimensionality of nonlinearly separable data is reduced by exploiting Self-Organizing Map (SOM) network. In the processing mode, we passed the prepared data to a straightforward classifier based on a Deep Learning (DL) approach. We used autoencoder networks in constructing a deep network, which is the Deep Stacked Autoencoder (DSAE). The extracted features by the DSAE are non-handcrafted and task dependent, which gives it the most discriminative power to work as an efficient classifier. We apply the proposed model to PAMAP2 Physical Activity Monitoring data set. The results show that DSAE achieves high accuracy (99.8%) compared to the state-of-the-art classifiers.
Selected Topics in Continuum Percolation Phase Transitions , Cover Times and Random Fractals

Mussini, F. 2019. Selected Topics in Continuum Percolation. Phase Transitions, Cover Times and Random Fractals. Uppsala Dissertations in Mathematics 117. 54 pp. Uppsala: Department of Mathematics. ISBN 978-91-506-2787-9. This thesis consists of an introduction and three research papers. The subject is probability theory and in particular concerns the topics of percolation, cover times and random fractals. Paper I deals with the Poisson Boolean model in locally compact Polish metric spaces. We prove that if a metric space M1 is mm-quasi-isometric to another metric space M2 and the Poisson Boolean model in M1 features one of the following percolation properties: it has a subcritical phase or it has a supercritical phase, then respectively so does the Poisson Boolean model in M2. In particular, if the process in M1 undergoes a phase transition, then so does the process in M2. We use these results to study phase transitions in a large family of metric spaces, including Riemannian manifolds, Gromov spaces and Caley graphs. In Paper II we study the distribution of the time it takes for a Poisson process of cylinders to cover a bounded subset of d-dimensional Euclidean space. The Poisson process of cylinders is invariant under rotations, reflections and translations. Furthermore, we add a time component, so that one can imagine that the cylinders are “raining from the sky” at unit rate. We show that the cover times of a sequence of discrete and well separated sets converge to a Gumbel distribution as the cardinality of the sets grows. For sequences of sets with positive box dimension, we determine the correct speed at which the cover times of the sets An grows. In Paper III we consider a semi-scale invariant version of the Poisson cylinder model. This model induces a random fractal set in the vacant region of the process. We establish an existence phase transition for dimensions d ≥ 2 and a connectivity phase transition for dimensions d ≥ 4. An important step when analysing the connectivity phase transition is to consider the restriction of the process onto subspaces. We show that this restriction induces a fractal ellipsoid model in the corresponding subspace. We then present a detailed description of this induced ellipsoid model. Moreover, the almost sure Hausdorff dimension of the fractal set is also determined.
Computation of reference crop evapotranspiration of Nasik station of Maharashtra, India

The study was conducted to know the best method for estimation of reference crop evapotranspiration (ETr) for Nasik station (Latitude 20° 53ʹ N, Longitude 75° 16ʹ E and altitude 565.0 m amsl), Maharashtra, India. Reference crop evapotranspiration is a basic parameter in irrigation system design. Six reference crop evapotranspiration methods were used for their daily performances under semi-arid climatic condition. The PenmanMonteith method standardized by FAO was used to compare with the Modified Penman, Hargreaves-Samani, Pan Evaporation, Blanney-Criddle and Radiation methods. Performances of these methods were made on the basis of the least root mean square error (RMSE) and regression analysis (RA). The value of RMSE ranges from 2.01 to 1.09 with the highest in Pan Evaporation method followed by Blanney-Criddle, Radiation method, modified Penman and HargreavesSamani method. Out of six methods Hargreaves-Samani was found to have least root mean square error (RMSE = 1.09 mm/day) and highest correlation coefficient (r=0.99) under semi-arid climatic conditions of Nasik station of Maharashtra.
The Role of Randomness and Noise in Strategic Classification

We investigate the problem of designing optimal classifiers in the strategic classification setting, where the classification is part of a game in which players can modify their features to attain a favorable classification outcome (while incurring some cost). Previously, the problem has been considered from a learning-theoretic perspective and from the algorithmic fairness perspective. Our main contributions include 1. Showing that if the objective is to maximize the efficiency of the classification process (defined as the accuracy of the outcome minus the sunk cost of the qualified players manipulating their features to gain a better outcome), then using randomized classifiers (that is, ones where the probability of a given feature vector to be accepted by the classifier is strictly between 0 and 1) is necessary. 2. Showing that in many natural cases, the imposed optimal solution (in terms of efficiency) has the structure where players never change their feature vectors (the randomized classifier is structured in a way, such that the gain in the probability of being classified as a 1 does not justify the expense of changing one's features). 3. Observing that the randomized classification is not a stable best-response from the classifier's viewpoint, and that the classifier doesn't benefit from randomized classifiers without creating instability in the system. 4. Showing that in some cases, a noisier signal leads to better equilibria outcomes -- improving both accuracy and fairness when more than one subpopulation with different feature adjustment costs are involved. This is interesting from a policy perspective, since it is hard to force institutions to stick to a particular randomized classification strategy (especially in a context of a market with multiple classifiers), but it is possible to alter the information environment to make the feature signals inherently noisier.
Ontogenetic and seasonal variation in movements and depth use, and evidence of partial migration in a benthopelagic elasmobranch

Publisher Rights Statement: Copyright © 2019 Thorburn, Neat, Burrett, Henry, Bailey, Jones and Noble. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
The ElasTest Platform: Supporting Automation of End-to-End Testing of Large Complex Applications

Quality and high speed is the new mantra: everyone wants the best products delivered as fast as possible. On the one side, managers aim at “ ​the fastest time to market”​; on the opposite side, the cliché recurs that “ ​a good user experience is the key to a successful product​”. Compounding both sides, developers know well that before they can release their software, ​testing is a must​. Notwithstanding, it is often ignored or given low priority, why? Are the integrated systems tested enough at their ends to meet the user-demanded functionalities? Software code is written, transformed, and updated, then it is checked-in and verified before a new product is finally launched. But not always this translates into the best software solution or the best experience for users. Is it all about continuous integration process or are there more reasons? With the increasing need of distributed and more interconnected software systems, are developers ready to satisfy this demand?
The Influence of Peers in Worker Misconduct: Evidence from Restaurant Theft

Problem definition: Misconduct, such as theft, is a major problem in operational settings, and staffing decisions can either amplify or mitigate this problem as workers influence their peers’ behavior. Peers are known to influence coworker productivity and likely also affect counterproductive behaviors. Academic/practical relevance: Many studies have shown how mechanisms such as helping, knowledge transfer, teaching, and social pressure generate productivity peer effects in service and other settings. Yet few papers empirically examine these effects in counterproductive behaviors. We argue that while the same mechanisms driving productivity spillovers also generate peer effects in misconduct, an additional effect—strategic peer response—reflects how coworkers, under managerial monitoring, adjust misconduct in response to peers’ daily behavior. An additional contribution of this paper is to identify the effect of peers on operational performance in a firm setting. Methodology: We use transaction and theft data from 83,153 servers at 1,049 restaurants across 46 states in the United States. We employ instrumental variable (IV) models to account for both reflection problems and correlated error terms in same-day peer theft. We use Monte Carlo simulations to present how biases identified by a combination of ordinary least squares (OLS) and IV models suggest that managerial oversight might generate negative correlation in the same-day error terms of peers that reflects strategic peer responses. Results: Our results show that although servers are more likely to steal when working with high-theft peers, they steal less as peers steal more on a given day. We also show that this negative correlation in daily peer theft is higher under an information technology system that increases managerial oversight by reporting likely theft to managers. Importantly, we demonstrate how reflection effects can significantly amplify even small peer-effect coefficients to have large organizational implications. Our parameter estimates indicate that doubling a single worker’s average theft amount will increase total theft in an average restaurant by 76%. Doubling all workers’ theft amounts increases totals by 550%. Finally, we show that the positive peer effect from high-theft coworkers only exists for new workers in their first three to five months on the job, consistent with imprinting mechanisms that include knowledge transfer and norms. Managerial implications: The results show that the costs of employing unethical workers is higher than the direct cost of those workers’ misconduct because their behavior spills over into coworkers’ actions and amplifies through reflection effects. Yet our results also suggest that this contagion can be mitigated by managerial oversight. So long as there is sufficient monitoring of misconduct, workers will strategically limit such behavior in response to peers.
TF-BIV: transparent and fine-grained binary integrity verification in the cloud

With the emergence of virtualization technologies, various services have been migrated to the cloud. Beyond the tenants' own security controls implemented in the virtual machine (VM), the binary integrity verification mechanism in the virtual machine manager (VMM) provides stronger protections against malware. Unfortunately, none of existing integrity verification mechanisms in the cloud provides complete transparency and fine-grained efficiency. Some schemes selectively check the integrity of sensitive binaries, but they require modifications to the VMs (e.g., integrating monitoring libraries) to trigger verification. Others, although need no modification to the VMs, have to enforce checking on all the binaries, because they cannot distinguish binary images for the sensitive processes from the binaries for insensitive ones, leading to significant performance overheads. In this paper, we present TF-BIV, a transparent and fine-grained binary integrity verification scheme, which does not require any modification or software/driver installation in the VM. TF-BIV identifies the sensitive processes at the creation, and checks the integrity of the binaries (including the guest OS kernel and the dependant binaries) related to these processes. The provided transparency and efficiency are achieved by leveraging existing hardware virtualization supports (i.e., Intel extended page table) and debugging features (i.e., monitor trap flag). We have implemented the TF-BIV prototype based on QEMU-KVM. To demonstrate the usability of TF-BIV, we adopted it for cloud-based cryptographic services, to achieve the strict invoking controls. In addition to the password-based authentication, TF-BIV further achieves process-level authorization to the invokers. Intensive evaluation shows that TF-BIV implements the designed binary integrity verification with only about 3.6% performance overhead.
Comparative study of the performance and exhaust gas emissions of biodiesels derived from three different feedstocks with diesel on marine auxiliary diesel engine

In order to reduce the global environmental impact from internal combustion engines in terms of emissions particularly in the production of greenhouse gases (GHG), one of the most promising ways is the use of bio-derived fuels. To this purpose, the utilisation of biodiesel (pure or in blends) in diesel engines is already quite common in some countries due to its properties comprehensively similar to petro-diesel. The major positive effect not only intrinsic to the significant reduction in CO2 emission but reducing CO, HC and smoke emissions, even though it typically causes an appreciable increase in NOx emissions. However, the use of the biodiesel has some limitation due to efficiency drops and long term complications to the engine. Therefore, this study aims on examine the effects of different biodiesel blends on engine performance and exhaust gas emissions of marine auxiliary diesel engine and recommends bio-fuel that optimising the engine performance and lower emissions. This study found there has been an increment in engine performance specifically on torque, brake power, brake thermal efficiency (BTE) and brake mean effective pressure (BMEP) along with lower and better in brake specific fuel consumption (BSFC) crude palm biodiesel oil (CPO) and jatropha cursas biodiesel oil (JCO) blended fuels, while decreased in engine performance associated with higher BSFC by waste cooking biodiesel oil (WCO) blends compared to neat petroleum diesel. Aside, the reductions of CO, CO2 and HC by CPO; slightly increment in CO2, NOx and HC by JCO and acceptable CO2, CO and NOx gas productions by WCO has been discovered. Conclusion can be made is biodiesel blend fuels is operable in diesel engines without engine alterations as well as advantageous for exhaust emission reductions yet beneficial to the environmental sustainability.
Subgroup Formation in Human-Robot Teams

Subgroup formation is vital in understanding teamwork. It was not clear whether subgroup formation takes place in human–robot teams and what the implications of the subgroups might be for the team’s success. Therefore, we conducted an experiment with 44 teams of two people and two robots, where each team member worked with a robot to accomplish a team task. We found that subgroups were formed when team members identified with their robots and were inhibited when they identified with their team as a whole. Robot identification and team identification moderated the negative impacts of subgroup formation on teamwork quality and subsequent team performance.
Constraint Based Reasoning over Mutex Relations in Graphplan Algorithm

This paper describes a work in progress. We address a problem of reachability of states in planning from the constraint programming perspective. We propose an alternative way how planning graphs known from the Graphplan algorithm can be constructed. We propose to do an additional consistency check every time when our variant of the planning graph is extended. This leads into a more accurate approximation of reachable states than it is done by the standard Graphplan algorithm. It is theoretically shown in this paper that our variant of planning graph forbids strictly more situations than it is forbidden by the standard planning graphs.
Summarization via User-Aware Sequence Network

We address personalized review summarization, which generates a condensed summary for a user’s review, accounting for his preference on different aspects or his writing style. We propose a novel personalized review summarization model named User-aware Sequence Network (USN) to consider the aforementioned users’ characteristics when generating summaries, which contains a user-aware encoder and a useraware decoder. Specifically, the user-aware encoder adopts a user-based selective mechanism to select the important information of a review, and the user-aware decoder incorporates user characteristic and user-specific word-using habits into word prediction process to generate personalized summaries. To validate our model, we collected a new dataset Trip, comprising 536,255 reviews from 19,400 users. With quantitative and human evaluation, we show that USN achieves state-ofthe-art performance on personalized review summarization. Introduction Review summarization aims to generate a condensed summary for a review or multiple reviews (Hu and Liu 2004; Ganesan 2010; Carenini, Cheung, and Pauls 2013; Gerani et al. 2014; Lu and Wang 2016). As this task can alleviate the information overload problem, it has been widely studied. This paper addresses personalization issues of review summarization1, which have not been discussed in previous research. Given a review, different users may care about different contents according to their own experiences or thoughts. Figure 1 illustrates the motivation with a hotel review sample. Bob may travel on business and he cares about location and room more than price, while John may travel on a tight budget and care about price more. What’s more, different users have their own writing styles. Alice often summarizes reviews with the words which can explicitly express her emotions, such as “love” or “hate”, while Bob and John don’t do that. Actually, personalized review summarization is applicable to a wide range of online consumer review platforms, Copyright c © 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. In this paper, we focus on single-review summarization and we leave adapting our model to multi-review summarization scenario to future work. Review: The hotel is right next to the airport (my room had a view of the runways) but the noise is pretty well dampened so that is not an issue at all. Very convenient to the airport obviously, but also the main highways. Room was clean and comfortable, no complaints there. The price is a little high, but it is ok for me. Summary: very quite room in a great location.
Adapting Lesson Study for Community College Mathematics Instruction: Early Observations

with mathematics faculty at three Oregon community colleges to adapt and pilot the Lesson Study model of professional development for use among developmental mathematics instructors. Teams of part-time and full-time faculty at Clackamas Community College, Lane Community College, and Portland Community College are participating in the project. A primary goal of this project is to understand if and how Lesson Study can be a feasible and sustainable model for faculty development in community college mathematics departments.
Audio Bandwidth Extension based on Maximum Lyapunov Prediction

In this paper, the maximum Lyapunov exponent and the correlation dimension are first utilized to determine the chaotic characteristics of audio spectrum based on nonlinear analysis method. Next, the maximum Lyapunov prediction and codebook mapping are used to recover the fine structure and envelope of high-frequency spectrum separately. By using above two approaches, the bandwidth of wideband audio is extended to super-wideband. Finally, this bandwidth extension method is applied into G.722. 1 audio codec. The test results indicate that the proposed method outperforms the conventional algorithms in most cases, and the perceived quality of the extended superwideband audio in the real wideband audio codec system is comparable with that of directly reconstructed audio using super-wideband audio coding at the bit-rate of 24 kb/s.
Partnership for Advanced Computing in Europe 1 Evaluating and Implementing Hybrid Approach for SPECFEM 3 D _ GLOBE

The entire project focused on an evaluation of the code for a possible introduction of OpenMP and its actual implementation and extensive tests. Major time consuming parts of the code were detected and thoroughly analyzed. The most time consuming part was successfully parallelized using OpenMP. Very extensive test simulations using the hybrid code allowed for many further improvements and validations of its results. Possible improvements have also been discussed with the developers to be implemented in the near future.