Reviewerly/exHarmony huggingface dataset:
An Empirical Comparison of the Hardness of Multi-Agent Path Finding under the Makespan and the Sum of Costs Objectives

In the multi-agent path finding (MAPF) the task is to find non-conflicting paths for multiple agents. Recently, existing makespan optimal SAT-based solvers for MAPF have been modified for the sum-of-costs objective. In this paper, we empirically compare the hardness of solving MAPF with SAT-based and search-based solvers under the makespan and the sum-of-costs objectives in a number of domains. The experimental evaluation shows that MAPF under the makespan objective is easier across all the tested solvers and domains.
Fault Tolerant Stencil Computation on Cloud-Based GPU Spot Instances

This paper describes a fault tolerant framework for distributed stencil computation on cloud-based GPU clusters. It uses pipelining to overlap the data movement with computation in the halo region as well as parallelises data movement within the GPUs. Instead of running stencil codes on traditional clusters and supercomputers, the computation is performed on the Amazon Web Service GPU cloud, and utilizes its spot instances to improve cost-efficiency. The implementation is based on a low-cost fault-tolerant mechanism to handle the possible termination of the spot instances. Coupled with a price bidding module, our stencil framework not only optimizes for performance but also for cost. Experimental results show that our framework outperforms the state-of-the-art solutions achieving a peak of 25 TFLOPS for 2-D decomposition running on 512 nodes. We also show that the use of spot instances yields good cost-efficiency, increasing the average TFLOPS/USD from 132 to 360.
UniDoSA: The Unified Specification and Detection of Service Antipatterns

Service-based Systems (SBSs) are developed on top of diverse Service-Oriented Architecture (SOA) technologies or architectural styles. Like any other complex systems, SBSs face both functional and non-functional changes at the design or implementation-level. Such changes may degrade the design quality and quality of service (QoS) of the services in SBSs by introducing poor solutions-service antipatterns. The presence of service antipatterns in SBSs may hinder the future maintenance and evolution of SBSs. Assessing the quality of design and QoS of SBSs through the detection of service antipatterns may ease their maintenance and evolution. However, the current literature lacks a unified approach for modelling and evaluating the design of SBSs in term of design quality and QoS. To address this lack, this paper presents a meta-model unifying the three main service technologies: REST, SCA, and SOAP. Using the meta-model, it describes a unified approach, UniDoSA (Unified Specification and Detection of Service Antipatterns), supported by a framework, SOFA (Service Oriented Framework for Antipatterns), for modelling and evaluating the design quality and QoS of SBSs. We apply and validate UniDoSA on: (1) 18 RESTful APIs, (2) two SCA systems with more than 150 services, and (3) more than 120 SOAP Web services. With a high precision and recall, the detection results provide evidence of the presence of service antipatterns in SBSs, which calls for future studies of their impact on QoS.
Global Convergence of Block Coordinate Descent in Deep Learning

Deep learning has aroused extensive attention due to its great empirical success. The efficiency of the block coordinate descent (BCD) methods has been recently demonstrated in deep neural network (DNN) training. However, theoretical studies on their convergence properties are limited due to the highly nonconvex nature of DNN training. In this paper, we aim at providing a general methodology for provable convergence guarantees for this type of methods. In particular, for most of the commonly used DNN training models involving both two- and three-splitting schemes, we establish the global convergence to a critical point at a rate of ${\cal O}(1/k)$, where $k$ is the number of iterations. The results extend to general loss functions which have Lipschitz continuous gradients and deep residual networks (ResNets). Our key development adds several new elements to the Kurdyka-Łojasiewicz inequality framework that enables us to carry out the global convergence analysis of BCD in the general scenario of deep learning.
Automatic Tuning of the RBF Kernel Parameter for Batch-Mode Active Learning Algorithms: A Scalable Framework

Batch-mode active learning algorithms can select a batch of valuable unlabeled samples to manually annotate for reducing the total cost of labeling every unlabeled sample. To facilitate selection of valuable unlabeled samples, many batchmode active learning algorithms map samples to the reproducing kernel Hilbert space induced by a radial-basis function (RBF) kernel. Setting a proper value to the parameter for the RBF kernel is crucial for such batch-mode active learning algorithms. In this paper, for automatic tuning of the kernel parameter, a hypothesis-margin-based criterion function is proposed. Three frameworks are also developed to incorporate the function of automatic tuning of the kernel parameter with existing batchmodel active learning algorithms. In the proposed frameworks, the kernel parameter can be tuned in a single stage or in multiple stages. Tuning the kernel parameter in a single stage aims for the kernel parameter to be suitable for selecting the specified number of unlabeled samples. When the kernel parameter is tuned in multiple stages, the incorporated active learning algorithm can be enforced to make coarse-to-fine evaluations of the importance of unlabeled samples. The proposed framework can also improve the scalability of existing batch-mode active learning algorithms satisfying a decomposition property. Experimental results on data sets comprising hundreds to hundreds of thousands of samples have shown the feasibility of the proposed framework.
Localization of a radioactive source in an urban environment using Bayesian Metropolis methods

Abstract We present a method for localizing an unknown source of radiation in an urban environment using a distributed detector network. This method employs statistical parameter estimation techniques, relying on an approximation for the response of a detector to the source based on a simplified model of the underlying transport phenomena, combined with a Metropolis-type sampler that is modified to propagate the effect of fixed epistemic uncertainties in the material macroscopic cross sections of objects in the scene. We apply this technique to data collected during a measurement campaign conducted in a realistic analog for an urban scene using a network of six mobile detectors. Our initial results are able to localize the source to within approximately 8 m over a scene of size 300 m × 200 m in two independent trials with 30 min count times, including a characterization of the uncertainty associated with the poorly known macroscopic cross sections of objects in the scene. In these measurements, the nearest detectors were between 20 m to 30 m from the source, and recorded count rates between approximately 3 and 13 times background. A few detectors had line-of-sight to the source, while the majority were obscured by objects present in the scene. After extending our model to account for the orientation of the detectors and correcting for anomalies in the measurement data we were able to further improve the localization accuracy to approximately 2 m in both trials.
On illumination-invariant variational optical flow for weakly textured scenes

This paper deals with variational optical flow approaches for motion estimation under varying illumination conditions in weakly textured scenes. It proposes a systematic and complete study on descriptor-based data-terms that lead to a robust variational optical flow model. Unlike the literature which most often only experimentally shows that a descriptor is illumination invariant, this contribution gives a theoretical proof of this invariance. First, a local illumination change model is proposed and used to mathematically check whether a descriptor is invariant or not with respect to illumination variations between images. Then, this contribution proposes two general mathematical formulations which can be used to design a wide variety of new illumination-invariant descriptors. To illustrate the interest of the proposed approach, two novel illumination-invariant descriptors are constructed using the proposed general formulations. Moreover, the performance of the descriptors was evaluated on numerous datasets with known ground truth optical flow, while the robustness of the variational optical flow approach was highlighted using complex medical image sequences without ground truth. These experimental results have shown that data-terms based on the proposed descriptors led to accurate and constant optical flow under varying illumination conditions.
Integration of an in-home monitoring system into home care nurses’ workflow: A case study

The healthcare system faces a major challenge in caring for an increasingly ageing population as this task requires more resources than are currently available. Adopting monitoring technologies could enable more efficient care practices and support ageing in place. To investigate how the use of an in-home motion monitoring system can be integrated into home care nurses’ workflows and to uncover the factors behind system adoption. A single case study adopting a qualitative approach. A home care unit serving older adults living in independent living residences within an apartment complex. Multiple data collection methods were used including individual and group interviews, a questionnaire with open-ended questions, evaluation probes, and system log data. The qualitative material was analysed using a stepwise-deductive inductive approach. A central factor behind system adoption was the perceived usefulness of gaining information about older adults’ night-time activities. In particular, monitoring older adults suffering from memory disorders was considered advantageous. The information that the system provided supported nurses in health assessments and assisted in adjusting care decisions. Previous negative experiences with similar technologies initially influenced the time for adoption. Further, although nurses were closely involved in the system design process, they took some time to get acquainted with the system and to integrate its use into daily practice. System reliability and accuracy issues influenced nurses’ trust in the sensory data. The findings suggests that in a home care setting, focusing on motion pattern monitoring for older adults with memory disorders can provide significant benefits and therefore also facilitate system adoption among nurses. Involving nurses in the design of the technology and providing opportunities to trial the system in real practice also appear to be important in achieving system adoption.
Heavy Metal Concentrations in the Groundwater of the Barcellona-Milazzo Plain (Italy): Contributions from Geogenic and Anthropogenic Sources

We collected and analysed 58 samples of groundwater from wells in the Barcellona-Milazzo Plain, one of the most important coastal aquifers of Sicily (Italy), to determine major, minor, and trace element concentrations. In this area, geogenic and anthropogenic sources of heavy metals and other pollutants co-act, making the individuation of the main pollution sources difficult. Our work was aimed at the application of geostatistical criteria for discriminating between these pollution sources. We used probability plots for separating anomalous values from background concentrations, which were plotted on maps and related to possible sources of pollutants. Our results indicate that hydrothermal fluid circulation and the water–rock interaction of country rocks that host mineralized ore deposits generate a significant flux of heavy metals to groundwater, as well as anthropogenic sources like intense agriculture and industrial activities. In particular, NO3, F, and Ni exceed the Maximum Admitted Concentrations (MACs) established by the WHO and Italian legislation for drinking-water. The spatial distributions of geogenic and anthropogenic sources were so deeply interlocked that their separation was not easy, also employing geostatistical tools. This complex scenario makes the implementation of human health risk mitigation actions difficult, since the flow of pollutants is in many cases controlled by simple water–rock interaction processes.
PODCAST: An alternative way to improve EFL students’ listening and speaking performance

This article aims to view the impacts of using podcast in improving listening and speaking performance. As there is a trend of using ICT in language teaching growths, the authors intend to discuss about the media that can be used in enhancing students’ ability in listening skill and speaking performance. The authors collected the information related to the topic of this article from several sources such as, books, journal articels, and previous studies. From these sources, it can be concluded that podcast can bring many benefits in teaching listening and speaking for EFL learners.
Learning Styles and the Use of ICT in University Students within a Competency-Based Training Model

The purpose of the study was to determine the learning styles (LS) and the use of Information and Communication Technologies (ICT) in university students within a competency-based training model, as well as to identify possible differences related to the frequency of use of ICT in terms of gender. A descriptive and correlational quantitative approach was applied with a non-experimental and transversal design. The study used an intentional non-probabilistic sample of 113 students in undergraduate psychology, public accounting and business administration programs that use a competency-based model at a private Colombian university. The outcome showed no significant differences among LS or in the frequency of ICT use in students with competency-based training. Additionally, the outcome showed a gender gap in
An examination of digital footprint awareness and digital experiences of higher education students

Rapid changes are occurring in life owing to information and communication technologies. During this change period, digitalidentities have begun to emerge. These identities are associated with actions that users perform in online environments. Adigital footprint is formed with these actions. The purpose of this study is to determine the digital footprint awareness anddigital footprint experiences of higher education students. In order to collect data for the study, researchers developed adata collection tool titled â€˜A survey for digital media useâ€™. The study group consisted of 508 higher education students from41 Turkish universities. Data were collected through an online-form where participation was on a volunteer basis. Thefindings of the research show that students have a high level of digital footprint awareness ( x = 4.251) and a low level ofdigital footprint experience ( x = 1.907).Keywords: Digital footprint, digital footprint awareness, digital footprint experiences, higher education students.
A Free Navigation of an AGV to a Non-Static Target with Obstacle Avoidance

The industry is changing in order to improve the economy sector. This is the reason why technology is improving and developing new devices. The autonomous guided vehicle with free navigation is a new machine, which uses different techniques to move such as mapping, localization, path planning, and path following. In this paper, a path following is proposed. The path following is called moving to a point, which uses the proportional distance between the target and the autonomous guided vehicles (AGV) to calculate the velocity and direction. If some obstacles appear in the trajectory, however, the vehicle stops. Instead of stopping the machine, by using moving to a point logic, an obstacle avoidance function will be implemented. In this implementation, different parameters can be configured, such as: security distance, which determinates when the obstacle avoidance must correct the pose; and proportional values, which modify the velocity and steering commands. It is also compared to a dynamic window approach (DWA) obstacle avoidance solution. Additionally, the AGV navigates to a non-static target with a path following algorithm.
Three-dimensional measurement based on optimized circular fringe projection technique

Circular fringe projection profilometry (CFPP) is a recently proposed optical three-dimensional (3D) measurement technique. Theoretically, its optimal measurement accuracy should precede that of the conventional fringe projection profilometry. In practice, the measurement accuracy is impacted by many factors, and much research remains to be done in order to make CFPP reach its optimal precision. One of the dominant factors is the zero-phase point. For the usage of the cotangent function, error near the zero-phase point will be significantly amplified. This makes the overall measurement accuracy very low for CFPP with coaxial layout. To address this critical issue, CFPP with off-axis layout (called OCFPP for simplicity) is presented in this paper. The core theory of OCFPP is briefly introduced. The zero-phase point detection problem coming with OCFPP is explained. Then, two methods, one based on a two-dimensional ruler and the other based on plane constraint, are proposed to solve this additional problem. Simulation and experiments validate the effectiveness of the proposed zero-phase point detection methods, and convince the advantage of OCFPP. This paper contributes to distinctly improving the 3D measurement capability of CFPP, and lays an indispensible foundation for its practical application.
Iterative refinement for lp-norm regression

We give improved algorithms for the lp-regression problem, minx ||x||p such that Ax = b, for all p ∈ (1,2) ∪ (2, ∞). Our algorithms obtain a high accuracy solution in [EQUATION] iterations, where each iteration requires solving an m × m linear system, with m being the dimension of the ambient space.Incorporating a procedure for maintaining an approximate inverse of the linear systems that we need to solve at each iteration, we give algorithms for solving lp-regression to 1/poly(n) accuracy that runs in time OP(mmax{ω7/3}), where ω is the matrix multiplication constant. For the current best value of ω > 2.37, this means that we can solve lp regression as fast as l2 regression, for all constant p bounded away from 1.Our algorithms can be combined with nearly-linear time solvers for linear systems in graph Laplacians to give minimum lp-norm flow / voltage solutions to 1/poly(n) accuracy on an undirected graph with m edges in [MATH HERE] time.For sparse graphs and for matrices with similar dimensions, our iteration counts and running times improve upon the p-norm regression algorithm by [Bubeck-Cohen-Lee-Li STOC'18], as well as general purpose convex optimization algorithms. At the core of our algorithms is an iterative refinement scheme for lp-norms, using the quadratically-smoothed lp-norms introduced in the work of Bubeck et al. Formally, given an initial solution, we construct a problem that seeks to minimize a quadratically-smoothed lp norm over a sub-space, such that a crude solution to this problem allows us to improve the initial solution by a constant factor, leading to algorithms with fast convergence.
Mobile technology and food access

Access to food is a basic pillar of human development. It is therefore unsurprising that it features so centrally on global development agendas and that a robust, interdisciplinary literature seeks to examine its determinants. This study focuses on the relationship between mobile technology and food access. Specifically, we ask whether mobile technology can strengthen the relationship between food access and certain social and political factors such as remittance flows and political participation. We use Afrobarometer surveys and highly disaggregated data on 2G network coverage to estimate a multilevel model testing how increased connectivity measured by mobile technology influences food access. We show that mobile phone use and higher frequency of use are significantly and positively correlated with food access, but we do not find evidence that remittances and political participation levels can explain the mechanisms linking mobile technology and food access. The study highlights that connectivity can play a powerful role in shaping food outcomes even when controlling for commonly identified impediments such as income constraints or physical isolation. These findings suggest that policies aimed at improving food access should devote attention to strengthening both communication and physical infrastructure.
Prediction of apoptosis protein subcellular localization via heterogeneous features and hierarchical extreme learning machine

Apoptosis is a fundamental process controlling normal tissue homeostasis by regulating a balance between cell proliferation and death. Predicting the subcellular location of apoptosis proteins is very helpful for understanding the mechanism of programmed cell death. Predicting protein subcellular localization with bioinformatic techniques provides quite a few opportunities in related fields. In this work, we propose the use of a hierarchical extreme learning machine (H-ELM) to make a classification of high-dimensional input data without demanding a dimension reduction process, which yields acceptable results. An attempt is made to extract features from different perspectives, and a feature fusion process is accomplished. Regarding the position-specific scoring matrix, the first type depicts the correlation within the sequence with the autocorrelation function for relatively random sections from the sequence; and the second type is the Kullback-Leibler (K-L) divergence of the two distributions formed by the amino acids’ constitutuent proportions. It is illustrated in an experiment with features from different sources mixed by simple concatenation yielding a poor result, but the synthetical feature fused with stochastic nonlinear embedding (t-SNE) greatly improved the classification. Finally, the highest overall accuracy of ZD98 is 87.5% by adjusting the hyper-parameters of H-ELM, and of CL317 is 92.4%.
A Fixed-Bed Column Study for Removal of Organic Dyes from Aqueous Solution by Pre-Treated Durian Peel Waste

A number of harmful effects on the ecosystem, the life of humankind, and living species caused by dye-contaminated wastewater have urged the development for an efficient and cost-efficient treatment method for colored effluents. The cellulose-based adsorbents have been considered as a facile and efficient approach to remove hazardous pollutants because of the abundance of inexpensive agricultural wastes in Viet Nam. This study aims to investigate the elimination of methylene blue (MB) and crystal violet (VL) from wastewater using a fixed-bed column of pre-treated durian peel. Examined variables in the process are bed depths (2–6 cm), flow rate (5–20 mL/min), and influent dye concentrations (200–600 mg/L). The highest adsorption amount of pre-treated DP was 235.80 mg/g and 527.64 mg/g, respectively, on a 600 mg/L of methylene blue and crystal violet achieved within a bed height of 4 cm and a flow rate of 10 mL/min. Accordingly, the breakthrough curves were constructed and modeled using the relevant theoretical models under the effects of different experimental conditions. Pre-treated durian peel was found to exhibit high adsorption capacity for cationic dye in an initial concentration of 200–600 mg/L with complete removal being obtained.