Miracle-dz/UniFAR-FTUs huggingface dataset:
AUTOMATIC ASSESSMENT OF PRODUCT DESIGN USABILITY – A DESIGN METHODOLOGY

Usability assessment enables improving environmental design so to facilitate independent living among older adults. A multi-phase system design methodology is presented which was used in developing video analysis techniques for product usability assessment. The multiphase scheme incorporates the output of each phase during the design of following phases so to ease the need for manual annotation.
A PRELIMINARY STUDY

This study is concerned with the organization of inter-lexical pauses of L2 English speech produced by Chinese students. Foreign language learners (L2 learners) have often been observed to lack a natural “rhythm” when reading aloud. This investigation analyzed read speech of 5 English sentences at three different speech tempos by 18 Chinese native speakers. We compared the patterns of inter-lexical pauses of the Chinese subjects in the collected speech with those of the native English speakers reported in previous literature. It was found that the Chinese speakers could hardly read English at a very fast speech rate. Their patterns of inter-lexical pauses were more similar to those of the native speakers at a slow or normal speech rate than at a fast speech rate. However, most interlexical pause patterns of L2 English by the Chinese speakers deviate from those of the native speakers, which may result in foreign-accented speech with degraded intelligibility. We suggest that the importance of the organization of pauses between words should be taken into account in L2 English speech learning and teaching.
RQP Generator Query Parser / Lexer OQP Generator Storm Deployment Minimal Steiner Tree Calculator Network Topology Discovery Query Deployment Engine Query Plan Compiler Query Expression

CHive is a new streaming analytics platform to run distributed SQL-style queries on edge clouds. However, CHive is currently tightly coupled to a specific stream processing system (SPS), Apache Storm. In this paper we address the decoupling of the CHive query planner and optimizer from the runtime environment, and also extend the latter to support pluggable runtimes through a common API. As runtimes, we currently support Apache Spark and Flink streaming. The fundamental contribution of this paper is to assess the cost of employing interstream parallelism in SPS. Experimental evaluation indicates that we can enable popular SPS to be distributed on edge clouds with stable overhead in terms of throughput.
Practical approaches to seismic design of deep foundations

This paper sets out a simplified approach by which the practical foundation designer can undertake the relevant calculations to satisfy the requirements for deep foundation design in seismic areas. The following matters are dealt with: a. Design issues that should be addressed; b. Pile design for axial loading, including the possible effects of liquefaction; c. Pile design for lateral loading where liquefaction does not occur; d. Pile design for lateral loading where liquefaction does occur; e. Measures to mitigate against liquefaction effects.
An Improvised Bottleneck Routing Algorithm for virtual Buffer based energy efficient NoC ’ s

Network on chip technology will be a major factor in future communication system which is based on intra core system, But when comes to power usage this practical NoC system consumes considerably huge power , then the Architecture of the crossbar routers will directly increases with respect to the no of intra core system . When comes to bulky systems, the average power consumption in crossbar switches relatively high. While detailing the components of the routers we found that buffers in the input terminals leads to major power consumption, when we try to remove buffers in NoC routers, the overall performance will reduce due to the bottleneck Increase. To make These Network on Chip crossbars efficiently without degradation in performance we propose a better systematic approach in the crossbar switches by incorporating virtual Memory system as a substitute of conventional register based memory, this will leads to energy efficiency with increase in overall performance. We are going to apply this architecture in NoC system which based on 5 Intra core system, our proposed system reduced nearly 30% of overall power usage.
c 0 MY 806 cfS---MODELING OF NUCLEATION DURING REC

The number density and spatial distribution of nuclei determine the kinetics, and the evolution of microstructure and texture during recrystallization. The potential nucleation sites are the high angle boundaries that exist in the deformed microstructure. These could be either the original grain boundaries prior to deformation or boundaries created due to nonuniform plastic deformation on the microscopic scale. Discontinuous subgrain growth with or without the formation of high angle boundaries may also occur during recrystallization in the presence of long range orientation gradients existing in the deformation substructure. The paper presents Monte Carlo simulation results on the evolution of substructures for each of the above nucleation mechanisms. The evolution of recrystallized grain structure from a realistic substructure obtained by modeling the deformation of fcc polycrystals at the microstructural level is presented, and shows the collective contribution of the above nucleation mechanisms during recrystallization and grain growth. The simulations capture not only the kinetics and microstructural evolution but also the evolution of texture during recrystallization. The submitted manuscript has been authored by a contractor of the U.S. Government under contract No. DE-AC05-960RZ2464. Accordingly, the US. Government retains a non-exclusive, royaltyfree license to publish or reproduce the published form of this contribution, or allow others to do so, for U.S. Government purposes.
Perspective of Political and Management in the Regional Financial Management Synergy

: Regional financial management is a complex and multi-dimensional matter. Financial performance achieved by the regions is a reflection of the synergy of various dimensions and actors. Various problems, distortions and relations in the political and managerial dimensions in the management of regional finances are very important to be parsed scientifically. This research was conducted in Jember Regency by analyzing financial management for the 2017-2019 fiscal year. Instruments for analyzing patterns, relationship distortions in political and managerial dimensions refer to the McNabb analysis stage (McNabb, 2002). The data in this study were obtained from mass media and informants through focus group discussions. Political and managerial relations in the management of regional finances emerged especially when making memorandum of understanding in the budget discussion process. Distortion occurs when on the one hand the executive offers program plans, activities and budgets accompanied by the wishes and interests of the regional head while the legislative requirements with political and constituent interests. If we look at the pattern of discussion of budget plans, one side of the executive carries the mandate of the regional head while on the other hand the legislature does not have enough knowledge in the field of budgeting so that the process of sharing and training among actors runs tough. The power context is not only limited to normative political authority, but can be related to the competence and quality of its human resources. While on the other hand, managerial patterns and character and leadership of regional heads greatly affect the effectiveness of planning, implementation and accountability of local budgets.
Algorithms for structural variation discovery using multiple sequence signatures (Çoklu dizi sinyalleri kullanarak yapısal varyasyon keşfi için algoritmalar)

ALGORITHMS FOR STRUCTURAL VARIATION DISCOVERY USING MULTIPLE SEQUENCE SIGNATURES Arda Söylev Ph.D. in Computer Engineering Advisor: Can Alkan September 2018 Genomic variations including single nucleotide polymorphisms (SNPs), small INDELs and structural variations (SVs) are known to have significant phenotypic effects on individuals. Among them, SVs, that alter more than 50 nucleotides of DNA, are the major source of complex genetic diseases such as Crohn’s, schizophrenia and autism. Additionally, the total number of nucleotides affected by SVs are substantially higher than SNPs (3.5 Mbp SNP, 15-20 Mbp SV). Today, we are able to perform whole genome sequencing (WGS) by utilizing high throughput sequencing technology (HTS) to discover these modifications unimaginably faster, cheaper and more accurate than before. However, as demonstrated in the 1000 Genomes Project, HTS technology still has significant limitations. The major problem lies in the short read lengths (<250 bp) produced by the current sequencing platforms and the fact that most genomes include large amounts of repeats make it very challenging to unambiguously map and accurately characterize genomic variants. Thus, most of the existing SV discovery tools focus on detecting relatively simple types of SVs such as insertions, deletions, and short inversions. In fact, other types of SVs including the complex ones are of crucial importance and several have been associated with genomic disorders. To better understand the contribution of these SVs to human genome, we need new approaches to accurately discover and genotype such variants. Therefore, there is still a need for accurate algorithms to fully characterize a broader spectrum of SVs and thus improve calling accuracy of more simple variants. Here we introduce TARDIS that harbors novel algorithms to accurately characterize various types of SVs including deletions, novel sequence insertions, inversions, transposon insertions, nuclear mitochondria insertions, tandem duplications and interspersed segmental duplications in direct or inverted orientations using short read whole genome sequencing datasets. Within our framework, we make use of multiple sequence signatures including read pair, read depth and iii
OF Meloidogyne enterolobii ON Psidium SPECIES AND INDUCED CELLULAR RESPONSES IN THE ROOTS 1

Meloidogyne enterolobii has severely compromised the main guava producing regions in Brazil, thereby stimulating the development of resistant varieties for efficient management of this nematode. This study aimed to assess the penetration, development, reproduction, and induced cellular responses of M. enterolobii in roots of four species of the genus Psidium (P. guajava ‘Paluma’, P. guineense, P. cattleyanum, and P. friedrichstalianum) from the Active Germplasm Bank (BAG) of the Center for Agricultural Research of the Semi-arid Tropics (Embrapa Semi-arid) located in Petrolina, PE, Brazil. Three experiments were carried out to assess the penetration, development, and reproduction of the nematode and, subsequently, histological analyses were performed. Nematode penetrated indistinctly in both resistant and susceptible species, but only in ‘Paluma’ an evolution was observed from vermiform juveniles to “sausage” and globose forms in experiment 1. In experiment 2, all species presented “sausage” forms of nematode, but only in ‘Paluma’, these forms reached adult female stages. In experiment 3, only ‘Paluma’ was susceptible to M. enterolobii, with reproduction factor above unity. Histological analyses showed that, except for P. guajava, the other Psidium species presented poorly developed feeding sites at 20 days after inoculation.
The Slope of the IS Curve : New Evidence ∗

The degree of asset market participation has important implications for the relationship between the real interest rate and the aggregate demand. Our objective is to provide a quantitative estimate of the proportion of asset holders in the US and Euro Area starting from 1960s and 2000s respectively. In particular, we unveil the sub-periods where this number has fallen so dramatically that the IS curve becomes more vertical, and eventually inverted. We endogenize the identification of these periods through an estimation of a Markov-switching model displaying two (low and high) participation regimes. To account for the zero low bound on the policy rates, our model also features a shift in time preference shock which allows to distinguish between two steady states of the real interest rate, one of which being negative. We show that in the US the number of optimizing agents varied only during the Great Inflation and on the edge of the recent crisis. The proportion of non-participants has increased sufficiently to invert the IS curve. In the EA the asset market participation decreases significantly during a substantial period of the Great Recession, but just enough to provoke an increase in the slope of the IS curve without reverting it. Our result have important implications for the recent discussion on the effects of the unconventional monetary policy. JEL classification: E32, E44, E52, .
SealedGRID: Scalable, trustEd, and interoperAble pLatform for sEcureD smart GRID

Currently, much effort is being made on a European and global level, to push towards realizing a sustainable development of the Smart Grid, with the minimum vulnerability to external attacks or to malicious Smart Grid nodes. Utility companies globally invest in an efﬁcient, controlled and ﬂexible distribution of the energy to optimize the services they provide to the end customers. On the other hand, individual clients call for more efﬁcient Smart Grid solutions with guaranteed highly secure Demand Response services that could reduce their electricity bill without sacriﬁcing their privacy or their energy-consuming habits. In this paper, we present the SealedGRID that provides an innovative platform that abides by the existing standardization work and is directly utilized by the shareholders to provide new tools towards a scalable, highly trusted, and interoperable Smart Grid security platform.
Simple , efficient and effective keypoint tracking

We propose a simple, efficient and effective approach to multi-person body keypoint tracking in videos. Our proposed method builds upon the state-of-the-art single image pose estimation systems (e.g. Mask-RCNN), and adds a lightweight tracking module on top of the frame level predictions to generate keypoint predictions linked in time. We conduct extensive ablative experiments on the newly released multi-person video pose estimation benchmark, PoseTrack, to validate various design choices of our model. Our final model achieves an accuracy of 55% on the validation and 51.8% on the test set using the Multi-Object Tracking Accuracy (MOTA) metric, and won first position in the ICCV’17 PoseTrack keypoint tracking challenge1.
Ensemble of Diverse Mappings: Improving Reliability of Quantum Computers by Orchestrating Dissimilar Mistakes

Near-term quantum computers do not have the ability to perform error correction. Such Noisy Intermediate Scale Quantum (NISQ) computers can produce incorrect output as the computation is subjected to errors. The applications on a NISQ machine try to infer the correct output by running the same program thousands of times and logging the output. If the error rates are low and the errors are not correlated, then the correct answer can be inferred as the one appearing with the highest frequency. Unfortunately, quantum computers are subjected to correlated errors, which can cause an incorrect answer to appear more frequently than the correct answer. We observe that recent work on qubit mapping (including the recent work on variation-aware mapping) tries to obtain the best possible qubit allocation and uses it for all the trials. This approach significantly increases the vulnerability to correlated errors -- if the mapping becomes susceptible to a particular form of error, then all the trials will get subjected to the same error, which can cause the same wrong answer to appear as the output for a significant fraction of the trials. To mitigate the vulnerability to such correlated errors, this paper leverages the concept of diversity and proposes an Ensemble of Diverse Mappings (EDM). EDM uses diversity in qubit allocation to run copies of an input program with a diverse set of mappings, thus steering the trials towards making different mistakes. By combining the output probability distributions of the diverse ensemble, EDM amplifies the correct answer by suppressing the incorrect answers. Our experiments with ibmq-melbourne (14-qubit) machine shows that EDM improves the inference quality by 2.3x compared to the current state-of-the-art mapping algorithms.
Non-linear effects of the financial cycle on fiscal multipliers ∗

We use a Smooth Transition VAR to capture the non-linear features often displayed by the economy when reacting to a fiscal shock by letting our model fluctuate around a continuum of intermediate phases between two extreme states of the financial cycle. Using generalized impulse response analysis, we examine the non-linear effects of unanticipated government expenditure shocks on U.S. GDP, controlling for private credit and public debt. We also perform a scenario analysis to investigate shock responses in typical recessions and expansions. We find that (i) the results support the inclusion of a measure of fiscal burden in the model; (ii) once allowing for non-linearity the GDP response to fiscal shocks is asymmetric in sign and magnitude; (iii) there exists a cap effect with regard to how expansionary a fiscal policy can be and such cap appears non-linearly related to the public debt dynamic; (iv) the scenario analysis shows stronger multipliers in typical recessions. JEL Classifications: C32; E17; E32; E37; E51; E62; H20; H63
An Efficient and Slight Convolutional Neural Network for Vehicle Type Classification

With the popularity of small storage embedded devices, recent researches on deep convolutional neural networks no longer only focus on improving the classification accuracy but start to attach great importance to the size of convolutional neural networks. This paper proposes a small deep convolutional neural network for vehicle type classification, based on the Fire module and the residual module. The Fire module is applied to maintain a small number of parameters. The residual module is employed to accelerate the training process and improves the classification accuracy. Experimental results show that our model achieves 95.57% accuracy of vehicle type classification on an open source dataset BIT-Vehicle. This outperforms previous researches and some classical convolutional neural networks such as AlexNet, ResNet-32 and SqueezeNet. What is more, the size of our model is only 5.3 MB, which has 40X fewer parameters than AlexNet and has 15X fewer parameters than ResNet-32.
Distributed Synthetic Minority Oversampling Technique

Most real world prediction problems have imbalanced data. Here an imbalance in dataset is represented by a mismatch in class representation. An application of classification algorithms on imbalanced data is biased in favor of the majority class and gets further biased in case of high-dimensional data. This classimbalance problem can be reduced by under-sampling of majority data or by oversampling of minority data. Synthetic Minority Oversampling Technique (SMOTE) is one such popular technique proposed by Nitesh Chawala [1] that vastly improves random oversampling. Unfortunately, existing SMOTE [1] implementations are inept to handle big data, demanding need for distributed computing solution. In this paper we investigate challenges for implementing SMOTE in distributed environment and present algorithm and results of our distributed SMOTE [1] implementation. Our implementation is done in Apache Spark using variations of high and low dimensional data as well as large and small datasets. The results are compared to existing monolithic implementation in python SMOTE [1]. We were able to successfully demonstrate a distributed version of Spark SMOTE which generated higher or equal quality artificial samples in comparison to python implementation while preserving spatial distribution.
Quantitative Analysis of Parametric Roll and Operational Guidance

In this paper, it is shown that a large number of numerical simulations are needed to get a stable long-term statistics of parametric roll due to the non-ergodicity of this physical problem. To this end, a very fast and effective tool with an acceptable accuracy is proposed. This method is based on 1 DOF roll equation accounting for GM variation and approximated GZ curve and tested in both of regular and irregular waves. Using this tool, the vulnerability to parametric roll is quantified for a post-Panamax container ship and VLCC by means of long-term prediction in North-Atlantic seas using Monte Carlo Method. For the operational guidance, state-of-art tool based on IRF method is introduced, and it is shown that the application to operational guidance is quite successful for a very large container ship. Based on this study, the parametric roll characteristic is studied in the view of ship operation.
Self-Concept , Social Isolation , and Academic Achievement in College Students with and without Learning Disabilities

This study examined correlates with academic success for students, both with and without identified learning disabilities. Often students with learning disabilities experience failure in academic settings due to a variety of causes. The investigation was designed to determine possible reasons for failure in a group of students with learning disabilities. Research suggests there is a relationship between student achievement in elementary and secondary school and their self-concept and social isolation behavior. There has yet to be sufficient examination of the relationship between college success and self-concept and social isolation behavior.
Human Gait Feature Extraction based-on Silhouette and Center of Mass

When someone walks, there is a repetitive movement or coordinated cycle that forms a gait. Gait is different, unique and difficult to imitate. This characteristic makes gait one of the biometrics to find out one's identity. Gait analysis is needed in the development of biometric technology, such as in the field of security surveillance and the health sector to monitor gait abnormalities. The center of mass is the unique point of every object that has a role in the study of humans walking. Each person has a different center of mass. In this research, through a series of processes in image processing such as video acquisition, segmentation, silhouette formation, and feature extraction, the center of mass of the human body can be identified using a webcam with the resolution of 640 x 480 pixels and the frame rate of 30 frames/second. The results obtained from this research were gait frames of 510 frames from 17 pedestrian videos. Segmentation process using background subtraction separates the pedestrian object image from the background. Silhouette gait was produced from a series of image enhancement processes to eliminate noise that interferes the image quality. Based on the silhouette, feature extraction provides the center of mass to distinguish each individual's gait. The sequence of center of mass can be further processed for characterizing human gait cycle for various purposes.