Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Motion Estimator Based on Variable Block Size Scheme

This paper presents an adaptive motion estimator that can be dynamically reconfigured by the best algorithm depending on the variation of the video nature during the lifetime of an application under running. The 4 Step Search (4SS) and the Gradient Search (GS) algorithms are integrated in the estimator in order to be used in the case of rapid and slow video sequences respectively. The Full Search Block Matching (FSBM) algorithm has been also integrated in order to be used in the case of the video sequences which are not real time oriented. In order to efficiently reduce the computational cost while achieving better visual quality with low cost power, the proposed motion estimator is based on a Variable Block Size (VBS) scheme that uses only the 16x16, 16x8, 8x16 and 8x8 modes. Experimental results show that the adaptive motion estimator allows better results in term of Peak Signal to Noise Ratio (PSNR), computational cost, FPGA occupied area, and dissipated power relatively to the most popular variable block size schemes presented in the literature. Keywords—H264, Configurable Motion Estimator, Variable Block Size, PSNR, Dissipated power.
Occupational Similarity and Spousal Support: A Study of the Importance of Gender and Spouse's Occupation

This paper examines how gender and the occupation of one's spouse may explain differences in the amounts and types of spousal support individuals receive when coping with the stress of their job. We analyze survey data from a sample of married lawyers, some of whom are married to other lawyers and others who have spouses who are not lawyers. The results show that men receive more emotional support from their spouse than women, regardless of their spouse's occupation. In contrast, lawyers receive more informational support from their spouse if they are also a lawyer, regardless of their gender. These fi ndings suggest that wives provide more understanding and empathy to their spouses than husbands, consistent with the literature on gender diff erences in social support. Our fi ndings also suggest that when it comes to providing informational support in terms of sharing advice, suggestions, solutions or relevant experiences in solving a work-related problem, a spouse who is in the same occupation may be better able to provide support. This is consistent with the literature demonstrating the importance of shared experiences in understanding the eff ectives of social support. Future research might explore not only the importance of shared statuses, such as occupation, but also the meaning of shared experiences in order to better understand spouses' support of one another.
Vistas: Hierarchial Boundary priors using Multiscale Conditional Random Fields

Boundary detection is a fundamental problem in computer vision. However, boundary detection is difficult as it involves integrating multiple cues (intensity, color, texture) as well as trying to incorporate object class or scene level descriptions to mitigate the ambiguity of the local signal. In this paper we investigate incorporating a priori information into boundary detection. We learn a probabilistic model that describes a prior for object boundaries over small patches of the image. We then incorporate this boundary model into a mixture of multiscale conditional random fields, where the mixture components represent different contexts formed by clustering overall spatial distributions of boundaries across images and image regions (vistas). We demonstrate this approach using challenging real-world road scenes. Importantly, we show that recent spectral methods that have been used in state-of-the-art boundary detection algorithms do not generalize well to these complex scenes. We show that our algorithm successfully learns these boundary distributions and can exploit this knowledge to improve state-of-the-art boundary detectors.
Model-based Development of Embedded Systems : Executable Models vs . Code Generation

The use of models during the development of embedded systems is nowadays fairly limited. During the evolution of the system, the implementation and the design models often tend to get out of sync. The use of Model Driven Architecture in the development of embedded systems makes this problem more imminent, as it is much more demanding by introducing two separate modeling levels. Thus, there is a need to introduce techniques that overcome this problem. Complete code generation and the use of executable models potentially eliminate the need for a manual implementation that may get out of sync with the design models. We discuss important properties of such approaches and outline the advantages and possibilities of using a UML virtual machine in embedded systems.
Crystal structure of HLA-DQ0602 that protects against type 1 diabetes and confers strong susceptibility to narcolepsy

The MHC class II molecule DQ0602 confers strong susceptibility to narcolepsy but dominant protection against type 1 diabetes. The crystal structure of DQ0602 reveals the molecular features underlying these contrasting genetic properties. Structural comparisons to homologous DQ molecules with differential disease associations highlight a previously unrecognized interplay between the volume of the P6 pocket and the specificity of the P9 pocket, which implies that presentation of an expanded peptide repertoire is critical for dominant protection against type 1 diabetes. In narcolepsy, the volume of the P4 pocket appears central to the susceptibility, suggesting that the presentation of a specific peptide population plays a major role.
The Internal Architecture of Leukocyte Lipid Body Organelles Captured by Three-Dimensional Electron Microscopy Tomography

Lipid bodies (LBs), also known as lipid droplets, are complex organelles of all eukaryotic cells linked to a variety of biological functions as well as to the development of human diseases. In cells from the immune system, such as eosinophils, neutrophils and macrophages, LBs are rapidly formed in the cytoplasm in response to inflammatory and infectious diseases and are sites of synthesis of eicosanoid lipid mediators. However, little is known about the structural organization of these organelles. It is unclear whether leukocyte LBs contain a hydrophobic core of neutral lipids as found in lipid droplets from adipocytes and how diverse proteins, including enzymes involved in eicosanoid formation, incorporate into LBs. Here, leukocyte LB ultrastructure was studied in detail by conventional transmission electron microscopy (TEM), immunogold EM and electron tomography. By careful analysis of the two-dimensional ultrastructure of LBs from human blood eosinophils under different conditions, we identified membranous structures within LBs in both resting and activated cells. Cyclooxygenase, a membrane inserted protein that catalyzes the first step in prostaglandin synthesis, was localized throughout the internum of LBs. We used fully automated dual-axis electron tomography to study the three-dimensional architecture of LBs in high resolution. By tracking 4 nm-thick serial digital sections we found that leukocyte LBs enclose an intricate system of membranes within their “cores”. After computational reconstruction, we showed that these membranes are organized as a network of tubules which resemble the endoplasmic reticulum (ER). Our findings explain how membrane-bound proteins interact and are spatially arranged within LB “cores” and support a model for LB formation by incorporating cytoplasmic membranes of the ER, instead of the conventional view that LBs emerge from the ER leaflets. This is important to understand the functional capabilities of leukocyte LBs in health and during diverse diseases in which these organelles are functionally involved.
The KIT object models database: An object model database for object recognition, localization and manipulation in service robotics

For the execution of object recognition, localization and manipulation tasks, most algorithms use object models. Most models are derived from, or consist of two-dimensional (2D) images and/or three-dimensional (3D) geometric data. The system described in this article was constructed specifically for the generation of such model data. It allows 2D image and 3D geometric data of everyday objects be obtained semi-automatically. The calibration provided allows 2D data to be related to 3D data. Through the use of high-quality sensors, high-accuracy data is achieved. So far over 100 objects have been digitized using this system and the data has been successfully used in several international research projects. All of the models are freely available on the web via a front-end that allows preview and filtering of the data.
Securing software by enforcing data-flow integrity

Software attacks often subvert the intended data-flow in a vulnerable program. For example, attackers exploit buffer overflows and format string vulnerabilities to write data to unintended locations. We present a simple technique that prevents these attacks by enforcing data-flow integrity. It computes a data-flow graph using static analysis, and it instruments the program to ensure that the flow of data at runtime is allowed by the data-flow graph. We describe an efficient implementation of data-flow integrity enforcement that uses static analysis to reduce instrumentation overhead. This implementation can be used in practice to detect a broad class of attacks and errors because it can be applied automatically to C and C++ programs without modifications, it does not have false positives, and it has low overhead.
Predictive value of platelet-to-lymphocyte ratio in severe degenerative aortic valve stenosis

Background: Aortic valve stenosis (AVS) is the most common cause of left ventricular outflow obstruction, and its prevalence among elderly patients causes a major public health burden. Recently, platelet-to-lymphocyte ratio (PLR) has been recognized as a novel prognostic biomarker that offers information about both aggregation and inflammation pathways. Since PLR indicates inflammation, we hypothesized that PLR may be associated with the severity of AVS due to chronic inflammation pathways that cause stiffness and calcification of the aortic valve. Materials and Methods: We retrospectively enrolled 117 patients with severe degenerative AVS, who underwent aortic valve replacement and 117 control patients in our clinic. PLR was defined as the absolute platelet count divided by the absolute lymphocyte count. Severe AVS was defined as calcification and sclerosis of the valve with a mean pressure gradient of >40 mmHg. Results: PLR was 197.03 ± 49.61 in the AVS group and 144.9 ± 40.35 in the control group, which indicated a statistically significant difference (P < 0.001). A receiver operating characteristic (ROC) curve analysis demonstrated that PLR values over 188 predicted the severity of aortic stenosis with a sensitivity of 87% and a specificity of 70% (95% confidence interval = 0.734–0.882; P < 0.001; area under ROC curve: 0.808). Conclusion: We suggest that the level of PLR elevation is related to the severity of degenerative AVS, and PLR should be used to monitor patients' inflammatory responses and the efficacy of treatment, which will lead us to more closely monitor this high-risk population to detect severe degenerative AVS at an early stage.
A Corporate Scandal that Hits Close to Home: Examining Owners’ Responses to the Volkswagen Diesel Emissions Scandal

ABSTRACT The 2015 Volkswagen Group (VW) diesel emissions scandal has affected no group of individuals more directly than owners of the affected vehicles. Yet to date no research has examined in depth how owners have responded to the scandal. This includes not only what owners have already done or plan to do with their vehicles (e.g. repair, participate in the buyback programme) but also their interpersonal communicative actions (e.g. talking with other owners) and attitudinal reactions (e.g. perceptions of the VW-EPA settlement). We conducted two surveys—one prior to and one after a settlement was reached between VW and US regulators—to examine owners’ behavioural and attitudinal responses. Here we present descriptive, topline results from these surveys, which reveal relatively strong but heterogeneous owner engagement with the scandal. Owners report significant ambivalence about their own and VW’s mitigative options moving forward. Given the role owners will play in mitigating the environmental and health impacts of the scandal, understanding their responses is of critical importance.
Binary Particle Swarm Optimization – A Forma Analysis Approach

PSO was originally designed as a numerical optimization technique based on swarm intelligence [1]. This paper aims to generalize PSO in a rigorous manner with forma analysis [2]. Thus, binary PSO operators can be formally derived in a principled manner. Forma analysis is a formal but practical method that allows the problem representation and its operators to be structured in a formal manner by using equivalence relations. Since equivalence relations/classes have the ability to capture the properties of solutions, concrete operators can thus be mathematically derived by manipulating these equivalence relations in a formal way. Our approach (as illustrated in Figure 1) can be explained as, given an operator template, any suitable description of the considered problem domain gives rise to a concrete operator, whose behaviors and performance are related to the assumption we make to describe the search space.
A categorical model of model merging and weaving

Model driven engineering advocates the separation of concerns during the design time of a system, which leads to the creation of several different models, using several different syntaxes. However, to reason on the overall system, we need to compose these models. Unfortunately, composition of models is done in an ad hoc way, preventing comparison, capitalisation and reuse of the composition operators. In order to improve comprehension and allow comparison of merging and weaving operators, we use category theory to propose a unified framework to formally define merging and weaving of models. We successfully use this framework to compare them, both through the way they are transformed in the formalism, and through several properties, such as completeness or non-redundancy. Finally, we validate this framework by checking that it correctly identifies three tools as performing merging or weaving of models.
Dialogue Strategy Optimization with Reinforcement Learning in an AT & T Call Routing Application

Reinforcement Learning (RL) algorithms are particularly well suited to some of the challenges of spoken dialogue systems (SDS) design. RL provides an approach for automated learning that can adapt to new environments without supervision. SDS are constantly subjected to new environments in the form of new groups of users, and developer intervention is costly. In this paper, I will describe some results from experiments that used RL to select the optimal prompts to maximize the success rate in a call routing application. A simulation of the dialogue outcomes were used to experiment with different scenarios and demonstrate how RL can make these systems more robust.
Region dependent linear transforms in multilingual speech recognition

In today's speech recognition systems, linear or nonlinear transformations are usually applied to post-process speech features forming input to HMM based acoustic models. In this work, we experiment with three popular transforms: HLDA, MPE-HLDA and Region Dependent Linear Transforms (RDLT), which are trained jointly with the acoustic model to extract maximum of the discriminative information from the raw features and to represent it in a form suitable for the following GMM-HMM based acoustic model. We focus on multi-lingual environments, where limited resources are available for training recognizers of many languages. Using data from GlobalPhone database, we show that, under such restrictive conditions, the feature transformations can be advantageously shared across languages and robustly trained using data from several languages.
Team Edit Automata for Testing Security Property

This paper introduces a mathematical model, called team edit automata, for evaluating software security properties. We use the model to describe security properties and their correlation in the software programs. The component automata can suppress and insert actions and report possible flaws. They are used to specify individual security properties. The team is composed of multiple component automata interacting through shared actions. It models the situation where some program events are concerned by multiple security properties jointly. The paper concludes by a case study of detecting memory management and pointer manipulation flaws in C/C++ programs.
An accurate and efficient performance analysis approach based on queuing model for network on chip

An accurate and highly-efficient performance analysis approach is extremely important for the early-stage designs of network-on-chip. In this paper, the novel M/G/1/N queuing models for generic routers are proposed to analyze various packet blockings and then the performance analysis algorithm is presented to estimate some key metrics in terms of packet latency, buffer utilization, etc. For single-channel and multi-channel routers, the comparisons between analysis and observed results validate that the proposed approach with mean errors of 6.9% and 7.8% achieve the speed-ups of 240 and 210 times respectively. In our design methodology, this approach can not only effectively direct NoC synthesis process but also be conveniently applied to multi-objective optimizations to find the best mapping solutions. Categories and Subject Descriptors: B.4. 3 [Hardware]: Input/Output and Data Communication-Interconnections. General Terms: Algorithms, Performance.
On the generation of rich content metadata from social media

This contribution proposes a framework to generate auxiliary rich TV content metadata by processing social networks data. Based on simple criteria to identify authoritative social media sources, we have analysed Twitter short messages relative to TV program content and devised a method to compute their informative value. We have extracted dozen of features and characterized such social data in terms of quality and relevancy. This is a first step towards integrating relevant social media information to enhance the description of TV content as well as for generating recommendations based on social data.
Cut the Tail : Mobile Energy Saving Using Radio Tail Prediction

There are growing number of people using mobile computing devices such as Smart phones and tablets, to run different types of network applications. The cellular radio interface for communication in those network applications can cause significant energy drains. This is mainly because of the RaidoTails when the cellular radio remains in high energy state after communication has ended. To solve this problem, many existing works have discussed to employ Fast Dormancy, which is a feature that can force client radio to quickly go into a low energy state. The key problem in this line of research is to predict the proper time to invoke Fast Dormancy. A proper time means that when we invoke Fast Dormancy we would save as much idle time in high energy state as possible for radio cellular and also be able to avoid high overhead caused by frequent state transitions. There are some existing works studying this problem and use data mining algorithms such as decision tree to predict the right time of End of Session(EOS) event in packet transmission. However, they only focus on applications running in background and cannot learn a general model for different types of network applications with all possible user actions. Thus our aim is to design a simple and general algorithm that can predict the proper time for Fast Dormancy in any applications with all possible user actions. We propose our effective and efficient algorithm to cut the RadioTail in this paper. We first analyze the characteristics of existing work. Then we propose a framework to reduce the dimensionality of the features used in prediction. After that, we apply the reduced features in different prediction algorithms. Lastly, we evaluate our proposed method on a data set tracked from Android OS in 18 days. Results show that our proposed algorithm can achieve 84% accuracy, which is more than 10% higher than the baseline.
A framework for medical text mining using a feature weighted clustering algorithm

Text categorization is the task of deciding whether a document belongs to a set of pre specified classes of documents. Categorization of documents is challenging, as the number of discriminating words can be huge. Many existing text classification algorithms simply do not work with these many number of words. Traditional text classification algorithm uses all training samples for classification, thereby increasing the storage requirements and calculation complexity as the number of features increase. Mining medical records for relationships between living factors and the symptoms of a disease is an important task, however there has been relatively little research into this area. The proposed work evolves a text classification algorithm where al l cluster centers are taken as training samples there by reducing the sample size and introduces a weight factor to indicate the different importance of each training sample. A similarity measure function is used to classify a new patient document, based on the measure. Experiments on real life data show that the proposed algorithm outperforms the state of art classification algorithms such as k-nearest neighbor.
A test of construct validity of the work meaning inventory: Based on employees

The purpose of this study was to test the eight factors model and a high order factor structure of the Work Meaning Inventory(WMI) developed by Tak et al.(2015) and with a sample of employees. Data were obtained from 1,000 employees using online survey. Results of exploratory factor analyses confirmed the original 8 factors of the WMI, confirming the construct validity of the WMI. Also results of high-order factor analyses showed that the three higher order factors(make a living, growth opportunity, self-actualization) were obtained. Finally, the implications, limitations, and future studies are discussed.