Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental assessment of a satisfaction based thermal comfort control for a group of occupants

The set point of the Heating, Ventilating and Air Conditioning (HVAC) system is usually given by the Predicted Mean Vote (PMV) model. However, the individual differences in thermal preference often incur dissatisfactions in a group, which indicates that the PMV model may have bias in predicting the thermal comfort for a group of occupants. It has great potential to find a more suitable model to determine room set points. In this paper, a satisfaction based group comfort model is developed based on our previous work. We build an individual comfort model for each person according to their satisfaction and take the convex hull of these comfort zones. Some experiments are implemented for the group under both cooling and heating conditions in an ordinary office environment. The experimental results including indoor environment control, users' acceptance, working efficiency and energy consumption are supporting the presented group model.
Nders at the NTCIR-12 STC Task: Ranking Response Messages with Mixed Similarity for Short Text Conversation

Short Text Conversation (STC) is a typical scenario in man-machine conversation, which simplifies the conversation into one round interaction and makes the related tasks more practical. This paper presents a simple approach to the Chinese STC task issued by NTCIR-12. Given a repository of post-comment pairs, for any query, we define three types of similarity and merged them according to empirical weights. We consider the similarity between a query and a post/comment. To catch more logic relevance, we train a LDA model to map a query/comment into a topic space, and then calculate the similarity between them. The evaluation results show that our approach performs better than average. Considering its simplicity, our approach can be used in quickly deploying related STC systems.
Immune Checkpoint Blockade in Cancer Treatment: A Double-Edged Sword Cross-Targeting the Host as an “Innocent Bystander”

Targeted immune checkpoint blockade augments anti-tumor immunity and induces durable responses in patients with melanoma and other solid tumors. It also induces specific “immune-related adverse events” (irAEs). IrAEs mainly include gastrointestinal, dermatological, hepatic and endocrinological toxicities. Off-target effects that arise appear to account for much of the toxicity of the immune checkpoint blockade. These unique “innocent bystander” effects are likely a direct result of breaking immune tolerance upon immune check point blockade and require specific treatment guidelines that include symptomatic therapies or systemic corticosteroids. What do we need going forward to limit immune checkpoint blockade-induced toxicity? Most importantly, we need a better understanding of the roles played by these agents in normal tissues, so that we can begin to predict potentially problematic side effects on the basis of their selectivity profile. Second, we need to focus on the predictive factors of the response and toxicity of the host rather than serially focusing on individual agents. Third, rigorous biomarker-driven clinical trials are needed to further elucidate the mechanisms of both the benefit and toxicity. We will summarize the double-edged sword effect of immunotherapeutics in cancer treatment.
MapIterativeReduce: a framework for reduction-intensive data processing on azure clouds

With the emergence of cloud computing as an alternative to supercomputers to support data intensive applications, MapReduce has arisen as a major programming model for data analysis on clouds. In this context, reduce-intensive algorithms are becoming increasingly useful in applications such as data clustering, classification and mining. However, platforms like MapReduce or Dryad lack built-in support for reduce-intensive workloads. This paper introduces MapIterativeReduce, a framework which 1) extends the MapReduce programming model to better support reduce-intensive applications and 2) substantially improves their efficiency by eliminating the implicit barrier between the Map and the Reduce phase. We evaluated MapIterativeReduce on the Microsoft Azure cloud with synthetic benchmarks and with a real-life application. Compared to state-of-art solutions, our approach reduces the execution times by up to 75%.
kinks - gradient flow and dynamics

The symmetric dynamics of two kinks and one antikink in classical (1 + 1)-dimensional theory is investigated. Gradient flow is used to construct a collective coordinate model of the system. The relationship between the discrete vibrational mode of a single kink, and the process of kink - antikink pair production is explored.
A Novel Load Balancing Algorithm in IEEE 802.11 Wireless LANs with Cell Breathing

Many mechanisms have been studied aimed at resolving load imbalance among access points (APs), which is a key issue in Wireless LANs (WLANs); meanwhile, cell breathing (CB), first appearing in cellular networks, provides another method in controlling coverage scale to realize load balance. In this paper, we present a new load balancing algorithm through power management based on cell breathing. Through establishing a centralized operation agent (COA), the load status of all the APs is monitored and power management is implemented accordingly, thus it avoids repetitive operations and extra management message consumption between APs in the distributive manner. Simulation results show that our algorithm can find a near optimal solution and improve the fairness as well as the performance of the whole network effectively.
Computational fluid dynamics based iterative learning control for smart rotor enabled fatigue load reduction in wind turbines

Smart rotors integrated into the blades of large-scale turbines offer, when used in conjunction with collective and individual pitch control, the potential to significantly improve aerodynamic performance and load control. Of the four main ways to modify the lift on the blades, this work seeks to modify the blade section aerodynamics by damping perturbations in the lift using circulation control by integrating smart devices, such as microtabs or active vortex generators into the blades. This paper uses a computational fluid dynamics model with nonlinear wake effects to represent the flow past an airfoil as an approximate model of the dynamics for the design of iterative learning control algorithms for this problem area. Under a 2-norm measure a two orders of magnitude reduction over the case with no control is established.
Analysis and improvement of fairness between TCP Reno and Vegas for deployment of TCP Vegas to the Internet

According to past research, a TCP Vegas version is able to achieve higher throughput than TCP Tahoe and Reno versions, which are widely used in the current Internet. However we need to consider a migration path for TCP Vegas to be deployed in the Internet. In this paper, by focusing on the situation where TCP Reno and Vegas connections share the bottleneck link, we investigate the fairness between two versions. From the analysis and the simulation results, we find that the performance of TCP Vegas is much smaller than that of TCP Reno as opposed to an expectation on TCP Vegas. The RED algorithm improves the fairness to some degree, but there may still be an inevitable trade-off between fairness and throughput. Accordingly, we consider two approaches to improve the fairness. The first one is to modify the congestion control algorithm of TCP Vegas, and the other is to modify the RED algorithm to detect misbehaved connections and drop more packets from those connections. We use both of analysis and simulation experiment for evaluating the fairness, and validate the effectiveness of the proposed mechanisms.
A toolkit for detecting and analyzing malicious software

We present PEAT: the Portable Executable Analysis Toolkit. It is a software prototype designed to provide a selection of tools that an analyst may use in order to examine structural aspects of a Windows Portable Executable (PE) file, with the goal of determining whether malicious code has been inserted into an application after compilation. These tools rely on structural features of executables that are likely to indicate the presence of inserted malicious code. The underlying premise is that typical application programs are compiled into one binary, homogeneous from beginning to end with respect to certain structural features; any disruption of this homogeneity is a strong indicator that the binary has been tampered with. For example, it could now harbor a virus or a Trojan horse program. We present our investigation into structural feature analysis, the development of these ideas into the PEAT prototype, and results that illustrate PEAT's practical effectiveness.
Transforming architectural descriptions for formal analysis

Model transformation is the process of automatically generating a target model from a source model according to a set of transformation rules. Automatic model transformation has the potential to eliminate the complexity, the inconsistencies and faults that are inherent in a manual model transformation process. Many of the existing tools that automate the model transformation process require the encoding of transformation rules within the transformation process, which limits their reuse and usability in different contexts. This paper presents a tool that automates the model transformation of component-based systems specification. The tool takes an architectural specification as input and generates a behavior protocol as output. The transformation rules are described independently from the transformation process. This allows changing the transformation rules without affecting the transformation process. We discuss in detail the transformation rules for transforming a trustworthy component-based system, formally specified in an architecture description language (TADL), to an extended timed automata specification. The goal is to formally verify trustworthiness properties claimed in the source model by model checking the trustworthiness properties in the target model. By varying the target model and the set of transformation rules the same tool can be used to obtain different target models and use different verification techniques.
Effect of Plate Thickness on the In-Plane and Through-Thickness Stresses at a Hole.

Abstract : A combined experimental and numerical study has been carried out to examine the influence of thickness on the surface and through-thickness stress distributions of aluminum alloy specimens with central holes. Thermographic and strain-gauge measurements were made on several thicknesses of specimens, the central holes of which were either reamed or cold-expanded by 3.6%. The experimental results were compared with those from numerical analyses of the specimens by three-dimensional finite-element modelling.
Effect of DNA Methylation in Various Diseases and the Probable Protective Role of Nutrition: A Mini-Review

DNA methylation, a process of adding a methyl group to DNA done by a DNA methyltransferase is a heritable (epigenetic) alteration leading to cancer, atherosclerosis, nervous disorders (Imprinting disorders), and cardiovascular diseases. The role of nutrition in DNA methylation is revealed by identification of methyl variable positions (MVP) on DNA. These regions are more susceptible to DNA methylations. Nutritional supplementation of folic acid and methionine in utero and in adults decreased epigenetic modifications due to its role in DNA metabolism (one carbon metabolism). Thus, in utero and adult supplementation of folic acid and methionine may reduce DNA methylation. This review attempts to highlight the process of DNA methylation, its effect on various diseases, and the probable protective role of nutrition.
Aversive Ableism: Modern Prejudice Towards Disabled People

The aim of this study was to examine the patterns of explicit (conscious) and implicit (unconscious) disability prejudice. The majority of participants were implicitly prejudiced against disabled people despite having low explicit prejudice. This pattern is in alignment with aversive ableism – disability prejudice was present among those who meant well. Aversive Ableism: Modern Prejudice Towards Disabled People by Carli Friedman is licensed under a Creative Commons Attribution 4.0 International License. Based on a work at http://rdsjournal.org/index.php/journal/article/view/811. Permissions beyond the scope of this license may be available at https://www.rds. hawaii.edu.
E-Playground: Simultaneous Identification of Multi-players in Educational Physical Games Using Low-cost RFID

We propose a design for an affordable interactive floor, and an accompanied software architecture for developing multi-player educational games. The floor uses passive RFID technology for robust tracking of the position and the identity of the students during play. The tracking results could be used to monitor and analyse the performances of individual students. A prototype was implemented using off-the-shelf electronics and thus it could be adopted in schools or community clubs with declined budget. To evaluate the latency and the scalability of the design, an analytical model and its empirical validation are presented.
Signal Modeling and Classification Using a Robust Latent Space Model Based on $t$ Distributions

Factor analysis is a statistical covariance modeling technique based on the assumption of normally distributed data. A mixture of factor analyzers can be hence viewed as a special case of Gaussian (normal) mixture models providing a mathematically sound framework for attribute space dimensionality reduction. A significant shortcoming of mixtures of factor analyzers is the vulnerability of normal distributions to outliers. Recently, the replacement of normal distributions with the heavier-tailed Student's-t distributions has been proposed as a way to mitigate these shortcomings and the treatment of the resulting model under an expectation-maximization (EM) algorithm framework has been conducted. In this paper, we develop a Bayesian approach to factor analysis modeling based on Student's-t distributions. We derive a tractable variational inference algorithm for this model by expressing the Student's-t distributed factor analyzers as a marginalization over additional latent variables. Our innovative approach provides an efficient and more robust alternative to EM-based methods, resolving their singularity and overfitting proneness problems, while allowing for the automatic determination of the optimal model size. We demonstrate the superiority of the proposed model over well-known covariance modeling techniques in a wide range of signal processing applications.
EVERYONE IS DIFFERENT ! EXPLORING THE ISSUES AND PROBLEMS WITH ERP ENABLED SHARED SERVICE INITIATIVES

In today’s increasingly competitive environment, there is constant pressure for corporate leaders to add value to their organizations. These contemporary organizations are increasingly moving into business models that attempt to reduce duplicate supporting processes and staff by streamlining business processes that are not central to the organization’s operations and concentrating on strategies on strategic or core, business processes. This concept, known as Shared Services, attempts to bundle some of the supporting processes and non-strategic activities into a separate organization, which in turn treats those processes and activities as the core of its own business. Shared Services consolidate and support redundant functions, such as accounts payable and procurement, for disparate business units. By leveraging economies of scale from a common IT infrastructure, such a group is able to market specific services to business units. Many organizations are employing Enterprise Resource Planning (ERP) systems, for example SAP, to facilitate Shared Service initiatives by aggregating backroom functionality across departments. This research-in-progress paper investigates issues and problems with ERP enabled Shared Services in 19 organizations. The results reveal five main issues that organizations face in implementing a Shared Services initiative.
Management of Spectral Resources in Elastic Optical Networks

Recent developments in the area of mobile technologies, data center networks, cloud computing and social networks have triggered the growth of a wide range of network applications. The data rate of these applications also vary from a few megabits per second (Mbps) to several Gigabits per second (Gbps), thereby increasing the burden on the Internet. To support this growth in Internet data traffic, one foremost solution is to utilize the advancements in optical networks. With technology such as wavelength division multiplexing (WDM) networks, bandwidth upto 100 Gbps can be exploited from the optical fiber in an energy efficient manner. However, WDM networks are not efficient when the traffic demands vary frequently. Elastic Optical Networks (EONs) or Spectrum Sliced Elastic Optical Path Networks (SLICE) or Flex-Grid has been recently proposed as a long-term solution to handle the ever-increasing data traffic and the diverse demand range. EONs provide abundant bandwidth by managing the spectrum resources as fine-granular orthogonal sub-carriers that makes it suitable to accommodate varying traffic demands. However, the Routing and Spectrum Allocation (RSA) algorithm in EONs has to follow additional constraints while allocating sub-carriers to demands. These constraints increase the complexity of RSA in EONs and also, make EONs prone to the fragmentation of spectral resources, thereby decreasing the spectral efficiency. The major objective of this dissertation is to study the problem of spectrum allocation in EONs under various network conditions. With this objective, this dissertation presents the author’s study and research on multiple aspects of spectrum allocation in EONs: how to allocate sub-carriers to the traffic demands, how to accommodate traffic demands that varies with time, how to minimize the fragmentation of spectral resources and how to efficiently integrate the predictability of user demands for spectrum assignment. Another important contribution of this dissertation is the application of EONs as one of the substrate technologies for network virtualization. INDEX WORDS: Elastic Optical Networks, Flex-Grid networks, Spectrum-sliced elastic optical networks, Routing and Spectrum Allocation, Spectrum fragmentation, Network virtualization, Virtual network reconfiguration TITLE: MANAGEMENT OF SPECTRAL RESOURCES IN ELASTIC OPTICAL NETWORKS
Cheap Cancellation of Strong Echoes for Digital Passive and Noise Radars

The problem of cancellation of strong, potentially nonstationary, echoes in noise radars and passive radars utilizing digital transmissions is considered. The proposed solution is a multi-stage procedure. Initial clutter estimates, obtained using the least mean squares (LMS) algorithm, are refined using specially designed filters, “matched” to spectral densities of targets and clutter. When the postprocessing filters are noncausal, the performance of the proposed canceler is improved compared to the solution based on causal filters.
Mapping Annual Forest Cover in Sub-Humid and Semi-Arid Regions through Analysis of Landsat and PALSAR Imagery

Accurately mapping the spatial distribution of forests in sub-humid to semi-arid regions over time is important for forest management but a challenging task. Relatively large uncertainties still exist in the spatial distribution of forests and forest changes in the sub-humid and semi-arid regions. Numerous publications have used either optical or synthetic aperture radar (SAR) remote sensing imagery, but the resultant forest cover maps often have large errors. In this study, we propose a pixel- and rule-based algorithm to identify and map annual forests from 2007 to 2010 in Oklahoma, USA, a transitional region with various climates and landscapes, using the integration of the L-band Advanced Land Observation Satellite (ALOS) PALSAR Fine Beam Dual Polarization (FBD) mosaic dataset and Landsat images. The overall accuracy and Kappa coefficient of the PALSAR/Landsat forest map were about 88.2% and 0.75 in 2010, with the user and producer accuracy about 93.4% and 75.7%, based on the 3270 random ground plots collected in 2012 and 2013. Compared with the forest products from Japan Aerospace Exploration Agency (JAXA), National Land Cover Database (NLCD), Oklahoma Ecological Systems Map (OKESM) and Oklahoma Forest Resource Assessment (OKFRA), the PALSAR/Landsat forest map showed great improvement. The area of the PALSAR/Landsat forest was about 40,149 km2 in 2010, which was close to the area from OKFRA (40,468 km2), but much larger than those from JAXA (32,403 km2) and NLCD (37,628 km2). We analyzed annual forest cover dynamics, and the results show extensive forest cover loss (2761 km2, 6.9% of the total forest area in 2010) and gain (3630 km2, 9.0%) in southeast and central Oklahoma, and the total area of forests increased by 684 km2 from 2007 to 2010. This study clearly demonstrates the potential of data fusion between PALSAR and Landsat images for mapping annual forest cover dynamics in sub-humid to semi-arid regions, and the resultant forest maps would be helpful to forest management.
Concept Pre-digestion Method for Image Relevance Reinforcement Learning

Relevance feedback (RF) is commonly used to improve the performance of CBIR system by allowing incorporation of user feedback iteratively. Recently, a method called image relevance reinforcement learning (IRRL) has been proposed for integrating several existing RF techniques as well as for exploiting RF sessions of multiple users. The precision obtained at the end of every iteration is used was a reward signal in the Q-learning based reinforcement learning (RL) approach. The objective of learning in IRRL is to estimate the optimal RF technique to be applied for a given query at a specific iteration. The main drawback of IRRL is its prohibitive learning time and storage requirement. We propose a way of addressing these difficulties by performing `pre-digestion' of concepts before applying IRRL. Experimental results on two databases of images demonstrated the viability of the proposed approach
A path planning algorithm based on genetic and ant colony dynamic integration

Genetic algorithm has strong global search ability and robustness, but can not use the feedback of system and easy to do a lot of unnecessary redundancy iteration in the latter, resulting in the decline of convergence rate. Ant colony algorithm has the good characteristic of feedback, but due to lacking of initial pheromone, solving speed is slow. A method of adaptive dynamic integration is presented in the paper based on two algorithms, it uses genetic algorithm to generate initial pheromone distribution, and uses ant colony algorithm for dynamic integration of genetic operators in the latter. It not only improves the convergence rate of solutions, but also solves the problem of precocious and poor global search ability caused by the excessive convergence of ant colony algorithm in the late.
Self-Calibration of Optical Lenses

Even high-quality lenses suffer from optical aberrations, especially when used at full aperture. Furthermore, there are significant lens-to-lens deviations due to manufacturing tolerances, often rendering current software solutions like DxO, Lightroom, and PTLens insufficient as they don't adapt and only include generic lens blur models. We propose a method that enables the self-calibration of lenses from a natural image, or a set of such images. To this end we develop a machine learning framework that is able to exploit several recorded images and distills the available information into an accurate model of the considered lens.