Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic Multi-Criteria Decision Making of Energy Recovery Ventilation Systems using Cumulative Prospect Theory

Recently, an Energy Recovery Ventilator (ERV) in a residential building was seen as an attractive ventilation option in terms of energy saving and indoor air quality. In order to identify a feasible set among many ventilation strategies in this situation, various decision-making approaches (deterministic or stochastic) using Building Performance Simulation (BPS) tools have been suggested. As a simulation-based decision-making approach, a Stochastic Multi-Criteria Decision Making (SMCDM) method based on Cumulative Prospect Theory (CPT) is presented in this paper to find the best ventilation strategy under model uncertainties. For this study, two ventilation strategies, considering air inlet positions and CO2 sensor positions, were chosen and modelled using two simulation tools: CONTAMW 3.1 for the airflow model and EnergyPlus for the thermal model. In addition, Latin Hypercube Sampling (LHS) was used to reflect the model uncertainties. In this study, it is shown that CPT can provide a more realistic and trustworthy framework than the Bayesian decision theory.
A review on the prevalent fabrication methods, microstructural, mechanical properties, and corrosion resistance of nanostructured hydroxyapatite containing bilayer and multilayer coatings used in biomedical applications

Surface treatments of the biomaterials are of great interest in many biomedical applications. Hydroxyapatite is a favorable candidate for surface modification of the implants. To date, a wide variety of methods have been developed to produce bio-active/biocompatible coatings with desirable features in order to improve the performance of the implants. This paper strives to overview the present prevalent methods for synthesizing the hydroxyapatite based multilayer coatings as well as the various properties of such coatings. The common methods for fabrication of HAp-containing multilayer coatings including electrochemical, sol-gel, and plasma spray routs. It was clearly highlighted that the main drawback in pure HAp coatings is their poor adhesion to the implants. The studies in the field of HAp-ceramic systems showed that the deposited ceramic layers, e.g. TiO2 and ZrO2, are strongly adherent to HAp and substrate which leads to a remarkable improvement in the mechanical properties of pure HAp coatings. Unlike HAp-ceramic systems, there are less studies dealt with the HAp-polymeric systems. The performed studies demonstrated that the corrosion resistance and adhesion strength of the coatings to the substrates can be considerably improved with the deposition of HAp/polymer multilayer coatings. Altogether, HAp based multilayer coatings have bright prospect since they benefit from HAp biocompatibility as well as the enhanced adhesion to the substrate.
Development of Experimental Simulation Coursewares of Principles of Computer Organization

Principles of Computer Organization is a professional basic course that is strongly practical. In order to promote the experiment teaching result, programs the experimental simulation coursewares, which regards C ++ as the programming language and uses C++ Builder 6.0 as a development tool. Expounds the developing procedures of simulation courseware of the semiconductor memory principle experiment. The courseware can not only be used for both the teachers teaching, but also for covering the disadvantages of traditional experiment, for the students' experimental simulation before and after class.
Recommendation System for Sightseeing Tours

The tourism sector has been launched a countinuous improvement mission to its stakeholders. Inside of a bunch of knowned challenges to explore, there is the “time” factor, that is directly related to the tourists who intend to participate in a touristic itinerary through a specific city, according to their preferences at that time. The study proposes to demonstrate a model of a recommendation system, able to present suggested tips of what to visit in a city, based on the available time, personal preferences, current geolocalization and the user’s context awareness. These suggestions are calculated based on the treatment of collected data in real time by externals Application Programming Interfaces, through a list of Points of Interest located within a radius that can be reached by the user. With the results obtained, it is possible to prove and validate the value of the model and its capacity to recommend a route to whom is looking for this kind of touristic offer and have a good time knowing a city or place according to his needs in a specific moment. Author
Reduced-Precision Memory Value Approximation for Deep Learning

Neural networks (NNs) and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. As the complexity of the NNs increases over the years, the performance of them are highly limited by the hardware resources. We identified that memory capacity and memory bandwidth are bottlenecks for highly parallel and high-performance deep NN implementations. In this work, we propose reduced-precision memory value approximation to reduce the required memory bandwidth. Our design highlights an approximator in the memory controller for reduced-precision access to the weight parameters. Our results show that reduced precision to 8-bit representation for a random 20% of the whole set of weights causes only 1% of accuracy loss in the object classification tasks on ImageNet. Another important observation is that the errorresilience of weight parameters varies from layer to layer, with fully connected layers being more prone to the reduced precision approximation. Our proposal is expected to achieve significant memory bandwidth savings with less than 1% loss of accuracy. External Posting Date: December 14, 2015 [Fulltext] Internal Posting Date: December 14, 2015 [Fulltext]  Copyright 2015 Hewlett Packard Enterprise Development LP Reduced-Precision Memory Value Approximation for Deep Learning Zhaoxia Deng, Cong Xu, Qiong Cai, and Paolo Faraboschi University of California, Santa Barbara
Aging and Declining Trends in the Real Interest Rate and Inflation : Japanese Experience ∗

This paper explores a causal link between aging of the labor force and declining trends in the real interest rate and inflation in Japan. We develop a new Keynesian search/matching model that features heterogeneities in age and firm-specific skill levels. Using the model, we examine the long-run implications of the sharp drop in labor force entry in the 1970s. We show that the changes in the demographic structure driven by the drop induce significant low-frequency movements in per-capita consumption growth and the real interest rate. It also leads to similar movements in the inflation rate when the monetary policy rule follows the standard Taylor rule, failing to recognize the timevarying nature of the natural rate of interest. The model suggests that aging of the labor force accounts for roughly 40% of the declines in the real interest rate observed between 1980s and 2000s in Japan. JEL Classification: E24, E31, E52
Wavelet pLeader Non-Gaussian Multiscale Expansions for EEG series : an Exploratory Study on Cold-Pressor Test

Brain dynamics recorded through electroencephalography (EEG) have been proven to be the output of a nonstationary and nonlinear system. Thus, multifractality of EEG series has been exploited as a useful tool for a neurophysiological characterization in health and disease. However, the role of EEG multifractality under peripheral stress is unknown. In this study, we propose to make use of a novel tool, the recently defined non-Gaussian multiscale analysis, to investigate brain dynamics in the range of 4-8Hz following a cold-pressor test versus a resting state. The method builds on the wavelet p-leader multifractal spectrum to quantify different types of departure from Gaussian and linear properties, and is compared here to standard linear descriptive indices. Results suggest that the proposed non-Gaussian multiscale indices were able to detect expected changes over the somatosensory and premotor cortices, over regions different from those detected by linear analyses. They further indicate that preferred responses for the contralateral somatosensory cortex occur at scales 2.5s and 5s. These findings contribute to the characterization of the so-called central autonomic network, linking dynamical changes at a peripheral and a central nervous system levels.
Ontology construction for web services

Ontologies have become the de-facto modeling tool of choice, employed in a variety of applications and prominently in the Semantic Web. Nevertheless, ontology construction remains a daunting task. Ontological bootstrapping, which aims at automatically generating concepts and their relations in a given domain, is a promising technique for ontology construction. Bootstrapping an ontology based on a set of predefined textual sources, such as Web services, must address the problem of multiple concepts that are largely unrelated. This paper exploits the advantage that Web services usually consist of both WSDL and free text descriptors. The WSDL descriptor is evaluated using two methods, namely Term Frequency/Inverse Document Frequency (TF/IDF) and Web context generation. We propose an ontology bootstrapping process that integrates the results of both methods and validates the concepts using the free text descriptors, thereby offering a more accurate definition of ontologies.
REACTION-RESTRICTION FRAGMENT LENGTH POLYMORPHISM TEST FOR THE OTHER PUBLICATIONS AUTHENTICATION OF HERBAL CHINESE MEDICINES David M. Hillis and Michael T. Dixon “Ribosomal DNA:

Molecular Evolution And Phylogenetic Inference' The 75) I torS: Wang: Pang Chi Shaw, both of (75) Inventors SARS E. E. Quarterly Review of Biology. (1991) vol. 66 No. 4:410–449. Ngor Karenda Ngan, Kowloon, all of Wen-Sheng Lang, Zhi-Cen Lou and Paul Pui-Hay But (HK) “High-Performance Liquid Chromatographical Analysis of Ginsenosides in Panax ginseng, P. quinquefalium and P notoginseng” Journal of Chinese Pharmaceutical Sciences (1993) pp. 133-143. (73) Assignee: The Chinese Univerisity of Hong Kong, Shatin (HK)
Appendix : Probabilistic Neural-symbolic models for Interpretable Visual Question

Let us first focus on the prior on (serialized) programs, log p(z). This is a sequence model, which factorizes as, log p(z) = log p(z1) + ∑T t=2 log p(zt|{zi} t−1 i=1), where p(zt|·) ∼ Cat(πt). We learn point estimates of the parameters for the prior (in a regular sequence model), however, the every sequence has an implicit distribution on p(πt). To see this, note that at every timestep (other than t = 1), the sampling distribution depends upon {zi}i=1, which is set of random variables. Let p(Π, z) denote the joint distribution of all the sampling distributions across multiple timesteps Π = (π1, · · · , πT ) and z, the realized programs from the distribution. Let G = {πi, zi}i=1 Since πt refers to the sampling distribution for zt, we immediately notice that zt ⊥ G/πt|πt. That is, zt only depends on the sampling distribution at time t, meaning that log p(z|Π) = ∑T t=1 log p(zt|πt).
DESIGN AND IMPLEMENTATION OF PHYSICAL UNCLONABLE FUNCTIONS FOR DEVICE AUTHENTICATION AND SECRET KEY GENERATION USING ASIC

Silicon physical unclonable functions (PUF) utilize the variation during silicon fabrication process to extract information that will be unique for each chip. There have been many recent approaches to how PUF can be used to improve security related applications. However, it is well-known that the fabrication variation has very strong spatial correlation and this has been pointed out as a security threat to silicon PUF. In fact, when we apply NIST’s statistical test suite for randomness against the random sequences generated from a population of 125 ring oscillator (RO) PUFs using classic 1-out-of-8 Coding and Neighbor Coding, none of them can pass all the tests. In this paper, we propose to decouple the unwanted systematic variation from the desired random variation through a regression-based distiller, where the basic idea is to build a model for the systematic variation so we can generate the random sequences only from the true random variation. Applying Neighbor Coding to the same benchmark data [2], our experiment shows that 2 and 3 order polynomials distill random sequences that pass all the NIST randomness tests. So does 4 order polynomial in the case of 1-out-of-8 Coding. Finally, we introduce two generic random sequence generation methods. The sequences they generate fail all the randomness tests, but with the help of our proposed polynomial distiller, all but one tests are passed. These results demonstrate that our method can provide statistically random PUF information and thus bolster the security characteristics of existing PUF schemes.
A Classification of Hyper-heuristics Approaches – Revisited

Hyper-heuristics comprise a set of approaches that aim to automate the development of computational search methodologies, initially to address operational research problems but more recently venturing into new domains such as bioinformatics, strategies for games, and software engineering. This chapter overviews previous categorisations of hyper-heuristics and provides a unified classification and definition. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail, and recent research trends are highlighted. Our goals are to clarify the main features of existing techniques and to suggest new directions for hyper-heuristic research. Edmund K. Burke Queen Mary University of London, UK. e-mail: ekb@qmul.ac. uk Graham Kendall University of Nottingham Malaysia Campus, Malaysia. Automated Scheduling, Optimisation and Planning (ASAP) Group, School of Computer Science, University of Nottingham, UK. Matthew Hyde at Automated Scheduling, Optimisation and Planning (ASAP) Group, School of Computer Science, University of Nottingham, UK. Gabriela Ochoa Computing Science and Mathematics, University of Stirling, UK. Ender Özcan Automated Scheduling, Optimisation and Planning (ASAP) Group, School of Computer Science, University of Nottingham, UK. John R. Woodward Queen Mary University of London, UK.
University of Birmingham Automated formal analysis of side-channel attacks on probabilistic systems

The security guarantees of even theoretically-secure systems can be undermined by the presence of side channels in their implementations. We present Sch-imp , a probabilistic imperative language for side channel analysis containing primitives for identifying secret and publicly-observable data, and in which resource consumption is modelled at the function level. We provide a semantics for Sch-imp programs in terms of discrete-time Markov chains. Building on this, we propose automated techniques to detect worst-case attack strategies for correctly deducing a program’s secret information from its outputs and resource consumption, based on veriﬁcation of partially-observable Markov decision processes. We implement this in a tool and show how it can be used to quantify the severity of worst-case side-channel attacks against a selection of systems, including anonymity networks, covert communication channels and modular arithmetic implementations used for public-key cryptography.
Eu3+-Activated NaGdF4 Nanorods for Near-Ultraviolet Light-Triggered Indoor Illumination

The Eu3+-activated NaGdF4 nanorods were achieved through a simple precipitation procedure at ambient temperature. With the irradiation at 396 nm, the designed nanorods exhibited visible red light with admirable color purity of 90.5% and the optimal doping concentration was 50 mol %. The concentration quenching mechanism was revealed to be decided by dipole–quadrupole interaction and the critical distance was 11.14 A. The optical transition parameters, which were estimated from Judd-Ofelt theory, of the Eu3+ ions were obtained to confirm its local symmetry behaviors. Moreover, the emission spectra of the designed nanorods at diverse temperatures were used to identify its thermal quenching performance. In comparison with that of the Y2O3:Eu3+ compound, the emission intensity of the Eu3+-activated NaGdF4 nanorods with optimum doping content was 2.47 times higher and its internal quantum efficiency was around 62.3%. Furthermore, the developed white light-emitting diode device, which was constructed by resulta.
Enabling Sensor Webs by Utilizing SWAMO for Autonomous Operations

The concept of sensor webs will require a certain amount of underpinning to enable the interaction of the various constituent components. The SWAMO project is providing an intelligent agent based framework for allowing sensor systems to intercommunicate via standard interfaces such as SensorML, Sensor Web Enablement standards, and web service calls. The culmination of the project will allow for the discovery of sensing assets, query capabilities, requesting task feasibilities, and even charge the system with direct tasking. This paper describes the work to date on designing and prototyping this framework.
Gaussian Process Filtering of non-Gaussian Beliefs

This work studies the problem of stochastic dynamic filtering and state propagation with complex beliefs. The main contribution is GPSUM, a filtering algorithm tailored to dynamic systems and observation models expressed as Gaussian Processes (GP), and to states represented as a weighted Sum of Gaussians. The key attribute of GP-SUM is that it does not rely on linearizations of the dynamic or observation models, or on unimodal Gaussian approximations of the belief, hence enables tracking complex state distributions. The algorithm can be seen as a combination of a sampling-based filter with a probabilistic Bayes filter. On the one hand, GP-SUM operates by sampling the state distribution and propagating each sample through the dynamic system and observation models. On the other hand, it achieves effective sampling and accurate probabilistic propagation by relying on the GP form of the system, and the sum-of-Gaussian form of the belief. We show that GP-SUM outperforms several GP-Bayes and Particle Filters on a standard benchmark. We also demonstrate its use in a pushing task, predicting with experimental accuracy the naturally occurring nonGaussian distributions.
3D reconstruction of synapses with deep learning based on EM Images

Recently, due to the rapid development of electron microscope (EM) with its high resolution, stacks delivered by EM can be used to analyze a variety of components that are critical to understand brain function. Since synaptic study is essential in neurobiology and can be analyzed by EM stacks, the automated routines for reconstruction of synapses based on EM Images can become a very useful tool for analyzing large volumes of brain tissue and providing the ability to understand the mechanism of brain. In this article, we propose a novel automated method to realize 3D reconstruction of synapses for Automated Tapecollecting Ultra Microtome Scanning Electron Microscopy (ATUM-SEM) with deep learning. Being different from other reconstruction algorithms, which employ classifier to segment synaptic clefts directly. We utilize deep learning method and segmentation algorithm to obtain synaptic clefts as well as promote the accuracy of reconstruction. The proposed method contains five parts: (1) using modified Moving Least Square (MLS) deformation algorithm and Scale Invariant Feature Transform (SIFT) features to register adjacent sections, (2) adopting Faster Region Convolutional Neural Networks (Faster R-CNN) algorithm to detect synapses, (3) utilizing screening method which takes context cues of synapses into consideration to reduce the false positive rate, (4) combining a practical morphology algorithm with a suitable fitting function to segment synaptic clefts and optimize the shape of them, (5) applying the plugin in FIJI to show the final 3D visualization of synapses. Experimental results on ATUM-SEM images demonstrate the effectiveness of our proposed method.
Binge-Watching and Media Franchise Engagement ∗ Current version : June 2019

We investigate the relationship between binge-watching and consumers’ engagement with media franchises in two areas: personal and interactive engagement. The former involves consumers’ adoption and consumption of franchise extensions and the latter concerns consumers’ content generation related to a focal media product they watched. Our novel data come from an online anime (Japanese cartoons) platform containing individual-level information on consumers’ anime watching behavior and their user-generated content. We find that the effects of binge-watching on personal engagement critically depend on the availability of a franchise extension at the time of watching the focal media product and the type of franchise extension (sequels versus other types of extensions). For interactive engagement, our results show that binge-watching is associated with lower submission rates but higher valence of anime ratings, the most prevalent form of UGC on the platform. Furthermore, we explore five common sources of heterogeneity: age, gender, geography, usage, and experience. We discuss managerial implications for TV networks and online streaming services regarding the timing of content release.
Adversarial Password Cracking

Password generation is a very common security issue. Users are faced with the problem of choosing good passwords and trying to make sure that the password they choose is as robust as possible. In this project, we train a generative adversarial network with a generator that tries to generate guesses to break a password scheme and a discriminator that tries to prevent the generator from generating a guess that breaks the password security scheme. We try to compare this scheme to Hashcat and also evaluate the strength of our adversarial system using zxcvbn, a low budget password estimator developed by Dropbox engineers. We also compare the effect of creating an extended dataset based on random permutations and language rules on the guessability of user passwords.
PEDESTRIAN DETECTION USING ADABOOST LEARNING OF FEATURES AND VEHICLE PITCH ESTIMATION

In this paper we propose a combination of different Haar filter sets and Edge Orientation Histograms (EOH) in order to learn a model for pedestrian detection. As we will show, with the addition of EOH we obtain better ROCs than using Haar filters alone. Hence, a model consisting of discriminant features, selected by AdaBoost, is applied at pedestrian-sized image windows in order to perform the classification. Additionally, taking into account the final application, a driver assistance system with realtime requirements, we propose a novel stereo-based camera pitch estimation to reduce the number of explored windows. With this approach, the system can work in urban roads, as will be illustrated by current results.
A Compact Four Concentric Rectangular Rings Slot Antenna for Mobile Handset Applications

A novel compact four concentric rectangular rings slot broadband microstrip antenna is proposed for mobile handsets in the paper. The antenna has a circular radiator with four concentric rectangular rings slot structure. The antenna can cover more than ten mobile applications with -10dB bandwidth of 88.7% (1.6-4.15GH) for DCS1800(1710-1880MHz), TD-SCDMA(1880-2025MHz, 2300-2400MHz), WCDMA(1920-2170MHz, 1755-1880MHz), LTE33-41/42/43 (1900-2690MHz, 3400-3800MHz), Bluetooth, GPS, COMPASS, GLONSS, GALILEO, WLAN (802.11b/g/n:2.4-2.48GHz), and WiMAX(3.3-3.8GHz) wireless applications. The proposed antenna is fabricated on a 1.6 mm-thick FR4 substrate with dielectric constant of 4.4, and the size is 50*40mm2. The good agreement between the measurement results and the simulation validates the proposed design approach and meets the requirements for mobile handset applications.