Miracle-dz/UniFAR-FTUs huggingface dataset:
FC$^2$N: Fully Channel-Concatenated Network for Single Image Super-Resolution

Most current image super-resolution (SR) methods based on convolutional neural networks (CNNs) use residual learning in network structural design, which favors to effective back propagation and hence improves SR performance by increasing model scale. However, residual networks suffer from representational redundancy by introducing identity paths that impede the full exploitation of model capacity. Besides, blindly enlarging network scale can cause more problems in model training, even with residual learning. In this paper, a novel fully channel-concatenated network (FC$^2$N) is presented to make further mining of representational capacity of deep models, in which all interlayer skips are implemented by a simple and straightforward operation, i.e., weighted channel concatenation (WCC), followed by a 1$\times$1 conv layer. Based on the WCC, the model can achieve the joint attention mechanism of linear and nonlinear features in the network, and presents better performance than other state-of-the-art SR models with fewer model parameters. To our best knowledge, FC$^2$N is the first CNN model that does not use residual learning and reaches network depth over 400 layers. Moreover, it shows excellent performance in both largescale and lightweight implementations, which illustrates the full exploitation of the representational capacity of the model.
FES-assisted Locomotion on an Inclined Treadmill : Use of Passive Dynamics

Functional electrical stimulation (FES) using surface electrodes has been used to facilitate walking in spinal cord injured (SCI) patients for decades [1,2]. Although established as an assistive technology, neuroprostheses for walking are not widely accepted by SCI patients and are seldom used in activities of daily living (ADL). Two limitations of surface FES are: 1) muscle fatigue occurs rapidly, within minutes after walking is initiated; and 2) there is a need to assist hip flexion by swinging the upper body.
Optimization without Programming or Lagrangians – Implications for Investing

We study alternative ways to optimize a portfolio to better assess the practical value of meanvariance optimizations. Mathematical programming methods do provide the correct solutions but in the process we lose sight of how security characteristics are reflected in the optimal weights. The first section of the study reviews pre-optimization challenges (data gathering, calculation of inputs). The second shows that early applications of mathematical programming to portfolio optimization were overloaded with features that are actually not needed in practice (e.g., semidefiniteness). This section also shows that through its history portfolio optimization has been hampered with inessential generality as well: the tendency to make things more “impressive” than they need to be to solve the problem. The third part of the study reviews alternative portfolio optimization methods –basis reduction, least squares, selective basis addition-all of which can get started from an unrestricted quadratic equation, referred to as the algebraic method. The application of alternative optimization approaches to portfolio optimization provides twelve critical observations that reveal exactly how the optimization works. These findings enable investors not only to properly assess the practical value of mean-variance optimizations, but also to benefit from this knowledge without optimization calculations. Although the main focus is on enhancing investment practice and security selection, this study presents pedagogical advantages as well.
Investigation and modeling of respiratory effects on SCG-signal fiducial points

Different measurement techniques exists for recording cardiac events and diagnosing mechanical heart diseases. These techniques are though often costly, and seismocardiography is a rediscovered and cheaper alternative. The seismocardiography signal is however not fully understood to this day, and part of the reason is that no common morphology has been agreed upon. Parts of this problem is that respiration changes the cardiac event timings and the hearts stroke volume changes up to 30%. Thereto have different interpretations respiration been used. These presentations of respiration have all been segmented and classified into two categories, but in this study is a non-discrete continuous presentation suggested. It is then proposed to create a model for seismocardiography fiducial point amplitudes and timings with respiration phase as an input for the first heart sound, to attempt modelling the morphology. Respiration is successfully derived from the seismocardiography signal and the respiration phase is modeled as a peak-to-peak sine wave, containing information on both respiration amplitude and gradient. Using second degree polynomial regression to fit the fiducial point amplitudes resulted in low and negligible adjusted r2 values for all 10 study subjects, due to high variance. Tendencies for both amplitude and timings between fiducial points were however discovered, although at different phases. This suggests that a phase delay between respiration and the recorded seismocardiography was evident. The sine wave representation of respiration is recommended for future use in research, since it is evident that non-discrete classification is not suitable for mapping the seismocardiography morphology.
Signal Analysis from Wireless ECG System

In this paper a novel mobile ECG analysis system is discussed. Existing stationary systems allow recording high quality ECG signal, but they place patients in an unlikely environment. Known wearable systems do not provide enough freedom as their sensors are connected to a mobile computer by wires. We propose a wireless ECG system with wired sensors and a mobile computer able to perform real-time analysis. Recorded signal is also transmitted to a central server where it can be processed more accurately. Special algorithms, based on well known methods, are proposed for signal filtering, baseline drift removal and P, Q, R, S and T peaks detection. Key–Words: Electrocardiogram (ECG), QRS complex detection, P-wave, T-wave, R-R intervals, Baseline drift, Mobile systems, Real-time processing, Kalman filter
A Codebook Generation and Search Scheme for FDD Downlink Massive MIMO Systems

Applying channel state information feedback scheme based on codebook for FDD downlink MIMO system to massive MIMO system directly causes following two problems. First, codebook generation becomes hard because resources needed for codebook generation increases exponentially in the number of antennas. In addition, long time is needed for channel state information feedback because users`s codebook search time increases linearly in the number of antennas. This paper proposes a codebook generation scheme and a codebook search scheme which can reduce resource for codebook generation and codebook search time drastically though they have similar performance as conventional approaches.
J 1 . 7 USING A MESOSCALE MODEL AND UAVS TO QUANTIFY THE UNDER-REPRESENTATION OF CLIMATE VARIABILITY IN THE NCEP REANALYSIS OF COASTAL REGIONS IN THE ARCTIC

The NCEP reanalyses are widely used to study panArctic climate including the evaluation of global climate model simulations of present day climate (e.g., Walsh et al. (2002) and the assessment of the arctic hydrological cycle (Cullather et al. 2000; Serreze and Hurst 2000; Serreze et al. 2003) and energy budget (Walsh et al. 2002). While these analyses provide a best guess of the atmospheric variability over polar regions for the past 40+ years, the horizontal resolution of this dataset and others like it (e.g., ERA-40) are inadequate for assessing the local climate in regions of complex surface characteristics such as those found in coastal regions.
Analyses of Avoidance Manipulability for Redundant Manipulators *

This paper is concerned with a new concept of avoidance manipulability inspired from manipulability, which represents the shape-changing ability of each intermediate link. Based on avoidance manipulability, we present the “Local Non-singular Configuration Assumptions”. By analyses of avoidance manipulability for redundant manipulators, we think that it gives the foundation to assess the shape-changing ability to improve the structure at the first step of design for a new robot.
Text Classification using Text Summarization- A case study on Telugu Text

This paper addresses the issue of curse of dimensionality in the Text Classification ( TC) problem using Text Summarization (TS). In this paper, an attempt is made to effectively tackle the curse of dimensionality problem using Text classification model. It is observed from the experimental results that proposed model improves the performance of text categorization. An empirical evaluation is performed on different summaries and it is concluded that performance of the TC is observed to be improved when the redundancy in the summary is minimal. Performance of the proposed model is evaluated with the F1measure and macro-averaged F1 measures using Support Vector Machine (SVM).
Extreme Programming Explained

Extreme Programming (XP) was conceived and developed to address the specific needs of software development conducted by small teams in the face of vague and changing requirements. This new lightweight methodology challenges many conventional tenets, including the longheld assumption that the cost of changing a piece of software necessarily rises dramatically over the course of time. XP recognizes that projects have to work to achieve this reduction in cost and exploit the savings once they have been earned.
An international review of experiences from on-demand public transport services

The aim of this report is to contribute to develop knowledge about what the developments in positioning and smartphone technology bring to the table for the public transport sector. The overarching question in the report is: can new technology improve demand-responsive transport (DRT)? The cases analysed in this report were selected using a number of criteria to delimit the sample and distinguish the cases from “traditional” DRT and from ride-hailing services. A total of 35 different services were identified that met the criteria for what this report refers to as on-demand public transport. The identified cases are located in nine different countries and 23 different cities or regions, and includes services that have been or are operating in major urban areas, smaller towns, suburbs, semi-rural and rural areas. Nine services, most of which are subsidised by the public sector, have been analysed in more detail. The comparison of the cases reveal differences and similarities concerning aspects such as vehicles and fleet sizes, and service partnerships. Different variants are also described regarding operational policies of the services. This includes origin-destination policies, areas covered by the services, where to pick up and drop off passengers, operating hours, booking method, time of booking, payment and pricing. For the nine cases that are the focus of the report a comparison of patronage, productivity and production costs are also made. A main conclusion from this part of the study is that so far there is scant evidence that new technology improves the productivity of DRT. This suggests that new technology is no panacea for fixing the problems of DRT and the study shows that thus far, at least, on-demand public transport hardly represents a transport revolution. (Less)
CLUSTER INITIATIVES IN THE CONTEXT OF THE BEE MODEL

The phenomenon of small and medium-sized enterprises has been known for decades. The importance of these businesses is important both from the perspective of macroeconomic and social. It is a fact that small and mediumsized enterprises have a competitive position in the market compared to large competitors, for example multinational concerns. The financial possibilities of SMEs are weaker and the possibility of investing in research and development is scarce. Solution to this problem is to link the business to the entrepreneurial networks and the cluster. The aim of this paper is to identify the benefits of linking SMEs to the network and to describe the possibility of using the BEE model to assess the state of the business environment.
Recognizing the Illegal Activities of Computer Users

son, 1996), preliminary research has concluded that frequent users of the Internet do find a need for its regulation (McCabe & Lee, 1997). From this comes the question as to what exactly is in need of regulation? Is it simply activities that result in financial loss, or any activity that causes a computer user a loss of time and talent? Most will agree that the production and distribution of child pornography as well as the publication of destructive materials are in need of regulation, but what of other crimes emerging as the computer now becomes the tool for criminal activity? To what extent do the regulation of unauthorized entry, piracy, or the programming of destructive viruses affect computer users? To what extent do the issues of data diddling, piggybacking, or impersonation plague those who use this technology? This report presents the findings of an exploration into the illegal area of computer abuse and the user’s knowledge of these abuses. In particular, the knowledge of Internet users regarding specific acts of computer abuse, which, as defined by law as illegal, were examined. In addition, this report identifies the demographic characteristics of users most associated with a knowledge of specific computer laws. We assert that before criminal acts using the computer may be regulated, they should first be recognized as illegal by those committing the acts. To curtail the spread of illegal activities, the computer user must be aware of the extent of regulations, if any, as well as the penalties that accompany illegal actions that were executed through the manipulation of a computer.
Time-Based Proxy Re-Encryption for User Revocation with Reduced UAKs in Cloud Storage

With rapid development of cloud computing, more and more enterprises will outsource their sensitive data for sharing in cloud. To keep the shared data confidential against un-trusted Cloud Service Providers (CSPs), a natural way is to store only the encrypted data in cloud. The key problems of this approach include establishing access control for the encrypted data and revoking the access rights from users when they are no longer authorized to access the encrypted data. Symmetric key Cryptography provides a single shared key for group of users. If a user leaves the group, secret key needs to changed and data needs to be encrypted again with new secret key. To overcome this, Proxy Re-Encryption (PRE) scheme efficiently handles user revocation by re-encrypting the cipher text again at proxy server. For fine-grained access control Attributed Based Encryption (ABE) uses attributes of the users to provide access to data. Time-based Proxy ReEncryption specifies time for every attribute of a user which is termed as access time of the attribute. Each user will be provided with set of User Attribute secret Keys (UAKs). Each UAK is associated with user, attributes and access time of the attribute. In this way, each attribute of a user will have a separate UAK. This results in creation of many numbers of UAKs for a user. To reduce the number of UAKs, the proposed system will generate UAKs for group of attributes in the access structure rather than generating UAKs for each of the attribute.
Experimental results and modeling techniques for substrate noise in mixed-signal integrated circuits

An experimental technique is described for observing the effects of switching transients in digital MOS circuits that perturb analog circuits integrated on the same die by means of coupling through the substrate. Various approaches to reducing substrate crosstalk (the use of physical separation of analog and digital circuits, guard rings, and a low-inductance substrate bias) are evaluated experimentally for a CMOS technology with a substrate comprising an epitaxial layer grown on a heavily doped bulk wafer. Observations indicate that reducing the inductance in the substrate bias is the most effective. Device simulations are used to show how crosstalk propagates via the heavily doped bulk and to predict the nature of substrate crosstalk in CMOS technologies integrated in uniform, lightly doped bulk substrates, showing that in such cases the substrate noise is highly dependent on layout geometry. A method of including substrate effects in SPICE simulations for circuits fabricated on epitaxial, heavily doped substrates is developed. >
D EVALUATION OF METABOLIC STATUS IN DAIRY COWS DURING TRANSITION PERIOD

The objective of this study was to evaluate organic and inorganic blood parameters, i.e. indicators of metabolic status in dairy cows (n=30) during the transition period. The cows were divided into two groups. The first group (n=15) comprised clinically healthy prepartal cows, and the second one included clinically health postpartal cows (n=15). Blood samples were taken from all the examined dairy cows. Blood glucose levels were statistically significantly lower (p<0.05) in the postpartum cows, which suggested an increased glucose uptake by the mammary gland and decreased gluconeogenesis in the liver. The significantly lower (p<0.05) blood triglyceride levels as well as the low total blood cholesterol levels (p>0.05) in the postpartum cows indicated their accumulation in liver cells. Significantly lower (p<0.05) blood levels of total protein, albumin and urea were recorded in the postpartum cows, which suggested the reduced synthetic capacity of liver cells. Blood bilirubin levels in the postpartum cows were significantly higher (p<0,01), which clearly indicated the reduced excretory capacity of the liver. Blood calcium, phosphorus and magnesium levels in the postpartum cows were lower, but they were not statistically significant (p>0.05), which most likely resulted from the increased uptake of these blood macroelements by the mammary gland. Biochemical evaluation suggested that early lactation cows had metabolic disturbances associated with some degree of hepatic lesions, probably due to fat infiltration.
Modeling of fault tolerance in neural networks

The authors present an analytical technique for assessing the fault tolerance of neural networks. The basis of the technique is developed through an analogy with magnetic spin systems using statistical mechanics. It is shown that neural networks can be analyzed using statistical mechanics. Simulated results are compared with analytical results, showing that the analytical model does indeed conform to the simulation model. The primary example presented is an associative memory. <<ETX>>
Structuring a new product development process portfolio using decoupling thinking

Abstract The purpose is to develop a structured new product development (NPD) process portfolio for manufacturing companies that facilitates the organization of NPD processes for both standardized products, focusing on time-to-market, and customized products, focusing on time-to-customer. The research combines different literature streams, enriching and advancing the understanding of decoupling thinking in NPD processes of manufacturing companies. It includes extensive empirical data from six manufacturing companies and presents testable propositions for further research. The resulting NPD process portfolio separates technology development from product development, acknowledges the different drivers and outcomes of processes and addresses the lead-time trade-offs. It provides an overview of potential options for NPD processes and shows different pathways through the processes. Companies can use the portfolio to support decisions related to the overall configuration of their NPD portfolios, the role and the range of different NPD processes, as well as to determine when and how to engage customers.
Usability engineering for code-based multi-factor authentication

The increase in the use of online banking and other alternative banking channels has led to improved flexibility for customers but also an increase in the amount of fraud across these channels. The industry recommendation for banks and other financial institutions is to use multi-factor customer authentication to reduce the risk of identity theft and fraud for those choosing to use such banking channels. There are few multi-factor authentication solutions available for banks to use that offer a convenient security procedure across all banking channels. The CodeSure card presented in this research is such a device offering a convenient, multi-channel, twofactor code-based security solution based on the ubiquitous Chip-and-PIN bank card. In order for the CodeSure card to find acceptance as a usable security solution, it must be shown to be easy to use and it must also be easy for customers to understand what they are being asked to do, and how they can achieve it. This need for a usability study forms the basis of the research reported here. The CodeSure card is also shown to play a role in combating identity theft. With the growing popularity of online channels, this research also looks at the threat of phishing and malware, and awareness of users about these threats. Many banks have ceased the use of email as a means to communicate with their customers as a result of the phishing threat, and an investigation into using the CodeSure card's reverse (sender) authentication mode is explored as a potential solution in regaining trust in the email channel and reintroducing it as a means for the bank to communicate with its customers. In the 8 experiments presented in this study the CodeSure card was rated acceptably high in terms of mean usability. Overall, the research reported here is offered in support of the thesis that a usable security solution predicated on code-based multifactor authentication will result in tangible improvements to actual security levels in banking and eCommerce services, and that the CodeSure card as described here can form the basis of such a usable security solution.
BAYESIAN CLIMATE CHANGE ASSESSMENT USING MULTI-AOGCM ENSEMBLES : GLOBAL AND REGIONAL SURFACE TEMPERATURES

There have been increasing efforts to apply Bayesian statistics to climate change detection and attribution in order to consider errors in the signal (or model response) as well as natural climate variability (Min et al., 2004, hereinafter referred to as M04, 2005a; Lee et al., 2005; Schnur and Hasselmann, 2005; IDAG, 2005 and references therein). However, uncertainties from inter-model differences could not be assessed reasonably due to lack of enough samples of model simulations. In this study, we use multi-model ensembles archived for the 4th Assessment Report (AR4) of Intergovernmental Panel on Climate Change (IPCC) in order to test the sensitivity of Bayesian detection and attribution of climate change to inter-model uncertainties. The Bayesian decision method by M04 is applied to global and regional mean surface air temperatures (SATs) from singlemodel ensembles (SMEs) with the ECHO-G model (Legutke and Voss, 1999; Min et al., 2005b,c,d) and multi-model ensembles (MMEs) with IPCC AR4 models (Min and Hense, 2005a, hereinafter referred to as MH05a) and their results are compared. In total, six scenarios are considered as a possible explanation of observed SAT changes over the 20th century: CTL (control), N (natural forcing; solar and volcanic), G (greenhousegas), S (sulfate aerosol), ANTHRO (anthropogenic forcing), and ALL (natural plus anthropogenic forcing). Given the scenarios, the Bayesian assessment is done for observed SATs for the whole 20th century and its first (1900-1949) and second half (1950-1999) separately. Parameters necessary to define the scenarios (means and covariance matrices, see section 2.1) are estimated from SMEs or MMEs and its effect on Bayesian decisions is examined as well as the effect of varying priors.