Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent advances in the treatment of C. difficile using biotherapeutic agents

Abstract Clostridium difficile (C. difficile) is rapidly becoming one of the most prevalent health care–associated bacterial infections in the developed world. The emergence of new, more virulent strains has led to greater morbidity and resistance to standard therapies. The bacterium is readily transmitted between people where it can asymptomatically colonize the gut environment, and clinical manifestations ranging from frequent watery diarrhea to toxic megacolon can arise depending on the age of the individual or their state of gut dysbiosis. Several inexpensive approaches are shown to be effective against virulent C. difficile in research settings such as probiotics, fecal microbiota transfer and immunotherapies. This review aims to highlight the current advantages and limitations of the aforementioned approaches with an emphasis on recent studies.
Email 'Message-IDs' helpful for forensic analysis?

Finding the source of spoofed email is a challenging task for forensic investigators. Header of an email has several fields that can be used for investigation. An investigator can easily understand the evidences embedded within most of the header fields of an email, except the message-id field. Therefore, there is a need to understand how message-ids are constructed and what useful information can be recovered from them. The immediate aim of the analysis is to find the message-id construction mechanism of ‘Sendmail’ mail transfer agent (MTA) version 8.14 and how the findings can be used successfully in forensic analysis. Source code of the ‘Sendmail’ MTA is made use of during analysis. This analysis will uncover several information that will help to find email source and validate other email header fields also. The drawbacks in message-id based forensic analysis also discussed here.
Dialogue and Connectivism: A New Approach to Understanding and Promoting Dialogue-Rich Networked Learning

Connectivism offers a theory of learning for the digital age that is usually understood as contrasting with traditional behaviourist, cognitivist, and constructivist approaches. This article will provide an original and significant development of this theory through arguing and demonstrating how it can benefit from social constructivist perspectives and a focus on dialogue. Similarly, I argue that we need to ask whether networked social media is, essentially, a new landscape for dialogue and therefore should be conceived and investigated based on this premise, through considering dialogue as the primary means to develop and exploit connections for learning. A key lever in this argument is the increasingly important requirement for greater criticality on the Internet in relation to our assessment and development of connections with people and resources. The open, participative, and social Web actually requires a greater emphasis on higher order cognitive and social competencies that are realised predominantly through dialogue and discourse. Or, as Siemens (2005) implies in his call to rethink the fundamental precepts of learning, we need to shift our focus to promoting core evaluative skills for flexible learning that will, for example, allow us to actuate the knowledge we need at the point that we need it. A corollary of this is the need to reorient educational experiences to ensure that we develop in our learners the ability “to think, reason, and analyse. ” In considering how we can achieve these aims this article will review the principles of connectivism from a dialogue perspective; propose some social constructivist approaches based on dialectic and dialogic dimensions of dialogue, which can act as levers in realising connectivist learning dialogue; demonstrate how dialogue games can link the discussed theories to the design and performance of networked dialogue processes; and consider the broader implications of this work for designing and delivering sociotechnical learning.
Diversity reservation ALOHA

A multiaccess protocol suitable for packet-switched satellite broadcast channels is considered. The proposed protocol, called diversity reservation ALOHA (DRA), combines the essential features of announced retransmission random access (ARRA), reservation ALOHA, and diversity ALOHA to achieve the goals of improved delay-throughput characteristics and high channel utilization. Two versions of DRA protocols differentiated by the requirement of a global queue distributed at users' terminals are analyzed. Important system performance measures, such as activity-factor-throughput characteristics of DRA, are compared with those of ARRA. Examples of numerical results show that with system parameters properly chosen, substantial increases in attainable channel utilization are possible. Furthermore, the use of diversity for reservation allows a smaller packet delay, especially under light traffic conditions. <<ETX>>
Spectrum Sensing based on Energy Detection for Cognitive Radio using FPGA

— Spectrum sensing in cognitive radio (CR) is essential method that identifies the presence of primary user signal in a channel. Cognitive technology solves the difficulty by access the unlicensed users from licensed bands. In this paper, energy detection technique algorithm is used for modulated signal to identify the presence of deterministic primary user (PU) signal in the channel whereas signal is present or absent. There are considered two types of modulated signals which are BPSK and QPSK under noise (AWGN). Also simulation performance characteristics of receiver operating curves for modulated signal are done on MATLAB with detection probability versus false alarm probability using the Monte Carlo method. The hardware implementation created on FPGA board for the switching the absent signal to required secondary user (SU).
Channel coding for cochannel interference suppression in wireless communication systems

Cochannel interference is a major impairment in cellular systems. In practice the performance of time and frequency division multiple access (TDMA and FDMA) systems is limited by a few dominant cochannel interferers. Conventional channel coding techniques treat this interference as noise to mitigate its effect. It is shown that cochannel interference can be totally suppressed by using a adaptive nonlinear decoder. We illustrate our ideas using codes in a real (complex) field as well as codes in a finite field. The decoding technique can be combined with antenna diversity to offer increased interference suppression capability.
MOS varactors with n- and p-type gates and their influence on an LC-VCO in digital CMOS

The influence of the gate doping type of the MOS varactor on frequency tuning, phase noise, and frequency sensitivity to supply-voltage variations of a fully integrated inductance-capacitance voltage-controlled oscillator (LC-VCO) is presented. Three varactors in multifinger layout with shallow trench isolation (STI) are compared. The polysilicon gate is either entirely n- or p-doped or the fingers have alternating n and p doping. Differences in capacitance and quality factor are shown. Two identical VCOs with the varactors having n gates or np gates are realized. Homogenous doping increases the VCO tuning range to 1.31 GHz (/spl plusmn/20%) in comparison to 1.06 GHz (/spl plusmn/15%) obtained by mixed doping. However, mixed doping has the advantages of more linear VCO frequency tuning, lower close-in phase noise, and reduced maximum sensitivity to variations in supply voltage. Several varactor parameters are introduced. They allow prediction of the influence of varactors on the performance of a given VCO. With a current consumption of only 1 mA from a supply voltage of 1.5 V, both VCOs show a phase noise of -115 dBc/Hz at 1-MHz offset from a 4-GHz carrier and a VCO figure of merit of -185.3 dBc/Hz.
Java as a First Programming Language

The purpose of this workshop was to explore the advantages, disadvantages and implications of adopting Java as a first programming language. Two workshop sessions were held. Both sessions were well attended and participants represented a wide range of interest groups. Academics present included some who have already had experience teaching Java at first year, those about to teach Java at first year in 1998 and others about to debate first language selection whose primary interest was gaining some idea of the issues. The presence of a group of students provided a valuable source of feedback from the consumers of our courses. A list of participants who chose to identify themselves is included in table 1. Many of the participants also contributed to the workshop Introducing OO into the Curriculumand at times it was difficult to separate the two themes.
Detection of EEG oscillatory patterns corresponding to human concentration of attention with help of perceptron type artificial neural network

In this paper we analyzed possibility for detection of EEG oscillatory patterns related to states of low and high levels of human concentration during perception of visual stimuli with help of artificial neural network. We analyzed different variation of EEG signals combination in order to find optimal one. We performed classification of brain states with perceptron-type artificial neural network and analyzed quality of classification.
Off-line frequency correction for equalization in the EDGE system

A robust and low complexity receiver is addressed, which meets the EDGE specification requirements by using an off-line non-adaptive equalizer. Specifically, the problem of correcting a random burst-by-burst frequency offset in an EDGE receiver is studied. Simulation results show that the EDGE (handset) requirements for the TU50 propagation scenario can be met for all coding schemes with 8-PSK modulation by using a simple off-line training-based DFE with decision-directed frequency offset correction.
Quantitative source spectra from acoustic array measurements

While phased microphone arrays are often applied with the focus on acoustic source localization, in many cases quantitative source information is also needed. The estimation of quantitative source spectra from array measurements is not straightforward and a number of methods exist that either use integration of the beamforming map or solve inverse problems for source estimation. An alternative, computationally efficient method is proposed that is based on a signal subspace approach. A short overview of the theory is given and the method is demonstrated using two examples, a four loudspeaker set-up and trailing edge noise measurement.
A scheme of on-line Chinese character recognition using neural networks

The paper proposes a scheme of online Chinese character recognition, based on neural networks. The supervised backpropagation algorithm is used to train the network. The input character is converted as a sequence of virtual stroke segments as well as real stroke segments, which is a good feature exactly describing the complete structure of a character, and is to be extracted by our system. In order to simplify the recognition process and reduce the recognition time, the neural network is divided into several subnetworks. Each of them is responsible for recognizing a group of about 75 character patterns. In other words, the huge set of Chinese characters is divided into several groups according to the numbers of stroke segments in the characters, and for each group of characters, a specific subnetwork is trained in order to recognize every character in the group. Whenever the system accepts an input Chinese character, it will calculate the number of stroke segments, including virtual stroke segments as well as real stroke segments in that character, and then determine which subnets to enter for recognition process. The system is allowed to accept and recognize some interconnected characters. The algorithm was experimentally implemented in a personal computer system, it accepts interconnected Chinese characters written on an electronic tablet, and performs recognition in real time. Our experiment showed that recognition accuracy exceeded 96% on the test example.
Asynchronous Pipeline for Processing Huge Corpora on Medium to Low Resource Infrastructures

Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.
Iterated Large-margin Discriminant Analysis for Feature Dimensionality Reduction in Medical Image Retrieval

Feature Dimensionality Reduction (DR) is an important topic in medical image retrieval. However, most medical image retrieval systems use naive principal component analysis for DR, which is not optimal. Recently, the class conditional nearest neighbour relation over pairs of points was introduced. In this paper, we extend it to class conditional K-nearest neighbour (ccknn). Based on ccknn, we define the within-class and between-class graph for Large-Margin Discriminat Analysis (LMDA). Moreover, an iterative expectation-maximisation framework is applied to LMDA to boost the performance. The experimental results demonstrate that the proposed approach yields significant improvements over the state-of-the-art DR algorithms.
A Review and Comparative Analysis of Security Risks and Safety Measures of Mobile Health Apps

In line with a patient-centred model of healthcare, Mobile Health applications (mhealth apps) provide convenient and equitable access to health and well-being resources and programs that can enable consumers to monitor their health related problems, understand specific medical conditions and attain personal fitness goals. This increase in access and control comes with an increase in risk and responsibility to identify and manage the associated risks, such as the privacy and security of consumers’ personal and health information. Based on a review of the literature, this paper identifies a set of risk and safety features for evaluating mHealth apps and uses those features to conduct a comparative analysis of the 20 most popular mHealth apps. The comparative analysis reveals that current mHealth apps do pose a risk to consumers. To address the safety and privacy concerns, recommendations to consumers and app developers are offered together with consideration of mHealth app future trends.
Data Sharing in Cloud Storage by RSA based Encryption

In cloud, the data sharing is the most important functionality that has to be done securely and efficiently. The main problem which we are facing in cloud storage is how to share the data effectively. Although, there are many techniques that are developed to encrypt the data in the cloud for secure storage and sharing, we cannot able to share data flexibly with others. In this paper, a public-key cryptosystem that produces a constant-size cipher text is introduced. Here, the secret keys are accumulated together and make them as a single compact key. Other than the files that are encrypted and present outside the set remains trustworthy. These accumulated keys are sent secretly to other users or to be stored within limited secure storage. Thus, our KAC enables efficient data sharing in cloud storage with confidentiality and integrity.
Non Path-Based Mutual Anonymity Protocol for Decentralized P2P System

Each and every end-to-end systems try to mask the information and details of their users for privacy considerations. Existing quality of being anonymous approaches are mainly path-based: peers have to pre- construct an anonymous path before transmissio of any files and queries.The overhead of maintaining and updating such paths is significantly high also introduce high cost. Its called as blindly assigned path. We propose Rumor Riding (RR), a lightweight and non-path-based mutual anonymity protocol for decentralized P2P systems. Employing a random walk mechanism, RR takes advantage of lower overhead by mainly using the symmetric cryptographic algorithm. Accelerating the query speed. Introducing mimic traffic to confuse attackers. Mimic traffic generates duplicate cipher and key rumor to confuse the attackers. This mimic traffic generates traffic in a purposefully irregular manner. Mimic is undetectable by any known detection technique at without loss of throughput. Index Terms: Mutual anonymity, non-path-based, random walk, peer-to-peer.
MATHEMATICAL ENGINEERING TECHNICAL REPORTS An Estimation Procedure for Contingency Table Models Based on the Nested Geometry

We propose a geometrical method for estimating the parameters of contingency tables. Our method–bisector regression for contingency tables–is based on a nested structure of models. The nested structure represents the variables that are independent. This means that a model includes smaller models allowing stronger independence, which also means that more parameters are eliminated in smaller models. Our method estimates parameters corresponding to the interactions of lower orders after those of higher orders are estimated or eliminated. Bisector regression generates a sequence of parameter estimates, each element of which represents a model and an estimate. The length of the sequence is much smaller than the total number of models. We describe the algorithm and show examples. In this paper, contingency tables are considered. We introduce parametrization of multinomial distributions and propose an algorithm for estimating parameters. The proposed algorithm is bisector regression for contingency tables (BRCT). The main idea of BRCT comes from our previous works. In [6, 7], we proposed the bisector regression algorithm, which is an extension of least angle regression [4]. Least angle regression is an algorithm for parameter estimation, which is related to the l1-regularization method (lasso, [3, 5, 11, 12]). In problems of contingency tables, our interest is to estimate parameters corresponding to interactions between factors. Factors, or random variables, are qualitative variables. Parameters are separated into groups depending on how many factors are involved. We apply the main idea of bisector regression for generalized linear regression ([6]) and Gaussian graphical models [7] to these parameter groups. We provide ∗hirose@stat.t. u-tokyo.ac. jp
SAR image segmentation by mathematical morphology and texture analysis

Presents a promising segmentation method for synthetic aperture radar (SAR) imagery designed in the context of agricultural remote sensing using a morphological region based image analysis. The region based approach is really appropriate for land use applications given that land cover is naturally built-up from regions. The method is divided into two principal steps: a morphological image partitioning resulting in a severe oversegmentation and a further region growing process exploiting information from a textural edge detector.
Cardiovascular variability modelling: some fundamental concepts

In parallel with individual analysis of the variability signals, namely; heart rate variability (HRV), blood pressure variation (BPV) and lung volume (LV), illustration of a systemic representation to depict their mutual interactions has been of increasing prominence. Various parametric and nonparametric methods have been employed to find a satisfactory relationship between the variability signals and their origins. However, it seems that some of the fundamental concepts, critical in modelling and identification, have been overlooked in this regard. The main purpose of this paper is the presentation of a conceptual discussion about some of these issues including the large signal and small signal cardiovascular modelling, proper definition of the variability signals, and lumped and distributed modelling.
The Genesis Story Understanding and Story Telling System A 21 st Century Step toward Artificial Intelligence

Story understanding is an important differentiator of human intelligence, perhaps the most important differentiator. The Genesis system was built to model and explore aspects of story understanding using simply expressed, 20-100 sentence stories drawn from sources ranging from fairy tales to Shakespeare's plays. I describe Genesis at work as it reflects on its reading, searching for concepts, reads stories with controllable allegiances and cultural biases, models personality traits, answers basic questions about why and when, notes concept onsets, anticipating trouble, calculates similarity using concepts, models question-driven interpretation, aligns similar stories for analogical reasoning, develops summaries, and tells and persuades using a reader model. I conclude with thoughts on how Genesis would describe people in pictures and video, thus engaging with the CBMM challenge problem.
A Conceptual Framework for Semantic Business Process Configuration

In this work, we present a conceptual framework for deriving executable business process models from conceptual business process models, based on the paradigm of Service-Oriented Architectures and Semantic Web technology. The framework yields higher automation in the process configuration phase through the use of semantic annotations of the process activities. Based on the annotations, a more direct link is established between a business view on a process and its execution driven by an IT system. This link enables the derivation of an execution-level model for newly created business process models as well as adaptation of the execution model after re-engineering processes, possibly under certain re-design goals (such as quality, cost, execution time, flexibility, or others). The framework includes a component architecture and an algorithm that describes how to combine executable artifacts, such as (Semantic) Web services, in order to find an implementation that matches a given business process model. An extensible set of criteria can be used for validating the composition
An automatic target recognition algorithm based on image matching with multiple sub templates

The paper presents an upgrade target recognition algorithm based on the analysis of normal template matching algorithm. The new algorithm firstly uses given template to generate sub templates at random or with certain principle, and then matches each sub templates in search image to get possible areas of target, lastly finds the target from comparison among the matching results. The paper at last gives the test results of the mentioned Algorithm. The results show: the new algorithm greatly improves the efficiency, real-time ability, precision of the normal algorithm, and also it has an advantage of easy realization, thus it is a good reference for solving the same problems.