Miracle-dz/UniFAR-FTUs huggingface dataset:
Allowing Exploratory Search from Podcasts: the Case of Secklow Sounds Radio

We present here the Secklow Sounds Radio App that was developed as one of the demonstrators in the context of the MK:Smart project. Secklow Sounds is a community radio based in the city of Milton Keynes (MK), providing digital recordings of their broadcasts online, where local issues are often discussed. We developed a mobile-friendly web app that, besides offering live streaming of the radio, allows users to perform an entity-based search of past broadcasts enhanced with information (e.g. areas, museums, topics, etc) provided by the city’s centralised repository. The paper presents first the data processing workflow which integrates a number of existing solutions, such as the Google Speech-To-Text API, Neural Networks, DBpedia Spotlight, and the DiscOU search engine, and finally shows how results were integrated into a web and mobile application providing an exploratory search service for radio podcasts.
Dose dependence and durability of the therapeutic effects of Asparagus cochinchinensis fermented extract in an ovalbumin-challenged asthma model

The butanol extract of Asparagus cochinchinensis roots fermented with Weissella cibaria (BAfW) significantly suppressed the inflammatory response induced by lipopolysaccharide (LPS) treatment in RAW264.7 cells. To investigate the dose dependence and durability of BAfW on the anti-asthma effects, alterations in key parameters were measured in ovalbumin (OVA)-challenged Balb/c mice treated with the different doses of BAfW at three different time points. The number of immune cells, OVA-specific IgE level, thickness of respiratory epithelium and mucus score decreased significantly in a dose-dependent manner in response to treatment with 125 to 500 mg/kg BAfW (P<0.05), although the highest level was detected in the 500 mg/kg treated group. Moreover, the decrease in these parameters was maintained from 24 to 48 h in the 500 mg/kg of BAfW treated group. At 72 h, the effects of BAfW on the number of immune cells, OVA-specific IgE level and thickness of respiratory epithelium partially disappeared. Overall, this study provides the first evidence that the anti-asthma effect of BAfW may reach the maximum level in OVA-challenged Balb/c mice treated with 500 mg/kg and that these effects can last for 48 h.
Computational Technologies for Brain Morphometry

In this paper, we described a set of computational technologies for image analysis with applications in Brain Morphometry. The proposed technologies are based one a new Variational Principle which constructs a transformation with prescribed Jacobian determinant (which models local size changes) and prescribed curl-vector (which models local rotations). The goal of this research is to convince the image research community that Jacobian determinant as well as curl-vector should be used in all steps of image analysis. Specifically, we develop an optimal control method for non-rigid registration; a new concept and construction of average transformation; and a general robust method for construction of unbiased template from a set of images. Computational examples are presented to show the effects of curl-vector and the effectiveness of optimal control methods for non-rigid registration and our method for construction of unbiased template.
Drowsiness Detection Using Adaptive Hermite Decomposition and Extreme Learning Machine for Electroencephalogram Signals

Automatic drowsiness detection system plays a vital role to prevent the road accidents caused by drowsiness. In this regard, the electroencephalogram (EEG) signal provides valuable information of brain physiology for detection of drowsiness. EEG signals exhibit non-stationary nature which is tough to explore by prior defined and fixed number of basis functions. In this paper, an adaptive Hermite decomposition is proposed for the detection of drowsiness EEG signals. In the proposed decomposition, Hermite functions are used as basis functions, which are adaptively selected for each EEG signal by evolutionary optimization algorithms (EOAs). The mean square error of decomposition is proposed as an objective function to EOAs. The minimum decomposition error provided artificial bee colony EOA is considered to Hermite coefficients-based feature extraction for EEG signals. The extracted features are tested with the extreme learning machine (ELM), decision tree, $k$ -nearest neighbor, least squares support vector machine, artificial neural network, and naive Bayes for detection of alertness and drowsiness EEG signals. The proposed method with ELM classifier obtained 95.45% and 87.92% detection rates for alertness and drowsiness states, respectively, which are better as compared to other existing methods.
FlowCloak: Defeating Middlebox-Bypass Attacks in Software-Defined Networking

Software-Defined Networking (SDN) greatly simplifies middlebox policy enforcement. Middleboxes need tag packet headers to avoid forwarding ambiguity on SDN switches. In this paper, we present a new attack, called middlebox-bypass attack, to breach SDN-based middlebox policy enforcement. Such an attack manipulates a compromised switch to locally tag attacking packets without handing them over to the attached middlebox for inspection. Existing SDN security solutions, however, cannot detect the middlebox-bypass attack under practical constraints of efficiency, robustness, and applicability. We design and implement FlowCloak, the first protocol for per-packet real-time detection and prevention of middlebox-bypass attacks. FlowCloak enables middleboxes to generate tags that are probabilistically unknown to an attacker and confines it to only random guessing. We propose a multi-tag verification technique to address the tradeoff between FlowCloak robustness and TCAM usage by tag verification rules on the egress switch. Experiment results show that dozens of verification rules can confine the attacking probability under 0.1 %. FlowCloak imposes only a 0.3 ms packet processing delay on middleboxes and no obvious delay on the egress switch.
Signal Tracking Using Commodity WiFi

Recently, WiFi has been the focus of much research on activity recognition, indoor positioning, trajectory tracking. Traditional methods dealing with the research depend on received signal strength (RSS) or signal phase of the channel state information (CSI) of the commercial WiFi chips. But a lot of the research has not been turned into business application because of the instability of RSS and the lack of time synchronized phase between the transmitter and the receiver. Therefore, finding out how the signal propagates affected by the surroundings is the key question. In this paper, we design and implement WFSense, which tracks the direction of signal propagation of commercial WiFi system. WFSense represents the indoor multipath by taking full advantage of the subcarriers in OFDM and the space diversity across multi-antennas in MIMO system to improve MUSIC algorithm to estimate the angle of arrival (AoA) and departure (AoD) of the multipath signals. The minimum description length (MDL) principle is exploited to estimate the number of the multipath signals. WFSense identifies the directpath from the multiple paths with the time of flight (ToF) deduced by sanitizing the phase. The experiment results in different scenarios demonstrate that WFSense achieves a great performance.
Error bounds on the Rayleigh approximation of the K-distribution

It is a well-known property in X-band maritime surveillance radar signal processing that the K-distribution limits to a Rayleigh as its shape parameter increases, justifying the Rayleigh approximation of the K-distribution in certain scenarios. In the analysis of real data, it has been observed that this approximation tends to be valid for shape parameters >20. Using Stein's method, it is possible to construct explicit bounds on the distributional differences to quantify this observation.
Relationships among advanced manufacturing technology, innovation, export, and firm performance: Empirical evidence from Turkish manufacturing companies

The purpose of this study is to find the relationships among advanced manufacturing technology (AMT), innovation, export and firm performance by using data obtained from 310 Turkish manufacturing firms. ,A survey study was performed to obtain data from manufacturing firms. Multiple-item scales were adapted from the literature to conduct the survey in this study. Data were collected from five cities located in the Southeastern Anatolia Region in Turkey (Gaziantep, Adiyaman, Kilis, Diyarbakir and Sanliurfa). Structural equation modeling was utilized to investigate the relationships among AMT, innovation, export and firm performance in Turkish manufacturing firms operating in several industries. The direct and indirect relations between these variables are examined in the proposed conceptual model. In addition, the complex relations including in the proposed model are assessed in detail through the mediation analysis. ,Six of the proposed ten hypotheses related to manufacturers are validated by the empirical evidence from manufacturing companies in Turkey. Significant findings obtained in this study include the following: there are strong positive associations between the use of AMT and innovation and between export and firm performance. In addition, innovation mediates the relationship between AMT and firm performance and between AMT and export. Finally, export mediates the relationship between AMT and firm performance and between innovation and firm performance. It is expected that the results obtained from this empirical analysis will help decision makers and managers to construct a good technology and production management strategy for manufacturing systems. ,In this study, a novel conceptual model is proposed to examine the relationships among AMT, innovation, export and firm performance via the use of survey responses from 310 Turkish manufacturing firms. To the authors’ best knowledge, this is the first study that proposes such a conceptual model in the literature.
String Matching Algorithm based on Letters' Frequencies of Occurrence

In this paper, we utilize the varying frequencies of occurrence of letters in the words in natural languages to propose a new string matching algorithm. Unlike standard string matching algorithms that compares letters in the pattern against the text in fixed order, the proposed algorithm ranks the letters of the pattern according to their frequency of occurrence. The letters of patterns are then matched against text according to that ranking, starting with the letter with the least frequency of occurrence. The proposed algorithm significantly outperformed KMP algorithm in terms number of comparisons.
Revisiting Outlier Rejection Approach for Non-Lambertian Photometric Stereo

Photometric stereo offers a single camera-based approach to recover 3D information and has attracted wide range of applications in computer vision. Presence of non-Lambertian reflections in almost all the real-world objects limits the usage of the Lambertian model for surface normal vector estimation. Previous methods proposed to address such non-Lambertian phenomena employ an outlier rejection approach, while more recent methods introduce bidirectional reflectance distribution function models that can generate more accurate results. However, results with comparable accuracy can also be achieved by simply filtering the observed intensity values. This paper presents two novel outlier rejection techniques that attempt to identify the data that are more reliable and likely to be Lambertian. In the first technique, observed intensity values with less reliability are automatically eliminated. This reliability is determined by the responses from a newly introduced inter-relationship function. In the second technique, those photometric ratio equations that are less likely to be Lambertian are identified by observing the residue of the equations. By eliminating the data that are unreliable and likely to be non-Lambertian, surface normal vectors are more accurately estimated. Our comparative and reproducible experimental results using both real and synthetic data sets illustrate superior performance over the state-of-the-art methods, which validate our theoretical arguments presented in this paper.
Using Workflows to Build Compositions of Read-Write Linked Data APIs on the Web of Things

We showcase an approach to use workflows for composing applications from components with Linked Data interfaces. Our approach consists in an ontology and corresponding operational semantics. We use the ontology to describe workflow models and workflow instances in Linked Data. To execute workflows, we developed operational semantics for the ontology using ASM4LD, a rule-based language for specifying computation in the context of Read-Write Linked Data. For our interactive showcase, we use as components: (1) networked devices with sensors and actuators from the Internet of Things / Web of Things, and (2) APIs from the Web that produce and consume RDF.
Promotion of Techno-entrepreneurship Programs in different Countries: A Review

An entrepreneur is a key to initiating entrepreneurial activities. Various countries have taken different steps for promoting technopreneurship. On this backdrop we have attempted to explore the promotional efforts in various countries and the factors which enhance propensity of technopreneurial activities. To address these research questions, we have identified research articles on technopreneurship in different countries and systematically reviewed them. Few European countries have begun providing entrepreneurship education at the higher stages of academia. In some nations governing bodies are set up to help budding entrepreneur grow their business, incubators are set up in various national institutes, and the concept of the entrepreneurial university has also emerged. Investment in Research and Development has also been looked as initiating step for technopreneurship in many countries. This will create an ecosystem of technopreneurship and will help improve the economy of a nation by generating more number of jobs after setting up of more number of MSMEs (Micro, Small and Medium Enterprises) in the field of high technology. These programs are also likely to help an individual think more towards the improvement of the community and not only about profit making. We have found that the efforts by various countries are dependent on their regional priorities. However, there is more which can be done to research the key issues for not getting the desired impact of such programs.
The Role of MicroRNAs in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common cancers, leading to the second cancer-related death in the global. Although the treatment of HCC has greatly improved over the past few decades, the survival rate of patients is still quite low. Thus, it is urgent to explore new therapies, especially seek for more accurate biomarkers for early diagnosis, treatment and prognosis in HCC. MicroRNAs (miRNAs), small noncoding RNAs, are pivotal participants and regulators in the development and progression of HCC. Great progress has been made in the studies of miRNAs in HCC. The key regulatory mechanisms of miRNAs include proliferation, apoptosis, invasion, metastasis, epithelial-mesenchymal transition (EMT), angiogenesis, drug resistance and autophagy in HCC. And exosomal miRNAs also play important roles in proliferation, invasion, metastasis, and drug resistance in HCC by regulating gene expression in the target cells. In addition, some miRNAs, including exosomal miRNAs, can be as potential diagnostic and prediction markers in HCC. This review summarizes the latest researches development of miRNAs in HCC in recent years.
Pleasure, risk perception and consent among group sex party attendees in a small Canadian Urban Centre

Abstract From a health perspective, group sex parties represent risk environments, as multiple sexual partners and polysubstance use associated with these parties may present risk practices for sexually transmitted infection (STI) transmission. At the same time, group sex parties exemplify sex as recreation, expressed in contemporary sociological theory as sex as leisure or play. In this paper, we report on the findings of an exploratory qualitative study conducted with group sex party attendees and hosts/organisers in a mid-sized Canadian city. Thematic analysis of semi-structured interviews with thirteen individuals identified several sexual communities based on gender composition, sexual orientation, and sexual practice, with variation among and within groups in terms of sexual consent and STI disclosure. All the interviewees discussed at length issues of harm reduction and consent, suggesting that these factors contribute to participants’ group sex party experiences regardless of their sexual community. These insights highlight the potential use of group sex parties as avenues to understanding the integration of harm reduction practices with the pursuit of sexual pleasure, information applicable in the context of a wide range of sexuality education programmes.
Background-Suppressed Correlation Filters for Visual Tracking

Correlation filters (CF) visual object tracking is a powerful framework, with excellent tracking accuracy and beyond realtime frame rate. Its performance, however, can be severely degraded in cluttered background images. In this paper, we propose background-suppressed correlation filters (BSCF), a better CF tracking scheme, which can significantly improve the reliability and accuracy of CF trackers, without harming their beyond real-time speed. Specifically, we present a unified BSCF object function. We show that both the correlation filters and BS weight map can be efficiently and jointly solved in frequency domain. Extensive experiments on OTB-100 benchmark validate the effectiveness and generality of BS in improving multiple CF trackers with higher accuracy and robustness while maintaining their fast tracking speed. We also show BS boosted CF tracker can achieve comparable accuracy of the state-of-the-art spatially-regularized CF tracker but is 14 times faster.
Chirp Interference Mitigation for FHSS Communication Based on Compressive Sensing

A challenge in frequency hopping spread spectrum (FHSS) communication chirp interference mitigation is the requirement of a high sampling rate for wideband FHSS signal. We demonstrate that we could filter out the interference in the system after compressed sampling. Using different sparsity feature of the FHSS signal and the chirp interference, a chirp interference mitigation model is proposed in this paper, we achieve the interference mitigation by suppressing the interference components in the signal sparse coefficient vector which is estimated using the compressed data. The algorithm is designed using two stage compressive sampling matching pursuit (CoSaMP) algorithm. In contrast with traditional methods, the proposed method could suppress the chirp interference effectively with the prior information of the interference sparse degree. The simulation results show a reliable chirp interference mitigation performance even in low SNR and low sub- sampling rate, the interference mitigation performance is robust to the variations of the interference intensity and the interference cycle.
A Fast MPEG’s CDVS Implementation for GPU Featured in Mobile Devices

The Moving Picture Experts Group’s Compact Descriptors for Visual Search (MPEG’s CDVS) intends to standardize technologies in order to enable an interoperable, efficient, and cross-platform solution for internet-scale visual search applications and services. Among the key technologies within CDVS, we recall the format of visual descriptors, the descriptor extraction process, and the algorithms for indexing and matching. Unfortunately, these steps require precision and computation accuracy. Moreover, they are very time-consuming, as they need running times in the order of seconds when implemented on the central processing unit (CPU) of modern mobile devices. In this paper, to reduce computation times and maintain precision and accuracy, we re-design, for many-cores embedded graphical processor units (GPUs), all main local descriptor extraction pipeline phases of the MPEG’s CDVS standard. To reach this goal, we introduce new techniques to adapt the standard algorithm to parallel processing. Furthermore, to reduce memory accesses and efficiently distribute the kernel workload, we use new approaches to store and retrieve CDVS information on proper GPU data structures. We present a complete experimental analysis on a large and standard test set. Our experiments show that our GPU-based approach is remarkably faster than the CPU-based reference implementation of the standard, and it maintains a comparable precision in terms of true and false positive rates.
Random Dopant Fluctuation and Random Telegraph Noise in Nanowire and Macaroni MOSFETs

We present a systematic investigation of random dopant fluctuations and random telegraph noise instabilities in Nanowire and Macaroni MOSFETs via 3D atomistic Monte Carlo simulations. We discuss their dependence on geometry and doping and show that different trends appear with respect to planar devices. Some unexpected results are explained in terms of 3D percolation and electrostatic intearitv of the structures.
Prospecting Information Extraction by Text Mining Based on Convolutional Neural Networks–A Case Study of the Lala Copper Deposit, China

With geological big data becoming a focus of geoscience research, the vast amount of textual geoscience data provides both opportunities and challenges for data analysis and data mining. In fact, it does not seem possible to meet the demands of the big data age through the traditional manual reading for information extraction and gaining knowledge. In this paper, a workflow is proposed to extract prospecting information by text mining based on convolutional neural networks (CNNs). The aim is to classify the text data and extract the prospecting information automatically. The procedure involves three parts: 1) text data acquisition; 2) text classification based on CNN; and 3) statistics and visualization. First, the large amount of available text data was acquired based on geoscience big data acquisition methodologies. After text preprocessing, the CNN was used to classify the geoscience text data into four categories (geology, geophysics, geochemistry, and remote sensing), with each category consisting of three levels of text scales (word, sentence, and paragraph). Second, the word frequency statistics, co-occurrence matrix statistics, and term frequency–inverse document frequency (TF-IDF) statistics were for words, sentences, and paragraphs, respectively, which aimed to obtain the key nodes and links derived from the content-words. Finally, the deep semantic information of the big data mining of relevant geoscience texts was visualized by word clouds, knowledge graphs (e.g., the chord and bigram graphs), and TF-IDF statistical graphs. The Lala copper deposit in Sichuan province was taken as a test case, for which the prospecting information was extracted successfully by the developed text mining methodologies. This paper provides a strong basis for research into establishing mineral deposits prospecting models based on logical knowledge trees. In addition, it shows the great potential of this method for intelligent information extraction within geoscience big data.