Miracle-dz/UniFAR-FTUs huggingface dataset:
Signalling Security Analysis: Is HTTP/2 Secure in 5G Core Network?

The incoming 5G mobile network owns many advanced features. In the core network respect, 5G features Control and User Plane Separation (CUPS), Service Based Architecture (SBA), Network Slicing (NS) and Access Agnostic. To get all those new features realized, a new 5G system architecture and protocol stack have been designed. 5G adopts HTTP/2 as application layer protocol which means all the network entities in control plane will communicate with each using HTTP/2. However, HTTP/2 may cause new problems in the totally new designed Service-based Architecture (SBA). In this paper, we present a comprehensive study on HTTP/2 to be used in 5G core network and introduce the possible vulnerabilities and security problems. Firstly, based on the technology standards released by 3GPP, we give an overview of 5G architecture—Service based architecture; secondly, we introduce the new features of HTTP/2 protocol; thirdly, we evaluate the security problems that may occur in 5G core network.
Detection of Conferences Attendies from Interactions Can We Know if Our Colleague Attend a Given Conference?

In this paper, we propose to apply pragma-linguistics and Natural Language Processing (NLP) techniques, in order to isolate researchers interests and their intention to attend a given conference, journey, colloquium, seminar, … This type of work can be applied in a laboratory or a research group. That help, for instance, to organize meetings in a conference, to ask proceedings and papers.
Survey on Extractive Text Summarization Methods with Multi-Document Datasets

Text summarization has been one of the key research areas in Natural Language Processing (NLP) for a while. The various methods to summarize one or more documents can be broadly classified into extractive and abstractive text summarization where the former involves selecting key parts in the document and embedding into the summary while balancing between salience and redundancy. The latter involves creating new sentences to provide a summary of the documents. Extractive summarization can further be done in a supervised manner with humans or an unsupervised manner without any human intervention. This paper provides the knowledge a few of the current methods to perform extractive text summarization where the input would be multi document sets. Multi document summarization can consider two types of document sets; a homogeneous set of documents which have a common topic or theme and a heterogeneous set where the main topic for the documents are unrelated but they contain some form information that is related to the summary. The first method uses sentence regression where they consider performing sentence ranking along with sentence relations followed by greedy selection process. The second is an unsupervised paragraph embedding method utilizing a density peaks clustering method. The third method proposes document-level reconstruction using a neural document model. The fourth method is a query focused, joint neural network based model with an attention mechanism. The fifth method concentrates on coherence by providing a graph-based model which does not require discourse analysis as a prerequisite. We also see a way to create a heterogeneous multi-documentcorpus along with the limitations of each of these methods.
CAS(ME)$^2$ : A Database for Spontaneous Macro-Expression and Micro-Expression Spotting and Recognition

Deception is a very common phenomenon and its detection can be beneficial to our daily lives. Compared with other deception cues, micro-expression has shown great potential as a promising cue for deception detection. The spotting and recognition of micro-expression from long videos may significantly aid both law enforcement officers and researchers. However, database that contains both micro-expression and macro-expression in long videos is still not publicly available. To facilitate development in this field, we present a new database, Chinese Academy of Sciences Macro-Expressions and Micro-Expressions (CAS(ME)<inline-formula><tex-math notation="LaTeX">$^2$</tex-math> <alternatives><inline-graphic xlink:href="fu-ieq2-2654440.gif"/></alternatives></inline-formula>), which provides both macro-expressions and micro-expressions in two parts (A and B). Part A contains 87 long videos that contain spontaneous macro-expressions and micro-expressions. Part B includes 300 cropped spontaneous macro-expression samples and 57 micro-expression samples. The emotion labels are based on a combination of action units (AUs), self-reported emotion for every facial movement, and the emotion types of emotion-evoking videos. Local Binary Pattern (LBP) was employed for the spotting and recognition of macro-expressions and micro-expressions and the results were reported as a baseline evaluation. The CAS(ME)<inline-formula><tex-math notation="LaTeX">$^2$</tex-math><alternatives> <inline-graphic xlink:href="fu-ieq3-2654440.gif"/></alternatives></inline-formula> database offers both long videos and cropped expression samples, which may aid researchers in developing efficient algorithms for the spotting and recognition of macro-expressions and micro-expressions.
Linear Discriminative Sparsity Preserving Projections for Dimensionality Reduction

Recently, some works have demonstrated that combining manifold learning or sparse subspace learning methods with global discriminative learning methods is an effective way to improve the performance for image recognition tasks. In this paper, to further exploit the robustness of those combinations, we propose a new algorithm called linear discriminative sparsity preserving projections (LDSPP). Different from the previous works which only consider within-class or between-class discriminative information, LDSPP takes both of them into account and adds them to sparse subspace learning model to establish objective function for dimensionality reduction. Experiments on Yale and ORL face image datasets demonstrate the effectiveness of the proposed method.
Isolated hepatitis B core antibody in HIV infected patients--can response to hepatitis B vaccine help to elucidate the cause?

Background: Concomitant hepatitis B and HIV infections are common. In some of these patients, HBcAb is the only serologic marker of hepatitis B. This study was conducted to elucidate the cause of isolated HBcAb in HIV-infected patients via hepatitis B vaccination. Methods: In this interventional study during 2014-15 in the HIV Clinic in Hamadan, thirty four patients with HIV infection and isolated HBcAb positive isolate, received hepatitis B vaccine and their responses to vaccination were investigated. Demographic data, stage of disease, and status of CD4 and HCV Ab were extracted from the patients' medical records and were entered in a checklist. Results: Of the 103 HIV positive patients, the prevalence of HBs Ag, and HBc Ab isolates were 6.79% (n=7) and 46.6% (n=48), respectively. All of the patients with isolated HBcAb were positive for HCV Ab. Among the 48 patients with isolated HBc Ab, 34 (70.8%) were available and examined for HBV DNA in serum samples. The result of PCR was negative in all. After the first round of hepatitis B vaccination, HBs Ab titer exceeded 10 International Units Per Liter (IU/L) in 58.8% of patients with isolated HBc Ab. With the completion of the three-dose of vaccine, this titer was observed in 97% of patients. Significant correlation was observed between titer of antibodies and values of CD4 cells. Conclusions: Due to favorable response to hepatitis B vaccination in HIV positive patients with isolated HBc Ab, false positive HBc Ab and recovery from previous infection were more probable than hidden hepatitis B.
A facile approach towards copper oxide nanoparticles synthesis using Spirulina platensis and assessment of its biological activities

There is a budding need to develop a method for environmentally benign metal nanoparticle synthesis, that do not use toxic chemicals in the synthesis protocols to avoid adverse effects in medical applications. The present investigation dealt with the synthesis of copper oxide (CuO) nanoparticles from blue green alga, Spirulina platensis. The algal extract consisting of phytochemicals was used as the reducing agent and copper sulphate as the substrate. Synthesised nanoparticles were characterized by UV-Vis spectrophotometry, FT-IR spectroscopy, XRD and SEM. Antibacterial and anticancer activities were assessed for the CuO nanoparticles. The results indicated that the formed CuO nanoparticles were observed to be nanosheets. FT-IR spectral analysis elucidated the occurrence of biomolecules required for the reduction of copper oxide ions. The synthesized nanoparticles were found to be effective at the concentration of 1 mg/mL against Escherichia coli, Bacillus subtilis, Pseudomonas aeruginosa and Serratia marcescens. The cytotoxicity activity of CuO nanoparticle was evaluated by MTT Assay against colon cancer cell lines and confirmed that CuO nanoparticle at a concentration of 125 μg/mL had cytotoxic activity. In conclusion, the CuO nanoparticles were synthesized at a low energy supply, in an ecologically safe mode which could be utilized for pharmacological applications and various biotechnological studies.
A prescription for “nature” – the potential of using virtual nature in therapeutics

Many studies suggest that increased exposure to urban greenness is associated with better population health. Accessing nature can in some circumstances, however, be difficult, especially for individuals with mobility constraints. Therefore, a growing body of work is investigating the ways to replace the in vivo experience with forms of “virtual” contact, in order to provide these individuals with at least some benefits of the natural environment. The aim of this paper is to provide a review of previous use of virtual reality (VR) nature in health and care settings and contemplate the potential use of this technology in future. Our central question is whether engaging with virtual nature can contribute to enhanced physical and emotional well-being in housebound or mobility-constrained individuals. We conclude that while contact with real-world nature is preferred, VR use can be an alternative in cases when in vivo contact with nature is not possible. There are many possibilities for the use of VR technology in psychiatric and medical care; however, the risks, benefits, and cost efficiency of these attempts should be carefully assessed and the outcomes should be measured in a scientifically valid manner. The current review has nonetheless demonstrated that VR nature could play a role in each of the proposed mediating mechanisms linking natural environments and health.
Find Optimal Model Among Various Neural Networks Models Using Monte-Carlo Tree Search

Nowadays, most researchers in machine learning field will agree that the deep neural networks(DNNs) provide the best performance in pattern recognition, computer vision, natural language processing and so on. So many DNNs architectures are built relying on mathematical heuristics and prior knowledge. We can make some new models by ourselves depending on the platforms, the type of data and applications. If we can experiment all possible cases, then we will find the best approximate solution. Unfortunately, it is hard to do like that because experimenting every possible case costs too much resources. In this paper, we suggest the method to find optimal neural networks model among various models by using Monte-Carlo Tree Search [1].
Data Augmentation via Latent Space Interpolation for Image Classification

Effective training of the deep neural networks requires much data to avoid underdetermined and poor generalization. Data Augmentation alleviates this by using existing data more effectively. However standard data augmentation produces only limited plausible alternative data by for example, flipping, distorting, adding noise to, cropping a patch from the original samples. In this paper, we introduce the adversarial autoencoder (AAE) to impose the feature representations with uniform distribution and apply the linear interpolation on latent space, which is potential to generate a much broader set of augmentations for image classification. As a possible “recognition via generation” framework, it has potentials for several other classification tasks. Our experiments on the ILSVRC 2012, CIFAR-10 datasets show that the latent space interpolation (LSI) improves the generalization and performance of state-of-the-art deep neural networks.
Semi-Supervised Cerebrovascular Segmentation by Hierarchical Convolutional Neural Network

Due to the tortuosity and the complexity of cerebral vasculature and the similar intensity distribution with the background, it remains challenging to accurately segment cerebral vessels from magnetic resonance angiography (MRA). The previous rule-based methods have limitations when applied to accurate clinical diagnosis, such as the under-segmentation on complex vessels, the dependence on domain knowledge, and the lack of quantification estimation. In this paper, we proposed a semi-supervised cerebrovascular segmentation method with a hierarchical convolutional neural network (H-CNN) that transfers the exquisite model/feature design in rule-based methods to solve the mapping from MRA images to cerebral vessels. First, we generated the tube-level labels of cerebral vessels with centerlines and estimated radii. Second, we constructed and trained an H-CNN with the MRA images and corresponding tube-level labels. Third, the stopping criterion of the proposed H-CNN was determined by the comprehensive index (CI) that was defined based on partially annotated voxel-level ground truth. The comparison of our H-CNN with the vesselness, bi-Gaussian, optimally oriented flux, vessel enhancing diffusion, hybrid diffusion with continuous switch, Mimics software, convolutional neutral network2D (CNN)2D, and CNN3D were conducted on six testing images. The mean sensitivity, accuracy, and the CI of our H-CNN are 94.69%, 97.85%, and 2.99%, respectively, outperforming the other methods. The curved planar reformation also visualized the performance of H-CNN for cerebrovascular segmentation. Given only the tube-level labels, the proposed H-CNN method accomplished the voxel-level vessel segmentation via the hierarchical update of CNN. The H-CNN can potentially to be applied for the accurate diagnosis of cerebrovascular diseases and other medical image segmentation with only partially correct labels.
How Management Consultancies Make Sense of Digital Strategy

Digital technologies are increasingly shaping our lives. They have become an integral part of many products and services, have changed the way companies interact with their customers, and have even opened up new business opportunities that challenge contemporary business models. The impact of digitalization is so fundamental that researchers demand strategic responses. For this purpose, they have introduced a new concept to the academic debate often referred to as “digital strategy”. However, beyond basic assumptions, there is not much consensus on what a digital strategy is. At the same time, management consultancies have made digitalization a core concern and use the notion of a digital strategy extensively. Hence, given the nascent state of research, we have taken a systematic look at how management consultancies make sense of the concept drawing on empirical content analysis. Our analysis revealed a couple of definitional characteristics as well as four prevailing ways of looking at digital strategy: (1) the strategy level, (2) the strategy governance (3) the devices of manifestation and (4) the strategizing logic. Our research contributes to the academic debate by providing a fresh look at how management consultancies make sense of digital strategy as well as the assumptions consultancies make about their development.
Self-Talk: Responses to Users' Opinions and Challenges in Human Computer Dialog

People like to be, or partly, encouraged when their opinions or challenges are supported by listeners, even the listeners are robots. Encouraging responses from the robot which seem to get users' points potentially improve users' feeling in human computer dialog. According to this hypothesis, this paper proposes a method to generate supporting responses to users' opinions or challenges. The core ideas and contributions of the proposed method are: (1) multiple search engines cooperate, and (2) each engine random asks itself or ask another one to obtain more related information from the internet in multiple turns; then (3) final responses are abstracted from the answers. We call these three steps as Self-Talk. The comparisons between Self-Talk and several commercial open speech assistants show that the proposed method does generate suitable answers to users when they present their opinions or challenges in dialog. The hypothesis is positively evaluated that encouraging responses could improve users' chat feeling.
Development of Technical Solution for Automated Compact Cogeneration Plant Using Search Engineering

The application of search design methods in the development of new products and technologies can significantly reduce the time and cost of engineering. When implementing new methods for the production of electric and thermal energy, designing of an appropriate device presents a challenge. The article describes the process of creating of such a device using the engineering and physical method of search design. The method involves constructing a model of the physical principle of operation and defining elementary functions of the structural elements associated with the model. This allows obtaining a large variety of possible options for technical solutions of the desired device, followed by the determination of the most promising options for the implementation using computing. Thus, the development of design options for a new device is reduced to a deterministic sequence of actions, leading to lower qualification requirements to the developers of new products. The theoretical foundations of the method can also be used for teaching students and novice designers. A device has been developed where the collateral energy, which is developed when receiving electric power, is used for obtaining thermal energy, with the electric power produced being redistributed for additional water heating by means of an adaptive control system in the absence of need for the use thereof by external consumers. This increases the efficiency of the plant. Owing to a more effective use of the thermal and electric energy received when fuel is combusted, the energy efficiency and functionality of the system increase generally. A prototype is designed and made for the pilot study of the device developed, which consists of a gasoline electric generator, a heat exchanger, a TEH (thermal electric heater), and pumps, as well as a control system on the basis of a microcontroller and sensors. The forward estimate of the operability of the plant developed has been carried out and also the influence of various factors on the efficiency of water heating has been investigated.
Software Defect Detection Using Machine Learning Techniques

Software defect detection is mainly used for identifying the defective modules that are present in the software so that it helps in improving the quality of the software system. Machine learning techniques are useful in terms of software defect detection. Software is divided into defective and non-defective modules. Decision Tree and Logistic Regression algorithms are implemented for the classification of defective data set. The datasets are taken from the promise data repository and thereby accuracy is computed. The algorithms are implemented using Weka tool as well as Python idle and the comparative analyses of the results are exhibited.
Learning Collaborative Model for Visual Tracking

This paper proposes a robust visual tracking method by designing a collaborative model. The collaborative model employs a two-stage tracker and a HOG-based detector, which exploits both holistic and local information of the target. The two-stage tracker learns a linear classifier from the patches of original images and the HOG-based detector trains a linear discriminant analysis classifier with the object exemplar. Finally, a result decision making strategy is developed by considering both the original template and the appearance variations, making the tracker and the detector collaborate with each other. The proposed method has been evaluated on OTB-50, OTB-100 and Temple-Color datasets, and results demonstrate that the proposed method is able to effectively address the challenging cases such as scale variation and out-of-view and gets better performance than the state-of-the-art trackers.
Design and Characterization of a New MV PLC Coupler for Smart Electric Energy Systems

This paper deals with the design, modeling and characterization of a new coupling system for medium voltage (MV) narrow band power line communications (NB-PLC). It exploits the capacitive divider of voltage detecting systems (VDSs) normally used to reveal the mains voltage presence, thus avoiding the use of dedicated MV couplers. The proposed coupling system was patented by the authors. In this paper, the modeling of the VDS capacitive divider is presented, using a proper characterization method. Moreover, the interface card design is described, based on simulation of the whole PLC transmission channel. Laboratory tests are also presented and discussed. More in detail, a frequency response analysis is performed to validate the developed model and the related simulations. Moreover, transmission tests with nPSK modems demonstrate that the proposed coupling system can be effective for narrowband modulated signal transmission. With respect to other tests presented in previous papers, in this paper the experimental characterization and validation was performed in an extended frequency range up to 200 kHz.
Performance Study of Spark on YARN Cluster Using HiBench

Recently, various kinds of Internet-of-Things (IoT) solutions and services are provided such as smart industry, smart city, smart factory, smart agriculture and etc. Those solutions and services generate large amount of data from various devices which are connected through networks while they communicate with each other. However, it is a difficult problem to process the fast and massively produced data efficiently. To solve the problems in the framework level, there are many open-source big data processing and analysis frameworks. To process large-scale data in a fast manner, those frameworks use a cluster consisting of multiple computing machines. However, to set the framework running on large-scale cluster properly is not simple and it is difficult to verify its performance in the distributed environment. In this paper, we evaluate the performance of Apache Spark which is one of the most popular big data processing and analysis frameworks. Especially, we conduct experiments by using a representative benchmark tool, called HiBench, and large-scale data in the cluster environment. From the experimental results, we can conclude that Spark is highly scalable for distributed machine learning as well as big data processing.