Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Security Compliance Demands and Resources on Security Compliance-An Exploratory Study in Vietnam

This study extends current information security compliance research by adapting "work-stress model" of the Job Demands-Resources (JD-R) model to explore how security compliance demands and security resources influence the system users' information security compliance. The paper proposes that security compliance burnout and security engagement as the mediating factors between security compliance demands, security resources and individual security compliance. We employed a multi-case study method to explore the characteristics of security compliance demands and security resources that could influence security compliance. Interviews with system users in three organisations in Vietnam revealed three types of security compliance and four types of security resources that may influence security compliance burnout and engagement respectively. Practical implications of the initial findings are also presented.
Weighted Local Directional Pattern for Robust Facial Expression Recognition

A novel low-cost highly discriminatory feature space is introduced for facial expression recognition, which incorporates a weight to the Local Direction Pattern (LDP), capable of robust performance over a range of image resolutions. In addition, we use Adaboost to pick a small set of high-flying features, which are used by the Support Vector Machine (SVM) to classify facial expressions proficiently. Experimental results show that the proposed technique improves both the accuracy and the speed of the final classifier compares to other existing state-of-the-art methods.
Real-time free-viewpoint video rendering from volumetric geometry

The aim of this work is to render high-quality views of a dynamic scene from novel viewpoints in real-time. An online system available at our institute computes the visual hull as a geometry proxy to guide the rendering at interactive rates. Because only a sparse set of cameras distributed around the scene is used to record the scene, only an coarse model of the scene geometry can be recovered. To alleviate this problem, we render textured billboards defined by the voxel model of the visual hull, preserving details in the source images while achieving excellent performance. By exploiting multi-texturing capabilities of modern graphics hardware, real-time frame rates are attained. Our algorithm can be used as part of an inexpensive system to display 3D-videos, or ultimately even in live 3D-television. The user is able to watch the scene from an arbitrary viewpoint chosen interactively.
Relationship between fractal property of gait cycle and severity of Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by degeneration of dopaminergic neurons, affecting motor controls by basal ganglia. Serious movement disorders such as tremor or gait disturbance are often observed, but early diagnosis of PD is difficult. From such a background, Detrended Fluctuation Analysis (DFA) attracts attention as one of the methods for analyzing the fluctuation of the gait cycle in recent years. Therefore, the aim of this study is to clarify the relationship between the fractal exponent of DFA and disease severity of the PD patients. We performed the DFA analysis of gait cycle in 200 meters' walk of 17 PD patients and 12 healthy young people. Particularly, we divided PD patients based on the Hoehn and Yahr (HY) scale into an HY2 group (n=9) and an HY3 group (n=8) in order to examine the relation to disease severity. Results indicate that fractal exponent was significantly lower in both PD groups (HY2, HY3) compared to the young healthy person. Fractal exponent also tended to be lower for the HY3 group compared to the HY2 group, although this tendency for fractal exponent decreasing with the disease severity was not significant here. From these results, the randomness of gait fluctuation seems to related to the severity of PD, suggesting a possibility for diagnosis of PD using fluctuation analysis of gait.
FAdo:Interactive Tools for Learning Formal Computational Models

FAdo is an ongoing project which aims the development of an interactive environment for symbolic manipulation of formal languages. In this paper we focus in the description of interactive tools for teaching and assisting research on regular languages. In particular we focus in the description of an interactive environment for editing and visualising finite automata and the conversion between automata and regular expressions.
RNA synthesis in preovulatory mouse oocytes

RNA synthesis, previously shown to take place during oocyte growth, has been demonstrated throughout the growth-quiescent period preceding ovulation of the mouse oocyte. In the final 7-day preovulatory period, the level of incorporation of (5,6-3H)uridine into ovulated oocytes decreased as the interval between exposure to precursor and ovulation decreased; significant incorporation was detectable within 2 days before ovulation. Analysis of the frequency and density of label in ovarian oocytes at successive stages of meiosis in relation to the interval between adminstration of labeled precursor and collection of oocytes revealed that RNA synthesis continues up to within 2 h before GVBD.
Sidelock: authentication on mobile device sides

This paper presents Sidelock, a tangible authentication prototype on mobile devices. Grasp events and finger gestures are sensed by twenty capacitive sensors on left and right sides. They were used in a two-phase authentication process, in which grasp pattern wakes up devices and a 1-D template-based gesture recognizer verifies if input matches pre-defined password templates. Compared with other popular authentication approachers like PINs or grid lock, Sidelock has much larger password space and approximate recognition speed, with only 5.3% critical errors. The prototype could be minimized and embedded broadly in many common mobile devices. The user feedback suggests it is an memorable and acceptable authentication method.
Haptic Interaction with a Guide Robot in Zero Visibility

Search and rescue operations are often undertaken in dark and noisy environment in which rescue team must rely on haptic feedback for exploration and safe exit. However, little attention has been paid specifically to haptic sensitivity in such contexts or the possibility of enhancing communicational proficiency in the haptic mode as a life-preserving measure. The potential of root swarms for search and rescue has been shown by the Guardians project (EU, 2006-2010); however the project also showed the problem of human robot interaction in smoky (non-visibility) and noisy conditions. The REINS project (UK, 2011-2015) focused on human robot interaction in such conditions. This research is a body of work (done as a part of he REINS project) which investigates the haptic interaction of a person wit a guide robot in zero visibility. The thesis firstly reflects upon real world scenarios where people make use of the haptic sense to interact in zero visibility (such as interaction among firefighters and symbiotic relationship between visually impaired people and guide dogs). In addition, it reflects on the sensitivity and trainability of the haptic sense, to be used for the interaction. The thesis presents an analysis and evaluation of the design of a physical interface (Designed by the consortium of the REINS project) connecting the human and the robotic guide in poor visibility conditions. Finally, it lays a foundation for the design of test cases to evaluate human robot haptic interaction, taking into consideration the two aspects of the interaction, namely locomotion guidance and environmental exploration.
Evaluation Metrics of Service-Level Reliability Monitoring Rules of a Big Data Service

This paper presents new metrics to evaluate the reliability monitoring rules of a large-scale big data service. Our target service uses manually-tuned, service-level reliability monitoring rules. Using the measurement data, we identify two key technical challenges in operating our target monitoring system. In order to improve the operational efficiency, we characterize how those rules were manually tuned by the domain experts. The characterization results provide useful information to operators supposed to regularly tune such rules. Using the actual production failure data, we evaluate the same monitoring rules by using standard metrics and the presented metrics. Our evaluation results show the strengths and weaknesses of each metric and show that the presented metrics can further help operators recognize when and which rules need to be re-tuned.
CoRe-MAC: A MAC-Protocol for Cooperative Relaying in Wireless Networks

Cooperative relaying methods can improve wireless links, but introduce overhead due to relay selection and resource reservation compared to non-cooperative transmission. In order to be competitive, a cooperative relaying protocol must avoid or compensate for this overhead. In this paper, we present a MAC protocol for relay selection and cooperative communication as an extension to CSMA/CA which addresses resource reservation, relay selection, and cooperative transmission while keeping the overhead in terms of time and energy low. We discuss the efficiency of this protocol for packet error rate, throughput, and message delay in a multi-hop network. Simulation results show that the protocol performs similar and without noticeable overhead compared to standard CSMA/CA for good SNR while it is able to significantly improve throughput and reliability at larger distances.
ScalaGiST: Scalable Generalized Search Trees for MapReduce Systems [Innovative Systems Paper]

MapReduce has become the state-of-the-art for data parallel processing. Nevertheless, Hadoop, an open-source equivalent of MapReduce, has been noted to have sub-optimal performance in the database context since it is initially designed to operate on raw data without utilizing any type of indexes. To alleviate the problem, we present ScalaGiST - scalable generalized search tree that can be seamlessly integrated with Hadoop, together with a cost-based data access optimizer for efficient query processing at run-time. ScalaGiST provides extensibility in terms of data and query types, hence is able to support unconventional queries (e.g., multi-dimensional range and k-NN queries) in MapReduce systems, and can be dynamically deployed in large cluster environments for handling big users and data. We have built ScalaGiST and demonstrated that it can be easily instantiated to common B+-tree and R-tree indexes yet for dynamic distributed environments. Our extensive performance study shows that ScalaGiST can provide efficient write and read performance, elastic scaling property, as well as effective support for MapReduce execution of ad-hoc analytic queries. Performance comparisions with recent proposals of specialized distributed index structures, such as SpatialHadoop, Data Mapping, and RT-CAN further confirm its efficiency.
Ultrafine carbon black particles enhance respiratory syncytial virus-induced airway reactivity, pulmonary inflammation, and chemokine expression.

Exposure to particulate matter (PM) may exacerbate preexisting respiratory diseases such as asthma, chronic obstructive pulmonary disease (COPD), bronchitis, and pneumonia. However, few experimental studies have addressed the effects of PM on lower respiratory tract (LRT) viral infection. Respiratory syncytial virus (RSV) is a major etiological agent for LRT infections in infants, the elderly, and the immunocompromised and may lead to chronic wheezing and the development of asthma in children. In this study, we examined the effects of carbon black (CB) on RSV-induced pulmonary inflammation, chemokine and cytokine expression, and airway hyperresponsiveness in a mouse model of RSV. Female BALB/c mice were instilled via the trachea (i.t.) with 1 x 106 plaque forming units (pfu) RSV or with uninfected culture media. On day 3 of infection, mice were i.t. instilled with either 40 micro g ultrafine CB particles or with saline. End points were examined on days 4, 5, 7, and 14 of RSV infection. Viral titer and clearance in the lung were unaffected by CB exposure. Neutrophil numbers were elevated on days 4 and 7, and lymphocyte numbers were higher on days 4 and 14 of infection in CB-exposed, RSV-infected mice. CB exposure also enhanced RSV-induced airway hyperresponsiveness to methacholine, bronchoalveolar lavage (BAL) total protein, and virus-associated chemokines monocyte chemoattractant protein (MCP-1), macrophage inflammatory protein (MIP-1 alpha), and regulated upon activation, normal T cell expressed and secreted (RANTES). MIP-1 alpha mRNA expression was increased in the alveolar epithelium, where ultrafine particles deposit in the lung. These data demonstrate a synergistic effect of ultrafine CB particles on RSV infection, and suggest a potential mechanism for increased respiratory infections in human populations after PM exposure.
Collaboratively Annotating Multilingual Parallel Corpora in the Biomedical Domain—some MANTRAs

The coverage of multilingual biomedical resources is high for the English language, yet sparse for non-English languages―an observation which holds for seemingly well-resourced, yet still dramatically low-resourced ones such as Spanish, French or German but even more so for really under-resourced ones such as Dutch. We here present experimental results for automatically annotating parallel corpora and simultaneously acquiring new biomedical terminology for these under-resourced non-English languages on the basis of two types of language resources, namely parallel corpora (i.e. full translation equivalents at the document unit level) and (admittedly deficient) multilingual biomedical terminologies, with English as their anchor language. We automatically annotate these parallel corpora with biomedical named entities by an ensemble of named entity taggers and harmonize non-identical annotations the outcome of which is a so-called silver standard corpus. We conclude with an empirical assessment of this approach to automatically identify both known and new terms in multilingual corpora.
Design of 5′ Untranslated Sequences in Retroviral Vectors Developed for Medical Use

ABSTRACT Utilizing genetic modules of simple retroviruses, we have developed a novel generation of gene transfer vectors with improved therapeutic potential. In the 5′ untranslated “leader” sequences, all AUG codons which may aberrantly initiate translation and all viral coding sequences were removed. Thus, the probability of expressing unwanted peptides and the potential for homologous recombination with retroviral genes were largely reduced, and the cloning capacity was increased. The transgene was inserted to replace the viral gag sequences, and a new minimal splice acceptor was introduced, resulting in increased expression with all genes tested (those coding for human multidrug resistance 1 and enhanced green fluorescent protein, as well as the lacZ gene). These vectors may represent attractive tools for human gene therapy, because they increase the efficiency of transgene expression and may also increase safety in medical applications.
Estimating the Traffic Impacts of Green Light Optimal Speed Advisory Systems Using Microsimulation

Even though signalised intersections are necessary for urban road traffic management, they can act as bottlenecks and disrupt traffic operations. Interrupted traffic flow causes congestion, delays, stop-and-go conditions (i.e., excessive acceleration/deceleration) and longer journey times. Vehicle and infrastructure connectivity offers the potential to provide improved new services with additional functions of assisting drivers. This paper focuses on one of the applications of vehicle-to-infrastructure communication namely Green Light Optimal Speed Advisory (GLOSA). To assess the effectiveness of GLOSA in urban road network an integrated microscopic traffic simulation framework is built into VISSIM software. Vehicle movements and vehicle-infrastructure communications are simulated through the interface of External Driver Model. A control algorithm is developed for recommending an optimal speed that is continuously updated in every time step for all vehicles approaching a signal-controlled point. This algorithm allows vehicles to pass a traffic signal without stopping or to minimise stopping times at a red phase. This study is performed with all connected vehicles at 100% penetration rate. Conventional vehicles are also simulated in the same network as a reference. A straight road segment composed of two opposite directions with two traffic lights per lane is studied. The simulation is implemented under 150 vehicles per hour and 200 per hour traffic volume conditions to identify how different traffic densities influence the benefits of GLOSA. The results indicate that traffic flow is improved by the application of GLOSA. According to this study, vehicles passed through the traffic lights more smoothly, and waiting times were reduced by up to 28 seconds. Average delays decreased for the entire network by 86.46% and 83.84% under traffic densities of 150 vehicles per hour per lane and 200 vehicles per hour per lane, respectively.
Risk Sharing in CO2 Delivery Contracts for the CCS-EOR Value Chain

A key reason for poor performance in large capital projects is the weak incentives facing the involved entities to deliver optimal project outcomes. The weak incentives arise from misalignment of interests of the individual entities with the common interest of the project, thus resulting in sub-optimal decisions that do not maximize the overall project value. Contractual risk-sharing is key to aligning the interests of the individual entities and incentivizing them to make optimal decisions. We develop a framework to quantify the impact of the project contract terms on the financial value of large energy capital projects. We focus on a prototype carbon capture and storage (CCS) project wherein the power plant company (that captures the CO2) is linked to the oil field company (that stores the CO2 for enhanced oil recovery, EOR) through a long-term CO2 delivery contract. We evaluate alternate CO2 contract structures in terms of the incentives the contracts provide to the individual entities to respond to changes in the market risk factors (oil price, electricity price, and CO2 emission penalty). The results show that inappropriate risk allocation, as in fixed price CO2 contracts, leads to significant loss in project value. This loss under fixed price CO2 contracts is due to high contracting risks associated with ex post insolvencies and weak incentives for contingent decision-making. We find that risk sharing offered by oil-indexed price CO2 contracts significantly reduces the contracting risks and thus considerably increases the project value compared to a fixed price CO2 contract. Analyzing the weaknesses of alternate CO2 contract structures gives insights into the design of optimal CO2 contracts for the CCS-EOR value chain.
Variable Star Network: World Center for Transient Object Astronomy and Variable Stars (Special Issue: Resent Results from VSNET)

Variable Star Network (VSNET) is a global professional-amateur network of researchers in variable stars and related objects, particularly in transient objects, such as cataclysmic variables, black-hole binaries, supernovae, and gamma-ray bursts. The VSNET has been playing a pioneering role in establishing the field of transient object astronomy, by effectivelyincorporating modern advancesin observationalastronomy and global electronic networks, as well as collaborative progress in theoretical astronomy and astronomical computing. The VSNET is now one of the best-featured global networks in this field of astronomy. We review the historical progress, design concept, associated technology, and a wealth of scientific achievements powered by VSNET.
Mapping Climate Change Vulnerabilities to Infectious Diseases in Europe

Background: The incidence, outbreak frequency, and distribution of many infectious diseases are generally expected to change as a consequence of climate change, yet there is limited regional information available to guide decision making. Objective: We surveyed government officials designated as Competent Bodies for Scientific Advice concerning infectious diseases to examine the degree to which they are concerned about potential effects of climate change on infectious diseases, as well as their perceptions of institutional capacities in their respective countries. Methods: In 2007 and 2009/2010, national infectious disease experts from 30 European Economic Area countries were surveyed about recent and projected infectious disease patterns in relation to climate change in their countries and the national capacity to cope with them. Results: A large majority of respondents agreed that climate change would affect vector-borne (86% of country representatives), food-borne (70%), water-borne (68%), and rodent-borne (68%) diseases in their countries. In addition, most indicated that institutional improvements are needed for ongoing surveillance programs (83%), collaboration with the veterinary sector (69%), management of animal disease outbreaks (66%), national monitoring and control of climate-sensitive infectious diseases (64%), health services during an infectious disease outbreak (61%), and diagnostic support during an epidemic (54%). Conclusions: Expert responses were generally consistent with the peer-reviewed literature regarding the relationship between climate change and vector- and water-borne diseases, but were less so for food-borne diseases. Shortcomings in institutional capacity to manage climate change vulnerability, identified in this assessment, should be addressed in impact, vulnerability, and adaptation assessments.
The effect of steroid hormones on the mRNA expression of oct4 and sox2 in uterine tissue of the ovariectomized mice model of menopause

Background: The uterus is a dynamic tissue responding to hormonal changes during reproductive cycles. As such, uterine stem cells have been studied in recent years. Transcription factors oct4 and sox2 are critical for effective maintenance of pluripotent cell identity. Objective: The present research evaluated the mRNA expression of oct4 and sox2 in the uterine tissues of ovariectomized mice treated with steroid hormones. Materials and Methods: In this experimental study, adult virgin female mice were ovariectomized and treated with estradiol 17β (E2), progesterone (P4), and a combination of E2 and P4 (E2 & P4) for 5 days. Uterine tissues were removed, and immunofluorescent (IF) staining and quantitative real-time PCR of oct4 and sox2 markers were performed. Results: IF showed oct4 and sox2 expression in the uterine endometrium and myometrium among all groups. The mRNA expression of oct4 (p=0.022) and sox2 (p=0.042) in the E2-treated group significantly were decreased compared to that in the control group. By contrast, the mRNA expression of oct4 and sox2 in the P4 (p=0.641 and 0.489 respectively) and E2 & P4-treated groups (p=0.267 and 0.264 respectively) did not show significant differences compared to the control group. Conclusion: The results indicate ovarian steroid hormones change the expression of oct4 and sox2 in the mice uterine tissues, which suggest the involvement of steroid hormonal regulation in uterine stem cells.