Miracle-dz/UniFAR-FTUs huggingface dataset:
Complying With Data Handling Requirements in Cloud Storage Systems

In past years, cloud storage systems saw an enormous rise in usage. However, despite their popularity and importance as underlying infrastructure for more complex cloud services, today’s cloud storage systems do not account for compliance with regulatory, organizational, or contractual data handling requirements by design. Since legislation increasingly responds to rising data protection and privacy concerns, complying with data handling requirements becomes a crucial property for cloud storage systems. We present Prada, a practical approach to account for compliance with data handling requirements in key-value based cloud storage systems. To achieve this goal, Prada introduces a transparent data handling layer, which empowers clients to request specific data handling requirements and enables operators of cloud storage systems to comply with them. We implement Prada on top of the distributed database Cassandra and show in our evaluation that complying with data handling requirements in cloud storage systems is practical in real-world cloud deployments as used for microblogging, data sharing in the Internet of Things, and distributed email storage.
Flat Knitting Loop Deformation Simulation Based on Interlacing Point Model

Abstract In order to create realistic loop primitives suitable for the faster CAD of the flat-knitted fabric, we have performed research on the model of the loop as well as the variation of the loop surface. This paper proposes an interlacing point-based model for the loop center curve, and uses the cubic Bezier curve to fit the central curve of the regular loop, elongated loop, transfer loop, and irregular deformed loop. In this way, a general model for the central curve of the deformed loop is obtained. The obtained model is then utilized to perform texture mapping, texture interpolation, and brightness processing, simulating a clearly structured and lifelike deformed loop. The computer program LOOP is developed by using the algorithm. The deformed loop is simulated with different yarns, and the deformed loop is applied to design of a cable stitch, demonstrating feasibility of the proposed algorithm. This paper provides a loop primitive simulation method characterized by lifelikeness, yarn material variability, and deformation flexibility, and facilitates the loop-based fast computer-aided design (CAD) of the knitted fabric.
Creating Virtual Synthetic Cultures for Intercultural Training

Currently, interactive virtual environments for inter-cultural training are mostly designed to train in a specific target culture, focusing on the communication aspects of that culture. We are currently pursuing a different approach, one where users learn to broadly cope with culture shock as they interact with virtual agents that can enact different synthetic cultures. To create such agents we propose an architecture that integrates cultural aspects in the way the agent feels and chooses its goals and actions, based on anthropological studies. This architecture was applied in an agent-based educational role-playing game that tries to promote inter-cultural empathy in young teenagers.
Sensitivity analysis of the tree distribution model Phenofit to climatic input characteristics: implications for climate impact assessment

Species distributions are already affected by climate change. Forecasting their long‐term evolution requires models with thoroughly assessed validation. Our aim here is to demonstrate that the sensitivity of such models to climate input characteristics may complicate their validation and introduce uncertainties in their predictions. In this study, we conducted a sensitivity analysis of a process‐based tree distribution model Phenofit to climate input characteristics. This analysis was conducted for two North American trees which differ greatly in their distribution and eight different types of climate input for the historic period which differ in their spatial (local or gridded data) and temporal (daily vs. monthly) resolution as well as their type (locally recorded, extrapolated or simulated by General Circulation Models). We show that the climate data resolution (spatial and temporal) and their type, highly affect the model predictions. The sensitivity analysis also revealed, the importance, for global climate change impact assessment, of (i) the daily variability of temperatures in modeling the biological processes shaping species distribution, (ii) climate data at high latitudes and elevations and (iii) climate data with high spatial resolution.
Magnetoencephalography and Magnetic Source Imaging

This chapter focuses on magnetoencephalography (MEG) used in brain imaging and its use in localizing the brain sources of externally recorded spontaneous activity and stimulus and task – induced activation. The chapter first describes the instruments used for recording the magnetoencephalographic signals and the neurogenesis of these signals. It then considers proposed solutions for the “inverse” problem and describes approaches for MEG source estimation, including a method that specifies only one or many equivalent current dipoles. It also explains the signal sourcelocalizing technique known as beamforming and concluding with a discussion of practical issues in MEG/ MSI, with emphasis on those arising in clinical applications of the method.
Sweat Gland Awakening on Physical Training: A Skin Capacitance Mapping Observation

Skin capacitance mapping (SCM) is an established real-time non- invasive method useful in the assessment of sweat production. We presently review and explore the sweat gland production following a mild 10-min physical training on a cycloergometer. Sprouts of sweat vapor are progressively increased by recruiting increasing numbers of active sweat pores. An overall moisturization of the stratum corneum ensues, and liquid sweat appears in a further step. These aspects are conveniently assessed using SCM while blind assessments of the skin surface water loss cannot distinguish these functional features.
Both flowering time and distance to conspecific plants affect reproduction in Echinacea angustifolia, a common prairie perennial

In small fragmented plant populations, reproductive failure due to pollen limitation is often attributed to spatial isolation of individuals. While flowering time has been shown to affect seed set, its role in pollen limited fragmented populations is less understood. In this study, we quantified near‐neighbour distances, flowering phenology, and how they interact to affect seed set in individual plants. We followed the daily flowering phenology of over 2400 heads on over 500 Echinacea angustifolia individuals and quantified the resulting seed set during three consecutive flowering seasons. The study was conducted in an experimental plot where we randomized planting locations to eliminate spatial patterns of mate availability which are common in fragmented populations of Echinacea, a self‐incompatible plant. We found that individual flowering time had a larger and more consistent effect on seed set than did spatial location. Seed set in the earliest flowering plants exceeded seed set in the latest by 46–70% in all three years. The role of spatial isolation, characterized both by individual distance to conspecific plants and by location in the plot, was less consistent and showed a weaker relationship with seed set than did flowering phenology. The most isolated plants set 20–27% less seed than the least isolated plants in 2005–2006 with no difference in 2007. In one year, we quantified seed set by floret position within a flowering head. We found significant positional effects; however, effects due to flowering time were much greater. These results were more consistent with the pollen limitation hypothesis than the resources limitation hypothesis. Synthesis. Our results illustrate that flowering time and distance to neighbouring conspecifics can cause reproductive failure in fragmented populations, even in the absence of mate limitation caused by mating incompatibility. These findings suggest that flowering time may be an underappreciated contributor to reproductive failure in small fragmented populations.
Intraguild predation reduces bacterial species richness and loosens the viral loop in aquatic systems: 'kill the killer of the winner' hypothesis

Viruses perform an ecological function (diversity regulation) and a biogeochemical function (viral loop) in microbial food webs. We used an idealized food web model to evaluate the impact of viral loss by protozoan grazing on these 2 functions. Viral loss by protozoan grazing is an example of intraguild predation (IGP); protozoan predators kill viruses, which in turn kill the winner of the nutrient competition among bacteria. The model considers the interactions among uninfected and infected host cells, free-living viruses, protozoa, and nutrients. Calculations showed that this 'kill the killer of the winner' (KKW) process has a negative effect on bacterial species richness (BSR). This is because the KKW process reduces the virus to bacteria ratio, which undermines the 'kill the winner' (KW) process. In particular, the intensity of the KKW process is influenced by the latent period of viruses; a longer latent period leads to a reduction in BSR by causing the viruses in host cells to be more at risk of being killed by grazing. In addition, under high IGP with a long latent period, eutrophication leads to a decrease in the virus to protozoa ratio, resulting in a lower BSR and a smaller contribution of the virus loop to nutrient cycling, whereas the changes are minimal under low IGP with a short latent period.
Open Source Multi-Head 3D Printer for Polymer-Metal Composite Component Manufacturing

As low-cost desktop 3D printing is now dominated by free and open source self-replicating rapid prototype (RepRap) derivatives, there is an intense interest in extending the scope of potential applications to manufacturing. This study describes a manufacturing technology that enables a constrained set of polymer-metal composite components. This paper provides (1) free and open source hardware and (2) software for printing systems that achieves metal wire embedment into a polymer matrix 3D-printed part via a novel weaving and wrapping method using (3) OpenSCAD and parametric coding for customized g-code commands. Composite parts are evaluated from the technical viability of manufacturing and quality. The results show that utilizing a multi-polymer head system for multi-component manufacturing reduces manufacturing time and reduces the embodied energy of manufacturing. Finally, it is concluded that an open source software and hardware tool chain can provide low-cost industrial manufacturing of complex metal-polymer composite-based products.
Voces. An R-based Dashboard for Lexical Semantics

Voces (from Lat. vox 'voice', 'word') is an analysis and visualisation dashboard for corpus-based research in lexical semantics. Currently developed as a Shiny application communicating with R session running in the background, Voces provides users with possibly exhaustive account of how selected Latin word is distributed across the corpus and what can be told about its meaning. The application is built around a corpus which currently consists of ca. 200M words from texts dating from the Classical era (1 BCE) to the Middle Ages (14th CE). Although Voces was originally conceived as a tool of historical semantics research, the application due to its modular design may be modified and the code basis can be re-used in new research contexts.
Fabric Selection for the Reference Clothing Destined for Ergonomics Test of Protective Clothing—Sensorial Comfort Point of View

Abstract The main purpose of this study is the selection of a proper fabric for the reference clothing for ergonomic tests of protective clothing. For research, seven fabric of different raw material content and different structure were chosen. We studied the handle of fabrics produced from blend of polyester/cotton and polyester/Tencel, which were designated by letters from A to G. The assessment of handle of the fabric was performed based on the mechanical properties of fabrics using Kawabata evaluation system (KES-system). It was proven that one of the tested fabrics (F) made of polyester and cotton fibers (85% PES / 15% cotton) with the reinforced twill weave is characterized by the highest total hand value (THV). The high THV results from the low value of koshi (stiffness) and the highest value of numeri (smoothness) and fukurami (fullness). However, in terms of physiological comfort, the lower value of fukurami is more preferred. It turned out that the fabric with the higher value of fukurami (including fabric F) is characterized by the lower air permeability and higher water vapor resistance. At the end, we decided that the reference clothing will be made of cotton/polyester fabric G with the lowest mass per square meter because of the very good physiological comfort parameters and the satisfactory sensorial comfort parameters.
Transfer of Mathematical Knowledge: Series

The aim of this study is to explain students' ability to transfer their knowledge about mathematical series to the problems that they encounter. The data of the study were obtained by using two different tests, namely "Problem Solving Test (PST)" and "Series Character Identification Test (SCT)" which were developed by the researchers. The study was conducted to third- grade students from department of elementary school mathematics education in the 2009­ 2010 academic year. In view of the analysis of the data, it was observed that the students experienced no difficulty in the SCT which required procedural knowledge. They experienced difficulty in the PST which required skill to transfer their knowledge about the series to the circumstances that they encounter. According to these results, it was determined that the students experienced difficulty in transforming real life problems to series and interpreting these problems
A Review of Emotion Recognition Using Physiological Signals

Emotion recognition based on physiological signals has been a hot topic and applied in many areas such as safe driving, health care and social security. In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals. A summary and comparation among the recent studies has been conducted, which reveals the current existing problems and the future work has been discussed.
Composite risk modeling for automated threat mitigation in medical devices

Medical device security is a growing concern with increasing incorporation of complex software and hardware. Security threats exploiting vulnerabilities in medical devices may directly impact patient safety. Standardization and federal organizations are hence, actively involved in setting up new paradigms for guidance and regulation of security throughout the lifecycle. To protect medical devices against threats a risk-based framework that continually manages and assesses security risks along with their proactive addressing is highly recommended. In this paper, we model a multi-modal design approach for risk assessment in medical devices and propose an adaptive remediation scheme to mitigate security threats. Our multi-modal approach is integrated into the hardware-software development with a middleware for interaction between the modes. This provides an effective premarket risk management while the adaptive remediation scheme pro-actively mitigate risk during postmarket deployment. We model our approaches in detail and demonstrate them in a pacemaker design model and deployment scenario.
Identifiying the Authorised User by Typing Speed Comparison

Computers have become a ubiquitous part of the modern society. Online attacks on companies resulted in the shutdown of their networks and compromised the passwords and personal information of millions of users. Since we depend so much on computers to store and process sensitive information, it has become all the more necessary to secure them from intruders. For user authentication and identification in computer based applications, there is a need for simple, low-cost and unobtrusive device. A user can be defined as a person who attempts to access information stored on the computer or online using standard input device such as the keyboard. Use of biometrics such as face, fingerprints and signature requires additional tools to acquire the biometric which leads to an increase in costs. Use of a behavioral biometric which makes use of the typing pattern of an individual can be obtained using existing systems such as the standard keyboard, making it an inexpensive and extremely attractive technique. One of the major advantages of this biometric is that it is nonintrusive and can be applied covertly to augment existing cyber-security systems. In this paper we implement new process for password text analysis of keystrokes that joins monograph and digraph analysis, and uses a neural network to predict missing digraphs based on the relation between the monitored keystrokes.
Consequences of Incidental Otter Trawl Capture on Survival and Physiological Condition of Threatened Atlantic Sturgeon

Abstract Atlantic Sturgeon Acipenser oxyrinchus aggregate in Minas Basin in the inner Bay of Fundy, Nova Scotia, during summer, presumably to feed on abundant intertidal invertebrates. The Atlantic Sturgeon aggregation is composed of multiple stocks from Canada and the USA. Government agencies from both nations have recently recognized Atlantic Sturgeon as threatened or endangered due to overfishing and habitat degradation. Little is known about the fate of Atlantic Sturgeon that are captured as bycatch in fisheries targeting other species, making it difficult to determine the extent to which bycatch is contributing to the Atlantic Sturgeon population's decline. To characterize the effects of otter trawl capture and release on Atlantic Sturgeon, we calculated a minimum survival rate for fish after catch and release by using acoustic telemetry, and we examined physiological indicators of stress. The minimum postrelease survival rate from otter trawl capture events was high (94% survival). Results also demo.
BILIARY DISEASE a 5 b 1-Integrins Are Sensors for Tauroursodeoxycholic Acid in Hepatocytes

Ursodeoxycholic acid, which in vivo is converted to its taurine conjugate tauroursodeoxycholic acid (TUDC), is a mainstay for the treatment of cholestatic liver disease. Earlier work showed that TUDC exerts its choleretic properties in the perfused rat liver in an a5b1 integrin-mediated way. However, the molecular basis of TUDC-sensing in the liver is unknown. We herein show that TUDC (20 lmol/L) induces in perfused rat liver and human HepG2 cells the rapid appearance of the active conformation of the b1 subunit of a5b1 integrins, followed by an activating phosphorylation of extracellular signal-regulated kinases. TUDCinduced kinase activation was no longer observed after b1 integrin knockdown in isolated rat hepatocytes or in the presence of an integrin-antagonistic hexapeptide in perfused rat liver. TUDC-induced b1 integrin activation occurred predominantly inside the hepatocyte and required TUDC uptake by way of the Na/taurocholate cotransporting peptide. Molecular dynamics simulations of a 3D model of a5b1 integrin with TUDC bound revealed significant conformational changes within the head region that have been linked to integrin activation before. Conclusions: TUDC can directly activate intrahepatocytic b1 integrins, which trigger signal transduction pathways toward choleresis. (HEPATOLOGY 2013;57:1117-1129)
On Learning to Identify Genders from Raw Speech Signal Using CNNs

Automatic Gender Recognition (AGR) is the task of identifying the gender of a speaker given a speech signal. Standard approaches extract features like fundamental frequency and cepstral features from the speech signal and train a binary classi-ﬁer. Inspired from recent works in the area of automatic speech recognition (ASR), speaker recognition and presentation attack detection, we present a novel approach where relevant features and classiﬁer are jointly learned from the raw speech signal in end-to-end manner. We propose a convolutional neural networks (CNN) based gender classiﬁer that consists of: (1) convolution layers, which can be interpreted as a feature learning stage and (2) a multilayer perceptron (MLP), which can be interpreted as a classiﬁcation stage. The system takes raw speech signal as input, and outputs gender posterior probabilities. Experimental studies conducted on two datasets, namely AVspoof and ASVspoof 2015, with different architectures show that with simple architectures the proposed approach yields better system than standard acoustic features based approach. Further analysis of the CNNs show that the CNNs learn formant and fundamental frequency information for gender identiﬁcation.
Using Coalition Games for QoS Aware Scheduling in mmWave WPANs

With the increasing quality of service (QoS) demands for indoor multimedia applications, millimeter wave (mmWave) communications are emerging as a promising candidate for the wireless personal area networks (WPANs). On the one hand, it has the advantage of providing several-Gbps transmission rate. However, due to the unique characteristics in 60-GHz frequency band, such as high propagation loss, beamforming is fully exploited for mmWave links to achieve directional transmission and reception. In this paper, we propose a novel QoS aware scheduling algorithm for concurrent transmission in mmWave WPANs based on coalition game. First, we formulate the problem of concurrent transmission scheduling into a non-convex integer programming problem. Then, we propose a coalition game based algorithm to maximize the number of flows satisfying the corresponding QoS requirements, while improving the network resource utilization effectively. Besides, our proposed algorithm converges to a Nash-stable equilibrium with greatly reduced complexity. Through extensive simulations under various system parameters, we demonstrate our scheme achieves better network performance in terms of the throughput and the number of flows scheduled successfully, compared with existed protocols.
Simplified Novel Protein Design Using Thermodynamic Environments

A new computational method is developed to create novel amino acid sequences that fold into specified protein structures. We utilize an ensemble model that generates a range of accessible protein conformations as well as a simplified “alphabet” of “thermodynamic environments. ” This design method was implemented on the well-studied SH3 domain of C. elegans as the design target. Four randomly selected designs exhibit primary, secondary, and tertiary structure consistent with the target in silico. Further, an unexpected correlation between the fitness, as measured by the design algorithm, of the most buried residues in the native state and protein stability was found.