Miracle-dz/UniFAR-FTUs huggingface dataset:
The moderating impact of national culture on the relationship between knowledge sharing and organisational effectiveness : The case of Iran ’ s private sector organisations

This paper investigates the relationship between knowledge sharing and organisational effectiveness, as well as testing for the possible moderating impact of national culture. Data for this paper was collected through the use of self-administered questionnaires. The results are based on responses from 357 managers of private sector organisations in Iran. To assess knowledge sharing, the researcher used the scale from Zheng, et al. (2009) and to investigate national culture and organisational effectiveness the scale from Nazarian (2013) was used. The regression analysis shows a significant relationship between knowledge sharing and organisational effectiveness; however, the moderating effect of national culture showed significant impact with the uncertainty avoidance dimension only. However, if national culture is taken as a single component in the regression analysis, a significant moderating correlation is evident.
The Seismic Response of Multi-Storey Semirigid Steel Structures

The present contribution is focused on statical and kinematic components of seismic response of multi-storey steel structures with semi-rigid beam-column connections. The dynamic model of these structures is a system with finite degrees of freedom and concentrated masses at each level. The degrees of freedom are expressed by the lateral level displacements. The semi-rigid behaviour of the beam-column connection is considered cyclic and is asociated to an analytic model with four parameters M-r. The structure is subjected to seismic action Vrancea 1977. Analysis are the type of time-history. The presented results refers to lateral level displacement and seismic base shear force. The principal aim of this work is comparing the afferent parameters (statical and kinematic).
Career Consequences of Firm Heterogeneity for Young Workers: First Job and Firm Size

I study the long-term effects of landing a first job at a large firm versus a small one using Spanish social security data. Size could be a relevant employer attribute for inexperienced workers since large firms are associated with greater training, higher wages, and enhanced productivity. The key empirical challenge is selection into first jobs – for instance, more able people may land jobs at large firms. I address this challenge developing an instrumental-variables approach that, while keeping business-cycle conditions fixed, leverages variation in the composition of labor demand that labor-market entrants face. I find that initially matching with a larger firm substantially improves long-term outcomes such as lifetime income, and that these benefits persist through subsequent jobs. Additional results point to mechanisms related to search frictions and better skill-development at large firms. Together, these findings shed light on how heterogeneous firms persistently impact young workers' trajectories.
Modeling and Evaluation of the Mathematical Educational Ontology

In this paper, we discuss the current stage of development of the educational mathematical ontology OntoMath Edu , firstly presented by us at INTED 2019 and CICM 2019. This ontology is intended to be used as a Linked Open Data hub for mathematical education, a linguistic resource for intelligent mathematical language processing and an end-user reference educational database. The ontology is organized in three layers: a foundational ontology layer, a domain ontology layer and a linguistic layer. The domain ontology layer contains language-independent concepts, covering secondary school mathematics curriculum. The linguistic layer provides linguistic grounding for these concepts, and the foundation ontology layer provides them with meta-ontological annotations. Our current work is dedicated to development of prerequisite relationships of the OntoMath Edu ontology. We introduce these relationships by manual arrangement of the concepts of OntoMath Edu by educational levels. After that, we conduct preliminary evaluation of the ontology. The ontology will be used as a foundation of the new digital educational platform of Kazan Federal University.
Soil Classification and Suitable Crop Prediction

This paper investigates the development of digital image analysis approach for estimation of physical properties of soil in lieu of conventional laboratory approach. The traditional laboratory approach attracts some drawbacks such as lot of manual involvement, time consuming, chances of creeping of human errors, uncertain prediction and always invasive in nature . The signal processing methods involve enhancing original image using filters and calculating the features of the transformed images. In this paper Low mask, Gabor Filter and color quantization are applied to the original images to extract the texture features of soil images for retrieval. Results on a database of 100 soil images belonging to 10 different types of Soils with different orientations, scales and translations shows that proposed method performs retrieval efficiency effectively. The features are constructed on preprocessed methods applied on the Soil texture image by considering different types of windows. These features offer a better classification rate.
Privacy at a Glance: The User-Centric Design of Glanceable Data Exposure Visualizations

Abstract Smartphone users are often unaware of mobile applications’ (“apps”) third-party data collection and sharing practices, which put them at higher risk of privacy breaches. One way to raise awareness of these practices is by providing unobtrusive but pervasive visualizations that can be presented in a glanceable manner. In this paper, we applied Wogalter et al.’s Communication-Human Information Processing model (C-HIP) to design and prototype eight different visualizations that depict smartphone apps’ data sharing activities. We varied the granularity and type (i.e., data-centric or app-centric) of information shown to users and used the screensaver/lock screen as a design probe. Through interview-based design probes with Android users (n=15), we investigated the aspects of the data exposure visualizations that influenced users’ comprehension and privacy awareness. Our results shed light on how users’ perceptions of privacy boundaries influence their preference regarding the information structure of these visualizations, and the tensions that exist in these visualizations between glanceability and granularity. We discuss how a pervasive, soft paternalistic approach to privacy-related visualization may raise awareness by enhancing the transparency of information flow, thereby, unobtrusively increasing users’ understanding of data sharing practices of mobile apps. We also discuss implications for privacy research and glanceable security.
Open Source IDS High Performance Shootout

For many years, Snort has been the de facto open-source IDS/IPS solution, with the program’s architects focused on improving the performance of this single-thread model. However, the demand for high-throughput IDS has increased as the average network throughput has increased from 1 Gbps to 10 Gbps, with 40 Gbps slated to become mainstream in the near future. In response, several other open-source projects have adopted a multi-threading approach to scaling IDS performance to meet the increasing demand for multi-gigabit analysis. As a result, Bro and Suricata are now viable candidates to replace Snort and are attempting to fill in the multithreading gap left by Snort while leveraging existing Snort rule sets and third-party tools. Suricata and Bro have also introduced new features that were not originally explored by Snort, such as GeoIP lookups. Suricata is attempting to introduce GPU acceleration and IP reputation features to increase its throughput and compete with commercial IDS/IPS solutions that currently offer IP reputation functionality. To remain competitive, Snort has added a multi-instance feature to its 2.9 release to address its single-thread limitation and has indicated that version 3.0 will be multithreaded by default. For a long time, Snort was the only player in the open-source IDS/IPS market, but now the industry is reaping the benefits of changing demands and increased competition through the introduction of new features and their integration with existing advantageous features.
A Survey of Symbolic Execution Tools

In the software development life cycle (SDLC), testing is an important step to reveal and fix the vulnerabilities and flaws in the software. Testing commercial off-the-shelf applications for security has never been easy, and this is exacerbated when their source code is not accessible. Without access to source code, binary executables of such applications are employed for testing. Binary analysis is commonly used to analyze on the binary executable of an application to discover vulnerabilities. Various means, such as symbolic execution, concolic execution, taint analysis, can be used in binary analysis to help collect control flow information, execution path information, etc. This paper presents the basics of the symbolic execution approach and studies the common tools which utilize symbolic execution in them. With the review, we identified that there are a number of challenges that are associated with the symbolic values fed to the programs as well as the performance and space consumption of the tools. Different tools approached the challenges in different ways, therefore the strengths and weaknesses of each tool are summarized in a table to make it available to interested researchers.
INTERNATIONAL JOURNAL OF HUMAN COMPUTER INTERACTION (IJHCI)

The aim of gesture recognition researches is to create system that can easily identify gestures, and use them for device control, or convey some formations. In this paper we are discussing researches done in the area of hand gesture recognition based on Artificial Neural Networks approaches. Several hand gesture recognition researches that use Neural Networks are discussed in this paper, comparisons between these methods were presented, advantages and drawbacks of the discussed methods also included, and implementation tools for each method were presented as well.
A Study of Reaction Products in Soft Clay- Lime Mixture

The deep deposits of highly compressible marine soils cover vast areas along the coastal belt of the Indian peninsula. These are very soft and contain large amount of shells. They possess low shear strength and high compressibility thereby causing larger settlements when structures are constructed in these soft soils. Many attempts were made in the past to improve the engineering properties of marine soil but still it is difficult to stabilize these soils. A laboratory investigation has been carried out by mixing the hydrated lime with soft marine clay at different proportions of 2%, 5% and 7% by weight of marine clay in the presence of sea water and distilled water separately. The mineralogical variations with XRD and fabric studies of marine clay are presented at curing periods of 10 and 40 days. The new minerals and reactive products formed by treating the soil with lime have been identified using XRD studies. A use of the SEM shows the increase in particle size and formation of flocculated structure in presence of sea water as well as distilled water. A use of the SEM shows the increase in particle size and formation of flocculated structure in presence of sea water as well as distilled water.
On the Importance of Learning Aggregate Posteriors in Multimodal Variational Autoencoders

We study joint latent variable models of two modalities: images and text. A common task for these multimodal models is to perform conditional generation; for instance, generating an image conditioned on text. This can be achieved by sampling the posterior of the text then generating the image given the latent variable. However, we ﬁnd that a problem with this approach is that the posterior of the text does not match the aggregate of the image posteriors corresponding to that text. The result is that the generated images are either of poor quality or don’t match the text. A similar problem is also encountered in the mismatch between the prior and the marginal aggregate posterior. In this paper, we highlight the importance of learning aggregate posteriors when faced with these types of distribution mismatches. We demonstrate this on modiﬁed versions of the CLEVR and CelebA datasets.
End-to-end Learning for Distributed Circuit Design

We present a neural network model for designing distributed circuits. Today, designing such circuits is a slow process that can take months from an expert engineer. Our model both automates and speeds up the process. The model learns to simulate the electromagnetic (EM) properties of distributed circuits. Hence, it can be used to replace traditional EM simulators, which typically take tens of minutes for each design iteration. Further, since neural networks are differentiable, we can use our model to solve the inverse problem – i.e., given desirable EM specifications, we propagate the gradient descent to optimize the template parameters to satisfy the specifications. We compare our model with a commercial simulator showing that it reduces simulation time by five orders of magnitude. We also demonstrate the value of our model by using it to design a Terahertz channelizer, a difficult task that requires a specialized expert. The results show that our model produces a channelizer whose performance is as good as a manually optimized design, and can save the expert several weeks of iterative parameter optimization.
Osteoprotegerin Is more than a Possible Serum Marker in Liver Fibrosis: A Study into Its Function in Human and Murine Liver

Osteoprotegerin (OPG) serum levels are associated with liver fibrogenesis and have been proposed as a biomarker for diagnosis. However, the source and role of OPG in liver fibrosis are unknown, as is the question of whether OPG expression responds to treatment. Therefore, we aimed to elucidate the fibrotic regulation of OPG production and its possible function in human and mouse livers. OPG levels were significantly higher in lysates of human and mouse fibrotic livers compared to healthy livers. Hepatic OPG expression localized in cirrhotic collagenous bands in and around myofibroblasts. Single cell sequencing of murine liver cells showed hepatic stellate cells (HSC) to be the main producers of OPG in healthy livers. Using mouse precision-cut liver slices, we found OPG production induced by transforming growth factor β1 (TGFβ1) stimulation. Moreover, OPG itself stimulated expression of genes associated with fibrogenesis in liver slices through TGFβ1, suggesting profibrotic activity of OPG. Resolution of fibrosis in mice was associated with decreased production of OPG compared to ongoing fibrosis. OPG may stimulate fibrogenesis through TGFβ1 and is associated with the degree of fibrogenesis. It should therefore be investigated further as a possible drug target for liver fibrosis or biomarker for treatment success of novel antifibrotics.
Motivation as the Mediator in Relationship between Non-verbal Communication of Arabic Language Teachers and Student Learning Outcomes

Effective communication requires a strong combination between verbal and nonverbal communication which is one of the skills that need to be mastered by a teacher. The estimate of 65-70 percent of message in communication is obtained by nonverbal and only 30-35 percent is obtained through speech. This study is aimed at investigating the impact of NVC practice of Arabic language teachers on the motivation of students and their learning outcomes in Arabic language in Malaysia. Quantitative study used a survey questionnaire and was involved a population of 84,841 students. Sampling was done through stratified random sampling and only 750 students were chosen in all over Malaysia as respondents. The SEM model was analysed using the approach by Baron and Kenny (1986). The findings show that the relationship NVC motivation in students was significant ( = .133, 0.01), NVC learning outcome ( = .350, p learning outcome ( = .439, 0.01). In the test of indirect effect (mediator), the findings show that motivation was statistically proven through the Sobel test ( = 0.133, Sobel Test = 4.68, SE = 0.01, 0.01) as the mediator in the relationship between NVC and learning outcome. Hence, the result shows that, the higher the motivation possessed by the students the better their learning outcome in Arabic language. Therefore, inevitably the teachers play vital roles during teaching and learning to use more NVC approach to enhance the higher learning outcomes for students in Arabic language learning.
TeraRack: A Tbps Rack for Machine Learning Training

We explore a novel, silicon photonics-based approach to build a high bandwidth rack designated for machine learning training. Our goal is to scale state-of-the-art ML training platforms, such as NVIDIA’s DGX and Intel’s Gaudi, from a handful of GPUs in one platform to 256 GPUs in a rack while maintaining Tbps communication bandwidth. Our design, called TeraRack, leverages the emergence of silicon photonics technology to achieve Tbps bandwidth in/out of the GPU chip. TeraRack enables accelerating the training time of popular ML models using (i) a scheduling algorithm that finds the best wavelength allocation to maximize the throughput between communicating nodes; and (ii) a device placement algorithm that partitions ML models across nodes to ensure a sparse and local communication pattern that can be supported efficiently on the interconnect. We build a small prototype with FPGA boards and a 10 mm × 10 mm silicon photonics chip. Simulation results show that TeraRack’s performance on realistic ML training workloads is equivalent to a full-bisection 256×1.2Tbps electrical fabric at 6× lower cost, enabling faster model/data parallel training.
Crocodilians : Fact vs . Fiction

The stressors of farmed and captive crocodiles consist of any limitations imposed on the expression of the natural behaviour of crocodiles: fear and the inability to escape a frightening event (capture, restraint, transport), the inability to thermoregulate (lack of environmental thermogradient) and the inability to establish or leave a territory (overcrowding). The effects depend on the severity and duration of the action of the stressor, on the age of the animal and on environmental conditions (immunosuppression by cold, malnutrition). Consequently, they may be expressed in different degrees of severity and they can be behavioural or somatic. The behavioural changes caused by stress are anorexia and hydrophobia or in less severe cases reduced appetite and growth rate. The somatic changes are immune suppression and septicaemia, gastric ulcers and chronic dermatitis, as well as metabolic disorders, particularly lowered calcium levels with resulting poor dental mineralization and osteoporosis.
Poster: AppPrivacy: Analyzing Data Collection and Privacy Leakage from Mobile Apps

While collecting legitimate usage data, many mobile applications (apps) have reportedly posed privacy threats to their hosted mobile devices and individuals, who are, unfortunately, unaware of data leaks and measures to protect themselves against these leaks. In this poster, we present a system that analyzes the two sides of mobile application ecosystem — data collection and privacy risk. The system consists of three main modules that correspond to mobile apps, users and service providers, respectively. To the best of our knowledge, this is the first work to evaluate privacy risk by analyzing data collection and privacy leakage from mobile apps.
Automating Expectation Maximixation

Existing probabilistic programming systems frequently treat machine learning problems as purely inference tasks. But real-world problems often require a novel combination of inference and optimization. For example when implementing Expectation Maximization for a model, one must alternate between performing an inference and an optimization step. We show that by treating optimization as a program transformation it is possible to implement Expectation Maximization in a generic and composable way.
“The Prolong Montroller System” using Wireless Technology

Almost in food/pharmaceutical industries there is a mounting need for quality, efficiency and machine environment monitoring systems. Moreover, so many industrial operation monitoring is necessary to record, analyze or control in order to have implementations of proper, cost-effective and wastage free or maintenance free Production and also to reduce repair/replacing overheads. Therefore, here “The Prolong Montroller System” using Wireless Technology is proposed as important system engineering. In this project, a wireless network for in-field operation, on time monitoring of multiple different area parameters like temperature, humidity is projected. The system network is arranged as able to sink operationrelated data of notable areas to one central point, called as master station. This master device is also automatically managing the communication with all remote monitoring stations distributed at different areas.
Citrus sinensis citrate transporter 1 CsCit 1 encoding a novel vacuolar citrate

We have isolated a cDNA, designated Citrus sinensis citrate transporter 1 CsCit1 encoding a novel vacuolar citrate/symporter. Immunoblots using antibodies raised against CsCit1 showed that the protein is localized to the juice sac cell vacuoles. The highest expression of CsCit1 and the amount of protein in the juice sac cell vacuoles coincided with the developmental stage at which the vacuolar citrate content began declining with the concomitant increase in vacuolar pH. Vacuoles from Sacharomyces cereviseae expressing CsCit1 displayed a citrate-dependent H efflux, and our results clearly demonstrate that CsCit1 is able to mediate the electroneutral co-transport of H and citrate ions, since the citrate-dependent H fluxes are not affected by changing the electrical potential difference across the tonoplast. The roles of CsCit1 in mediating citrate efflux from the vacuole and on citric acid homoestasis in Citrus juice sac cells are discussed.
Accuracy Assessment of DEMs Using Modern Geoinformatic Methods

Digital Elevation Models (DEMs), which can come in the form of digital surface models or digital terrain models, are key tools in land analyses and other purposes. Classical methods such as field surveying and photogrammetry can yield high-accuracy terrain data, but they are time consuming and labor-intensive. Nowadays, different modernistic height-finding methods have emerged, including Global Positioning System (GPS) and airborne methods. In contrast to the airborne ways that are suited to gain highly precise, fine-resolution DEMs at a local scale. The airborne ways are complementary to their space-borne matches, such as Light Detection and Ranging (LiDAR), Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflection RadiometerGlobal Digital Elevation Model (ASTER GDEM) and Advanced Land Observing Satellite (ALOS). LiDAR data acquisition has become the standard approach for collecting point data to interpolate high-resolution ground and aboveground surface. In this study, we assessed elevation accuracy of three modern geoinformatic methods (STRM, ASTER GDEM and ALOS); by comparing standard deviations of elevation differences for these methods versus more than 6,000,000 points from LiDAR. From case study results, standard deviations of elevation differences between LiDAR points vs ASTER DEM equal 9.09 m, LiDAR points vs STRM DEM equal 5.28 m and LiDAR point’s vs ALOS DEM equal 2.08 m, based on these results, ALOS DEM shows a good agreement with LiDAR data.