Miracle-dz/UniFAR-FTUs huggingface dataset:
Teacher' attitudes towards e-learning in higher education in Macedonia Case study: University of Tetovo

Nowadays in higher education it is important integration of new information and communication technologies into the learning and teaching process. Fastest development of computer technologies and the Internet as a communication platform has motivated higher education institutions to start to think about integration of e-learning system on teaching and learning process on their university. E-learning is becoming progressively more important in higher education environment. Teacher is one of the most important stockholder for integration of e-learning. The purpose of this paper is to analyze the perception of teachers' attitudes towards e-learning. In this study were analyzed 49 teachers teaching in 2 major faculties in University of Tetovo, in Macedonia. The results show that a significant number of teachers agree about the plan to blend the present teaching method with e-learning, on the other hand, a great number of teachers not agree to replace traditional teaching method with e-learning. The results show that the number that prefer using traditional teaching method is the same with those that not agree. There is no any significant correlation between teacher' attitudes towards various e-learning according to gender and faculty. Results shows that teachers' attitudes have important role towards e-learning in teaching process. These results can be a good example for creation a platform or framework for the implementation of e-learning environment in the teaching and learning process on higher education. In addition, the results of the study can serve as an important factor for the improvement and development of the educational process supported by the use of technology, thus implementing an e-learning system in order to help and facilitate the students in their studies.
Ultrasound-guided artificial insemination: a randomized controlled trial.

BACKGROUND The aim was to ascertain whether using ultrasound guidance during intrauterine insemination (IUI) could increase pregnancy rates (PRs). METHODS The population under study consisted of 73 consecutive couples subjected to IUI in our Human Reproduction Unit, between June and December 2006, with a total of 231 IUI cycles performed. The patients were randomized using a computer-generated random numeric table into two groups: ultrasound-guided IUI group (n = 33) and clinical IUI group (n = 40). RESULTS The PR was 16.0% per cycle in ultrasound-guided IUI and 16.8% in the control group, no statistically significant differences being observed between the groups. The 95% confidence interval for the difference in PRs of 0.8% was -8.8 to 10. There were no differences in PR per woman, nor in first-cycle PR. The cumulative PR was also similar in both populations. Although the initial intention was to perform a study involving a larger number of cases, after a first interim analysis, the study was interrupted due to its futility. There were no differences in PR according to the different cervico-uterine angles. CONCLUSIONS Ultrasound-guided IUI does not produce better results than blind insemination, because the PR per cycle is similar. ClinicalTrials.gov ID NCT00809952.
On Petri Nets and Predicate-Transition Nets

Petri nets are used to study many types of networked systems. [1] have designed a software tool for analysis and simulation of Petri nets. We extend this tool to handle a form of Petri net known as Predicate Transition (PrT) nets. We implement mechanisms that automate the process of folding a Petri net to a PrT net and finding invariants. We can compute invariants for systems that use either Petri net or the extended PrT net. Invariants are required to prove certain properties of the system being modeled, such as liveness and safety. Finally, for Petri net, we use the invariants to prove these two properties.
Weak distinction and the optimal definition of causal continuity

Causal continuity is usually defined by imposing the conditions (i) distinction and (ii) reflectivity. It is proved here that a new causality property which stays between weak distinction and causality, called feeble distinction, can actually replace distinction in the definition of causal continuity. An intermediate proof shows that feeble distinction and future (past) reflectivity imply past (resp. future) distinction. Some new characterizations of weak distinction and reflectivity are given.
On Bitcoin as a public randomness source

1 Stanford University 2 Concordia University 3 Princeton University Abstract. We formalize the use of Bitcoin as a source of publiclyverifiable randomness. As a side-effect of Bitcoin’s proof-of-work-based consensus system, random values are broadcast every time new blocks are mined. We can derive strong lower bounds on the computational min-entropy in each block: currently, at least 68 bits of min-entropy are produced every 10 minutes, from which one can derive over 32 nearuniform bits using standard extractor techniques. We show that any attack on this beacon would form an attack on Bitcoin itself and hence have a monetary cost that we can bound, unlike any other construction for a public randomness beacon in the literature. In our simplest construction, we show that a lottery producing a single unbiased bit is manipulation-resistant against an attacker with a stake of less than 50 bitcoins in the output, or about US$12,000 today. Finally, we propose making the beacon output available to smart contracts and demonstrate that this simple tool enables a number of interesting applications.
Detecting deception: does the cognitive interview impair discrimination with CBCA criteria in elderly witnesses?*

ABSTRACT The accuracy of an eyewitness statement depends on the interviewing method used. Therefore, empirically tested interviewing techniques have been developed, in particular for vulnerable witnesses. Sometimes witnesses may not tell the truth based on personally experienced events but give testimony about events they have only heard about, thus lying about the event. In this experiment, 80 witnesses over 60 years old either saw a film or heard an audiotaped summary. They were interviewed either with a standard interview (SI) or an Enhanced Cognitive Interview (ECI). The ECI led to an increase in the amount of information gained without jeopardizing its accuracy compared to the SI. Trained coders evaluated transcripts of the interviews with Criteria-Based Content Analysis and judged the truthfulness of each account. Some differences in CBCA criteria were in the expected direction, others opposite to expectation, thus prohibiting the use of a summary score. Accounts from the SI group (80%) were judged correctly significantly more often than accounts from the ECI group (40%). Only 15% of the lies in the ECI group were judged correctly vs. 80% in the SI group. Findings are discussed regarding the importance of testing interview techniques with respect to their potential to detect deception.
Bayesian Classification of FMRI Data: Evidence for Altered Neural Networks in Dementia

The alterations in functional relationships among brain regions associated with senile dementia are not well understood. We present a machine learning technique using dynamic Bayesian networks (DBNs) that extracts causal relationships from functional magnetic resonance imaging (fMRI) data. Based on these relationships, we build neural-anatomical networks that are used to classify patient data as belonging to healthy or demented subjects. Visual-motor reaction time task data from healthy young, healthy elderly, and demented elderly patients (Buckner et al. 2000) was obtained through the fMRI Data Center. To reduce the extremely large volume of data acquired and the high level of noise inherent in fMRI data, we averaged data over neuroanatomical regions of interest. The DBNs were able to correctly discriminate young vs. elderly subjects with 80% accuracy, and demented vs. healthy elderly subjects with 73% accuracy. In addition, the DBNs identified causal neural networks present in 93% of the healthy elderly studied. The classification efficacy of the DBN was similar to two other widely used machine learning classification techniques: support vector machines (SVMs) and Gaussian naïve Bayesian networks (GNBNs), with the important advantage that the DBNs provides candidate neural anatomical networks associated with dementia. Networks found in demented but not healthy elderly patients included substantial involvement of the amygdala, which may be related to the anxiety and Burge et al., Page 2 agitation associated with dementia. DBNs may ultimately provide a biomarker for dementia in its early stages, and may be helpful for the diagnosis and treatment of other CNS disorders.
Fast mode decision for H.264/AVC based on macroblock correlation

H.264/AVC encoder complexity is remarkable due to mainly variable block size ME and exhaustive RDO mode decision. This makes application of H.264/AVC in network especially wireless environments very difficult. To reduce encoder complexity, statistical characteristics of modes among adjacent macroblocks (MBs) spatially and temporally are first studied using H.264 reference software. Then a fast algorithm is presented to reduce jointly the computational load of mode decision and ME. The main ideas are as follows: (1) candidate modes for current MB are first inferred from mode and motion information of defined reference MBs and then partial RDO approach is applied to make target selection; (2) ME is considered as a part of mode decision and no ME is required for particular modes that are eliminated in advance; (3) the exhaustive RDO method is invoked only if average distortion of the reference MBs goes beyond a predefined threshold. Simulation results demonstrate that our scheme can reduce encoder complexity significantly while coding efficiency is only slightly decreased.
The Relationship Between Participation in Out-of-Class Activities and Cognitive and Social Outcomes of Korean College Students

In the era of the 4th Industrial Revolution, higher education institutions should change practices of educational programs and services, which are mainly based on traditional classroom-based instructions, to allow students to have more diverse experiences. Since college students spend relatively more time engaged in out-of-class activities than attending regular courses, it is necessary to examine how participating in out-of-class programs is related to cultivation of the competencies that the future demands. This study explores the relationship between out-of-class activity participation and perceived change in cognitive and social outcomes of Korean college students. Five out-of-class activities were examined: learning community, undergraduate research, service learning, internship, and residential college programs. K-NSSE (Korea-National Survey of Student Engagement) data were analyzed using hierarchical linear model analysis. The study findings are consistent with the results of previous research that demonstrated a positive association between participating in out-of-class activities and students’ cognitive and social outcomes.
Gravitational Phase Transitions and the Sine-Gordon Model

We consider the Sine-Gordon model coupled to 2D gravity. We find a nonperturbative expression for the partition function as a function of the cosmological constant, the SG mass and the SG coupling constant. At genus zero, the partition function exhibits singularities which are interpreted as signals of phase transitions. A semiclassical picture of one of these transitions is proposed. According to this picture, a phase in which the Sine-Gordon field and the geometry are frozen melts into another phase in which the fields and geometry become dynamical.
Undifferentiated Bronchial Fibroblasts Derived from Asthmatic Patients Display Higher Elastic Modulus than Their Non-Asthmatic Counterparts

During asthma development, differentiation of epithelial cells and fibroblasts towards the contractile phenotype is associated with bronchial wall remodeling and airway constriction. Pathological fibroblast-to-myofibroblast transition (FMT) can be triggered by local inflammation of bronchial walls. Recently, we have demonstrated that human bronchial fibroblasts (HBFs) derived from asthmatic patients display some inherent features which facilitate their FMT in vitro. In spite of intensive research efforts, these properties remain unknown. Importantly, the role of undifferentiated HBFs in the asthmatic process was systematically omitted. Specifically, biomechanical properties of undifferentiated HBFs have not been considered in either FMT or airway remodeling in vivo. Here, we combine atomic force spectroscopy with fluorescence microscopy to compare mechanical properties and actin cytoskeleton architecture of HBFs derived from asthmatic patients and non-asthmatic donors. Our results demonstrate that asthmatic HBFs form thick and aligned ‘ventral’ stress fibers accompanied by enlarged focal adhesions. The differences in cytoskeleton architecture between asthmatic and non-asthmatic cells correlate with higher elastic modulus of asthmatic HBFs and their increased predilection to TGF-β-induced FMT. Due to the obvious links between cytoskeleton architecture and mechanical equilibrium, our observations indicate that HBFs derived from asthmatic bronchi can develop considerably higher static tension than non-asthmatic HBFs. This previously unexplored property of asthmatic HBFs may be potentially important for their myofibroblastic differentiation and bronchial wall remodeling during asthma development.
Ontogenetic Expression of Sonic Hedgehog in the Chicken Subpallium

Sonic hedgehog (SHH) is a secreted signaling factor that is implicated in the molecular patterning of the central nervous system (CNS), somites, and limbs in vertebrates. SHH has a crucial role in the generation of ventral cell types along the entire rostrocaudal axis of the neural tube. It is secreted early in development by the axial mesoderm (prechordal plate and notochord) and the overlying ventral neural tube. Recent studies clarified the impact of SHH signaling mechanisms on dorsoventral patterning of the spinal cord, but the corresponding phenomena in the rostral forebrain are slightly different and more complex. This notably involves separate Shh expression in the preoptic part of the forebrain alar plate, as well as in the hypothalamic floor and basal plates. The present work includes a detailed spatiotemporal description of the singular alar Shh expression pattern in the rostral preoptic forebrain of chick embryos, comparing it with FoxG1, Dlx5, Nkx2.1, and Nkx2.2 mRNA expression at diverse stages of development. As a result of this mapping, we report a subdivision of the preoptic region in dorsal and ventral zones; only the dorsal part shows Shh expression. The positive area impinges as well upon a median septocommissural preoptic domain. Our study strongly suggests tangential migration of Shh-positive cells from the preoptic region into other subpallial domains, particularly into the pallidal mantle and the intermediate septum.
Influence Maximization in Near-Linear Time: A Martingale Approach

Given a social network G and a positive integer k, the influence maximization problem asks for k nodes (in G) whose adoptions of a certain idea or product can trigger the largest expected number of follow-up adoptions by the remaining nodes. This problem has been extensively studied in the literature, and the state-of-the-art technique runs in O((k+l) (n+m) log n ε2) expected time and returns a (1-1 e-ε)-approximate solution with at least 1 - 1/n l probability. This paper presents an influence maximization algorithm that provides the same worst-case guarantees as the state of the art, but offers significantly improved empirical efficiency. The core of our algorithm is a set of estimation techniques based on martingales, a classic statistical tool. Those techniques not only provide accurate results with small computation overheads, but also enable our algorithm to support a larger class of information diffusion models than existing methods do. We experimentally evaluate our algorithm against the states of the art under several popular diffusion models, using real social networks with up to 1.4 billion edges. Our experimental results show that the proposed algorithm consistently outperforms the states of the art in terms of computation efficiency, and is often orders of magnitude faster.
Distinguishing Behavioral Models of Momentum

This paper compares the extent to which different irrational behavior patterns explain the momentum effect. I find that price momentum increase in the average correlation of analysts’ forecast errors. I argue that these results provide evidence that the momentum effect may be better understood within a framework built on overconfidence about correlated signals than one built on limited attention and gradual information flow.
Design of high speed Vedic multiplier using multiplexer based adder

Real time applications such as controlling environmental conditions demand quick response of the processor for processing the acquired signals. Multiplier is an important feature of signal processing. Vedic Mathematics provides principles of high speed multiplication. Motivated by this, a high speed Vedic multiplier using multiplexer based adder is proposed in this paper. Proposed design is simulated using ModelSim and synthesized using Xilinx ISE 14.7. When compared with existing Vedic multipliers, proposed design shows a significant improvement in speed.
Fusion of Dense SURF Triangulation Features and Dense Trajectory based Features Do Hang Nga

In this paper, we describe our method used to achieve our results which was submitted to the Recognition Task of the challenge. As for video features, we combined our proposed feature [1] and the dense trajectories based feature presented in [2]. We employed Fisher Vector encoding to represent videos using these features and trained multiclass linear SVMs to perform action recognition. We conducted experiments on UCF-101 dataset following the suggested competition track evaluation setup and obtained precision rate as 66.5%.
Higher ratio immune versus constitutive proteasome level as novel indicator of sensitivity of pediatric acute leukemia cells to proteasome inhibitors

The ex vivo sensitivity of pediatric leukemia cells to the proteasome inhibitor bortezomib was compared to 3 next generation proteasome inhibitors: the epoxyketone-based irreversible proteasome inhibitors carfilzomib, its orally bio-available analog ONX 0912, and the immunoproteasome inhibitor ONX 0914. LC50 values were determined by MTT cytotoxicity assays for 29 childhood acute lymphoblastic leukemia and 12 acute myeloid leukemia patient samples and correlated with protein expression levels of the constitutive proteasome subunits (β5, β1, β2) and their immunoproteasome counterparts (β5i, β1i, β2i). Acute lymphoblastic leukemia cells were up to 5.5-fold more sensitive to proteasome inhibitors than acute myeloid leukemia cells (P<0.001) and the combination of bortezomib and dexamethasone proved additive/synergistic in the majority of patient specimens. Although total proteasome levels in acute lymphoblastic leukemia and acute myeloid leukemia cells did not differ significantly, the ratio of immuno/constitutive proteasome was markedly higher in acute lymphoblastic leukemia cells over acute myeloid leukemia cells. In both acute lymphoblastic leukemia and acute myeloid leukemia, increased ratios of β5i/β5, β1i/β1 and β2i/β2 correlated with increased sensitivity to proteasome inhibitors. Together, differential expression levels of constitutive and immunoproteasomes in pediatric acute lymphoblastic leukemia and acute myeloid leukemia constitute an underlying mechanism of sensitivity to bortezomib and new generation proteasome inhibitors, which may further benefit from synergistic combination therapy with drugs including glucocorticoids.
Cortical Glial Fibrillary Acidic Protein-Positive Cells Generate Neurons after Perinatal Hypoxic Injury

Glial fibrillary acidic protein-positive (GFAP+) cells give rise to new neurons in the neurogenic niches; whether they are able to generate neurons in the cortical parenchyma is not known. Here, we use genetic fate mapping to examine the progeny of GFAP+ cells after postnatal hypoxia, a model for the brain injury observed in premature children. After hypoxia, immature cortical astroglia underwent a shift toward neuronal fate and generated cortical excitatory neurons that appeared synaptically integrated into the circuitry. Fate-mapped cortical GFAP+ cells derived ex vivo from hypoxic, but not normoxic, mice were able to form pluripotent, long-term self-renewing neurospheres. Similarly, exposure to low oxygen conditions in vitro induced stem-cell-like potential in immature cortical GFAP+ cells. Our data support the conclusion that hypoxia promotes pluripotency in GFAP+ cells in the cortical parenchyma. Such plasticity possibly explains the cognitive recovery found in some preterm children.
A deep 6.7 μm survey in the SSA13 field with ISO

We present results of a deep mid-infrared survey in the SSA13 field with the Infrared Space Observatory (ISO). In order to probe the near-infrared light at high redshifts, we surveyed the field with the broad band LW2 (5-8.5 µm) filter of the mid-infrared camera ISOCAM. Adopting a highly redundant imaging strategy for the 23 h observation and carefully treating gradual changes in the detector responsivity caused by a very high rate of cosmic ray impacts, we succeeded in reaching an 80% completeness limit of 16 µJy in the central 7 arcmin 2 region. Utilizing the signal-to-noise ratio map, we detected 65 sources down to 6 µJy in the 16 arcmin 2 field. Integral galaxy number counts at 6.7 µm are then derived, reaching 1.3 × 10 4 deg −2 at the faint limit with a slope of −1.6 between 13 µJy and 130 µJy. Integrating individual sources in this flux range, the resolved fraction of the extragalactic background light at 6.7 µm is estimated to be 0.56 nW m −2 sr −1 . These results, which reach a flux limit three times fainter than those in the Hubble Deep Fields, are in fairly good agreement with a model prediction by Franceschini et al. (1997). Finally, we discuss the relation of distant massive E/S0 galaxies to the faint 6.7 µm galaxy population.
D_EDF: An efficient scheduling algorithm for real-time multiprocessor system

A scheduling algorithm decides a schedule for a set of tasks. There are numbers of algorithm for scheduling tasks on a processor. Some of these algorithms are used for scheduling tasks on multiprocessor system either under the partitioning scheme or under the global scheduling scheme. The most common scheduling algorithms are: Earliest Deadline First (EDF) and Least Laxity First (LLF). They are optimal scheduling algorithms for single processor system, but problem arises when algorithms are used for multiprocessor system. In this paper, we have proposed a new algorithm, D_EDF. D_EDF scheduling algorithm overcomes the limitations of dynamic algorithm during overloaded conditions. The proposed algorithm D_EDF, simulated and tested for independent, preemptive, periodic tasks on tightly coupled real-time multiprocessor system under global scheduling. The performance is measured in terms of Success Ratio and Effective CPU Utilization. From experiments and result analysis it concludes that the proposed algorithm is very efficient in both underloaded and overloaded conditions. It performs always better than conventional EDF algorithm. The algorithm proposed in the paper performs quite well during overloaded conditions.