Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Some Drought Indices in Iraq

Drought in Iraq was assessed using three drought indices for two different time periods, past period from 1970 to 2015, and future period from 2016 to 2050 for 4 stations in Iraq. These indices named: the Standardized Precipitation Index (SPI), Percentage of Precipitation Anomaly (PPA), and Z-Score Index (ZSI). The main sources of data were the monthly rainfall archive from Iraqi Meteorological Organization and Seismology (IMOS) for past period, and projection monthly precipitation data from Representative Concentration Pathway scenario (RCP4.5) for Fifth Assessment Report (AR5) affiliate to the Intergovernmental Panel on Climate Change (IPCC) for future period. The results shown good correlation of among 3 indices, with different rank of them, were lowest rank was 0.85. The three indices refer to were good evaluate the drought severity of Iraq. Found too the best index was PPA during past period, and the significant index was ZSI for the future period. The North of Iraq (Mosul Station) was the least vulnerable to drought from the analysis of the results of the three indices for the last period, where the number of dry seasons were 12 on the basis of the PPA classification. South of Iraq (Basra station) has the highest number of wet season on the PPA rating were (15) season.
An Assessment of CO 2 Emission and Absorption in Response to LandCover Changes in the Seoul Metropolitan Area

In order to cope with climate change, which has been becoming a global issue, there are measures such as fundamentally reducing energy use or converting energy sources into renewable energy, but this is difficult to apply due to limitations on human activities. Based on the guidelines provided by the IPCC, this study drew a countermeasure to climate change considering the emission and absorption of CO2 by land use or land cover. Especially, by using the land-use change simulation technique to predict future land use, expected problems which are caused by urban development were prevented in advance. In addition, CO2 emissions sources are classified into direct emissions and indirect emissions, and the extent to which each region contributes to greenhouse gas emissions is analyzed to provide alternatives that meet the characteristics of each region. Moreover, to calculate greenhouse gas emissions in the transportation sector, the network analysis of ArcGIS was used to calculate CO2 emissions from vehicle’s movements and to propose alternatives.
Improving foreign language listening through subtitles

Understanding speech in a second language can be hard, as there are many sounds in this language that do not match with the sounds of a listener’s native language (Best & Tyler, 2007). Luckily, listeners are able to adapt to foreign speech by shifting their phonetic patterns, using lexical information (Norris, McQueen, & Cutler, 2003). This process is therefore known as lexically-guided perceptual learning (Norris et al., 2003). Previous studies have found that watching videos in a foreign languages with subtitles can aid this process of perceptual learning (Birulés-Muntané & Soto-Faraco, 2016; Charles & Trenkic, 2015; Mitterer & McQueen, 2009). Two of these studies also showed that while foreign-language subtitles improve perceptual learning, native-language subtitles harm perceptual learning (Birulés-Muntané & Soto-Faraco, 2016; Mitterer & McQueen, 2009). All of these results are derived from experiments using participants with a high level of proficiency in their second language. However, the results might be different when using participants with a lower proficiency level. This study investigates the influence of watching subtitled videos on perceptual learning in a foreign language, and whether language proficiency related to this influence. Four subquestions are investigated in this study: 1. What is the influence of the subtitle language on perceptual tuning in English of Dutch university students? 2. What is the influence of the subtitle language on perceptual tuning in English of Dutch high school and university students? 3. Does language proficiency modulate this possible influence? 4. If there is a learning effect, will this effect generalize to speakers who were not heard during the exposure phase? These four subquestions were investigated by having Dutch high school and university students watch an English video. This video was accompanied by either English, Dutch or no subtitles. The participants’ perception of English was measured using a dictation task before and after watching the video. These scores therefore give an estimation of the amount of perceptual learning that took place while watching the video. Both the video and the sentences where spoken by speakers with a Glaswegian accent. The results show that using English subtitles and no subtitles leads to similar scores of speech perception. They also show that using Dutch subtitles might lead to less perceptual learning taking place. This seems to be the case mostly for listeners with a lower proficiency level. Listeners with a low proficiency level had lower scores in the Dutch subtitle condition than in the other conditions. The results were however not completely straightforward under closer inspection, making it difficult to interpret them. It can therefore be concluded that the subtitle language relates to the
Analysis of the Mechanism of Zhichuanling Oral Liquid in Treating Bronchial Asthma Based on Network Pharmacology

Zhichuanling oral liquid (ZOL) as a preparation of traditional Chinese medicine is widely used for the treatment of asthma in China; therefore, it is necessary to systematically clarify bioactive chemical ingredients and the mechanism of action of ZOL. Information on ZOL ingredients and asthma-related targets was collected, and we used the latest systematic pharmacological methods to construct protein-protein interaction network and compound-target network and then visualized them. Finally, GO and KEGG pathway enrichment analysis was conducted through the clusterProfiler package in the R software. The results showed that 58 bioactive ingredients and 42 potential targets of ZOL related to asthma were identified, following six important components and nine hub genes screened. Further cluster and enrichment analysis suggested that NF-κB signaling pathway, PI3K/Akt signaling pathway, IL-17 signaling pathway, Toll-like receptor signaling pathway, and TNF signaling pathway might be core pathways of ZOL for asthma. Our work successfully predicted the active ingredients and potential targets of ZOL and provided the explanation for the mechanism of action of ZOL for asthma through the systematic analysis, which suggested that ZOL played a major role in many ways including reducing airway inflammation and inhibiting airway remodeling and mucus secretion. Moreover, ZOL combined with glucocorticoids may have some effects on severe asthma.
Entrepreneurship strategy for improving business performance using internet technology-based business application

Purpose – to conduct an empirical test over the effect of entrepreneurship competency on business performance using Internet technologybased business applications. Design/Method/Approach. The research population is from small-and-medium Enterprises (SMEs) in the city of Tarakan. The determined sample is based on the observed area; hence, the technique is called probability sampling area. The data analysis technique is Partial Least SquareStructural Equation Modelling (PLS-SEM), run by WarpPls 6.0. Findings. This research's results indicate that entrepreneurship competency has a positive impact on Internet technology-based business applications. Besides, internet technology-based business application mediates the effect of entrepreneurship competency on business performance. Theoretical implications. The results support the Resource-Based View Theory, stating that the competitive advantage of an enterprise is derived from its unique resource. Practical implications. Internet technology-based business applications can be used as entrepreneurship strategies to develop business performance. Originality/Value. The originality of this research is the internet technology-based business application as a mediating variable between entrepreneurship competence and business performance. Research limitations/Future research. The research has its limitations; it only uses limited SME research samples in the city of Tarakan. Future research can expand the sample through other Indonesian cities.
Relational Influence

An uninformed principal elicits non-contractible recommendations from a privately informed agent regarding the quality of projects. The agent is biased in favor of implementation and no credible communication is possible in a one-shot setting. In a repeated setting, the fear of losing future influence can sustain informative communication, but the agent's willingness to remain truthful depends on the extent to which he expects the principal to listen to him. In a stationary equilibrium, the principal always implements mediocre projects at a sub-optimally high frequency to reward honesty, while she may either favor or discriminate against high-quality projects. In a non-stationary equilibrium, the principal will further condition the agent's future influence on today's proposals, with the admission of mediocre alternatives rewarded with increased future influence while rejections of high-quality proposals are further punished by lowering the agent's future influence.
The Relationship between Quality of Life and Health Literacy among Nurses of the Largest Heart Center in the North West of Iran

Background and Objective: Quality of life is a multidimensional concept which is influenced by several important factors, such as physical and mental status. Health literacy is also one of the factors with a significant impact on quality of life. The purpose of this study was to investigate the relationship between quality of life and health literacy of nurses in the largest heart center in northwest of Iran in 2018. Materials and Methods: This was a descriptive cross-sectional study performed on 217 nurses working in the largest heart center of Tabriz. The study data were collected using the 36-item Quality of Life Questionnaire (SF-36) and the Health Literacy for Iranian Adults (HELIA) questionnaire. Data analysis was performed using ANOVA, Pearson correlation test, and SPSS 25 software. Results: The overall mean and standard deviation of nurses' quality of life of (59.7 Â± 17.6) were obtained and the majority of the subjects (84.7) had a good quality of life. Also the overall mean and standard deviation of health literacy were estimated at (75.9 Â± 13.3) which indicate adequate level of health literacy in nurses. There was a significant relationship between different dimensions of quality of life and total score of health literacy (P <0.001), but there was no significant relationship between understanding and perception of health literacy with most of the dimensions of quality of life. Conclusion: Findings showed that increasing nurses' health literacy had a positive effect on some aspects of their quality of life. Therefore, nursing supervisors should pay attention to health and quality of life while communicating with nursing staff and, try to improve nurses' quality of life by developing educational programs and promoting their health literacy.
‘GPU-based Desktop Supercomputing’

It is interesting to note a major force impacting Graphics Processor Unit (GPU) evolution has been a seemingly endless demand for increased Personal Computer (PC) graphics subsystem performance. In this context, the GPU is understood to exist as an ancillary coprocessor subsystem, attached to some internal high-speed bus and memory-mapped into global system memory resources. In particular, gaming, computer vision, and advanced graphics design applications have driven sharp MIPS performance improvements and increased diversity and algorithmic sophistication on part of relevant graphics standards {1}{2}{15}{16}. Arguably, this is all part and parcel of a larger evolutionary trend whereby PCs supplant dedicated workstations for a host of compute intensive applications.
Performance comparison of Korean keyword-based document classifiers using convolutional neural networks – A case of Science and Technology research report data

Recently, with the rapid development of deep neural network (DNN) technology, application research is increasing in various fields of artificial intelligence. Particularly, activity is increasing in the image recognition field using DNN. Deep learning-based technology is also used in document classification, having been studied in the natural language processing field for a long time. Document classification using sentence unit texts has been attempted in other countries with high performance. In this study, document classification is performed based on an existing convolutional neural network (CNN) containing Korean/English keyword data from Korean reports; the results are summarized. A single-layer CNN was used. Owing to the experimentation with bi-gram, we obtained 24% more accurate results than simple word-based learning using Korean learning data.
Which way? Direction-Aware Attributed Graph Embedding

Graph embedding algorithms are used to efficiently represent (encode) a graph in a low-dimensional continuous vector space that preserves the most important properties of the graph. One aspect that is often overlooked is whether the graph is directed or not. Most studies ignore the directionality, so as to learn high-quality representations optimized for node classification. On the other hand, studies that capture directionality are usually effective on link prediction but do not perform well on other tasks. This preliminary study presents a novel text-enriched, direction-aware algorithm called DIAGRAM , based on a carefully designed multi-objective model to learn embeddings that preserve the direction of edges, textual features and graph context of nodes. As a result, our algorithm does not have to trade one property for another and jointly learns high-quality representations for multiple network analysis tasks. We empirically show that DIAGRAM significantly outperforms six state-of-the-art baselines, both direction-aware and oblivious ones,on link prediction and network reconstruction experiments using two popular datasets. It also achieves a comparable performance on node classification experiments against these baselines using the same datasets.
Weakly Supervised Instance Segmentation by Deep Multi-Task Community Learning

We present an object segmentation algorithm based on community learning for multiple tasks under the supervision of image-level class labels only, where individual instances of the same class are identified and segmented separately. This problem is formulated as a combination of weakly supervised object detection and semantic segmentation and is addressed by designing a unified deep neural network architecture, which has a positive feedback loop of object detection with bounding box regression, instance mask generation, instance segmentation, and feature extraction. Each component of the network makes active interactions with others to improve accuracy, and the end-to-end trainability of our model makes our results more reproducible. The proposed algorithm achieves competitive accuracy in the weakly supervised setting without any external components such as Fast R-CNN and Mask R-CNN on the standard benchmark dataset.
Conditioning Autoencoder Latent Spaces for Real-Time Timbre Interpolation and Synthesis

We compare standard autoencoder topologies’ performances for timbre generation. We demonstrate how different activation functions used in the autoencoder’s bottleneck distributes a training corpus’s embedding. We show that the choice of sigmoid activation in the bottleneck produces a more bounded and uniformly distributed embedding than a leaky rectified linear unit activation. We propose a one-hot encoded chroma feature vector for use in both input augmentation and latent space conditioning. We measure the performance of these networks, and characterize the latent embeddings that arise from the use of this chroma conditioning vector. An open source, real-time timbre synthesis algorithm in Python is outlined and shared.
A Finite-Time Algorithm for the Distributed Tracking of Maneuvering Target

This paper presents a novel distributed algorithm for tracking a maneuvering target using bearing or direction of arrival measurements collected by a networked sensor array. The proposed approach is built on the dynamic average-consensus algorithm, which allows a networked group of agents (nodes) to reach consensus on the global average of a set of local time-varying signals in a distributed fashion. Since the average-consensus error corresponding to the presented dynamic average-consensus algorithm converges to zero in finite time, the proposed distributed algorithm guarantees that the tracking error converges to zero in finite time. Numerical simulations are provided to illustrate the effectiveness of the proposed algorithm.
Developing an Augmented Reality Tourism App through User-Centred Design (Extended Version)

Augmented Reality (AR) bridges the gap between the physical and virtual world. Through overlaying graphics on natural environments, users can immerse themselves in a tailored environment. This offers great benefits to mobile tourism, where points of interest (POIs) can be annotated on a smartphone screen. While a variety of apps currently exist, usability issues can discourage users from embracing AR. Interfaces can become cluttered with icons, with POI occlusion posing further challenges. In this paper, we use user-centred design (UCD) to develop an AR tourism app. We solicit requirements through a synthesis of domain analysis, tourist observation and semi-structured interviews. Whereas previous user-centred work has designed mock-ups, we iteratively develop a full Android app. This includes overhead maps and route navigation, in addition to a detailed AR browser. The final product is evaluated by 20 users, who participate in a tourism task in a UK city. Users regard the system as usable and intuitive, and suggest the addition of further customisation. We finish by critically analysing the challenges of a user-centred methodology.
An Implicit Attention Mechanism for Deep Learning Pedestrian Re-identification Frameworks

Attention is defined as the preparedness for the mental selection of certain aspects in a physical environment. In the computer vision domain, this mechanism is of most interest, as it helps to define the segments of an image/video that are critical for obtaining a specific decision. This paper introduces one 'implicit' attentional mechanism for deep learning frameworks, that provides simultaneously: 1) masks-free; and 2) foreground-focused samples for the inference phase. The main idea is to generate synthetic data composed of interleaved segments from the original learning set, while using class information only from specific segments. During the learning phase, the newly generated samples feed the network, keeping their label exclusively consistent with the identity from where the region-of-interest was cropped. Hence, as the model receives images of each identity with inconsistent unwanted areas, it naturally pays the most attention to the label consistent consistent regions, which we observed to be equivalent to learn an effective receptive field. During the test phase, samples are provided without any mask, and the network naturally disregards the detrimental information, which is the insight for the observed improvements in performance. As a proof-of-concept, we consider the challenging problem of pedestrian re-identification and compare the effectiveness of our solution to the state-of-the-art techniques in the well known Richly Annotated Pedestrian (RAP) dataset. The code is available at this https URL
Kelly Betting with Quantum Payoff: a continuous variable approach

The main purpose of this study is to introduce a semi-classical model describing betting scenarios in which, at variance with conventional approaches, the payoff of the gambler is encoded into the internal degrees of freedom of a quantum memory element. In our scheme, we assume that the invested capital is explicitly associated with the quantum analog of the free-energy (i.e. ergotropy functional by Allahverdyan, Balian, and Nieuwenhuizen) of a single mode of the electromagnetic radiation which, depending on the outcome of the betting, experiences attenuation or amplification processes which model losses and winning events. The resulting stochastic evolution of the quantum memory resembles the dynamics of random lasing which we characterize within the theoretical setting of Bosonic Gaussian channels. As in the classical Kelly Criterion for optimal betting, we define the asymptotic doubling rate of the model and identify the optimal gambling strategy for fixed odds and probabilities of winning. The performance of the model are hence studied as a function of the input capital state under the assumption that the latter belongs to the set of Gaussian density matrices (i.e. displaced, squeezed thermal Gibbs states) revealing that the best option for the gambler is to devote all their initial resources into coherent state amplitude.
Polyol-mediated synthesis of size-controlled copper nanoparticles under microwave irradiation

In this research, well-dispersed zero-valent copper nanoparticles (CuNPs) were successfully synthesized from a welldefined complex of [Cu(OH)(TMEDA)]2Cl2 in neat glycerol via a polyol method, under microwave irradiation (MW) assistance. The as-prepared CuNPs were thoroughly characterized by means of various techniques such as inductively coupled plasma mass spectrometry (ICP-MS), Ultraviolet-visible spectroscopy (UV-vis), X-ray diffraction (XRD) and transmission electron microscopy (TEM) analyses, evidencing the formation of the spherical nanoparticles with the range of 3.24.2 nm in mean diameter. In addition, the size control of obtained CuNPs was examined via reaction time, thereby showing that the formation of CuNPs conformed to the model of “mono-dispersion”.
Is the High-Resolution Coronal Imager Resolving Coronal Strands? Results from AR 12712

Following the success of the first mission, the High-Resolution Coronal Imager (Hi-C) was launched for a third time (Hi-C 2.1) on 2018 May 29 from the White Sands Missile Range, NM, USA. On this occasion, 329 s of 17.2 nm data of target active region AR 12712 were captured with a cadence of ≈4 s, and a plate scale of 0. ″129 pixel−1. Using data captured by Hi-C 2.1 and co-aligned observations from SDO/AIA 17.1 nm, we investigate the widths of 49 coronal strands. We search for evidence of substructure within the strands that is not detected by AIA, and further consider whether these strands are fully resolved by Hi-C 2.1. With the aid of multi-scale Gaussian normalization, strands from a region of low emission that can only be visualized against the contrast of the darker, underlying moss are studied. A comparison is made between these low-emission strands and those from regions of higher emission within the target active region. It is found that Hi-C 2.1 can resolve individual strands as small as ≈202 km, though the more typical strand widths seen are ≈513 km. For coronal strands within the region of low emission, the most likely width is significantly narrower than the high-emission strands at ≈388 km. This places the low-emission coronal strands beneath the resolving capabilities of SDO/AIA, highlighting the need for a permanent solar observatory with the resolving power of Hi-C.