Miracle-dz/UniFAR-FTUs huggingface dataset:
Secured Color Image Compression based on Compressive Sampling and Lü System

An efficacious and unharmed approach is vital for transmission of sensitive and secret images over unsecure public Internet. In this paper, secured color image compression method based on compressive sampling and Lü system is proposed. Initially, the plain-image is sparsely represented in transform basis. Compressive sampling measurements are obtained from these sparse transform coefficients by employing incoherent sensing matrix. To upgrade the security level, permutation-substitution operations are performed on pixels based on Lü system. To concoct input sensitivity in the scheme, the keys are obtained from input image. Lastly, fast and efficient greedy algorithm is utilized for sparse signal reconstruction. To evaluate the performance of the proposed scheme Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Average Difference (AD), Structural Content (SC), Normalized Cross Correlation (NCC), Normalized Absolute Error (NAE), Edge Strength Similarity (ESSIM), Maximum Difference (MD), Correlation Coefficient, Unified Average Changing Intensity (UACI), Key Sensitivity, Number of pixel change Rate (NPCR), Key Space and Histogram metrics are used. Experimental results demonstrate that the proposed scheme produced highly satisfactory security.
The synoptic-dynamics of summertime heatwaves in the Sydney area (Australia)

Motivated by the record-breaking heatwaves of early 2017, the synoptic structure and evolution of summer (December–February) heatwaves in the Sydney area is investigated through composite and trajectory analyses. In the upper troposphere, the main features of the composite structure are an isolated upper-tropospheric anticyclonic potential vorticity (PV) anomaly to the south-east of Australia and cyclonic anomalies to the east and south. Back trajectories starting from within the upper-tropospheric anticyclonic PV anomaly on the first day of the heatwave fall into two groups: those that are diabatically cooled in the final 72 h and those that are diabatically heated. Those that are cooled come predominantly from the upstreammiddle troposphere over the Indian Ocean. The change in the potential temperature of these parcels is less than 3K, and so their motion is effectively adiabatic. In contrast, those parcels that are heated in the final 72 h are drawn predominantly from the lower half of the troposphere over the south-western part of the continent. As they ascended, their potential temperature increases by 10K in the mean due to latent heating. At low-levels, the main features of the composite are an anticyclone centred in the Tasman Sea, a broad low over the Southern Ocean and associated anomalous warm northwesterlies over the Sydney area. Five days prior to the heatwave, air parcels that become part of the near surface air mass are located predominantly offshore to the east and south of the continent. The anomalously high surface temperatures can be explained by adiabatic compression and surface sensible heating. For the next 48 h, the air parcels subside and their potential temperature changes little, whereas their temperature increases by around 15Kthrough adiabatic compression. In the final 72 h, as the parcels approach the surface and are entrained into the boundary layer, the potential temperature and temperature both increase by 5K, presumably through surface sensible heating. The record-breaking heatwaves of January and February 2017 are found to be very representative of previous heatwaves in the Sydney area, and in the mean they are synoptically very similar to heatwaves in Victoria, although dynamically there are differences.
Influence-based provenance for dataflow applications with taint propagation

Debugging big data analytics often requires a root cause analysis to pinpoint the precise culprit records in an input dataset responsible for incorrect or anomalous output. Existing debugging or data provenance approaches do not track fine-grained control and data flows in user-defined application code; thus, the returned culprit data is often too large for manual inspection and expensive post-mortem analysis is required. We design FlowDebug to identify a highly precise set of input records based on two key insights. First, FlowDebug precisely tracks control and data flow within user-defined functions to propagate taints at a fine-grained level by inserting custom data abstractions through automated source to source transformation. Second, it introduces a novel notion of influence-based provenance for many-to-one dependencies to prioritize which input records are more responsible than others by analyzing the semantics of a user-defined function used for aggregation. By design, our approach does not require any modification to the framework's runtime and can be applied to existing applications easily. FlowDebug significantly improves the precision of debugging results by up to 99.9 percentage points and avoids repetitive re-runs required for post-mortem analysis by a factor of 33 while incurring an instrumentation overhead of 0.4X - 6.1X on vanilla Spark.
Evaluating the impact of different types of crossover and selection methods on the convergence of 0/1 Knapsack using Genetic Algorithm

Genetic Algorithm is an evolutionary algorithm and a metaheuristic that was introduced to overcome the failure of gradient based method in solving the optimization and search problems. The purpose of this paper is to evaluate the impact on the convergence of Genetic Algorithm vis-a-vis 0/1 knapsack. By keeping the number of generations and the initial population fixed, different crossover methods like one point crossover and two-point crossover were evaluated and juxtaposed with each other. In addition to this, the impact of different selection methods like rank-selection, roulette wheel and tournament selection were evaluated and compared. Our results indicate that convergence rate of combination of one point crossover with tournament selection, with respect to 0/1 knapsack problem that we considered, is the highest and thereby most efficient in solving 0/1 knapsack.
Analysis of consumer preferences for prepaid mobile internet packages in Iran: A Discrete Choice Experiment

As a first discrete choice experiment in Iran emerging telecommunication market, this paper studies consumer preferences for prepaid mobile internet packages with a combined software and paper-based interview. A two-stage Bayesian D-optimal design procedure is deployed to design choice sets of mobile internet packages from four main attributes. The utility structure and customers’ willingness-to-pay for mobile internet packages are analyzed. Findings/originality: The results indicate that even with a considerable price reduction, consumers avoid prepaying for data plans with commitment periods longer than six months and high traffic volume. Traffic volume and brand attributes are recognized as the two most influential factors on consumers’ behavior. Simulating the market demonstrates the competition between mobile internet operators in Iran market. The statistics express a significant effect of consumers’ current mobile operator on their preferences for the brand attribute.
The Impact of Network Literature Website Service Quality on Reader Loyalty Based on Technology Acceptance Model

Based on the Technology Acceptance Model (TAM), this paper introduces the service quality theory into the field of network literature websites, and studies the mechanism of the service quality of network literature websites on reader loyalty. It is found through research that the service quality of online literature websites has a significant positive impact on reader loyalty; the service quality of network literature websites has a significant positive impact on perceived convenience; the perceived convenience has a significant positive impact on reader loyalty; and perceived convenience plays a part of the mediating effect in the service quality of network literature websites on reader loyalty. This research not only provides a certain support for the improvement of the related theories about the service quality of online literature websites in China, but also further explores the feasible path for the development of current online literature websites in China.
WEEE Recycling and Circular Economy Assisted by Collaborative Robots

Considering the amount of waste of electrical and electronic equipment (WEEE) generated each year at an increasing rate, it is of crucial importance to develop circular economy solutions that prioritize reuse and recycling, as well as reducing the amount of waste that is disposed of at landfills. This paper analyses the evolution of the amount of WEEE collection and its recycling rate at the national and European levels. It also describes the regulatory framework and possible future government policy measures to foster a circular economy. Furthermore, it identifies the different parts and materials that can be recovered from the recycling process with a special emphasis on plastics. Finally, it describes a recycling line that has been designed for the dismantling of computer cathodic ray tubes (CRT)s that combines an innovative participation of people and collaborative robots which has led to an effective and efficient material recovery solution. The key issue of this human–robot collaboration relies on only assigning tasks that require human skills to operators and sending all other tasks to robots. The first results from the model show a better economic performance than current manual processes, mainly regarding the higher degree of separation of recovered materials and plastic in particular, thus reaching higher revenues. This collaboration also brings considerable additional benefits for the environment, through a higher recovery rate in weight and for workers, who can make intelligent decisions in the factory and enjoy a safer working environment by avoiding the most dangerous tasks.
Using Contextual Embeddings and External Geophysical Information for Toponym Resolution in Text

Toponym resolution refers to the disambiguation of place names and other references to places present in textual documents, linking the corresponding spans of text to unambiguous geographical identifiers (e.g., geographic coordinates of latitude and longitude). One of the significant challenges in this task is the high ambiguity of place names since there are, for instance, several locations on the surface of the Earth that share the same name. In this article, we describe a novel approach for toponym resolution using a deep neural network, that does not directly involve gazetteer matching, to predict geospatial coordinates such as latitude and longitude. The proposed neural network architecture uses recurrent units with multiple inputs (e.g., the toponym to disambiguate along with the surrounding words), leveraging pre-trained contextual word embeddings (i.e., ELMo or BERT embeddings) and bi-directional Long Short-Term Memory (LSTM) units, both commonly used for textual data modeling. We tested the proposed model on three datasets that have been used to evaluate other toponym resolution systems, namely on the (1) War of the Rebellion , (2) Local-Global Lexicon , and (3) SpatialML corpora. Additionally, we evaluated the impact of using (1) external information in the form of raster datasets with geophysical properties, including information on land coverage, terrain elevation, among others, and (2) additional training data collected from Wikipedia articles, to guide and further help model training. The obtained results show a significantly higher quality of the proposed method, in comparison to previous approaches and particularly in the setting that involves BERT embeddings and additional data.
Discovery and Functional Prediction of Long Non-Coding RNAs Common to Ischemic Stroke and Myocardial Infarction

Objective Ischemic stroke and myocardial infarction are 2 of the leading causes of mortality. Both conditions are caused by arterial occlusion, resulting in ischemic necrosis of the cells in the cortex and heart. Long non-coding RNAs (lncRNAs) are a group of non-coding RNAs longer than 200 nucleotides without protein-coding potential. Thousands of lncRNAs have been identified but their involvement in ischemic stroke and myocardial infarction has not been studied extensively. Therefore, this study aimed to identify the role of lncRNAs, particularly those that are commonly altered in these two ischemic injuries. Methods We combined diverse RNA sequencing data obtained from public databases and performed extensive bioinformatics analyses to determine reliable lncRNAs commonly identified from these datasets. Using sequence analysis, we also detected the lncRNAs that may act as microRNA (miRNA) regulators. Results We found several altered lncRNAs that were common in ischemic stroke and myocardial infarction models. Some of these lncRNAs, including zinc finger NFX1-type containing 1 antisense RNA 1 and small nucleolar RNA host gene 1, were previously reported to be involved in the pathogenesis of each of these models. Interestingly, several lncRNAs had binding sites for miRNAs that were previously reported to be involved in the hypoxic response, suggesting the possible role of these lncRNAs as regulators in ischemic responses. Conclusion The lncRNAs identified in this study will be useful in determining the regulatory networks in ischemic stroke and myocardial infarction and in identifying potential specific markers for each of these ischemic diseases.
Robust contracting under double moral hazard

We study contracting when both principal and agent have to exert noncontractible effort for production to take place. An analyst is uncertain about what actions are available and evaluates a contract by the expected payoffs it guarantees to each party in spite of the surrounding uncertainty. Both parties are risk‐neutral; there is no limited liability. Linear contracts, which leave the agent with a constant share of output in exchange for a fixed fee, are optimal. This result holds both in a preliminary version of the model, where the principal only chooses to supply or not supply an input, and in several variants of a more general version, where the principal may have multiple choices of input. The model thus generates nontrivial linear sharing rules without relying on either limited liability or risk aversion.
High-Frame-Rate 3-D Vector Flow Imaging in the Frequency Domain

Ultrasound vector Doppler techniques for three-dimensional (3-D) blood velocity measurements are currently limited by low temporal resolution and high computational cost. In this paper, an efficient 3-D high-frame-rate vector Doppler method, which estimates the displacements in the frequency domain, is proposed. The novel method extends to 3-D an approach so far proposed for two-dimensional (2-D) velocity measurements by approximating the (x, y, z) displacement of a small volume through the displacements estimated for the 2-D regions parallel to the y and x directions, respectively. The new method was tested by simulation and experiments for a 3.7 MHz, 256-element, 2-D piezoelectric sparse spiral array. Simulations were also performed for an equivalent 7 MHz Capacitive Micromachined Ultrasonic Transducer spiral array. The results indicate performance (bias ± standard deviation: 6.5 ± 8.0) comparable to the performance obtained by using a linear array for 2-D velocity measurements. These results are particularly encouraging when considering that sparse arrays were used, which involve a lower signal-to-noise ratio and worse beam characteristics with respect to full 2-D arrays.
Cell proliferation in the amputated limb of lizard leading to scarring is reduced compared to the regenerating tail

After amputation, the tail of lizards regenerates while the limb forms a short scarring outgrowth. Using phospho-histone-H3 immunohistochemistry the mitotic activity of limb tissues at 12–25 days after amputation has been studied, when a limb outgrowth of 0.5–2 mm in length is covered by wound epidermis and the underlying connective is turning into a dense scar. In comparison with a regenerating tail of 3–5 mm in length, the number of dividing cells is reduced of 40–70% in different tissues of the scarring limb 1–2 mm in length at 18 days postamputation. Dividing cells are still present at 12–25 days postamputation in the cartilaginous epiphyses of the transected tibia and fibula and of the untransected femur. Also, the injured muscles present at the base of the scarring outgrowth still contain sparse dividing cells after 25 days postamputation of the limb. Together previous studies, the present observations suggest that after the initial proliferation of fibroblasts deriving from the injured tissues, especially from the dermis and intermuscle connectives during the initial 7–15 days postinjury, these cells cover the injured tissues underneath the wound epidermis, but rapidly produce high levels of collagen turning the initial blastema into a scar.
THE CHALLENGE OF WIRELLES CONNECTIVITY TO SUPPORT INTELLIGENT MINES *V

The need for continuous safety improvements and inc reased operational efficiency is driving the mining industry through a transition towards large-scale a utomation of operations, i.e., “intelligent mines”. The technology promises to remove human operators from harsh or dangerous conditions and increase productivity, from extraction all the way to the de liv ry of a processed product to the customer. In t his context, one of the key enablers is wireless connec tivity since it allows mining equipment to be remot ely monitored and controlled. Simply put, dependable wi reless connectivity is essential for unmanned mine operations. Although voice and narrowband data radi os have been used for years to support several type s of mining activities, such as fleet management (dispat ch) and telemetry, the use of automated equipment introduces a new set of connectivity requirements a d poses a set of challenges in terms of network pl anning, management and optimization. For example, the data rates required to support unmanned equipment, e.g. a teleoperated bulldozer, shift from a few kilobits/s econd to megabits/second due to live video feeds. T his traffic volume is well beyond the capabilities of P rofessional Mobile Radio narrowband systems and mandates the deployment of broadband systems. Furth ermore, the (data) traffic requirements of a mine a lso vary in time as the fleet expands. Additionally, w ireless networks are planned according to the chara teristics of the scenario in which they will be deployed, but mines change by definition on a daily-basis. There fore, a careful and continuous effort must be made to ensur e the wireless network keeps up with the topographi c and operational changes in order to provide the nec essary network availability, reliability, capacity and coverage needed to support a new mining paradigm. B y means of simulations, we analyze the effects on t he wireless network along 7 years of constant topograp hic changes in an open-pit mine coupled with much higher data requirements. The authors also present a new network topology that is able to partially me et the requirements posed by mining automation and discuss the consequences of not providing connectivity for all applications. The work also discusses how the c areful positioning of the heavy communications infrastructure (tall towers) from the early stages of the mine site project can make the provision of incremental capacity and coverage simpler.
Functional limit theorems for multitype branching processes and generalized P& olya urns

A functional limit theorem is proved for multitype continuous time Markov branching processes. As consequences, we obtain limit theorems for the branching process stopped by some stopping rule, for example when the total number of particles reaches a given level. Using the Athreya–Karlin embedding, these results yield asymptotic results for generalized P& olya urns. We investigate such results in detail and obtain explicit formulas for the asymptotic variances and covariances. The general formulas involve integrals of matrix functions; we show how they can be evaluated and simpli7ed in important special cases. We also consider the numbers of drawn balls of di8erent types and functional limit theorems for the urns. We illustrate our results by some examples, including several applications to random trees where our theorems and variance formulas give simple proofs of some known results; we also give some new results. c © 2003 Elsevier B.V. All rights reserved.
Adaptive Matrix Metrics for Attribute Dependence Analysis in Differential High-Throughput Data

Data-driven metric adaptation is proposed for proteome analysis of 2D-gel electrophoretic plots aiming at identi-ﬁcation of stress related proteins in two barley cultivars with different response towards different salt stress conditions. Gradient descent is applied to the ratio of intra-and inter-class distance sums to optimize the matrix parameters of generalized Mahalanobis distances in order to separate the several hundred dimensional data of protein intensities in the transformation space. The resulting matrix contains mutual dependence of spots, explaining differential stress reactions and putative protein interactions. We present interesting results obtained by the new metric learning method that possesses general applicability in biomedical data analysis.
Ecological site classification of Florida keys terrestrial habitats

Site and vegetation characteristics were examined in 113 Florida Keys locations that had been undisturbed for at least 50 years. Detrended correspondence analysis (DECORANA) indicated that Keys vegetation was arranged along two major environmental gradients: an elevational gradient within islands, and a geographic gradient associated with position along the NE-SW trending island chain. Both were complex gradients, with soil depth and type, periodicity of tidal inundation, ground water depth and salinity, climate, and geological substrate as potential contributing factors. Two-way indicator species analysis (TWINSPAN) was used to divide the samples into 14 major groups on the basis of plant species composition. Finally, the TWINSPAN classification was modified to recognize 13 Ecological Site Units which were homogeneous in important site factors as well as vegetation characteristics. Plant species diversity increased from intertidal to upland site units, while canopy height, basal area, and fine litter production increased both upslope and downslope of the supratidal units.