Miracle-dz/UniFAR-FTUs huggingface dataset:
A smart litho friendly design method to enable fast lithography hotspots detection in design flow

When technology comes to 28nm and beyond, chip size and design complexity are increasing. Lithography simulation is computing intensive and it may takes days to see whether there is any hotspot hard to solve other than change the design. Pattern classification is quite a matured technique and it can be used to locate unique patterns on a chip, then it is possible to do lithography simulation on these unique pattern locations first. This is an efficient approach to quickly detect any unfriendly design style on the layout and give designers enough time to fix these unfriendly designs and do an incremental check to validate the fixing. Once the fixing approach is validated, a pattern matching based pattern substitution can be used to fix all the problematic areas on the layout.
Going Farther Together: The Impact of Social Capital on Sustained Participation in Open Source

Sustained participation by contributors in opensource software is critical to the survival of open-source projects and can provide career advancement benefits to individual contributors. However, not all contributors reap the benefits of open-source participation fully, with prior work showing that women are particularly underrepresented and at higher risk of disengagement. While many barriers to participation in open-source have been documented in the literature, relatively little is known about how the social networks that open-source contributors form impact their chances of long-term engagement. In this paper we report on a mixed-methods empirical study of the role of social capital (i.e., the resources people can gain from their social connections) for sustained participation by women and men in open-source GitHub projects. After combining survival analysis on a large, longitudinal data set with insights derived from a user survey, we confirm that while social capital is beneficial for prolonged engagement for both genders, women are at disadvantage in teams lacking diversity in expertise.
ARTificial intelligence raters: Neural networks for rating pictorial expression

Previous studies on classification of fine art show that features of paintings can be captured and categorized using machine learning approaches. This progress can also benefit art psychology by facilitating data collection on artworks without the need to recruit experts as raters. In this study a machine learning approach is used to predict the ratings of RizbA, a Rating instrument for two-dimensional pictorial works. Based on a pre-trained model, the algorithm was fine-tuned via transfer learning on 886 pictorial works by contemporary professional artists and non-professionals. As quality criterion, artificial intelligence raters (ART) are compared with generic raters (GR) created from the real human expert raters, using error rate and mean squared error (MSE). ART ratings have been found to have the same error range as randomly chosen human ratings. Therefore, they can be seen as equivalent to real human expert raters for almost all items in RizbA. Further training with more data will close the gap to the human raters on all items.
Hybrid database system using runtime reconfigurable hardware

A system may include a routines repository that is configured to store and maintain hardware libraries, software libraries and metadata, a hybrid query engine that may be configured to receive a query, parse the query, compute a query execution plan and output the query execution plan using metadata and operators from the hardware libraries and/or the software libraries, and a routines management module that may be configured to provide the metadata and the operators from the routines repository to the hybrid query engine. The system may include an execution engine module that may be configured to receive the query execution plan, the execution engine module including a reconfigurable hardware execution engine (236) having a reconfigurable fabric, where the reconfigurable hardware execution engine (236) may be configured to process the query execution plan.
Wireless inductive power transfer

1. The transmitter power for the transmission power in power receiver using a wireless inductive power signal, the power transmitter comprises: an inductor for providing power signal, the power signal generator for the excitation coil to provide the power signal; calibration controller for determining whether there was formed calibration power loss for the pair of transmitter power and receiver power, the calibration of the power loss determines the expected relationship between dy indicating received power provided by the power receiver and the indication of the transmitted power to the transmitter power, power limiter configured to limit the power supplied to the coil, for the purpose of not exceeding the threshold value until the calibration power loss for the pair of power and receiver transmitter will be performed power; a receiver for receiving an indication of the received power from the power receiver; a sensor for detecting the parasitic power consumption in response to the deviation exceeding the threshold value of the ratio between the transmitted power indication and an indication of the received power and the expected relation between the indication of the transmitted power and the received indication moschnosti.2. power transmitter according to claim 1, further comprising: a. calibrator to calibrate the power loss to determine the expected ratio for a calibration phase, the expected ratio is determined by comparing the at least one transmitted power indication and at least one indication received
In silico Prediction of C_4-related Genes by Finding Duplications Causing Pattern Deviation and Comparative Analysis of Phylogenetic Trees

This study is focused on the development of a pattern-finding method for analyzing evolutionary trees to predict genes that may be involved in C_4 photosynthesis. It relies on publicly available phylogenetic data which is processed with the authors' own Python scripts and opensource software. The pattern recognition in the topology of the trees is an essential part of the process and the result is then validated by comparing the expression levels of the selected candidates. The same approach can be applied in studying the evolution of other important traits just by changing the type of pattern.
A Identification and Key Establishment Scheme based on Self-Certified Public Key for MANETs

Due to the mobility of nodes, lack of infrastructure and limited computing and storage resources in mobile ad hoc networks (MANETs), this scheme uses the self-certification public key, combined with the interactive zero-knowledge proof and KEA+ key exchange method in the GPS identity authentication protocol, uses four interactions to complete the two-way identity authentication and key exchange of both parties, and which security is analyzed subsequently. The scheme effectively reduces the leakage of the claimant’s secret knowledge in the identity authentication process, and enhances the reliability of the identity authentication and key exchange process.
Accessible and Usable Security: Exploring Visually Impaired Users’ Online Security and Privacy Strategies

Visually impaired individuals are increasingly reliant on the Internet in their daily lives. Yet, existing security mechanisms may not sufficiently help these users protect their online security and privacy. We explore this issue through two complementary studies. First, we conduct an expert evaluation to assess web-based security cues through JAWS. We propose a set of 9 heuristics combining usable security and web accessibility principles to guide our expert evaluation. We uncover several severe issues that are not identified by automated accessibility checkers. Second, we conduct a task-based user study with 14 visually impaired users to observe their security habits and concerns when navigating the web. Again, our findings suggest that severe usability issues lead users to take risks or force them to choose between accessibility or security. Based on our findings, we provide practical recommendations to remedy these issues by tailoring security information to effectively communicate with visually impaired users.
Sensors are Power Hungry: An Investigation of Smartphone Sensors Impact on Battery Power from Lifelogging Perspective

Smartphones are ubiquitous devices with millions of units are sold around the globe every year. To meet the growing performance needs of innovative applications, smartphones industry has shown tremendous developments in computing, storage capacity, communication, and battery power technologies. The integration of sensors has turned smartphones into powerful sensing methods with unlimited opportunities for devising novel applications for solving realworld problems. This has given rise to a new area of research called smartphone sensing which have potential applications in different domains including lifelogging. However, continuous and inefficient usage of sensors in lifelogging can consume significant amount of battery power and can drain out fully charged battery within a few hours. In this paper, we have presented the importance of smartphone sensors in monitoring users' daily life activities and their usage effects on smartphone battery lifetime. For this purpose, an Android app Energy Monitoring System for Smartphones Sensors (EM3S) is developed. EM3S is experimented in several real world scenarios for estimating smartphones sensors battery power consumptions information. It is found that smartphone sensors consume varying amount of battery power during different daily life activities. However, collectively, they can affect smartphone performance and is a major hurdle for smartphone sensors-based applications. This study is aimed to help researchers, manufacturers and developers in exploring optimal sensors battery power consumption methods while developing pragmatic smartphone sensors-based applications.
Development of design solutions for electric delivery vehicles with a GVM of 4.25 t

Van bodies are based on frame structures produced of aluminum alloy profiles. Searching for new directions in the development of vehicle bodies for newly designed eVans led to becoming focused on currently unused polymer composites in this area. The authors of the article designed a composite body for a delivery vehicle. They conducted detailed, numerical comparative analyses of the commercial body and the developed solution. The obtained results clearly confirmed the correctness of the formulated thesis about the possibility of designing bodies dedicated to electric vehicles made of structural composites.
Enrichment of Requirements Specifications with Videos - Enhancing the Comprehensibility of Textual Requirements

Requirements for a software product are mainly shared through a textual specification. One key ability in successful software organizations is a good requirements communication based on understandable information. Developers can only implement a useful and satisfying software product if they obtain and understand requirements properly. One challenge of writing requirements is to describe complex and interactive contents in an understandable manner. Videos offer a large potential to achieve such an easy-to-understand representation. Attached videos can enhance the reader’s understanding by using them as supplementary material for specifications. Despite their large potential, videos are not an established part of requirements specifications: The effort to produce videos is high, the corresponding motivation is low and the use of videos is cumbersome due to missing links between requirements and videos. We propose guidelines to support the identification of content which is appropriate to be supplemented by videos. We develope a starting set of guidelines that consider the different information types of a requirements specification with their presentation modes and characteristics. This paper presents an overview of our findings about improving the content-related linking between requirements and videos. We discuss the perspectives, advantages and obstacles for enhancing the compre-hensibility of textual requirements conveyed by videos.
Assessing the Reliability and Validity of Online Tax System Determinants: Using A Confirmatory Factor Analysis

This paper examined the determinants of an online tax system model using a confirmatory factor analysis onto the income taxpayersâ€™ in Nigeria. The data were collected through a questionnaire distributed to the income taxpayers in Nigeria. This questionnaire is constructed based on five variables which are the intention to adopt, information security, government support, compatibility, and complexity. The data were distributed to 600 income taxpayers with 400 returned. Out of which 55 were deleted as a result of missing data. In all, 345 completed questionnaires were used for the analysis. The data were analyzed using Analysis of Moment of Structural (AMOS) Version 21.0 through model specification, model evaluation, model modification, model verification and model estimation to improve the reliability and validity of the online tax system determinant. The result signifies that the variables are statistically significant and practically important as a determinant of the online tax system in Nigeria. The paper is accomplished by discussing the implication, limitation and suggestion for future study
An Integrated Event Summarization Approach for Complex System Management

Event mining is a useful way to understand computer system behaviors. The focus of recent works on event mining has been shifted to event summarization from discovering frequent patterns. Event summarization seeks to provide a comprehensible explanation of the event sequence on certain aspects. Previous methods have several limitations such as high time complexity, a low precision especially with the presence of noise and phase shifts, and providing a summary which is difficult for a human to understand. In this paper, we propose an integrated event summarization approach toward the understanding of the chaotic temporal data. Our approach focuses on two kinds of temporal relationships, the periodic pattern and the correlation pattern, hidden in event sequences of at most two event types. For the periodic patterns, we propose an event periodicity detection algorithm to discover them directly. For the correlation patterns, we make a simple statistical test based on low order statistics to check temporal dependency of events of two types and to eliminate event correlation candidate space dramatically, and then apply inter-arrival histograms to summarize an event sequence and capture the correlation patterns. In order to balance between accuracy and brevity, the minimum description length principle is used to guide the summarization process. Further, the event relationship network is built to describe discovered patterns. We conduct several groups of experiments on synthetic and real data. Experimental results show our approach is capable of producing usable event summarization, robust to noises, and scalable. The average compression ratio of event sequences reaches 99.7%.
Acoustic Event Detection from Weakly Labeled Data Using Auditory Salience

Acoustic Event Detection (AED) is an important task of machine listening which, in recent years, has been addressed using common machine learning methods like Non-negative Matrix Factorization (NMF) or deep learning. However, most of these approaches do not take into consideration the way that human auditory system detects salient sounds. In this work, we propose a method for AED using weakly labeled data that combines a Non-negative Matrix Factorization model with a salience model based on predictive coding in the form of Kalman filters. We show that models of auditory perception, particularly auditory salience, can be successfully incorporated into existing AED methods and improve their performance on rare event detection. We evaluate the method on the Task2 of DCASE2017 Challenge.
Storm naming and forecast communication: A case study of Storm Doris

On February 23, 2017, a significant low‐pressure system named Storm Doris crossed the Republic of Ireland and the UK causing widespread disruption. As an early example of a storm named through the Met Office and Met Eireann “Name Our Storms” project, this provided an excellent opportunity to study how information about extreme weather in the UK spread through the media. In traditional media, the forecast of Storm Doris was widely reported upon on February 21–22. On the February 23, newspaper coverage of the event rapidly switched to reporting the impact of the storm. Around three times the number of words and twice the number of articles were published on the impacts of Storm Doris in comparison with its forecast. Storm Doris rapidly became a broader cultural topic with an imprint on political news because of two by‐elections that occurred by coincidence on February 23. In the social media, the rapid growth in the number of tweets about Storm Doris closely mirrored the growth of newspaper articles about the impacts of the storm. The network structure of the tweets associated with Storm Doris revealed the importance of both the Met Office official Twitter account and newspaper and rail company accounts in disseminating information about the storm. Storm names, in addition to their benefit for forecast communication, also provide researchers with a useful and easily collected target to study the development and evolution of public understanding of extreme weather events.
Effect of some selected soil properties, moisture content, yield and consumptive water use on two Cassava (TMS 0581 and TME 419) varieties

The increasing demand of Cassava for our dietary needs and shortage experienced going by the burgeoning global population is a cause for concern that require urgent attention. The study therefore considered the effect of some selected soil properties, nutrients, moisture content, yield and consumptive water use (CWU) on two selected Cassava varieties TMS 0581 and TME 419 respectively. The design was a Randomized Complete Block Design (RCBD) of four treatments and three replicates. Treatment A had fertigation, B used poultry manure, C employed NPK, 15-15-15 while D with no treatment was used as control. Soil properties such as Bulk Density (BD), Particle Density (PD), soil classification and nutrients such as Cation Exchange Capacity (CEC), Organic Matter (OM), Nitrogen (N), Potassium (K) and others were determined using standard procedures. Penman-Monteith (PM) model was used in estimating reference evapotranspiration (ETr) while its product with crop coefficient (Kc) produced crop evapotranspiration (ETc). Moisture content was measured at depths 10, 20 and 30 cm respectively while water use efficiency (WUE), irrigation water applied and tuber yield were also determined. The findings of this study showed that all the soil properties were within permissible levels to encourage optimum agronomic development of Cassava plant and the class was entirely a loamy soil which permits well-developed roottuber configuration. Tuber yields varied from 21.96 Kg to 25.13 Kg for TMS 0581 variety while TME 419 had 17.04 Kg to 31.63 Kg in all the treatments. Deficiencies were observed in some of the nutrients which were replenished with the introduction of the fertilizers among the plots. Moisture content at 30 cm depth is suggestive of adequate water availability sufficient enough to encourage proper tuber development for optimum yield while fertigation technique was adjudged the best as it improved Cassava Tuberization and WUE in all the treatments considered.
Linear

The quotient of two linearly independent solutions of a four-term linear recurrence relation is represented in the form of a branched continued fraction with two branches of branching by analogous with continued fractions. Formulas of partial numerators and partial denominators of this branched continued fraction are obtained. The solutions of the recurrence relation are canonic numerators and canonic denominators of B -ﬁgured approximants. Two types of ﬁgured approximants A -ﬁgured and B -ﬁgured are often used. A n th A -ﬁgured approximant of the branched continued fraction is obtained by adding a next partial quotient to the ( n − 1 ) th A -ﬁgured approximant. A n th B -ﬁgured approximant of the branched continued fraction is a branched continued fraction that is a part of it and contains all those elements that have a sum of indexes less than or equal to n . A -ﬁgured approximants are widely used in proving of formulas of canonical numerators and canonical denominators in a form of a determinant, B -ﬁgured approximants are used in solving the problem of corresponding between multiple power series and branched continued fractions. A branched continued fraction of the general form cannot be transformed into a constructed branched continued fraction. For calculating canonical numerators and canonical denominators of a branched continued fraction with N branches of branching, N > 1, the linear recurrent relations do not hold. B -ﬁgured convergence of the constructed fraction in a case when coefﬁcients of the recurrence relation are real positive numbers is investigated.
Dynamic Nonparametric Random Forest Using Covariance

As the representative ensemble machine learning method, the Random Forest (RF) algorithm has widely been used in diverse applications on behalf of the fast learning speed and the high classification accuracy. Research on RF can be classified into two categories: (1) improving the classification accuracy and (2) decreasing the number of trees in a forest. However, most of papers related to the performance improvement of RF have focused on improving the classification accuracy. Only some papers have focused on reducing the number of trees in a forest. In this paper, we propose a new Covariance-Based Dynamic RF algorithm, called C-DRF. Compared to the previous works, while ensuring the good-enough classification accuracy, the proposed C-DRF algorithm reduces the number of trees. Specifically, by computing the covariance between the number of trees in a forest and F-measure at each iteration, the proposed algorithm determines whether to increase the number of trees composing a forest. To evaluate the performance of the proposed C-DRF algorithm, we compared the learning time, the test time, and the memory usage with the original RF algorithm under the different areas of datasets. Under the same or higher classification accuracy, it is shown that the proposed C-DRF algorithm improves the performance of the original RF algorithm by as much as 58.68% at learning time, 47.91% at test time, and 68.06% in memory usage on average. As a practical application area, we also show that the proposed C-DRF algorithm is more efficient than the state-of-the-art RF algorithms in Network Intrusion Detection (NID) area.