Miracle-dz/UniFAR-FTUs huggingface dataset:
Sneaky Spatial Segmentation

Currently, it is nearly impossible for an artificial neural network to generalize a task from very few examples. Humans, however, excel at this. For instance, it is not necessary for a designer to see thousands or millions of unique examples of how to place a given drawing symbol in a way that meets the economic, aesthetic, and performative goals of the project. In fact, the goals can be (and usually are) communicated abstractly in natural language. Machine learning (ML) models, however, do need numerous examples. The methods that we explore here are an attempt to circumvent this in order to make ML models more immediately useful. In this work, we present progress on the application of contemporary ML techniques to the design process in the architecture, engineering, and construction (AEC) industry. We introduce a technique to partially circumvent the data hungriness of neural networks, which is a significant impediment to their application outside of the ML research community. We also show results on the applicability of this technique to real-world drawings and present research that addresses how some fundamental attributes of drawings as images affect the way they are interpreted in deep neural networks. Our primary contribution is a technique to train a neural network to segment real-world architectural drawings after using only generated pseudodrawings. A real-world architectural drawing overlaid with its hand-drawn segmentation label. This is the ultimate goal for the output of the neural network.
Molecular phylogenetic relationships and intra-species diversities of three euroscaptor spp. (Talpidae: Lipotyphla: Mammalia) from Vietnam

Until 2008, three species of fossorial moles (tribe Talpini, Talpidae, Lipotyphla, Mammalia) were recognised in Vietnam: Euroscaptor longirostris from the northern highlands, E. parvidens from the southern Annamese mountains and Mogera latouchei from the northern lowlands. Recently, a new species (Eursocaptor subanura) was described from northern Vietnam. This new mole is externally similar to E. parvidens, but the skull and dental characters are rather similar to E. longirostris. In this study, we determined the complete mitochondrial cytochrome b (Cytb; 1140 bp), partial mitochondrial 12S ribosomal RNA (12S; ca. 850 bp) and partial nuclear recombination activating gene 1 (Rag1; 1010 bp) gene sequences of 19 specimens of E. subanura collected from three localities in northern Vietnam and estimated the phylogenetic relationships among the Southeast Asian moles. Our results strongly support the full species status of E. subanura and that it is a sister taxon to E. parvidens of central Vietnam, but not E. longirostris of northern Vietnam. Although we analysed samples from only three localities, intraspecies diversity in E. subanura was lower than in the other two species. These results suggest that E. subanura diverged from E. parvidens and that it is a relict species that survived past climatic changes.
Survey on Securing Medical Image Transmission using Visual Cryptography Techniques

Visual cryptography scheme is a cryptographic technique which allows visual information text or image to be encrypted in such a way that the decryption can be performed by the human visual system and without the aid of computers. It encodes the secret image into shares of different patterns. Visual Cryptography is done on black and white image as well as on color image. This paper includes the literature survey regarding Visual Cryptography techniques for secure medical image transmission.
Menggunakan Metode k-Nearest Neighbour

Abstrak—Penelitian mengenai pengenalan emosi pada manusia beberapa diantaranya menggunakan ekspresi wajah, suara, dan sinyal EEG sebagai datanya. Pada penelitian kali ini digunakan sinyal EEG. Electroencephalography atau EEG merupakan suatu teknik pencitraan medis yang mampu mencatat hasil perekaman aktivitas listrik di kulit kepala manusia. Data berupa sinyal EEG didapatkan dari DEAP Dataset yang terdiri dari rekaman gelombang otak 32 orang responden. Selama proses perekaman, setiap responden distimulus dengan beberapa video musik dan diminta melakukan sebuah self assessment terhadap tingkat valence dan arousal. Sinyal EEG yang digunakan diambil dari empat channel yaitu F8, T7, F3, dan C4. Sinyal tersebut kemudian didekomposisi menjadi lima kelompok frekuensi berbeda (alpha, beta, gamma, delta, theta) dan ditransformasikan menggunakan Continous Wavelet Transform fungsi sym8. Proses klasifikasi dilakukan menggunakan metode k-Nearest Neighbour dan dilakukan pengujian untuk mengetahui kinerjanya. Dengan mengubah beberapa parameter seperti nilai k, frekuensi, serta channel, maka didapatkan nilai f-measure tertinggi sebesar 0,82 ketika melibatkan frekuensi delta dan 0,78 ketika melibatkan seluruh frekuensi tanpa frekuensi theta.
Real-time Adaptive Crawler for Tracking Unfolding Events on Twitter

When a major event such as a crisis situation occurs, people post messages on social media sites such as Twitter, in order to exchange information or to share emotions. These posts can provide useful information to raise situation awareness and support decision making, e.g., by aid organizations. In this paper, we propose a novel method for social media crawling, which exploits a Bayesian inference framework to keep track of keyword changes over time and uses a counter-stream to gauge the inclusion of noise and irrelevant information. In addition, we present a framework to evaluate real-time adaptive social search algorithms in a reproducible manner, which relies on a semi-automated approach for ground-truth construction. We show that our method outperforms previous methods for very large scale events.
Extending GPenSIM for Model Checking on Petri Nets

Cyber-physical systems involve hardware and software that are highly interconnected and complex. Unexpected failures in these systems can cause material damages, cost a lot of money and reputation, and can risk human lives too. The definite way of avoiding unexpected failures is to make a model of the system and to perform model checking on it. Petri nets are a highly effective way of modelling discrete-event systems. General-purpose Petri Net Simulator (GPenSIM) is a tool for modelling, simulation, performance evaluation, and control of discrete-event systems. GPenSIM lacks the automatic model checking facility until now. Firstly, this paper explores the potentials of incorporating the model checking functions to GPenSIM. Secondly, three functionalities are identified and proposed for extending GPenSIM for automatic model checking.
Measuring Cultural Intelligence : Psychometric Evaluation of the Cultural Intelligence Scale with a Military Sample in the Canadian Armed Forces

Military personnel are often deployed to foreign countries on short notice and are expected to quickly forge collaborative relationships in a multi-national environment. In more favourable situations, personnel will operate in an atmosphere of peace working alongside cooperative partners. Other times, they may be mired in difficult situations where one is tasked to manage a minor disagreement between opposing groups or even to prevent the outbreak of armed conflict between groups. Regardless of the perceived situational ‘ease’, it is apparent that cultural intelligence is critically important to the operational effectiveness of multi-national organizations and has been the topic of interest for many researchers. 1
ResQu: A Framework for Automatic Evaluation of Knowledge-Driven Automatic Summarization

JAYKUMAR, NISHITA. M.S., Department of Computer Science and Engineering, Wright State University, 2016. ResQu: A Framework for Automatic Evaluation of Knowledge-Driven Automatic Summarization. Automatic generation of summaries that capture the salient aspects of a search resultset (i.e., automatic summarization) has become an important task in biomedical research. Automatic summarization offers an avenue for overcoming the information overload problem prevalent in large online digital libraries. However, across many of the knowledge-driven approaches for automatic summarization it is not always clear which features highly impact or influence the quality of a summary. Instead, there has been considerable focus on utilizing schema knowledge to facilitate browsing and exploration of generated summaries a posteriori. Informative features should not be ignored, since they could be utilized to help optimize the models that generate these semantic summaries in the first place. In this research, we adopt a leave-one-out approach to assess the impact of various features on the quality of automatically generated summaries that contain structured background knowledge. We first create the gold standard summaries, using information-theoretic methods, by extraction and validation, then the semantic summaries are transformed into an equivalent textual format. Finally, various similarity metrics, such as cosine similarity, euclidean distance, and jensen-shannon divergence are computed under different feature combinations, to assess summary quality against the textual gold standard. We report on the relative importance of the various features used to automatically generate the semantic summaries in a biomedical application. Our evaluation suggests that the proposed approach is an effective automatic evaluation method for assessing feature importance in automatically generated semantic summaries.
Case Report - Adenosine: A Cause of Angina

*Correspondence: Jadhav Ajit, Department of Cardiology, Baroda Heart Institute and Research Centre, 102, Wingsville appt. 41 Arunodaya Society, Alkapuri, Vadodara, Gujarat, 390007, India, Tel: 919028898494; E-mail: ajit92698@gmail.com Received Date: 21 Jun 2018 Accepted Date: 03 Aug 2018 Published Date: 10 Aug 2018 Citation: Ajit J, Suhas H. Adenosine: A Cause of Angina. Ann Cardiol Cardiovasc Med. 2018; 2(2): 1017.
COMPOSING CONTEXT-AWARE DISTRIBUTED SYSTEMS USING QOS AND TRUST PRINCIPLES

The emergence of ubiquitous computing and the wide adoption of smart phones over the past few years require many Web Services to function in a context-aware manner. In such services, not only the functional attributes, but also the QoS attributes (e.g., response time) and the trust (i.e., the degree of compliance of a service to its specification) depend on the context of the services. Hence, when designing systems composed out of such services, it is important to consider the entire system execution context in addition to its functional and non-functional requirements. Achieving this goal requires the consideration of the context to trust-QoS dependencies, and interaction patterns between individual services. In this work, we tackle this challenge by first proposing a model that uses the context-QoS dependency information of individual services and inter-service interaction patterns to make predictions about the QoS and trust of compositions at the design phase. Then we apply the prediction model to select the optimum set of service to create the composed system. Our approach allows for better design and implementation decisions about composed systems in the early phases of the software lifecycle thereby reducing cost, time, and effort. The preliminary results show that the proposed model provides more accurate predictions and results in optimum composed systems than the prevalent approaches.
3-D scanning PIV of the flow within a two-stroke water analogue combustion engine

Goal of this work is to characterize the cylinder scavenging process in a water analogue, backward scavenging, two-stroke internal combustion engine (ICE). The measurements are performed using time resolved 3D scanning particle image velocimetry (3D SPIV). The result is 2C-3D dataset. Stagnation flow is typical for the two-stroke IC engine. It is induced by the transfer and the booster ports, resulting in a complex three dimensional flow. Due to the stagnation flow high frequency fluctuations of the flow direction can occur. In order to maintain the two-stroke IC engine competitive in its different applications it is necessary to obtain a better insight of the three dimensional scavenging process. Therefore, 3D measurement techniques are necessary. Recent developments like tomographic PTV allow the reconstruction of 3D velocity fields. However, a plurality of optical accesses is necessary. Furthermore, the long processing time for data evaluation is a disadvantage of this technique. Hence, it is unsuitable for the two-stroke engine, due to a limited optical access. Instead, a single camera set-up combined with a scanning light sheet is used. This method is also suitable to investigate the cycle to cycle fluctuations. As Bown et al. (2007) showed, it is possible to calculate the third velocity-component from the velocity fields of the scanned planes, using conservation of mass for incompressible flow. The result is a complete three dimensional velocity field (3D 3C) for every scanning cycle of this procedure. Brücker (1997) demonstrated the working principle on IC engines in specific regions of the combustion chamber. With this time resolved measurement technique it is also possible to investigate fluctuations of the flow direction discovered by Hauke et al. (1998). This phenomenon was called flip-flop. The results of the time averaged measurements can qualitatively be compared with experiments in an external driven engine (Britsch 2010). The time averaged flow measurements show a typical tumble motion. Thereby, the flow leaves the transfer and boost ports upwards to the cylinder head, where it moves in direction of the exhaust port and back down. The time resolved three dimensional flow fields are similar to the time averaged results. However, the clearly structured tumble motion of the averaged results is blurred in the time resolved measurements, due to the fluctuations induced by the stagnation flow. Fig. 1 Presentation of the time averaged flow forming the tumble motion shown by iso-surfaces and streamtraces
of Achievements in Materials and Manufacturing Engineering Fusion and friction stir welding of X 6 Cr 17 stainless steel

Purpose: The σ-phase is the most serious of these secondary phases due to its impact on the mechanical properties, corrosion resistance or weldability of stainless steels among other properties. The purpose of this study is getting free σ-phase welding joints by using very novel welding method such a called friction stir welding. Design/methodology/approach: Both of fusion welding methods (MIG and TIG) and friction stir welding method were used to compare microstructure analysis of AISI 430 ferritic stainless steels. After welding, the formation of σ-phase was investigated by mechanical, chemical and micro structure analyses of welded joints. Findings: As a result, the formation of σ-phase have not been observed in FSW processes while compared to traditional fusion welding processes. Not only σ-phase have not been observed in metallurgical investigations but also micro hardness of all specimen have not been over 400HV on friction stir welded joints. Research limitations/implications: It is very difficult to be constant vertical force during friction stir welding. For keeping constant this force, hydraulic controlled welding machines could be used in further researches. Practical implications: This paper and its’ results shown that the friction stir welding could be used for joining of ferritic stainless steels, when you select suitable welding parameters. Originality/value: This study was performed in the frame of the Pamukkale University Scientific Research Projects Coordination Unit project no 2009FBE022 “The research of the factors affecting the friction stir weldability of ferritic stainless steels”.
The Effect of Industry Homogeneity on the Magnitude of Post-Earnings Announcement
 Drift: Evidence from Korea

We examine the relationship between industry homogeneity and the magnitude of postearnings announcement drift (hereafter, PEAD). Given that firms with more homogeneous operating cost structure are conducive to spillover of knowledge among investors, we expect that firms in highly homogenous industries have relatively low magnitude of PEAD. Using firms listed on the Korean Stock Exchange, we find that the magnitude of PEAD is negatively related to industry homogeneity, indicating that investors are more efficient for firms in a highly homogenous industry in which information is likely to be referred to peer firms. In addition, we find that the effect of industry homogeneity on the magnitude of PEAD is robust even after controlling to the effect of industry concentration. Our study contributes to the literature on exploring determinants of PEAD by linking information transfer effect in a homogenous industry to investors’ informational efficiency in capital markets.
DeepJet : Generic physics object based jet multiclass classification for LHC experiments

The detectors at the Large Hadron Collider at CERN reconstruct, among other objects, collimated sprays of particles, which are referred to as “jets”. An important task is to identify the type of the elementary particle that initiated the jet, i.e. whether it is a light quark, a heavy quark or a gluon, leading to a multiclass classification problem. We present results from a realistic simulation of one of the two multi-purpose detectors at the LHC, the Compact Muon Solenoid. The basic network architecture relies heavily on using convolutional layers on low-level physics objects, like individual particle objects, and uses much more information than previous algorithms in the literature. It stands out as the first proposal that can be applied to multiclass classification for all types of jet initiators as well as for jets of different widths. We demonstrate significant improvements by the new approach on the classification capabilities for several of the tested particle classes. In one specific case, and at high momentum, a decrease of nearly 90% in the rate of false positives is achieved for a constant true positive rate of 40%.
An analysis of numerical model applications to river hydrodynamics

Double Master of Science in Hydraulic Engineering (Civil Engineering and Geosciences) and Applied Mathematics (Electrical Engineering, Mathematics and Computer Science) - It is of importance to know how the water is behaving in rivers due to the dangers, such as flooding and eroding of land, it can bring with it. Present day we are still reshaping rivers, building structures in rivers or adjusting them, which results in changes in the hydrodynamics and morphology. In order to obtain more insight in the consequences of these adjustments, it is convenient to use a numerical model to simulate the river hydrodynamics. For the last decades numerical models have been developed that are able to simulate hydrodynamics. Currently, new software packages are proposed that are more efficient in predicting this. Two of these new software packages are D-Flow Flexible Mesh (FM) and 3Di. Whereas the first is especially designed to model the hydrodynamics of rivers and other flows such as in estuaries, the latter is mostly used for the simulation of flooding of land (for example due to dike breaches). The usage of numerical model applications results usually in many choices that have to be made. Based on these choice there are considerations that need to be taken into account. Both these choices and considerations will have influence on the river hydrodynamics. With two new packages available that can simulate hydrodynamics, the choices and possibilities for each package should be clear. When multiple numerical model applications are considered the similarities and differences should be known. The aim of this thesis is to indicate which considerations and consequences for simulations, that follow from the choices in numerical model applications, are important for the suitability of results given a specific problem.
Functional Role of Tyr 12 in the Catalytic Activity of Novel Zeta-like Glutathione S-transferase from Acidovorax sp . KKS 102

Glutathione S-transferases (GSTs) are a family of enzymes that function in the detoxification of variety of electrophilic substrates. In the present work, we report a novel zeta-like GST (designated as KKSG9) from the biphenyl/polychlorobiphenyl degrading organism Acidovorax sp. KKS102. KKSG9 possessed low sequence similarity but similar biochemical properties to zeta class GSTs. Functional analysis showed that the enzyme exhibits wider substrate specificity compared to most zeta class GSTs by reacting with 1-chloro-2,4-dinitrobenzene (CDNB), p-nitrobenzyl chloride (NBC), ethacrynic acid (EA), hydrogen peroxide, and cumene hydroperoxide. The enzyme also displayed dehalogenation function against dichloroacetate, permethrin, and dieldrin. The functional role of Tyr12 was also investigated by site-directed mutagenesis. The mutant (Y12C) displayed low catalytic activity and dehalogenation function against all the substrates when compared with the wild type. Kinetic analysis using NBC and GSH as substrates showed that the mutant (Y12C) displayed a higher affinity for NBC when compared with the wild type, however, no significant change in GSH affinity was observed. These findings suggest that the presence of tyrosine residue in the motif might represent an evolutionary trend toward improving the catalytic activity of the enzyme. The enzyme as well could be useful in the bioremediation of various types of organochlorine pollutants.
Structured design automation

The next stage in product development process evolution should be the automation of labour intensive repetitive steps. The design automation strategy should follow the trend of the supply chain management approach and deliver a flexible framework, allowing local specification and adjustment The proposed Adjoined Design Automation Process Trajectory (ADAPT) and associated tools provide such a framework. It covers the process from fuzzy front-end to the evaluation of automated processes. Considerable lead time and cost savings are shown for electrical component design and the feasibility of a Domain Specific Language approach is an important step towards acceptance of design automation.
Data Challenges as a Tool for Time-domain Astronomy

Data challenges are emerging as powerful tools with which to answer fundamental astronomical questions. Time-domain astronomy lends itself to data challenges, particularly in the era of classification and anomaly detection. With improved sensitivity of wide-field surveys in optical and radio wavelengths from surveys like the Large Synoptic Survey Telescope (LSST) and the Canadian Hydrogen Intensity Mapping Experiment, we are entering the large-volume era of transient astronomy. We highlight some recent time-domain challenges, with particular focus on the Photometric LSST Astronomical Time series Classification Challenge, and describe metrics used to evaluate the performance of those entering data challenges.