Miracle-dz/UniFAR-FTUs huggingface dataset:
Ground Penetrating Radar Image Processing Towards Underground Utilities Detection for Robotic Applications

During the last decades, the development of powerful novel sensor topologies, namely Ground Penetrating Radar (GPR) antennas, gave a thrust in the modeling of underground space. Subsurface mapping is of particular importance for future robotic applications that aim to operate in underground space. However, the current processing methods of GPR images (B-Scans) towards detecting underground utilities is typically laborious, semi-automatic and prone to errors, especially on cluttered subsurface sceneries that produce unclear signatures, and still require the manual annotation of experts. Due to lack of large scale datasets from such sceneries, the adoption of deep learning and model specific methods with increased repeatability is not feasible yet. In this scope, and working towards subsurface mapping for underground robotic applications, the paper at hand introduces an application specific method tailored to operate with realistic GPR data for the automatic detection of underground utilities, by applying a hyperbola detection framework on the radar images obtained from a surface GPR. To achieve this, a segmentation step on the GPR data is applied to prepare them for hand-designed feature extraction. The extracted representative features are used to train a SVM, while a final fitting step is applied to the detected hyperbola segments. The developed methodology has been evaluated on cluttered radar images that correspond to a subsurface area with dense buried pipes and exhibited remarkable performance.
Distribution Characteristics of the Transportation Network in China at the County Level

Evaluation of the rationality of transportation network distribution and its impact on social and economic development have great significance for building an efficient and comprehensive transportation system, which is also an important research topic in economic geography and regional economics. Because evaluation indicators to express the characteristics of transportation network distribution at the macro scale are lacking, this paper selects three indicators—highway network density, transportation network proximity, and location dominance related to the central city—to form the basis of an integrated transportation network distribution index for characterizing China’s transportation network distribution using geographical information system technology in 2015. The research shows that transportation network distribution is low in most Chinese counties, and that there are obvious differences in distribution between counties in the east and west in addition to identifying multiple dense areas of transport layout distribution. Transportation network distribution is intrinsically coupled with social economic development: the more complete are transportation network facilities, the greater the population density, and the higher the level of economic development. The results highlight the need to focus on strengthening population concentration and economic development in areas with good transportation network distribution. The study can guide the optimization of transportation network structures and provide a macro decision-making reference for planning and evaluation of major transportation projects in China.
Fingerprint Generation and Presentation Attack Detection using Deep Neural Networks

Performance evaluation of fingerprint recognition systems requires large-scale databases. Unfortunately, collecting fingerprints is expensive and time-consuming, and publishing them is restricted due to the privacy protection legislation. Hence, an algorithm which can generate huge fingerprint datasets would be of great help. With the popularization of fingerprint authentication systems, detecting fake fingerprints, also known as presentation attack detection, is an essential problem. Inspired by the fast development of deep learning, this paper demonstrates novel algorithms to generate artificial fingerprints and detect fake fingerprints using deep neural networks. The experimental results prove that the proposed system can generate fingerprints which have the same characteristics as real fingerprints. Regarding presentation attack detection, the proposed system shows an average detection error rate of 1.57% on three LivDet databases, including LivDet 2011, 2013, and 2015.
Numericware i: Identical by State Matrix Calculator

We introduce software, Numericware i, to compute identical by state (IBS) matrix based on genotypic data. Calculating an IBS matrix with a large dataset requires large computer memory and takes lengthy processing time. Numericware i addresses these challenges with 2 algorithmic methods: multithreading and forward chopping. The multithreading allows computational routines to concurrently run on multiple central processing unit (CPU) processors. The forward chopping addresses memory limitation by dividing a dataset into appropriately sized subsets. Numericware i allows calculation of the IBS matrix for a large genotypic dataset using a laptop or a desktop computer. For comparison with different software, we calculated genetic relationship matrices using Numericware i, SPAGeDi, and TASSEL with the same genotypic dataset. Numericware i calculates IBS coefficients between 0 and 2, whereas SPAGeDi and TASSEL produce different ranges of values including negative values. The Pearson correlation coefficient between the matrices from Numericware i and TASSEL was high at . 9972, whereas SPAGeDi showed low correlation with Numericware i (.0505) and TASSEL (.0587). With a high-dimensional dataset of 500 entities by 10 000 000 SNPs, Numericware i spent 382 minutes using 19 CPU threads and 64 GB memory by dividing the dataset into 3 pieces, whereas SPAGeDi and TASSEL failed with the same dataset. Numericware i is freely available for Windows and Linux under CC-BY 4.0 license at https://figshare.com/s/f100f33a8857131eb2db.
at UWindsor

Stereo-based 3D distributed smart camera networks are useful in a broad range of applications. Knowledge of the relative locations and orientations of nodes in the network is an essential prerequisite for true 3D sensing. A novel spatial calibration method for a network of pre-calibrated stereo smart cameras is presented, which obtains pose estimates suitable for collaborative 3D vision in a distributed fashion using two stages of registration on robust 3D point sets. The method is initially described in a geometrical sense, then presented in a practical implementation using existing vision and registration algorithms. Experiments using both software simulations and physical devices are designed and executed to demonstrate performance.
Process Patterns for Personal Practice

There is more to software development than analysis, programming, debugging, and even organising organic organisational structures. Whether working solo, in small groups, or larger teams, the biggest problems can be our own personal misconceptions, demons, paranoia and firmly held beliefs. Development is always done by people; clients and users are people; and under strict genetic testing most managers can be shown to share at least 50% of their genetic code with homo montipythonus. People, of course, are famously fallible, when not downright stupid or obstructive. It’s a miracle anyone manages to get anything developed ever. Of course, people do develop software every day, after many years of rigorous training and then by dint of weeks of hard work, bad skin tone, low lighting conditions, and the consumption of mind-altering quantities of pizza. Now, this is obviously far too much work, not very much fun, and bad for your physical and mental health. So, how can you succeed in development without really trying?
The Story Gestalt : A Model of Knowledge-Intensive Processes in Text Comprehension

Norvig, P. (1987). A unified theory of inference for text understanding (Rep: No. UCB/CSD '87-339). Berkeley: University of California, Computer Science Department. Norvig, P. (1989). Marker passing as a weak method for text inferencing. Cognitive Science, 13, 569-620 . ) Ortony, A. , Schallert, D. , Reynolds; R. , & Antos, S. (1978). Interpreting metaphors and idioms: Some effects of context on comprehension. Journal of Verbal Learning and Verbal Behavior, 17, 465-477.
Transcriptome‐wide changes in testes reveal molecular differences in photoperiod‐induced seasonal reproductive life‐history states in migratory songbirds

We investigated the molecular basis of seasonal testicular states in black‐headed buntings (Emberiza melanocephala), in which seasonal migrations limit reproduction to a narrow time‐window during the year. We examined testicular gene expressions in buntings during short day‐induced photosensitive nonreproductive state (SDSE), and during long day‐induced early (LDES) and late (LDLS) gonadal maturation and regressed photorefractory (LDRF) seasonal states. Using Kallisto, we pseudoaligned the RNA‐Seq transcripts and calculated the transcript abundance. We found 1,799 differentially expressed genes (DEGs) between the four photoperiod‐induced seasonal states. Further pairwise comparison with SDSE revealed 1,187, 1,224, and 1,238 DEGs in LDES, LDLS, and LDRF, respectively; 852 genes were common to three comparisons. We then identified genetic pathways putatively involved in seasonal testicular cycle. DEGs that enriched calcium ion binding were highly expressed in testicular maturation states. Similarly, DEGs that enriched glycolytic pathway were highly expressed in LDES and LDRF states. More specifically, quantitative polymerase chain reaction showed significant differences between the photoperiod‐induced states in testicular expression of genes (HOOK1, RGS2, PRDX4, BCL6, CYFIP2, PDCD4, P2RX4, and GABRA1) involved in gametogenesis and associated pathways. Overall, these results show significant transcriptional differences, and provide insights into the molecular basis of seasonal changes in the reproductive life‐history states in a photoperiodic species.
Penrose: From Mathematical Notation to Beautiful Diagrams

We introduce a system called Penrose for creating mathematical diagrams. Its basic functionality is to translate abstract statements written in familiar math-like notation into one or more possible visual representations. Rather than rely on a fixed library of visualization tools, the visual representation is user-defined in a constraint-based specification language; diagrams are then generated automatically via constrained numerical optimization. The system is user-extensible to many domains of mathematics, and is fast enough for iterative design exploration. In contrast to tools that specify diagrams via direct manipulation or low-level graphics programming, Penrose en- ables rapid creation and exploration of diagrams that faithfully preserve the underlying mathematical meaning. We demonstrate the effectiveness and generality of the system by showing how it can be used to illustrate a diverse set of concepts from mathematics and computer graphics.
Mind the Gap: A Practical Framework for Classifiers in a Forensic Context

In this paper, we present a practical framework that addresses six, mostly forensic, aspects that can be considered during the design and evaluation of biometric classifiers for the purpose of forensic evidence evaluation. Forensic evidence evaluation is a central activity in forensic case work, it includes the assessment of strength of evidence of trace and reference specimens and its outcome may be used in a court of law. The addressed aspects consider the modality and features, the biometric score and its forensic use, and choice and evaluation of several performance characteristics and metrics. The aim of the framework is to make the design and evaluation choices more transparent. We also present two applications of the framework pertaining to forensic face recognition. Using the framework, we can demonstrate large and explainable variations in discriminating power between subjects.
A Sequence of Developmental Events Occurs Underneath Growing Bacillus subtilis Pellicles

Biofilms are structured communities of bacteria that exhibit complex spatio-temporal dynamics. In liquid media, Bacillus subtilis produces an opaque floating biofilm, or a pellicle. Biofilms are generally associated with an interface, but the ability of Bacillus subtilis to swim means the bacteria are additionally able to reside within the liquid phase. However, due to imaging complications associated with the opacity of pellicles, the extent to which bacteria coexist within the liquid bulk as well as their behavior in the liquid is not well studied. We therefore develop a high-throughput imaging system to image underneath developing pellicles. Here we report a well-defined sequence of developmental events that occurs underneath a growing pellicle. Comparison with bacteria deficient in swimming and chemotaxis suggest that these properties enable collective bacterial swimming within the liquid phase which facilitate faster surface colonization. Furthermore, comparison to bacteria deficient in exopolymeric substances (EPS) suggest that the lack of a surface pellicle prevents further developmental steps from occurring within the liquid phase. Our results reveal a sequence of developmental events during pellicle growth, encompassing adhesion, conversion, growth, maturity, and detachment on the interface, which are synchronized with the bacteria in the liquid bulk increasing in density until the formation of a mature surface pellicle, after which the density of bacteria in the liquid drops.
Term extraction + term clustering: an integrated platform for computer-aided terminology

A novel technique for automatic thesaurus construction is proposed. It is based on the complementary use of two tools: (1) a Term Extraction tool that acquires term candidates from tagged corpora through a shallow grammar of noun phrases, and (2) a Term Clustering tool that groups syntactic variants (insertions). Experiments performed on corpora in three technical domains yield clusters of term candidates with precision rates between 93% and 98%.
Detection and Prevention of Android Malware Thru Permission Analysis

With immense use of smartphone for accessing online services, users store general, personal, and confidential information on mobile device. Availability and accessibility of sensitive information has provoked cyber criminals to use features of smartphones for cyber attacks. In this paper, we are providing a solution which will detect harmful permissions and will allow user to disable such permissions. To demonstrate the same, a sample malware attack is created. Ignorance of smartphone user is the biggest reason behind success of malware attacks. Some guidelines are given which will help in reducing malware attack and safeguard users from propagating the same.
Communications of the IIMA Communications of the IIMA

When developing IT systems it is important to know the requirements of the system. Requirements can be classified into different categories. In this study we look at functional and non-functional requirements. Respectively these describe the behaviour of the system (functional requirements) and the criteria under which the system works (the non-functional requirements). Furthermore this paper contributes to the understanding of the relation between the focus on non-functional requirements and the maturity of the requirements engineering process. The research question this paper addresses is: "How does the focus on non-functional requirements influence the maturity of the requirements engineering process? ” The method of the International Requirements Engineering Board is used as the standard for the requirements engineering process. Based on this a questionnaire was developed to measure both the maturity of the requirement engineering process and whether non-functional requirements are taken into account or not. This questionnaire was then sent to a population of people with an IT function, like IT architects and developers. 69 people from different organizations participated in the survey; of this 65 responses were valid and used for the analysis. The results of the analysis show that there is a positive correlation between the focus on non-functional requirements and the maturity.
Deep Learning-Based Wafer-Map Failure Pattern Recognition Framework

In integrated circuit (IC) manufacturing, wafer-map analysis has been essential for yield improvement. In this study, we focused on wafer-map failure pattern recognition. We proposed a deep learning-based failure pattern recognition framework. The proposed framework needs only wafer-maps with and without target failure patterns to recognize, and ascertains the features of the target failure patterns automatically. Conventional deep learning methods need a large amount of wafer-maps with the target failure patterns as training data for achieving high recognition accuracy. In the proposed framework, a data augmentation technique with noise reduction is proposed, and it is the key to achieve high recognition accuracy if the number of wafer-maps with the target failure patterns is small. Experimental results using a benchmark dataset showed that the proposed framework achieves high recognition accuracy with a failure pattern recognition problem and also multiple failure pattern recognition problem, and we confirmed the effectiveness of the proposed data augmentation technique.
Prominent Face Region Based Gender Classification Using Deep Learning

The social interaction among people is always influenced by human face. The complex characteristics of human face are gender, emotions, identity, etc. In this world of visual technology, the challenge lies in automatic extraction of these facial characteristics. Hence research in this area has broad applications in biometric authentication, surveillance systems, human-computer interface, etc. This paper focuses on gender classification using face images. In proposed paper, deep learning based gender classification approach is used. In this paper, six prominent face regions are extracted using Haar classifier. These extracted face regions are then learned using deep learning architecture. The experimental results shows accuracy from 99.4% under various artifacts such as illumination variation, pose variation which are inherent in most of unconstrained environment. The database used is Psychological Image Collection at Stirling (PICS) 2D face database of Aberdeen.
Gaps in Current Autism Research: The Thoughts of the Autism Research Editorial Board and Associate Editors

David G. Amaral, George M. Anderson, Anthony Bailey, Raphe Bernier, Somer Bishop, Gene Blatt, Ricardo Canal-Bedia , Tony Charman , Geraldine Dawson, Petrus J. de Vries, Emanuel Dicicco-Bloom, Cheryl Dissanayake , Yoko Kamio , Rajesh Kana , Naila Z. Khan, Allison Knoll, Frank Kooy , Janet Lainhart, Pat Levitt, Katherine Loveland, Nancy Minshew, Ralph-Axel Mueller , Declan Murphy, Peter Mundy, Sara Palencia, Jennifer Pinto-Martin, Alexia Rattazzi, Sally Rogers, Wendy L. Stone, Sara Jane Webb, and Andrew Whitehouse
Blockchain Technology and Implementation : A Systematic Literature Review

As research on blockchain continues to grow, blockchain technology was adopted to develop several information systems. There are many opportunities to examine the utilization of blockchain technology to be used in developing systems as needed. This paper summarizes the conditions of blockchain research in terms of technology and its implementation. This paper was written using a systematic literature review (SLR) as one of the methodologies used to solve problems by tracing the results of previous studies. The problem that you want to study in SLR is usually referred to as a research question (RQ). The defined RQ is related to the topic and clarifies each question by tracing previous research papers indexed in reputable journal databases such as IEEE Xplore, Springerlink, Scopus, and ScienceDirect. After synthesizing 41 articles, the result is: blockchain can be used in many applications, some applications that adopt blockchain technology are banking applications, e-voting applications and digital forensic applications. Often, applications that use blockchain technology only focus on developing one of the blockchain technologies that suits their needs. Some developers maximize the components of the contract on the blockchain, some further develop the data structure of the blockchain itself. The use of blockchain technology is still wide open for the implementation of other information systems. The expected contribution of this paper is to provide a general overview for researchers who want to build applications that use blockchain as a basis for researching so they can conduct further studies to better understand whether the applications they will develop are suitable for using blockchain technology as a basis.
Ancestry informative SNP panels for discriminating the major East Asian populations: Han Chinese, Japanese and Korean

Ancestry informative markers play an important role in medical genetics and forensic analyses. Several ancestry informative SNP panels have been developed and validated that can differentiate global populations into continental or major regional groups. These global panels have served as good first‐tier genetic markers; however, their performance in discriminating populations within regions appears unsatisfactory. To boost ancestry inference for regional populations, second‐tier panels with more refined discrimination power among subpopulations within each of the regions need to be developed. In East Asia, Han Chinese, Japanese, and Korean show highly similar externally visible characteristics and are genetically closely related. Reliable ancestry informative genetic markers appear invaluable in discriminating these populations. In the present study, we compiled a genome‐wide SNP dataset composing of 317,439 clean SNPs for a total of 1101 unrelated individuals from Han Chinese (817), Koreans (184), and Japanese (100). From this starting dataset, we developed a set of four nested ancestry informative SNP panels including 36, 59, 98, and 142 SNPs, respectively. The results of cross‐validation tests indicate that these panels can discriminate the Chinese Han, Japanese, and Korean populations with overall average accuracies ranging from 90% to 99%. In the further performance assessments, these panels also manifested high sensitivity and specificity. In combination with the first‐tier global panels, these second‐tier panels would contribute to medical genetics and forensic research in East Asia.
T Multi Objective Micro Genetic Algorithm for Combine and Reroute Problem

modeling, greedy heuristic and decision support system are well-known approaches in solving irregular airline operation problem. This paper presents an alternative approach based on Multi Objective Micro Genetic Algorithm. The aim of this research is to introduce the concept of Multi Objective Micro Genetic Algorithm as a tool to solve irregular airline operation, combine and reroute problem. The experiment result indicated that the model could obtain optimal solutions within a few second.
Arabidopsis Reactome : A Foundation Knowledgebase for Plant Systems Biology

Nicolas Tsesmetzis,a Matthew Couchman,a Janet Higgins,a Alison Smith,b John H. Doonan,c Georg J. Seifert,c Esther E. Schmidt,d Imre Vastrik,d Ewan Birney,d Guanming Wu,e Peter D’Eustachio,e,f Lincoln D. Stein,e Richard J. Morris,a Michael W. Bevan,c,1 and Sean V. Walsha,1 a Department of Computational and Systems Biology, John Innes Centre, Norwich NR4 7UH, United Kingdom b Department of Metabolic Biology, John Innes Centre, Norwich NR4 7UH, United Kingdom c Department of Cell and Developmental Biology, John Innes Centre, Norwich NR4 7UH, United Kingdom d European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, United Kingdom e Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724 f New York University, School of Medicine, New York, New York 10016