Reviewerly/exHarmony huggingface dataset:
AX Semantics’ Submission to the SIGMORPHON 2019 Shared Task

This paper describes the AX Semantics' submission to the SIGMORPHON 2019 shared task on morphological reinflection.We implemented two systems, both tackling the task for all languages in one codebase, without any underlying language specific features.The first one is an encoder-decoder model using AllenNLP; the second system uses the same model modified by a custom trainer that trains only with the target language resources after a specific threshold.We especially focused on building an implementation using AllenNLP with out-of-the-box methods to facilitate easy operation and reuse.
LUCIDAH Ligative and Unligative Characters in a Dataset for Arabic Handwriting

Arabic script is inherently cursive, even when machine-printed.When connected to other characters, some Arabic characters may be optionally written in compact aesthetic forms known as ligatures.It is useful to distinguish ligatures from ordinary characters for several applications, especially automatic text recognition.Datasets that do not annotate these ligatures may confuse the recognition system training.Some popular datasets manually annotate ligatures, but no dataset (prior to this work) took ligatures into consideration from the design phase.In this paper, a detailed study of Arabic ligatures and a design for a dataset that considers the representation of ligative and unligative characters are presented.Then, pilot data collection and recognition experiments are conducted on the presented dataset and on another popular dataset of handwritten Arabic words.These experiments show the benefit of annotating ligatures in datasets by reducing error-rates in character recognition tasks.
Meaning Representation of Null Instantiated Semantic Roles in FrameNet

Humans have the unique ability to infer information about participants in a scene, even if they are not mentioned in a text about that scene.Computer systems cannot do so without explicit information about those participants.This paper addresses the linguistic phenomenon of null-instantiated frame elements, i.e., implicit semantic roles, and their representation in FrameNet (FN).It motivates FN's annotation practice, and illustrates the three types of null-instantiated arguments that FrameNet tracks, noting that other lexical resources do not record such semanticpragmatic information, despite its need in natural language understanding (NLU), and the elaborate efforts to create new datasets.It challenges the community to appeal to FN data to develop more sophisticated techniques for recognizing implicit semantic roles, and creating needed datasets.While the annotation of null-instantiated roles was lexicographically motivated, FN provides useful information for text processing, and therefore it must be considered in the design of any meaning representation for NLU.
Robust Image Forgery Detection Using Point Feature Analysis

Day for day it becomes easier to temper digital images.Thus, people are in need of various forgery image detection.In this paper, we present forgery image detection techniques for two of the most common image tampering techniques; copy-move and splicing.We use match points technique after feature extraction process using SIFT and SURF.For splicing detection, we extracted the edges of the integral images of Y , C b , and Cr image components.GLCM is applied for each edge integral image and the feature vector is formed.The feature vector is then fed to a SVM classifier.For the copy-move, the results show that SURF feature extraction can be more efficient than SIFT, where we achieved 80% accuracy of detecting tempered images.On the other hand, processing the image in Y C b Cr color model is found to give promising results in splicing image detection.We have achieved 99% true positive rate for detecting splicing images.
Granger causality of the local Hadley cell and large-scale cloud cover over South Africa

This study demonstrates that Hadley cell dynamics could be used as a proxy to determine cloud cover and thus solar energy potential over South Africa. Granger causality was used to investigate causal interactions between the Hadley cell and cloud cover for the period 1980–2015, and such links were established. Areas of strong causality are found over the northwestern parts of South Africa. Moreover, weak causality from cloud cover to the Hadley cell does exist, with vertical velocity being the main variable responsible for this causality, which hence indirectly links cloud cover to Hadley cell causality.&#x0D; Significance:&#x0D; &#x0D; Hadley cell dynamics may be used to identify regions of cloudlessness over South Africa.&#x0D; Hadley cell dynamics may further be used as a proxy for cloud cover towards understanding the solar energy potential in South Africa within the context of climate variability and change.&#x0D;
Information Systems Development and Usage with Consideration of Privacy and Cyber Security Aspects

One of the contemporary problems, and at the same time a challenge, with development und usage of supply chain Information Systems are the issues associated with privacy and cyber security, which emerged due to new requirements of legal regulations and directives.The human factor belongs to the biggest risks within these issues.Leak of information, phishing, unauthorized access are the main problems.Also vulnerability of the systems due to new information technologies is an important topic.In this paper we discuss development and usage of Information Systems with regard to the security aspects associated to the software development lifecycle.We present our approach on examples of a user authentication process in logistics.
Distinguishing the effects of object-scene association strength and real-world object size in scene priming

Scenes are complex visual entities and often contain multiple objects. Since specific objects are often expected to be found in particular scenes (e.g., beach chair-beach), such regularities appear to facilitate object and scene perception. Recent findings suggested that object properties, such as real-world size and portability, may modulate scene processing. Specifically, big objects, compared with small objects, are often less portable and are more likely to be found in a specific scene. However, since real-world object size and object-scene association strength are often confounded, it remains unclear which factor may contribute to the facilitation of scene processing. Here we aimed to distinguish the roles of object-scene association strength and real-world object size on scene perception. In a priming task, we used images of big and small objects that are strongly associated with particular scenes (e.g., cheese slicer-kitchen) as primes, and related or unrelated scene images as targets. Participants judged if a scene target was presented upright or inverted. As the association strength between the objects and related scenes were rated high and comparable across big and small object sets, as confirmed in a pilot study, if the facilitation is due to association strength, the magnitude of scene priming should be comparable across big and small object primes. However, if object size is critical, big objects should evoke stronger scene priming than small objects. In two experiments with long (250ms) vs. short (50ms) presentation durations of the primes, participants (n=26 and n=27 in Experiments 1 and 2) showed significant priming with faster judgments for related than unrelated upright scenes (p’s<.045) and no interaction between real-world size and priming (p’s>.38). These results suggest that although the two factors are often confounded, facilitation of scene perception evoked by objects is unlikely due to object size but depends on the strength of object-scene associations.
The strong influence of contour geometry in Structure from Motion (SFM)

The SFM literature focuses almost entirely on the role of image motion, ignoring contributions of contour geometry. However, our previous work (He et al., VSS, 2017, 2018) has shown that contour geometry can play a dominant role in SFM, often even overriding inconsistencies with image motion. Here we investigate the role of contour geometry by manipulating inconsistencies with image motion. Specifically, we manipulate the shape of the aperture through which the same image motion is shown. We start with a “motion region” (such as rectangle, trapezoid, ellipse) containing dot motion consistent with 3D rotation, and transform the shape of this motion region to define a smaller aperture. In many conditions, rather than looking like dots moving behind an aperture, the contour captures and defines the perceived 3D shape. In Experiment 1, we generate smaller apertures by compressing the horizontal width of the motion region. In Experiments 2 & 3, we break coaxiality (the aperture and motion region no longer share the same axis) by either translating or rotating the aperture within the motion region. The question is how much the aperture can be transformed and still determine the 3D percept. We used Method of Constant Stimuli to find the thresholds for perceiving a rotating 3D object defined by the contour shape. In Experiment 1, even with average width ratios as low as 0.2 (ellipse) or 0.6 (rectangle), the SFM percept was still dominated by contour shape. In experiment 2, the average threshold was around 0.35 (hexagon, barrel) or 0.65 (rectangle), indicating the strong effect of contour geometry on SFM percepts. These experiments document the conditions where contour geometry overrides image motion in SFM, thereby highlighting its critical, but neglected role. They also emphasize the need for mathematical models combining contour geometry and image motion in predicting SFM percepts.
Efficient 3D object tracking approach based on convolutional neural network and Monte Carlo algorithms used for a pick and place robot

Currently, Deep Learning (DL) shows us powerful capabilities for image processing. But it cannot output the exact photometric process parameters and shows non-interpretable results. Considering such limitations, this paper presents a robot vision system based on Convolutional Neural Networks (CNN) and Monte Carlo algorithms. As an example to discuss about how to apply DL in industry. In the approach, CNN is used for preprocessing and offline tasks. Then the 6- DoF object position are estimated using a particle filter approach. Experiments will show that our approach is efficient and accurate. In future it could show potential solutions for human-machine collaboration systems.
Layout analysis of historical Tibetan documents

Layout analysis of historical Tibetan document is very important task in the processing of document rebuilding. The result of layout analysis can be used to segment text regions. The task includes skew correction, region segmentation, page layout and structure. Skew correction process uses Tibetan baseline feature and Hough transform to get baseline, so as to get the skew angle of the document. After skew correction, getting the position of border is to realize region segmentation. To obtain the border, we have adopted a series of treatments such as median filtering, Gaussian smoothing, Sobel edge and edge smoothing, removing small area regions, and obtaining the border position. we determine the location of other regions according to the position relationship between border and other regions, e.g. text region, left comments, right comments. Finally, we use XML format to save page information such as author, date time and page information and region positions. Our method is simple and realize batch image layout analysis.
Feature-aided NN-JPDAF Algorithm for Multiple Target Tracking

Feature aided tracking can often yield improved tracking performance over the standard multiple target tracking (MTT) algorithms with only kinematic measurements. In many applications, the feature signal of the targets consists of sparse Fourier-domain signals. It changes quickly and nonlinearly in the time domain, and the feature measurements are corrupted by missed detections and mis-associations. These two factors have made it hard to extract the feature information to be used in MTT. In this paper, we develop a feature-aided nearest neighbour joint probabilistic data association filter (NN-JPDAF) for joint MTT and feature extraction in dense target environments. We use the atomic norm constraint to formulate the sparsity of feature signal and use the $\ell_{1}$ -norm to formulate the sparsity of the corruption induced by mis-associations. The feature signal are estimated by solving a semidefinite program (SDP) which is convex. Re-filtering is then performed to estimate the kinematic states of the targets, where the association makes use of both kinematic and feature information. Simulation results are presented to illustrate the performance of the proposed algorithm in a radar application.
Markov Model Based Real Time Speaker Recognition using K-Means, Fast Fourier Transform and Mel Frequency Cepstral Coefficients

In this study, which was carried out using a combination of machine learning and sound processing methods, a speaker recognition system and application were developed using real-time Mel Frequency Cepstral Coefficients (MFCC) features and Markov chain model classifier. A sound sample was taken from each speaker for the training of the system and these sound samples were processed in Fast Fourier Transform and MFCC feature extraction algorithms. The MFCC features were clustered using the k-means clustering algorithm. A Markov chain model was created for each speaker by using the outputs obtained after clustering. By deducting the characteristic features of the voice of the speaker, the person who was talking in the society and how long and at which time intervals they spoke during the conversation was determined in real time with high accuracy.
The Role of Self-Efficacy in Students’ Writing Ability (A Case Study at Second Grade Students of a Senior High School in Indramayu)

This case study aimed at investigating the role of self-efficacy in students’ recount text especially in the area of writing as one of the most difficult skills in learning English. Writing difficulties faced by the students are not only based on their cognitive skill but also their own efficacy beliefs. Three problems can be found related to self-efficacy beliefs in English writing. They are related with confidence to write anxiety, and the third problem is about the students’ interest. The participants of this study are three students from a Senior High School in Indramayu. The data were obtained from three data collection: questionnaire, writing test, and also the interview. The result of this study shows that the participants of this study perceived mastery experience accounted for the greatest proportion in their writing ability. It can be seen from how students rate their confidence and given overall feelings about the way to write successfully. Social persuasions and physiological states also were influential while working on writing. Meanwhile, the common experience did not predict too much in students' writing ability. Ultimately, the results of this research concluded that self-efficacy has an important role in students’ ability in writing.
Study on Lip Prints—To Estimate the Reliability as a Personal Identification Method

Aim: Lip prints are unique for any individual.The potential of lip prints as one of the biometric records to determine the identity has been well-recognized.However, studies focused on their reliability by comparing the developed latent lip prints were scarce.This study focused on the reliability of the lip prints for the personal identification by comparing the registered lip prints with the developed latent lip prints made on the porcelain cups.Materials and methods: Samples of 102 subjects (52 males and 50 females) within the age group of 18-30 years were included.Latent and superimposed lip impressions were made on a standard porcelain cup.The latent prints were developed with the fingerprint powder.Then, the lip prints with lip rouge were registered on a transparent adhesive tape.Both the developed latent lip prints and the registered lip prints were photographed with a standard ruler using the digital camera and were compared.The lip prints were classified with the scheme proposed by Tsuchihashi.The statistical analysis was done using the Pearson Chi-square test (IBM SPSS version 20) with a p value of 0.05. Results:The lip prints were unique to any individual irrespective of the gender variation.Their interpretation of comparing the digital pictures confirmed the presence of unique patterns and the possibility of the feature extraction similar to the fingerprints.Type III was the most frequent pattern observed in the study group. Conclusion:We conclude that the lip prints are highly reliable as a biometric record due to their uniqueness.The lip prints have demonstrated enough evidence that is intentionally registered and the developed latent prints were compared, which can be applied as one of the easiest and simplest methods for comparison.Yet, the authenticity of the lip print is in the preliminary level and need more systematic studies to be accepted for the legal disputes.Clinical significance: The study result can strengthen the reliability of the lip prints as an identification tool and discusses the future possibilities of lip print application.
Rancang Bangun Aplikasi Panduan Pariwisata di Kabupaten Banyuwangi Mobile Berbasis Android

Tourism is a very important sector for the development of a region and tourism is one of the means of promotion to introduce the beauty of nature and the uniqueness of the culture in the area. Including for Banyuwangi Regency which is one of the regencies in East Java Province. This regency has many objects or tourist destinations which are a mainstay and the development of the tourism sector must be accompanied by the development of information technology. Smartphone is one form of technological development and one of the most popular smartphone technology bases based on its operating system is Android.The use of android smartphones in the development of the world of tourism in the information technology era is important for the public and tourists to have knowledge about objects or tourist destinations that will be targeted. In supporting the needs of the tourism information system in Banyuwangi Regency, an Android-based application was created. An application on an android smartphone that suits the needs of travelers equipped with interesting features. This application contains information about tourist objects that are equipped with visual media such as pictures, videos and Google Maps facilities to facilitate in finding tourist locations that will be addressed by the public and tourists.
Localised Generative Flows

We show that normalising flows become pathological when used to model targets whose supports have complicated topologies. In this scenario, we prove that a flow must become arbitrarily numerically noninvertible in order to approximate the target closely. This result has implications for all flow-based models, and especially Residual Flows (ResFlows), which explicitly control the Lipschitz constant of the bijection used. To address this, we propose Continuously Indexed Flows (CIFs), which replace the single bijection used by normalising flows with a continuously indexed family of bijections, and which can intuitively clean up mass that would otherwise be misplaced by a single bijection. We show theoretically that CIFs are not subject to the same topological limitations as normalising flows, and obtain better empirical performance on a variety of models and benchmarks.
PARALLEL AND DISTRIBUTED ASSOCIATION RULE MINING ALGORITHMS: A RECENT SURVEY

Data investigation is an essential key factor now a days due to rapidly growing electronic technology.It generates a large number of transactional data logs from a range of sources devices.Parallel and distributed computing is a useful approach for enhancing the data mining process.The aim of this research is to present a systematic review of parallel association rule mining (PARM) and distributed association rule mining (DARM) approaches.We have observed that the parallelized nature of Apriori, Equivalence class, Hadoop (MapReduce), and Spark proves to be very efficient in PARM and DARM environment.We conclude that this comprehensive review, references cited in this article will convey foremost hypothetical issues and a guideline to the researcher an interesting research direction.The most important hypothetical issue and challenges include the large size of databases, dimensionality of data, indexing schemes of data in the database, data skewness, database location, load balancing strategies, methods of adaptability in incremental databases and orientation of the database.
Data Monitoring Platform for Submarine Cable Tunnel Operation Based on Data Mining

Qin, D., 2019. Data monitoring platform for submarine cable tunnel operation based on data mining. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 960–968. Coconut Creek (Florida), ISSN 0749-0208.In order to ensure the safe and stable operation effect of submarine cable tunnel and solve the problems of low data accuracy, long running time, poor stability and high cost consumption during the operation of traditional monitoring platform, the optimal design of data monitoring platform for submarine cable tunnel operation based on data excavation is proposed. By evaluating and calculating the clarity level of the monitoring image, combining with the calculation results, the network topology of the monitoring platform is improved, and the software and hardware systems of the data monitoring platform are designed, so as to effectively improve the accuracy of the data monitoring image, and according to the network complex structure, the structure module of the main monitoring station of the submarine cable tunnel is configured to improve the submarine cable tunnel. In order to ensure the stability of data monitoring, the coordinator circuit of platform hardware equipment is improved. Finally, the experiment proves that the operation data monitoring platform for submarine cable tunnel based on data mining has significantly improved compared with the traditional data monitoring platform. During the operation process, the data accuracy is improved, the operation stability is enhanced, the running time and cost are shortened, and the safe and stable operation of submarine cable tunnel is guaranteed.
3D Image Processing &amp;#x2013; from Capture to Display &amp;#x2013;

Over the last 25 years, we have been involved in 3D image processing research field. We started our researches related to 3D image processing with &#x201C;Data Compression of an Autostereoscopic 3-D Image&#x201D; and presented our work in SPIE SD&amp;A session in 1994. We first proposed the ray space representation of 3D images which is a common data format for various 3D capturing and displaying devices. Based on the ray space representation, we have conducted various researches on 3D image processing, which include: ray space coding and data compression, view interpolation, ray space acquisition, display, and a full system from capture to display of ray space. In this paper, we introduce some of our 25-year researches in terms of 3D image processing &#x2013; from capture to display &#x2013;.