Reviewerly/exHarmony huggingface dataset:
Shearography for non-destructive testing of specular reflecting objects using scattered light illumination

Shearography has been widely used in non-destructive testing due to its advantages of fast, full-field, and high sensitivity for nondestructive test and strain measurement. Shearography has been applied successfully to various industry applications. However, traditional shearography requires the surface under test to be sufficiently rough. This study presents a modified shearography setup that can be applied to workpieces with specular surface which is illuminated by light scattered from a rough plane to generate speckle patten. In the optical setup, the specular surface is conjugated to the phase map to simplify the positional correspondence between the interferogram and the internal defects. The sensitivity factor is analyzed through ray tracing method based on geometric optics which is influenced by the surface normal. The experimental results are described and presented as well.
Using Lexical Chains to Identify Text Difficulty: A Corpus Statistics and Classification Study

Our goal is data-driven discovery of features for text simplification. In this paper, we investigate three types of lexical chains: exact, synonymous, and semantic. A lexical chain links semantically related words in a document. We examine their potential with a document-level corpus statistics study (914 texts) to estimate their overall capacity to differentiate between easy and difficult text and a classification task (11 000 sentences) to determine usefulness of features at sentence-level for simplification. For the corpus statistics study we tested five document-level features for each chain type: total number of chains, average chain length, average chain span, number of crossing chains, and the number of chains longer than half the document length. We found significant differences between easy and difficult text for average chain length and the average number of cross chains. For the sentence classification study, we compared the lexical chain features to standard bag-of-words features on a range of classifiers: logistic regression, naive Bayes, decision trees, linear and RBF kernel SVM, and random forest. The lexical chain features performed significantly better than the bag-of-words baseline across all classifiers with the best classifier achieving an accuracy of ~90% (compared to 78% for bag-of-words). Overall, we find several lexical chain features provide specific information useful for identifying difficult sentences of text, beyond what is available from standard lexical features.
BlockChain Based Certificate Verification Platform

This research is still in progress and focuses on using the blockchain for the verification of the authenticity of issued certificates. In this first stage of the research we are presenting a prototype that allows the registration of academic institutions and its respective institutes/faculties, registration of student cohorts and issuing of certificate awards. The issued certificates are registered on the blockchain so that any third party who would need to verify the authenticity of a certificate can do so, independently of the academic institution, even in the event that such institution has closed. The next phase of this research aims to extend the prototype to the registration of medical records on the blockchain with a focus on the privacy of sensitive data and allowing the owner of the information to control user access to the documents. The final phase of the research will involve gathering user and corporate feedback on the proposed prototypes.
Software Effort Interval Prediction via Bayesian Inference and Synthetic Bootstrap Resampling

Software effort estimation (SEE) usually suffers from inherent uncertainty arising from predictive model limitations and data noise. Relying on point estimation only may ignore the uncertain factors and lead project managers (PMs) to wrong decision making. Prediction intervals (PIs) with confidence levels (CLs) present a more reasonable representation of reality, potentially helping PMs to make better-informed decisions and enable more flexibility in these decisions. However, existing methods for PIs either have strong limitations or are unable to provide informative PIs. To develop a “better” effort predictor, we propose a novel PI estimator called Synthetic Bootstrap ensemble of Relevance Vector Machines (SynB-RVM) that adopts Bootstrap resampling to produce multiple RVM models based on modified training bags whose replicated data projects are replaced by their synthetic counterparts. We then provide three ways to assemble those RVM models into a final probabilistic effort predictor, from which PIs with CLs can be generated. When used as a point estimator, SynB-RVM can either significantly outperform or have similar performance compared with other investigated methods. When used as an uncertain predictor, SynB-RVM can achieve significantly narrower PIs compared to its base learner RVM. Its hit rates and relative widths are no worse than the other compared methods that can provide uncertain estimation.
Web document summarization by exploiting social context with matrix co-factorization

In the context of social media, users usually post relevant information corresponding to the contents of events mentioned in a Web document. This information posses two important values in that (i) it reflects the content of an event and (ii) it shares hidden topics with sentences in the main document. In this paper, we present a novel model to capture the nature of relationships between document sentences and post information (comments or tweets) in sharing hidden topics for summarization of Web documents by utilizing relevant post information. Unlike previous methods which are usually based on hand-crafted features, our approach ranks document sentences and user posts based on their importance to the topics. The sentence-user-post relation is formulated in a share topic matrix, which presents their mutual reinforcement support. Our proposed matrix co-factorization algorithm computes the score of each document sentence and user post and extracts the top ranked document sentences and comments (or tweets) as a summary. We apply the model to the task of summarization on three datasets in two languages, English and Vietnamese, of social context summarization and also on DUC 2004 (a standard corpus of the traditional summarization task). According to the experimental results, our model significantly outperforms the basic matrix factorization and achieves competitive ROUGE-scores with state-of-the-art methods.
Arabic Word Segmentation With Long Short-Term Memory Neural Networks and Word Embedding

In this paper, we propose an Arabic word segmentation technique based on a bi-directional long short-term memory deep neural network. This paper addresses the two tasks of word segmentation only and word segmentation for nine cases of the rewrite. Word segmentation with a rewrite concerns inferring letters that are dropped or changed when the main word unit is attached to another unit, and it writes these letters back when the two units are separated as a result of segmentation. We only use binary labels as indicators of segmentation positions. Therefore, label 1 is an indicator of the start of a new word (split) in a sequence of symbols not including whitespace, and label 0 is an indicator for any other case (no-split). This is different from the mainstream feature representation for word segmentation in which multi-valued labeling is used to mark the sequence symbols: beginning, inside, and outside. We used the Arabic Treebank data and its clitics segmentation scheme in our experiments. The trained model without the help of any additional language resources, such as dictionaries, morphological analyzers, or rules, achieved a high F1 value for the Arabic word segmentation only (98.03%) and Arabic word segmentation with the rewrite (more than 99% for frequent rewrite cases). We also compared our model with four state-of-the-art Arabic word segmenters. It performed better than the other segmenters on a modern standard Arabic text, and it was the best among the segmenters that do not use any additional language resources in another test using classical Arabic text.
Using Bessel Beams to Induce Optical Waveguides

Fabricating integrated waveguides using light is key to realizing miniaturized optical circuits and networks in a full three-dimensional volume, an achievement that presently is hampered by diffraction in the writing beams. The authors demonstrate the use of nondiffracting Bessel beams to write waveguides that support localized modes, so that no diffraction occurs during fabrication. Creations include single, double, and multiwaveguide splitters and couplers, along with electro-optic modulators, a family of components that can pave the way to densely packed passive optical devices for applications ranging from communication to classical and quantum optical computing.
MSG-GAN: Multi-Scale Gradient GAN for Stable Image Synthesis.

While Generative Adversarial Networks (GANs) have seen huge successes in image synthesis tasks, they are notoriously difficult to use, in part due to instability during training. One commonly accepted reason for this instability is that gradients passing from the discriminator to the generator can quickly become uninformative, due to a learning imbalance during training. In this work, we propose the Multi-Scale Gradient Generative Adversarial Network (MSG-GAN), a simple but effective technique for addressing this problem which allows the flow of gradients from the discriminator to the generator at multiple scales. This technique provides a stable approach for generating synchronized multi-scale images. We present a very intuitive implementation of the mathematical MSG-GAN framework which uses the concatenation operation in the discriminator computations. We empirically validate the effect of our MSG-GAN approach through experiments on the CIFAR10 and Oxford102 flowers datasets and compare it with other relevant techniques which perform multi-scale image synthesis. In addition, we also provide details of our experiment on CelebA-HQ dataset for synthesizing 1024 x 1024 high resolution images.
Design and analysis of an effective graphics collaborative editing system

With the rapid development of computer-supported cooperative work (CSCW) technology, graphical collaborative editing plays an increasingly important role in CSCW. The most important technique in graphics co-editing is the consistency of graphics co-editing, which mainly includes causality consistency, consistency of results, and consistency of intention. Most of the previous research was abstract and ineffective, lacking theoretical depth and scalability. However, because the algorithm proposed in this paper can solve the contradictions in the consistency of graphical collaborative editing, the research in this paper has particularity, and the results will be proven by the experiment described in the paper. In order to solve the consistency conflict problem of graphic collaborative editing, the common graphics collaborative editing algorithm (CGCE algorithm) is proposed. It is proposed not only to perfect and expand the definition of graphics collaborative editing but also to merge with HTML5 Canvas, WebSocket, jQuery, Node.js and other network programming languages and technologies. The graphic collaborative editing based on the design and implementation of this paper can effectively solve the consistency conflict problem of many users during the collaborative editing of graphics, which ensures that the graphics of each graphical collaborative editing interface is consistent and the collaborative work can achieve the desired effect.
Enhancing the Ambient Assisted Living Capabilities with a Mobile Robot

Ambient assisted living (AAL) environments are currently a key focus of interest as an option to assist and monitor disabled and elderly people. These systems can improve their quality of life and personal autonomy by detecting events such as entering potentially dangerous areas, potential fall events, or extended stays in the same place. Nonetheless, there are areas that remain outside the scope of AAL systems due to the placement of cameras. There also exist sources of danger in the scope of the camera that the AAL system cannot detect. These sources of danger are relatively small in size, occluded, or nonstatic. To solve this problem, we propose the inclusion of a robot which maps such uncovered areas looking for new potentially dangerous areas that go unnoticed by the AAL. The robot then sends this information to the AAL system in order to improve its performance. Experimentation in real-life scenarios successfully validates our approach.
Understanding the Role of Actor Heterogeneity in Blockchain Splits: An Actor-Network Perspective of Bitcoin Forks

This paper investigates the focal actors and their heterogeneity in blockchain splits. Disagreements in blockchain communities often lead to splits in the blockchain and the community. For example, disagreements within the Bitcoin community on increasing the block size led to the blockchain split and creation of Bitcoin Cash. We build on actor-network theory to investigate blockchain split as a translation process, and employ case study methodology to examine Bitcoin splits. We identify several human actors, such as miners, developers, merchants, and investors, as well as non-human actors including ideologies, exchanges and computer programs involved in Bitcoin splits. Our results show that actor heterogeneity, that is, the complex constellation of diverse actors, plays a key role in blockchain splits. We further describe how the human and non-human actors’ fluid moves into micro and macro positions in the network affect the development of the split. We also discuss the role of these actors and their engagement in forming micro and macro agencies in blockchain splits. Our study adds to the understanding of actor behavior and network dynamics in decentralized information systems such as blockchain and open source software.
Deep-learning-based pixel compensation algorithm for local dimming liquid crystal displays of quantum-dot backlights

Local dimming techniques have been widely studied to achieve a high contrast ratio and low power consumption for liquid crystal displays. The luminance of a backlight is reduced according to some characteristics of an input image and the pixel data are boosted to compensate for the dimmed backlight. In addition, because a backlight block is affected by adjacent ones, the pixel compensation algorithm requires huge processing power as well as many iterations along with the overall luminance profile information of a backlight. However, a proposed deep-learning-based local dimming algorithm generates the compensated image directly from an input image without any information of backlight's dimming levels. The proposed compensation network is constructed on the basis of the U-net to maintain the high-resolution features in the up-sampling paths through skip-connections. In addition, it is also ensured that the bi-linear interpolation can be used without visible image quality degradation for the reduction on the number of parameters. The proposed networks are trained and verified on a DIV2K 2K image dataset.
Clustering algorithms subjected to K-mean and gaussian mixture model on multidimensional data set

This paper explored the method of clustering.Two main categories of algorithms will be used, namely k-means and Gaussian Mixture Model clustering.We will look at algorithms within thesis categories and what types of problems they solve, as well as what methods could be used to determine the number of clusters.Finally, we will test the algorithms out using sparse multidimensional data acquired from the usage of a video games sales all around the world, we categories the sales in three main standards of high sales, medium sales and low sales, showing that a simple implementation can achieve nontrivial results.The result will be presented in the form of an evaluation of there is potential for online clustering of video games sales.We will also discuss some task specific improvements and which approach is most suitable.
Open

Colin Lockard, Prashant Shiralkar, Xin Luna Dong. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Design and trajectory control of universal drone system

In this paper, a new universal drone design is proposed to be used in different applications. The drone system has a variable number of rotors from four to twelve with portable arms. As a comparative study, a conventional eight rotors (Octocopter) and independently controlled twelve rotors (Dodecacopter) configures are chosen for the proposed universal drone simulations to benchmark the trajectory performance. Besides, since the arm lengths can be changed in this drone system, the effect of different arm lengths on this performance is also examined. The comparisons are performed on both systems in five different working condition, such as without disturbance, with periodic disturbances and non-periodic disturbances. In the simulations, when the amplitude was increased by 100% under periodic disturbing effect, an increase of 69.7% and 47.6% was observed in the root mean square of the position errors of the Octocopter and Dodecacopter systems, respectively. Similarly, for non-periodic disturbances, an increase of %13 and %7 was observed for both systems, respectively. According to the obtained results, the octocopter system is partially more stable without disturbing effect while the dodecacopter system is more stable flight than octocopter systems as the disturbance effect increases.
Round5: Compact and Fast Post-quantum Public-Key Encryption

We present the ring-based configuration of the NIST submission Round5, a Ring Learning with Rounding (RLWR)- based IND-CPA secure public-key encryption scheme. It combines elements of the NIST candidates Round2 (use of RLWR as underlying problem, having $$1+x+\ldots +x^n$$ with $$n+1$$ prime as reduction polynomial, allowing for a large design space) and HILA5 (the constant-time error-correction code XEf). Round5 performs part of encryption, and decryption via multiplication in $$\mathbb {Z}_{p}[x]/(x^{n+1}-1)$$ , and uses secret-key polynomials that have a factor $$(x-1)$$ . This technique reduces the failure probability and makes correlation in the decryption error negligibly low. The latter allows the effective application of error correction through XEf to further reduce the failure rate and shrink parameters, improving both security and performance. We argue for the security of Round5, both formal and concrete. We further analyze the decryption error, and give analytical as well as experimental results arguing that the decryption failure rate is lower than in Round2, with negligible correlation in errors. IND-CCA secure parameters constructed using Round5 and offering more than 232 and 256 bits of quantum and classical security respectively, under the conservative core sieving model, require only 2144 B of bandwidth. For comparison, similar, competing proposals require over 30% more bandwidth. Furthermore, the high flexilibity of Round5’s design allows choosing finely tuned parameters fitting the needs of diverse applications – ranging from the IoT to high-security levels.
Learning Implicitly Recurrent CNNs Through Parameter Sharing

We introduce a parameter sharing scheme, in which different layers of a convolutional neural network (CNN) are defined by a learned linear combination of parameter tensors from a global bank of templates. Restricting the number of templates yields a flexible hybridization of traditional CNNs and recurrent networks. Compared to traditional CNNs, we demonstrate substantial parameter savings on standard image classification tasks, while maintaining accuracy. Our simple parameter sharing scheme, though defined via soft weights, in practice often yields trained networks with near strict recurrent structure; with negligible side effects, they convert into networks with actual loops. Training these networks thus implicitly involves discovery of suitable recurrent architectures. Though considering only the design aspect of recurrent links, our trained networks achieve accuracy competitive with those built using state-of-the-art neural architecture search (NAS) procedures. Our hybridization of recurrent and convolutional networks may also represent a beneficial architectural bias. Specifically, on synthetic tasks which are algorithmic in nature, our hybrid networks both train faster and extrapolate better to test examples outside the span of the training set.
Instance Retrieval Using Region of Interest Based CNN Features

Recently, image representations derived by convolutional neural networks (CNN) have achieved promising performance for instance retrieval, and they outperform the traditional hand-crafted image features. However, most of existing CNN-based features are proposed to describe the entire images, and thus they are less robust to background clutter. This paper proposes a region of interest (RoI)-based deep convolutional representation for instance retrieval. It first detects the region of interests (RoIs) from an image, and then extracts a set of RoI-based CNN features from the fully-connected layer of CNN. The proposed RoI-based CNN feature describes the patterns of the detected RoIs, so that the visual matching can be implemented at image region-level to effectively identify target objects from cluttered backgrounds. Moreover, we test the performance of the proposed RoI-based CNN feature, when it is extracted from different convolutional layers or fully-connected layers. Also, we compare the performance of RoI-based CNN feature with those of the state-of-the-art CNN features on two instance retrieval benchmarks. Experimental results show that the proposed RoI-based CNN feature provides superior performance than the state-of-the-art CNN features for in-stance retrieval.
The Unfairness of Popularity Bias in Recommendation

Recommender systems are known to suffer from the popularity bias problem: popular (i.e. frequently rated) items get a lot of exposure while less popular ones are under-represented in the recommendations. Research in this area has been mainly focusing on finding ways to tackle this issue by increasing the number of recommended long-tail items or otherwise the overall catalog coverage. In this paper, however, we look at this problem from the users' perspective: we want to see how popularity bias causes the recommendations to deviate from what the user expects to get from the recommender system. We define three different groups of users according to their interest in popular items (Niche, Diverse and Blockbuster-focused) and show the impact of popularity bias on the users in each group. Our experimental results on a movie dataset show that in many recommendation algorithms the recommendations the users get are extremely concentrated on popular items even if a user is interested in long-tail and non-popular items showing an extreme bias disparity.