Reviewerly/exHarmony huggingface dataset:
Oil Tank Detection Using Co-Spatial Residual and Local Gradation Statistic in SAR Images

The advance in synthetic aperture radar (SAR) technology reduces the difficulty of data acquisition, along with greatly increased computational complexity. This paper cares about this and aims to accomplish oil tank detection in SAR images from the perspective of computer vision. The whole model includes three main steps. The first is a saliency-driven clustering to accomplish the single image saliency analysis according to the intensity specificity and texture distribution. The second step introduces a common visual saliency analysis based on the co-spatial residual and local gradation statistic to extract the common visual salient parts within the input series. The final step considers the distribution of adjacent highlight points obtained from the saliency analysis to accomplish the location of tanks. Three competing methods are established in the experimental part. The evaluation in pixel level and geometric segmentation accuracy both verify the superiority of the proposed model in target detection and interferences exclusion.
Performance Evaluation of AES, ARC2, Blowfish, CAST and DES3 for Standalone Systems : Symmetric Keying Algorithms

The paper presents a comparative analysis of different symmetric keying cryptographic algorithms. The algorithms under study are AES, ARC2, Blowfish, CAST and DES3, and the performance metrics for the basis of the study are Encryption time, Decryption time, Throughput, Plain text size vs Cipher text size. The study is conducted for a standalone system, where the algorithms are executed on a quad core i5 processor running Windows 10 and, Python is used as the programming language.
Coordination in Large-Scale Agile Software Development

Agile practices are popular within software development. But when applied to large projects with many teams, coordination challenges arise. The projects working title “Coordination in large-scale agile software development: An investigation of coordination mechanisms, communication, roles, autonomy and interdependencies” summarizes the main topics of investigation. While all theoretical and analytical approaches to the data material is not yet determined, I have already started fieldwork in one company which will serve as a main longitudinal case, with more to follow as the project proceeds. Initial fieldwork has revealed that there are differences in how agile teams coordinate their work across teams. I will continue to explore these differences. End goals of the project include to identify success criteria for coordination in large-scale agile software development projects.
User-depth Customized Men’s Shirt Design Framework Based on BI-LSTM

The current user customization method of clothing has a low degree of consumer participation, which results in the lack of effective data for garment manufacturers and poorly-recognized personalized products. The personalized shirt framework based on deep learning uses the long-short-term memory neural network (LSTM) to process the textual information of the characters described by the consumers themselves, providing the garment factory with more consistent design data, replacing the traditional clothing component combination design method. This method replaces the traditional clothing component combination design method, can effectively improve the accuracy of data in personalized production, the degree of matching between user participation and recommendation system, thus improving the predictability of garment factory for personalized production and the production efficiency of factory design department.
Explicit Learning of Feature Orientation Estimation

While many learning-driven algorithms for local feature detection and description have submerged during recent years. One key component in the pipeline, namely orientation estimation, still remains underdeveloped. Among all sorts of difficulties, the impracticality and tedium of finding a "ground truth" feature orientation as a learning target is one big challenge. In this paper, we bypass this "thinking trap" and propose an unsupervised scheme that explicitly trains a simple convolutional neural network to predict orientations for feature points. Together with a carefully designed loss term, the network manages to provide accurate orientation estimations. We further evaluate the capability of this estimator in two experiments: orientation estimation and feature matching. Results showed the proposed method outperforms other compared methods on multiple benchmark datasets. The pretrained model is publicly available.
Tamil talk: What you speak is what you get!

Tamil is one of the longest surviving classical languages in the world. Speech to text in Tamil would benefit to a lot of native Tamil speakers throughout the world. There are many speech recognition and speech to text applications available for a wide variety of languages but many minority languages, such as Tamil are overlooked. In this paper, we propose to develop a system for Tamil speech to text that will be consistent with the pronunciation of the user and conforms with the syntax of the language.
Safe-Net: Solid and Abstract Feature Extraction Network for Pedestrian Attribute Recognition

Pedestrian attribute analysis, which is a vital component in the intelligent video surveillance area, can facilitate person retrieval, searching and indexing. However, the resolution of the surveillance video is relatively low. The ability of neural networks to learn abstract features (age, gender) and solid features (hat, backpack, clothes) on low-resolution images is limited due to the inability to accurately locate the human body and the disturbance of background noise. In this study, we adopt a Semantic Parsing Technique (SPT) as a pedestrian extractor to localize informative regions of human effectively. Experiments are conducted on the RAP, PA-100k and other datasets, we show that our proposed SAFE-Net is capable of capturing abstract features and solid features, and produces competitive performance with the state-of-the-art methods.
Machine-Assisted Annotation of Forensic Imagery

Image collections, if critical aspects of image content are exposed, can spur research and practical applications in many domains. Supervised machine learning may be the only feasible way to annotate very large collections. However, leading approaches rely on large samples of completely and accurately annotated images. In the case of a large forensic collection that we are aiming to annotate, neither the complete annotation nor the large training samples can be feasibly produced. We, therefore, investigate ways to assist manual annotation efforts done by forensic experts. We present a method that can propose both images and areas within an image likely to contain desired classes. Evaluation of the method with human annotators showed highly accurate classification and reasonable segmentation accuracy that was strongly affected by transfer learning. We hope this effort can be helpful in other domains that require weak segmentation and have limited availability of qualified annotators.
Group Re-Identification with Hybrid Attention Model and Residual Distance

Group re-identification (Re-ID) is an important task of computer vision and involves multiple challenges. In this paper, we propose a Hybrid Attention Model (HAM) to address the problem of group Re-ID, solving the spatial variation in the challenging still-image-based group Re-ID task. HAM consists of both the position and channel attention to make the network focus more on the crucial areas and features of the group images. Furthermore, we propose a novel Least Squares Residual Distance (LSRD) based on the least squares algorithm. LSRD can leverage the residual of the fitting function achieved by least squares method, better learning the metric between group image pairs. To evaluate the performance, we propose a new largest group Re-ID dataset. Extensive experimental results conducted on the dataset demonstrate the effectiveness of our approaches.
Theoretical Analysis on Multiple Layer Fast Optical Switch Based Data Center Network Architecture

We propose a novel multi-parallel layer DCN architecture based on low radix FOS. Results show cost and power consumption reduction of 4 parallel-layer architecture is 60% and 26.7% with respect to the FatTree.
An optical processor for matrix computation on Silicon-On-Insulator

In this paper, we present a matrix computation processor based on an on-chip optical linear neural network. This processor can solves different matrix equations, showing possibility of usage in the field of optical artificial intelligence. We present a matrix computation processor based on an on-chip optical linear neural network, which solves different matrix equations by self-learning technology.
Siegel's lemma is sharp for almost all linear systems

The well-known Siegel Lemma gives an upper bound c U m / ( n − m ) for the size of the smallest non-zero integral solution of a linear system of m ⩾ 1 equations in n > m unknowns whose coefficients are integers of absolute value at most U ⩾ 1 ; here c = c ( m , n ) ⩾ 1 . In this paper, we show that a better upper bound U m / ( n − m ) / B is relatively rare for large B ⩾ 1 ; for example, there are θ = θ ( m , n ) > 0 and c ′ = c ′ ( m , n ) such that this happens for at most c ′ U m n / B θ out of the roughly ( 2 U ) m n possible such systems.
Building On-Demand Test Forms in R

Automated Test Assembly (ATA) plays important role in test development, especially in large scale test administrations. However, there is a lack of tutorials showing how to solve ATA problems. This tutorial aims to show to how build on-demand test forms easily for researchers and practitioners, and share the R codes for their use. The study presents the annotated R codes for thirty-nine unique examples. The examples include building one form, multiple forms and more complex ones under different constraint conditions across equal or different form lengths. All examples were solved by using “xxIRT” R package. The graphical depictions of the form-level information functions for all examples were also provided. Some important notes about the codes were also provided at the end of the paper in case one did not find a solution. The thirty-six examples were provided in the main body of the paper, the other three complex examples were given in the Supplementary material.
Code-Based Zero Knowledge PRF Arguments

Pseudo-random functions are a useful cryptographic primitive that, can be combined with zero-knowledge proof systems in order to achieve privacy-preserving identification. Libert et al. (ASIACRYPT 2017) has investigated the problem of proving the correct evaluation of lattice-based PRFs based on the Learning-With-Rounding (LWR) problem. In this paper, we go beyond lattice-based assumptions and investigate, whether we can solve the question of proving the correct evaluation of PRFs based on code-based assumptions such as the Syndrome Decoding problem. The answer is affirmative and we achieve it by firstly introducing a very efficient code-based PRG based on the Regular Syndrome Decoding problem and subsequently, we give a direct construction of a code-based PRF. Thirdly, we provide a zero-knowledge protocol for the correct evaluation of a code-based PRF, which allows a prover to convince a verifier that a given output y is indeed computed from the code-based PRF with a secret key k on an input x, i.e., $$y=f(k,x)$$ . Finally, we analytically evaluate the protocol’s communication costs.
Directional modulation based on chaos scrambling and artificial noise for physical layer security enhancement

Directional modulation (DM), as an emerging promising physical layer security (PLS) transmission technique for wireless communications, has attracted much attention over the past decade. It endows transmitters the ability to directly transmit the confidential messages to legitimate receivers along with pre-specified directions while distorting signal waveform signatures projected along all other spatial directions to guarantee the security of information transmission. Traditional DM designs are based on the assumption that eavesdroppers (Eves) and legitimate users (LUs) are in different directions. Nevertheless, it is not always the scenario in practical applications, as it is possible that Eves and LUs are in the same directions or even at the same positions, which results in that signals received by Eves will be approximately the same as LUs’. To address this problem, the chaos scrambling (CS) technique is employed in this paper. A DM technique based on CS and artificial noise (AN) is proposed for PLS enhancement. The symbol error rate, secrecy rate, and robustness of the proposed CS-AN-aided scheme are analysed and simulated. Simulation results show the effectiveness of the proposed method and the PLS can also be guaranteed even if Eves are aligning with the desired directions or very close to the LUs.
Beautified QR Code with Security Based on Data Hiding

In this paper, we combine beautified QR (Quick Response) code with data hiding algorithm. The QR code beautification algorithm based on error correction mechanism is used to generate the beautified QR code. In the process of generating the beautified QR code, the QR code modules are removed with the error correction capability allowed. The verified QR code is added to the beautified QR code to prevent the tampering of the QR code. Thus, we can enhance the security of the QR code when user scans it. The original image covered by the QR code and the verified QR code are embedded in the beautified QR code by using the LSB (Least Significant Bit) data hiding method. The experimental results show that the beautified QR code generated by the proposed algorithm is superior to the reference method in visual quality and security.
Cloud Security Architecture Based on Fully Homomorphic Encryption

Cloud computing is a new environment in computer-oriented services. The high costs of network platforms, development in client requirements, data volumes and weight on response time pushed companies to migrate to cloud computing, providing on-demand web facilitated IT services. Cloud storage empowers users to remotely store their information and delight in the on-demand high quality cloud applications without the affliction of local hardware management and programming administration. In order to solve the problem of data security in cloud computing system, by introducing fully homomorphism encryption algorithm in the cloud computing data security, another sort of information security solution to the insecurity of the cloud computing is proposed, and the scenarios of this application is hereafter constructed. This new security arrangement is completely fit for the processing and retrieval of the encrypted data, successfully prompting the wide relevant prospect, the security of data transmission, and the stockpiling of the cloud computing.
Event Detection Based on Open Information Extraction and Ontology

Most of the information is available in the form of unstructured textual documents due to the growth of information sources (the Web for example). In this respect, to extract a set of events from texts written in natural language in the management change event, we have been introduced an open information extraction (OIE) system. For instance, in the management change event, a PERSON might be either the new coming person to the company or the leaving one. As a result, the Adaptive CRF approach (A-CRF) [15] has shown good performance results. However, it requires a lot of expert intervention during the construction of classifiers, which is time consuming. To palpate such a downside, we introduce an approach that reduces the expert intervention during the relation extraction. The named entity recognition and the reasoning which are automatic and based on techniques of adaptation and correspondence. Carried out experiments show the encouraging results of the main approaches of the literature.
Quantitative Issues in Cyberinsurance: Lessons From Behavioral Economics, Counterfactuals, and Causal Inference

In this article, I discuss quantitative issues in cyberinsurance with 57 experts and present a tutorial summary of how behavioral economics can quantitatively capture cybersecurity behaviors. I also examine how counterfactuals and causal inference can contribute to cyberattribution and liability quantification and suggest possible first steps to improve practicing cyberinsurance.
Neural Network Based Transaction Classification System for Chinese Transaction Behavior Analysis

With the rapid development of Chinese economy, it is significant to examine the economic activities in China. Each transaction behavior is recorded by the invoice. The invoice contains the transaction content, the classification of the transaction behavior (in accordance with the Tax Classification and Coding for Commodities and Services issued by the state) and transaction price, etc. Our work uses real mass invoice data collected from Zhejiang Province and conducts a multi-dimensional analysis of Chinese transaction behavior based on transaction behavior classification model. Firstly, we propose a compositional CNN-RNN model with attention mechanism to recommend the corresponding categories of transaction behavior collected from tax invoices. It maps the transaction behavior recorded in the invoice to transaction code in the Tax Classification and Coding for Commodities and Services issued by the state. Preliminary experiments show that the top-one accuracy of classifying transaction behavior achieves 75%. Then, we focus on the quantity distribution of invoice data and draw a conclusion that the major category with larger number of invoice records is more diversified in subdivided categories. After that, we studied the price distribution of various transaction behaviors to discover the difference in price distribution between different industries. Prices in the major categories of goods are more concentrated in the middle or lower prices. We can analyze the regional industrial structure through the price distribution of the industry which makes sense to study the economy of the region from the perspective of industry.
Johns Hopkins University Submission for WMT News Translation Task

We describe the work of Johns Hopkins University for the shared task of news translation organized by the Fourth Conference on Machine Translation (2019).We submitted systems for both directions of the English-German language pair.The systems combine multiple techniques -sampling, filtering, iterative backtranslation, and continued training -previously used to improve performance of neural machine translation models.At submission time, we achieve a BLEU score of 38.1 for De-En and 42.5 for En-De translation directions on newstest2019.Post-submission, the score is 38.4 for De-En and 42.8 for En-De.Various experiments conducted in the process are also described.
The Relationship Between Game Elements and Player Emotions by Comparing Game Frameworks

Emotion has become an important part in games. The purpose of this paper is to find whether game elements could influence players emotion? Which elements in a game could evoke player emotions? Previous theories showed game elements could be classified into two layers: the underlying layer and the metaphorical layer. Moreover, aesthetics, narrative, social interaction, and players' ability in games could be emotional triggers. In this paper, we performed a case study to compare the two versions of a game: Game for Peace and PUBG Mobile. By using NLPIR (Natural Language Processing & Information Retrieval) platform, we analyzed 149 reviews of the new version to estimate the influence of specific game elements on player experience and emotion. Our study showed that: (1) even the small change of game elements has potential to influence players experience and emotion. (2) both the metaphorical layer and the underlying layer play a significant role in evoking players' emotion. (3) Without the change of core gameplay, the alteration in theme and a metaphorical layer could alter players experience and emotions.
Single Frame Image Super Resolution Using ANFIS Interpolation: An Initial Experiment-Based Approach

Image super resolution is a classical problem in image processing. Different from most of the existing super resolution algorithms that work on sufficient training data, in this work, a new super resolution method is proposed to handle the situation where the training data is insufficient by the use of ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation. ANFIS interpolation aims to interpolate an effective ANFIS given only sparse data in the problem area of interest, with the assistance of two well trained ANFISs in the neighbourhood areas. The interpolated ANFIS constructs mappings from low resolution images to high resolution ones, which provides an effective mechanism for further inference of high resolution images from given low resolution ones. Experimental results indicate that the proposed approach entails improved super resolution performance for situations where there is a shortage of training data.
Feature Based Fuzzy Framework for Sentimental Analysis of Web Data

Social mass media has emerged as a projectile platform for the evolution of web data. The sentimental Analysis where the huge textual online reviews are analyzed to extract the actual sentiment or emotions hidden in the reviews. In this paper an effective approach for sentimental analysis of web data is proposed which deploys the fuzzy based machine learning algorithm to accomplish fine-level sentiment analysis of huge online opinions by assimilating the fuzzy linguistic hedges influence on opinion descriptors. The seven layered categories are designed that uses SentiWordNet which has three stages: Pre-processing phase, Feature Selection Phase and Fuzzy based Sentiment Analysis phase. Various machine learning algorithms like AdaBoost, (IBK) K-Nearest Neighbour, (NB) Naïve Bayes and (SVM)/SMO Support Vector Machine are used for classification. Jsoup is implemented for gathering web opinions which are subjected to initial processing task later applied with stemming and tagging. This fuzzy based methodology is investigated for Mobile, Laptops dataset, also compared with state-of-the-art approaches which demonstrate upper indication of 94.37% accurateness through Kappa indicators showcasing lesser error rates. The investigational outcomes are tested on training data using Ten-Fold cross validation which concludes that this approach can be efficaciously used in Sentimental analysis as an aid for online decision.
Output and Input Data Perturbations for Differentially Private Databases

In today's ultra-connected world, the production and consumption of digital data has become immensely huge in volume. Differential privacy is a relatively new approach which attempts to provide strong privacy protection to users' data, while still maintaining outside access to data. However, the comparison of these methods in terms of performance and database suitability remains an open question. In this paper, we will provide a comprehensive comparison of input and output data perturbations for differentially private databases and evaluate the results in terms of accuracy, privacy, efficiency and scalability.