Reviewerly/exHarmony huggingface dataset:
MP <sup>2</sup> SDA

Sparse Discriminant Analysis (SDA) has been widely used to improve the performance of classical Fisher’s Linear Discriminant Analysis in supervised metric learning, feature selection, and classification. With the increasing needs of distributed data collection, storage, and processing, enabling the Sparse Discriminant Learning to embrace the multi-party distributed computing environments becomes an emerging research topic. This article proposes a novel multi-party SDA algorithm, which can learn SDA models effectively without sharing any raw data and basic statistics among machines. The proposed algorithm (1) leverages the direct estimation of SDA to derive a distributed loss function for the discriminant learning, (2) parameterizes the distributed loss function with local/global estimates through bootstrapping, and (3) approximates a global estimation of linear discriminant projection vector by optimizing the “distributed bootstrapping loss function” with gossip-based stochastic gradient descent. Experimental results on both synthetic and real-world benchmark datasets show that our algorithm can compete with the aggregated SDA with similar performance, and significantly outperforms the most recent distributed SDA in terms of accuracy and F1-score.
Secure Multiparty Computation via Homomorphic Encryption Library

Secure multiparty computation (MPC) is required when individuals want to privately evaluate a function over their inputs. While evaluating a common function, the participants do not reveal their inputs to each other. A homomorphic encryption (HE) scheme allows the evaluation of arbitrary computations on encrypted data without decrypting it. In theory, realizing MPC through a HE scheme is a simple and efficient approach. However, despite its promising theoretical power, the practical side of the approach remains underdeveloped. In this work, motivated by the rising MPC applications, e.g. cloud computation, a HE library is extended to provide the necessary methods for MPC. In particular HElib that implements Brakerski-Gentry-Vaikuntanathan (BGV), a HE scheme, is extended to support MPC protocols. This extension provides a broadcast protocol for the generation of a global public key by N parties, where each party maintains a share of the corresponding private key. In addition, the homomorphic evaluation of functions on ciphertexts encrypted by the public key is extended. Furthermore, a decryption broadcast protocol is provided where ciphertexts are decrypted using the individual shares of the private key. The proposed extension can be adapted to other HE libraries. A second contribution of this work, is a 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> factorial experimental design and analysis to study the memory, computation, and communication costs of HElib and the proposed extension. Four main factors are identified: the security parameter, the plaintext space, the number of levels of the evaluation function, and the number of parties. The proposed extensions are shown to be effective and efficient. On the experimented setup, it takes about 0.2 sec for multiparty key generation and 0.06 sec for multiparty decryption.
Automated Twitter Author Clustering with Unsupervised Learning for Social Media Forensics

Twitter is one of the key social media platforms, which is also used for cyber-crimes. Hence, monitoring and detecting the malicious activities of Twitter users is critically important for cybersecurity concerns around the globe since cybercriminals are heavily using Twitter for illegal purpose. It is increasingly common for cybercriminals signing up many accounts while masquerading different users for malicious behaviors. This fact has brought forward the issue of identifying the authors of Twitter accounts. In this paper, we propose a novel approach through a combination of feature extraction methods and then convert high dimensional data to kernel matrix for Twitter author clustering. The experimental results show that our approach can be used to effectively identify the groups among more than one hundred Twitter aliases even without knowing the number of authors.
Data Augmentation for Blind Signal Classification

The Army Rapid Capabilities Office (RCO) sponsored a Blind Signal Classification Competition seeking Artificial Intelligence/Machine Learning (AI/ML) algorithms to automatically identify the modulation schemes of RF signal from complex-valued IQ (in-phase quadrature) symbols. Our team, dubbed “Deep Dreamers”, participated in the competition and placed 3rd out of 42 active teams across industry, academia, and government. Deep learning methods such as CNN, Residual Neural Network (ResNet), and Long Short-Term Memory (LSTM) are the fundamental neural network models we used to develop a multi-class classifier. The key to our success was to use ensemble learning to average the outputs of multiple diverse classifiers. In this following study, we apply Data Augmentation (DA) to the data set in order to further increase the performance of our models. The goal of data augmentation is to push the decision boundary learned from the data set toward a better decision boundary by adding more meaningful data points. An effective data augmentation method for RF signals is to add white Gaussian noise to the existing RF signals. Individual DL models and ensemble learning methods such as blending trained over the augmented data set significantly improve the prediction accuracies for weak RF signals and achieve comparable results to the two leading teams in the competition.
Forecast Cloudy—Fair or Stormy Weather: Cloud Computing Insights and Issues

ABSTRACT To engage theory and practice of cloud computing in business, the third annual Journal of Information Systems Conference (JISC) brought together 29 academic researchers and 14 practitioners. This commentary reviews and synthesizes discussions and insights provided by three keynote presentations and panel discussions. In addition to sharing insights from the conference, this commentary identifies major themes, incorporates relevant current literature, and suggests potential research questions expressed by practitioners. The JISC emphasized the impact of the rapid maturing of cloud computing services on accounting information systems (AIS). Several recurring themes emerged throughout the conference: (1) rapid growth and evolution of cloud managed services, (2) security, privacy, and risk in the cloud ecosystem, (3) impact of cloud computing for data analytics, and (4) emerging and disruptive financial technologies and trends for the cloud. Most of the discussion surrounding these themes predominantly focused on the perspectives of cloud assurance and cloud service providers.
A Variable Length Key Based Cryptographic Approach on Cloud Data

Security has emerged to be a concerning issue in cloud computing as numerous sensitive data are processed and transferred over the cloud servers. In this paper, a variable length key based security mechanism has been designed, developed, and implemented by using the Advanced Encryption Standard (AES) protocol for the secured delivery of cloud data which also works as SaaS (Software as a Services). The length of the used key is given as input by the user followed by the selection of a secret key which is fed into the AES cryptographic system with the desired cloud data message thus producing the Cipher text that is to be transmitted to the destination. In the receiver end, the reverse process is performed with the same key on the received Cipher text and the plaintext is retrieved. Using Python to implement and validate the security process, several messages from cloud users are used and the result for each input is analyzed. The result of our study is then compared with the two existing approaches which clearly shows the advancement of the proposed approach. This process can be applied in any secured electronic message transactions for cloud data.
Unsupervised Word Embedding Learning by Incorporating Local and Global Contexts

Word embedding has benefited a broad spectrum of text analysis tasks by learning distributed word representations to encode word semantics. Word representations are typically learned by modeling local contexts of words, assuming that words sharing similar surrounding words are semantically close. We argue that local contexts can only partially define word semantics in the unsupervised word embedding learning. Global contexts, referring to the broader semantic units such as the document or paragraph where the word appears, can capture different aspects of word semantics and complement local contexts. We propose two simple yet effective unsupervised word embedding models that jointly model both local and global contexts to learn word representations. We provide theoretical interpretations of the proposed models to demonstrate how local and global contexts are jointly modeled, assuming a generative relationship between words and contexts. We conduct a thorough evaluation on a wide range of benchmark datasets. Our quantitative analysis and case study show that despite their simplicity, our two proposed models achieve superior performance on word similarity and text classification tasks.
Parallelized Key Expansion Algorithm for Advanced Encryption Standard

Advance Encryption Standard (AES) is an algorithm widely used for encryption, authentication, authenticated encryption as well as in security protocols for providing security services in many modern-day applications. The primary reasons for the extensive use of AES are its stronger security and faster speed than its competitors. In the AES algorithm, different keys derived from the main key are used in encryption rounds, to provide strength to the security of the algorithm. However, in standard AES key expansion algorithm, the sub-keys are derived sequentially, that is one after other. This dependence on the previous sub-key has two problems. Firstly, the key generation cannot be parallelized, secondly, if any single sub-key is known to the attacker, all the other sub-keys, including master key, can be extracted. Also, Differential Power Analysis attack is known effective on standard AES key schedule. In this paper, we proposed a new key parallelized expansion algorithm for AES. The proposed algorithm removes the dependence on other sub-keys to provide faster speed and enhanced security. The proposed algorithm has been implemented in MATLAB software and on FPGA. The results of implementation validate that the computational efficiency of the proposed algorithm is superior to standard AES Key expansion.
Extremely Low Resource Text simplification with Pre-trained Transformer Language Model

Recent text simplification approaches regard the task as a monolingual text-to-text generation inspired by machine translation. In particular, the transformer-based translation model outperform previous methods. Although machine translation approaches need a large-scale parallel corpus, parallel corpora for text simplification are very small compared to machine translation tasks. Therefore, we attempt a simple approach which fine-tunes the pre-trained language model for text simplification with a small parallel corpus. Specifically, we conduct experiments with the following two models: transformer-based encoder-decoder model and a language model that receives a joint input of original and simplified sentences, called TransformerLM. Thus, we show that TransformerLM, which is a simple text generation model, substantially outperforms a strong baseline. In addition, we show that fine-tuned TransformerLM with only 3,000 supervised examples can achieve performance comparable to a strong baseline trained by all supervised data.
Revising Probabilities and Full Beliefs

Abstract A new formal model of belief dynamics is proposed, in which the epistemic agent has both probabilistic beliefs and full beliefs. The agent has full belief in a proposition if and only if she considers the probability that it is false to be so close to zero that she chooses to disregard that probability. She treats such a proposition as having the probability 1, but, importantly, she is still willing and able to revise that probability assignment if she receives information that gives her sufficient reasons to do so. Such a proposition is (presently) undoubted, but not undoubtable (incorrigible). In the formal model it is assigned a probability 1 − δ , where δ is an infinitesimal number. The proposed model employs probabilistic belief states that contain several underlying probability functions representing alternative probabilistic states of the world. Furthermore, a distinction is made between update and revision, in the same way as in the literature on (dichotomous) belief change. The formal properties of the model are investigated, including properties relevant for learning from experience. The set of propositions whose probabilities are infinitesimally close to 1 forms a (logically closed) belief set. Operations that change the probabilistic belief state give rise to changes in this belief set, which have much in common with traditional operations of belief change.
Software Engineering for Dataset Augmentation using Generative Adversarial Networks

Software engineers require a large amount of data for building neural network-based software systems.The engineering of these data is often neglected, though, it is a critical and time-consuming activity.In this work, we present a novel software engineering approach for dataset augmentation using neural networks.We propose a rigorous process for generating synthetic data to improve the training of neural networks.Also, we demonstrate our approach to successfully improve the recognition of handwritten digits using conditional generative adversarial networks (cGAN).Finally, we shortly discuss selected important issues of our process, presenting related work and proposing some improvements.
Syn

User Interface design is an iterative process that progresses through low-, medium-, and high-fidelity prototypes. A few research projects use deep learning to automate this process by transforming low fidelity (lo-fi) sketches into front-end code. However, these research projects lack a large scale dataset of lo-fi sketches to train detection models. As a solution, we created Syn, a synthetic dataset containing 125,000 lo-fi sketches. These lo-fi sketches were synthetically generated using our UISketch dataset containing 5,917 sketches of 19 UI elements drawn by 350 participants. To realize the usage of Syn, we used it to train a UI element detector, Meta-Morph. It detects UI elements from a lo-fi sketch with 84.9% mAP and 72.7% AR. This work aims to support future research on UI element sketch detection and automating prototype fidelity transformation.
Asymptotically Optimal Codebooks Derived From Generalised Bent Functions

Codebooks are required to have small inner-product correlation in many practical applications, such as direct spread code division multiple access communications, space-time codes and compressed sensing. In general, it is difficult to construct optimal codebooks. In this paper, two kinds of codebooks are presented and proved to be optimally optimal with respect to the Welch bound. Additionally, the constructed codebooks in this paper have new parameters.
State Estimation Of a Quantum Cavity Driven by single-photon

The cavity is a fundamental ingredient of quantum optical systems. This paper concerns the behavior of a quantum cavity driven by non-classical field in single-photon state. To this end, the number operator has been opted to reveal the number of photons inside the cavity. Then, the quantum filtering equations have been employed to derive a stochastic master equation for the cavity which is driven by single-photon and is observed by either Homodyne or photon-counting detector. Finally, the state of the cavity has been estimated by the derived equations, and the results have been compared with the conventional master equation.
A three-stage method for batch-based incremental nonnegative matrix factorization

The main issue in incremental nonnegative matrix factorization (INMF) is how to update base matrix and coefficient matrix. The re-training scheme(RT-NMF) and the scheme proposed by Bucak and Gunsel(BG-INMF) are two common methods. However, both of them have problems in balancing root mean square error(RMSE) and time cost when incremental samples appear in a batch form. In this paper, a three-stage method(3S-INMF) is proposed to derive a good balance between RMSE and time cost. In the first stage, only the coefficient matrix of incremental samples is updated while the base matrix and the coefficient matrix of old samples are fixed. If the RMSE does not meet the required precision after this stage, the second stage, i.e. BG-INMF, is carried out. In the second stage, the base matrix and the coefficient matrix of incremental samples are updated alternatively while the coefficient matrix of old samples is fixed. If the RMSE still does not meet with the required precision after BG-INMF, the coefficient matrix of old samples will be updated in the third stage while the base matrix and the coefficient matrix of incremental samples are fixed. In the three consecutive stages, the initial values of base matrix and coefficient matrix in each stage are the corresponding output values in the previous stage. In addition, extensive experiments on the three popular datasets show that 3S-INMF obtains the best balance between RMSE and time cost compared with RT-NMF and BG-INMF. Furthermore, the 3S-INMF is extended to graph nonnegative matrix factorization(GNMF) and kernel nonnegative matrix factorization(KNMF), which also has a superior performance examined by further experiments.
A Survey on Semantic Similarity

This paper provides a survey of semantic similarity of text documents. Semantic Similarity is an important task in Natural Language Processing (NLP). It is widely used for information retrieval, text classification, question answering, and plagiarism detection. This survey will classify different types of semantic similarity approaches such as corpus-based, knowledge-based and string-based. Various papers are reviewed and prepared performance analysis in this survey.
Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images

Malaria is a life-threatening disease affecting millions. Microscopy-based assessment of thin blood films is a standard method to (i) determine malaria species and (ii) quantitate high-parasitemia infections. Full automation of malaria microscopy by machine learning (ML) is a challenging task because field-prepared slides vary widely in quality and presentation, and artifacts often heavily outnumber relatively rare parasites. In this work, we describe a complete, fully-automated framework for thin film malaria analysis that applies ML methods, including convolutional neural nets (CNNs), trained on a large and diverse dataset of field-prepared thin blood films. Quantitation and species identification results are close to sufficiently accurate for the concrete needs of drug resistance monitoring and clinical use-cases on field-prepared samples. We focus our methods and our performance metrics on the field use-case requirements. We discuss key issues and important metrics for the application of ML methods to malaria microscopy.
A Unified Framework of Intelligent Vehicle Damage Assessment based on Computer Vision Technology

Due to the development of deep learning, in recent years, the field of computer vision grows rapidly. A large amount of computer vision technologies have been applied in actual problems. At present, the industry of vehicle damage assessment requires a lot of manpower, and new automatic intelligent damage assessment technology can greatly reduce industrial costs. In this paper, a framework of intelligent vehicle damage assessment algorithm based on object detection technology and image classification technology is proposed. This algorithm can automatically identify the damage position, type and degree according to photos provided by users, so as to offer appropriate maintenance price and reach the accuracy that can meet actual application requirements.
A Line Matching Method Based on Multiple Intensity Ordering with Uniformly Spaced Sampling

This paper presents a line matching method based on multiple intensity ordering with uniformly spaced sampling. Line segments are extracted from the image pyramid, with the aim of adapting scale changes and addressing fragmentation problem. The neighborhood of line segments was divided into sub-regions adaptively according to intensity order to overcome the difficulty brought by various line lengths. An intensity-based local feature descriptor was introduced by constructing multiple concentric ring-shaped structures. The dimension of the descriptor was reduced significantly by uniformly spaced sampling and dividing sample points into several point sets while improving the discriminability. The performance of the proposed method was tested on public datasets which cover various scenarios and compared with another two well-known line matching algorithms. The experimental results show that our method achieves superior performance dealing with various image deformations, especially scale changes and large illumination changes, and provides much more reliable correspondences.
Small Objects Detection in Satellite Images Using Deep Learning

Using the deep Convolution Neural Networks (CNNs) for Object detection in satellite images accomplish promising results, especially for large objects. While Small objects detection in the same spatial resolution images does not attain the same results. For instance, vehicle detection in high-resolution satellite images, the targeted object maybe existed in an area that does not exceed 15 square pixels, which will not make a sufficient effect in the deeper layers. In addition; the interfering with the surrounding background, noise effect, the neighboring object's shadows, and various vehicle colors. In the proposed paper, an analysis study is performed to evaluate the effect of changing the object size on the detection results. A separate resampling algorithm is applied to the input test images to change its size - bear in mind the built-in detection model resampling layer-, which results in changing the object size, and accordingly extends the object impact in deep layers. Through Transfer Learning, the Faster R-CNN pre-trained object detection model with Inception-V2is applied to submeter satellite images and passenger vehicles as the target objects. The Experimental results show the change in detection accuracy with the change of the object size.
Machine Learning Techniques to Understand Partial and Implied Data Values for Conversion of Natural Language to SQL Queries on HPCC Systems

There has been an exponential growth in the amount of data produced daily in recent years, owing to the widespread use of technology. Ease of access to this data is of utmost importance in this day and age. Although in the past, use of structured query languages to query the data stored in the hard-drive was satisfactory, use of natural language to access the data is more desired. This paper talks about mapping partial data values to its corresponding data values and attributes in the schema to enrich the natural language query. Machine learning algorithm, Long Short Term Memory, preceded by an Embedding layer has been used on the HPCC Systems platform. The resulting model gives an accuracy of 99.6%, while its implementation with the experimental setup gives an accuracy of 92%.